From 46d5b10775a1c47a5b997df09e0c53b9a472d85c Mon Sep 17 00:00:00 2001 From: Rodion Lim Date: Sun, 18 Jan 2026 01:15:19 +0800 Subject: [PATCH 1/2] feat: add system skeleton with ported accounts --- pyproject.toml | 5 +- src/quantlib_st/config/__init__.py | 0 src/quantlib_st/config/configdata.py | 313 +++++++ src/quantlib_st/config/defaults.py | 36 + src/quantlib_st/config/defaults.yaml | 368 ++++++++ .../config/fill_config_dict_with_defaults.py | 25 + src/quantlib_st/config/instruments.py | 103 +++ src/quantlib_st/config/private_config.py | 29 + src/quantlib_st/config/private_directory.py | 15 + src/quantlib_st/core/constants.py | 21 + src/quantlib_st/core/dateutils.py | 132 ++- src/quantlib_st/core/exceptions.py | 39 + src/quantlib_st/core/fileutils.py | 195 +++++ src/quantlib_st/core/genutils.py | 391 +++++++++ src/quantlib_st/core/maths.py | 117 +++ src/quantlib_st/core/objects.py | 118 +++ src/quantlib_st/core/pandas/find_data.py | 81 ++ src/quantlib_st/core/pandas/frequency.py | 141 ++++ .../pandas/full_merge_with_replacement.py | 200 +++++ .../pandas/merge_data_keeping_past_data.py | 318 +++++++ .../pandas/merge_data_with_label_column.py | 420 ++++++++++ src/quantlib_st/core/pandas/pdutils.py | 392 +++++++++ .../core/pandas/strategy_functions.py | 106 ++- src/quantlib_st/core/text.py | 123 +++ .../data/test/csvconfig/instrumentconfig.csv | 248 ++++++ src/quantlib_st/estimators/turnover.py | 29 + src/quantlib_st/execution/__init__.py | 0 src/quantlib_st/execution/orders/__init__.py | 0 .../execution/orders/base_orders.py | 507 +++++++++++ .../execution/orders/named_order_objects.py | 16 + src/quantlib_st/execution/trade_qty.py | 253 ++++++ src/quantlib_st/init/__init__.py | 0 src/quantlib_st/init/futures/__init__.py | 0 .../build_multiple_prices_from_raw_data.py | 337 ++++++++ src/quantlib_st/logging/__init__.py | 0 src/quantlib_st/logging/adaptor.py | 102 +++ src/quantlib_st/logging/logger.py | 86 ++ src/quantlib_st/objects/__init__.py | 4 + src/quantlib_st/objects/adjusted_prices.py | 194 +++++ .../objects/contract_dates_and_expiries.py | 622 ++++++++++++++ src/quantlib_st/objects/contracts.py | 388 +++++++++ .../dict_of_futures_per_contract_prices.py | 151 ++++ ...ct_of_named_futures_per_contract_prices.py | 337 ++++++++ src/quantlib_st/objects/fills.py | 124 +++ src/quantlib_st/objects/instruments.py | 393 +++++++++ src/quantlib_st/objects/multiple_prices.py | 219 +++++ .../objects/production/__init__.py | 0 .../objects/production/tradeable_object.py | 245 ++++++ src/quantlib_st/objects/rolls.py | 451 ++++++++++ src/quantlib_st/objects/spot_fx_prices.py | 110 +++ src/quantlib_st/sysdata/__init__.py | 3 + src/quantlib_st/sysdata/base_data.py | 76 ++ src/quantlib_st/sysdata/csv/__init__.py | 13 + .../sysdata/csv/csv_adjusted_prices.py | 84 ++ .../sysdata/csv/csv_instrument_data.py | 138 +++ .../sysdata/csv/csv_multiple_prices.py | 100 +++ .../sysdata/csv/csv_roll_parameters.py | 120 +++ src/quantlib_st/sysdata/csv/csv_spot_fx.py | 126 +++ src/quantlib_st/sysdata/data_blob.py | 415 +++++++++ src/quantlib_st/sysdata/futures/__init__.py | 13 + .../sysdata/futures/adjusted_prices.py | 115 +++ .../sysdata/futures/instruments.py | 157 ++++ .../sysdata/futures/multiple_prices.py | 126 +++ .../sysdata/futures/rolls_parameters.py | 111 +++ .../sysdata/futures/spread_costs.py | 44 + src/quantlib_st/sysdata/fx/__init__.py | 3 + src/quantlib_st/sysdata/fx/spotfx.py | 242 ++++++ src/quantlib_st/sysdata/production_config.py | 8 + src/quantlib_st/sysdata/sim/__init__.py | 15 + src/quantlib_st/sysdata/sim/base_data.py | 3 + .../sysdata/sim/csv_futures_sim_test_data.py | 113 +++ .../sysdata/sim/futures_sim_data.py | 266 ++++++ .../sim/futures_sim_data_with_data_blob.py | 130 +++ src/quantlib_st/sysdata/sim/sim_data.py | 325 +++++++ .../accounts/account_buffering_subsystem.py | 208 +++++ .../accounts/account_buffering_system.py | 116 +++ .../systems/accounts/account_costs.py | 366 ++++++++ .../systems/accounts/account_forecast.py | 213 ++++- .../systems/accounts/account_inputs.py | 317 +++++++ .../systems/accounts/account_instruments.py | 225 +++++ .../systems/accounts/account_portfolio.py | 71 ++ .../systems/accounts/account_subsystem.py | 206 +++++ .../systems/accounts/account_trading_rules.py | 216 +++++ .../accounts/account_with_multiplier.py | 163 ++++ .../systems/accounts/accounts_stage.py | 12 + .../accounts/curves/account_curve_group.py | 169 ++++ .../accounts/curves/dict_of_account_curves.py | 41 + .../curves/nested_account_curve_group.py | 125 +++ .../systems/accounts/curves/stats_dict.py | 254 ++++++ .../pandl_calculation_dict.py | 106 +++ .../pandl_calculators/pandl_cash_costs.py | 262 ++++++ .../pandl_calculators/pandl_using_fills.py | 173 ++++ .../systems/accounts/tests/test_accounts.py | 26 + src/quantlib_st/systems/basesystem.py | 428 ++++++++++ src/quantlib_st/systems/forecasting.py | 248 ++++++ .../systems/provided/config/__init__.py | 0 .../provided/config/test_account_config.yaml | 26 + .../provided/futures_chapter15/basesystem.py | 47 ++ src/quantlib_st/systems/stage.py | 53 ++ src/quantlib_st/systems/system_cache.py | 793 ++++++++++++++++++ src/quantlib_st/systems/tools/__init__.py | 0 src/quantlib_st/systems/tools/autogroup.py | 275 ++++++ src/quantlib_st/systems/trading_rules.py | 554 ++++++++++++ src/tests/core/test_fileutils.py | 90 ++ uv.lock | 59 +- 105 files changed, 17084 insertions(+), 8 deletions(-) create mode 100644 src/quantlib_st/config/__init__.py create mode 100644 src/quantlib_st/config/configdata.py create mode 100644 src/quantlib_st/config/defaults.py create mode 100644 src/quantlib_st/config/defaults.yaml create mode 100644 src/quantlib_st/config/fill_config_dict_with_defaults.py create mode 100644 src/quantlib_st/config/instruments.py create mode 100644 src/quantlib_st/config/private_config.py create mode 100644 src/quantlib_st/config/private_directory.py create mode 100644 src/quantlib_st/core/constants.py create mode 100644 src/quantlib_st/core/exceptions.py create mode 100644 src/quantlib_st/core/fileutils.py create mode 100644 src/quantlib_st/core/genutils.py create mode 100644 src/quantlib_st/core/maths.py create mode 100644 src/quantlib_st/core/objects.py create mode 100644 src/quantlib_st/core/pandas/find_data.py create mode 100644 src/quantlib_st/core/pandas/full_merge_with_replacement.py create mode 100644 src/quantlib_st/core/pandas/merge_data_keeping_past_data.py create mode 100644 src/quantlib_st/core/pandas/merge_data_with_label_column.py create mode 100755 src/quantlib_st/core/pandas/pdutils.py create mode 100644 src/quantlib_st/core/text.py create mode 100644 src/quantlib_st/data/test/csvconfig/instrumentconfig.csv create mode 100644 src/quantlib_st/estimators/turnover.py create mode 100644 src/quantlib_st/execution/__init__.py create mode 100644 src/quantlib_st/execution/orders/__init__.py create mode 100644 src/quantlib_st/execution/orders/base_orders.py create mode 100644 src/quantlib_st/execution/orders/named_order_objects.py create mode 100644 src/quantlib_st/execution/trade_qty.py create mode 100644 src/quantlib_st/init/__init__.py create mode 100644 src/quantlib_st/init/futures/__init__.py create mode 100644 src/quantlib_st/init/futures/build_multiple_prices_from_raw_data.py create mode 100644 src/quantlib_st/logging/__init__.py create mode 100644 src/quantlib_st/logging/adaptor.py create mode 100644 src/quantlib_st/logging/logger.py create mode 100644 src/quantlib_st/objects/__init__.py create mode 100644 src/quantlib_st/objects/adjusted_prices.py create mode 100644 src/quantlib_st/objects/contract_dates_and_expiries.py create mode 100644 src/quantlib_st/objects/contracts.py create mode 100644 src/quantlib_st/objects/dict_of_futures_per_contract_prices.py create mode 100644 src/quantlib_st/objects/dict_of_named_futures_per_contract_prices.py create mode 100644 src/quantlib_st/objects/fills.py create mode 100644 src/quantlib_st/objects/instruments.py create mode 100644 src/quantlib_st/objects/multiple_prices.py create mode 100644 src/quantlib_st/objects/production/__init__.py create mode 100644 src/quantlib_st/objects/production/tradeable_object.py create mode 100644 src/quantlib_st/objects/rolls.py create mode 100644 src/quantlib_st/objects/spot_fx_prices.py create mode 100644 src/quantlib_st/sysdata/__init__.py create mode 100644 src/quantlib_st/sysdata/base_data.py create mode 100644 src/quantlib_st/sysdata/csv/__init__.py create mode 100644 src/quantlib_st/sysdata/csv/csv_adjusted_prices.py create mode 100644 src/quantlib_st/sysdata/csv/csv_instrument_data.py create mode 100644 src/quantlib_st/sysdata/csv/csv_multiple_prices.py create mode 100644 src/quantlib_st/sysdata/csv/csv_roll_parameters.py create mode 100644 src/quantlib_st/sysdata/csv/csv_spot_fx.py create mode 100644 src/quantlib_st/sysdata/data_blob.py create mode 100644 src/quantlib_st/sysdata/futures/__init__.py create mode 100644 src/quantlib_st/sysdata/futures/adjusted_prices.py create mode 100644 src/quantlib_st/sysdata/futures/instruments.py create mode 100644 src/quantlib_st/sysdata/futures/multiple_prices.py create mode 100644 src/quantlib_st/sysdata/futures/rolls_parameters.py create mode 100644 src/quantlib_st/sysdata/futures/spread_costs.py create mode 100644 src/quantlib_st/sysdata/fx/__init__.py create mode 100644 src/quantlib_st/sysdata/fx/spotfx.py create mode 100644 src/quantlib_st/sysdata/production_config.py create mode 100644 src/quantlib_st/sysdata/sim/__init__.py create mode 100644 src/quantlib_st/sysdata/sim/base_data.py create mode 100644 src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py create mode 100644 src/quantlib_st/sysdata/sim/futures_sim_data.py create mode 100644 src/quantlib_st/sysdata/sim/futures_sim_data_with_data_blob.py create mode 100644 src/quantlib_st/sysdata/sim/sim_data.py create mode 100644 src/quantlib_st/systems/accounts/account_buffering_subsystem.py create mode 100644 src/quantlib_st/systems/accounts/account_buffering_system.py create mode 100644 src/quantlib_st/systems/accounts/account_costs.py create mode 100644 src/quantlib_st/systems/accounts/account_inputs.py create mode 100644 src/quantlib_st/systems/accounts/account_instruments.py create mode 100644 src/quantlib_st/systems/accounts/account_portfolio.py create mode 100644 src/quantlib_st/systems/accounts/account_subsystem.py create mode 100644 src/quantlib_st/systems/accounts/account_trading_rules.py create mode 100644 src/quantlib_st/systems/accounts/account_with_multiplier.py create mode 100644 src/quantlib_st/systems/accounts/accounts_stage.py create mode 100644 src/quantlib_st/systems/accounts/curves/account_curve_group.py create mode 100644 src/quantlib_st/systems/accounts/curves/dict_of_account_curves.py create mode 100644 src/quantlib_st/systems/accounts/curves/nested_account_curve_group.py create mode 100644 src/quantlib_st/systems/accounts/curves/stats_dict.py create mode 100644 src/quantlib_st/systems/accounts/pandl_calculators/pandl_calculation_dict.py create mode 100644 src/quantlib_st/systems/accounts/pandl_calculators/pandl_cash_costs.py create mode 100644 src/quantlib_st/systems/accounts/pandl_calculators/pandl_using_fills.py create mode 100644 src/quantlib_st/systems/accounts/tests/test_accounts.py create mode 100644 src/quantlib_st/systems/basesystem.py create mode 100644 src/quantlib_st/systems/forecasting.py create mode 100644 src/quantlib_st/systems/provided/config/__init__.py create mode 100644 src/quantlib_st/systems/provided/config/test_account_config.yaml create mode 100644 src/quantlib_st/systems/provided/futures_chapter15/basesystem.py create mode 100644 src/quantlib_st/systems/stage.py create mode 100644 src/quantlib_st/systems/system_cache.py create mode 100644 src/quantlib_st/systems/tools/__init__.py create mode 100644 src/quantlib_st/systems/tools/autogroup.py create mode 100644 src/quantlib_st/systems/trading_rules.py create mode 100644 src/tests/core/test_fileutils.py diff --git a/pyproject.toml b/pyproject.toml index 4847757..ef4c46a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ dependencies = [ "numpy>=2.4.0", "pandas>=2.3.3", "scipy>=1.17.0", + "PyYAML>=6.0.2", ] [project.optional-dependencies] @@ -41,8 +42,8 @@ where = ["src"] include = ["*"] [tool.pytest.ini_options] -testpaths = ["src/tests"] -pythonpath = ["src/quantlib_st"] +testpaths = ["src", "tests"] +pythonpath = ["src"] [build-system] requires = ["setuptools>=80.9", "wheel"] diff --git a/src/quantlib_st/config/__init__.py b/src/quantlib_st/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/config/configdata.py b/src/quantlib_st/config/configdata.py new file mode 100644 index 0000000..3acf201 --- /dev/null +++ b/src/quantlib_st/config/configdata.py @@ -0,0 +1,313 @@ +""" +Configuration is used to control the behaviour of a system + +Config can be passed as a dict, a filename from which a YAML spec is read in +and then parsed + +There are no set elements for configurations, although typically they will +contain: + +parameters - a dict of values which override those in system.defaults +trading_rules - a specification of the trading rules for a system + +""" + +from pathlib import Path +from typing import Optional, Union + +import os +import yaml + +from quantlib_st.core.exceptions import missingData +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package +from quantlib_st.config.defaults import get_system_defaults_dict +from quantlib_st.config.fill_config_dict_with_defaults import ( + fill_config_dict_with_defaults, +) +from quantlib_st.config.private_config import ( + get_private_config_as_dict, + PRIVATE_CONFIG_FILE, +) +from quantlib_st.config.private_directory import ( + get_full_path_for_private_config, + PRIVATE_CONFIG_DIR_ENV_VAR, +) +from quantlib_st.logging.logger import * + + +RESERVED_NAMES = [ + "log", + "_elements", + "elements", + "_default_filename", + "_private_filename", +] + + +class Config(object): + def __init__( + self, + config_object: Optional[Union[str, dict, list]] = None, + default_filename=None, + private_filename=None, + ): + """ + Config objects control the behaviour of systems + + :param config_object: Either: + a string (which points to a YAML filename) + or a dict (which may nest many things) + or a list of strings or dicts or configs (build config from + multiple elements, latter elements will overwrite + earlier ones) + + :type config_object: str or dict + + :returns: new Config object + + >>> Config(dict(parameters=dict(p1=3, p2=4.6), another_thing=[])) + Config with elements: another_thing, parameters + + >>> Config("systems.provided.example.exampleconfig.yaml") + Config with elements: base_currency, ... trading_rules + + >>> Config(["systems.provided.example.exampleconfig.yaml", dict(parameters=dict(p1=3, p2=4.6), another_thing=[])]) + Config with elements: another_thing, ... parameters, ...trading_rules + + """ + + # this will normally be overridden by the base system + self.log = get_logger( + "config", {TYPE_LOG_LABEL: "config", STAGE_LOG_LABEL: "config"} + ) + + self._default_filename = default_filename + self._private_filename = private_filename + + if config_object is None: + config_object = dict() + + self._init_config(config_object) + + @property + def elements(self) -> list: + elements = getattr(self, "_elements", []) + + return elements + + def add_elements(self, new_elements: list): + _ = [self.add_single_element(element_name) for element_name in new_elements] + + def remove_element(self, element: str): + current_elements = self.elements + current_elements.remove(element) + self._elements = current_elements + + def add_single_element(self, element_name): + if element_name not in RESERVED_NAMES: + elements = self.elements + if element_name not in elements: + elements.append(element_name) + self._elements = elements + + def get_element(self, element_name): + try: + result = getattr(self, element_name) + except AttributeError: + raise missingData("Missing config element %s" % element_name) + return result + + def get_element_or_default(self, element_name, default): + result = getattr(self, element_name, default) + return result + + def get_element_or_arg_not_supplied(self, element_name): + return self.get_element_or_default(element_name, None) + + def __repr__(self): + elements = self.elements + elements.sort() + return "Config with elements: %s" % ", ".join(self.elements) + + def _init_config(self, config_object): + if isinstance(config_object, list): + # multiple configs, already a list + config_list = config_object + else: + config_list = [config_object] + + self._create_config_from_list(config_list) + + def _create_config_from_list(self, config_object): + for config_item in config_object: + self._create_config_from_item(config_item) + + def _create_config_from_item(self, config_item): + if isinstance(config_item, dict): + # its a dict + self._create_config_from_dict(config_item) + + elif isinstance(config_item, str) or isinstance(config_item, Path): + # must be a file YAML'able, from which we load the + filename = resolve_path_and_filename_for_package(os.fspath(config_item)) + with open(filename) as file_to_parse: + dict_to_parse = yaml.load(file_to_parse, Loader=yaml.FullLoader) + + self._create_config_from_dict(dict_to_parse) + + elif isinstance(config_item, Config): + self._create_config_from_dict(config_item.as_dict()) + else: + error_msg = ( + "Can only create a config with a nested dict or the " + "string of a 'yamable' filename, or a list " + "comprising these things" + ) + self.log.critical(error_msg) + + def _create_config_from_dict(self, config_object): + """ + Take a dictionary object and turn it into self + + When we've close self will be an object where the attributes are + + So if config_object=dict(a=2, b=2) + Then this object will become self.a=2, self.b=2 + """ + base_config = config_object.get("base_config") + if base_config is not None: + self._create_config_from_item(base_config) + + attr_names = list(config_object.keys()) + [setattr(self, keyname, config_object[keyname]) for keyname in config_object] + + self.add_elements(attr_names) + + def system_init(self, base_system): + """ + This is run when added to a base system + + :param base_system + :return: nothing + """ + + # fill with defaults + self.fill_with_defaults() + + def __delattr__(self, element_name: str): + """ + Remove element_name from config + + >>> config=Config(dict(parameters=dict(p1=3, p2=4.6), another_thing=[])) + >>> del(config.another_thing) + >>> config + Config with elements: parameters + >>> + """ + # to avoid recursion, we must first avoid recursion + super().__delattr__(element_name) + + self.remove_element(element_name) + + def __setattr__(self, element_name: str, value): + """ + Add / replace element_name in config + + >>> config=Config(dict(parameters=dict(p1=3, p2=4.6), another_thing=[])) + >>> config.another_thing="test" + >>> config.another_thing + 'test' + >>> config.yet_another_thing="more testing" + >>> config + Config with elements: another_thing, parameters, yet_another_thing + >>> + """ + # to avoid recursion, we must first avoid recursion + super().__setattr__(element_name, value) + self.add_single_element(element_name) + + def fill_with_defaults(self): + """ + Fills with defaults - private stuff first, then defaults + """ + # self.log.debug("Adding config defaults") + + self_as_dict = self.as_dict() + defaults_dict = self.default_config_dict + private_dict = self.private_config_dict + + ## order is - self (backtest filename), private, defaults + new_dict_with_private = fill_config_dict_with_defaults( + self_as_dict, private_dict + ) + new_dict_with_defaults = fill_config_dict_with_defaults( + new_dict_with_private, defaults_dict + ) + + self._create_config_from_dict(new_dict_with_defaults) + + @property + def default_config_dict(self) -> dict: + default_filename = self.default_config_filename + default_dict = get_system_defaults_dict(filename=default_filename) + + return default_dict + + @property + def default_config_filename(self) -> Optional[str]: + default_filename = getattr(self, "_default_filename", None) + + return default_filename + + @property + def private_config_dict(self) -> dict: + private_filename = self.private_config_filename + private_dict = get_private_config_as_dict(private_filename) + + return private_dict + + @property + def private_config_filename(self): + private_filename = getattr(self, "_private_filename", None) + + return private_filename + + def as_dict(self): + element_names = sorted(getattr(self, "_elements", [])) + self_as_dict = {} + for element in element_names: + self_as_dict[element] = getattr(self, element, "") + + return self_as_dict + + def save(self, filename): + config_to_save = self.as_dict() + with open(filename, "w") as file: + yaml.dump(config_to_save, file) + + @classmethod + def default_config(cls): + if hasattr(cls, "evaluated"): + return cls.evaluated + + if os.getenv(PRIVATE_CONFIG_DIR_ENV_VAR): + config = Config( + private_filename=get_full_path_for_private_config(PRIVATE_CONFIG_FILE) + ) + else: + config = Config() + config.fill_with_defaults() + + cls.evaluated = config + return cls.evaluated + + @classmethod + def reset(cls): + if hasattr(cls, "evaluated"): + delattr(cls, "evaluated") + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/config/defaults.py b/src/quantlib_st/config/defaults.py new file mode 100644 index 0000000..11ee66c --- /dev/null +++ b/src/quantlib_st/config/defaults.py @@ -0,0 +1,36 @@ +""" +All default parameters that might be used in a system are stored here + +Order of preferences is - passed in command line to calculation method, + stored in system config object + found in defaults + +""" + +from typing import Optional + +import yaml + +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package + +DEFAULT_FILENAME = "config.defaults.yaml" + + +def get_system_defaults_dict(filename: Optional[str] = None) -> dict: + """ + >>> get_system_defaults_dict()['average_absolute_forecast'] + 10.0 + """ + if filename is None: + filename = DEFAULT_FILENAME + default_file = resolve_path_and_filename_for_package(filename) + with open(default_file) as file_to_parse: + default_dict = yaml.load(file_to_parse, Loader=yaml.FullLoader) + + return default_dict + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/config/defaults.yaml b/src/quantlib_st/config/defaults.yaml new file mode 100644 index 0000000..dfb84f3 --- /dev/null +++ b/src/quantlib_st/config/defaults.yaml @@ -0,0 +1,368 @@ +# YAML +# +# PRODUCTION STUFF +# +# +## Strategy configuration +# This won't actually work unless you have a strategy called example.. so don't uncomment use as a template +#strategy_list: +# example: +# load_backtests: +# object: sysproduction.strategy_code.run_system_classic.runSystemClassic +# function: system_method +# reporting_code: +# function: sysproduction.strategy_code.report_system_classic.report_system_classic +# +# Allocate capital to strategies +# strategy_weights *must* be defined in private config +strategy_capital_allocation: + function: sysproduction.strategy_code.strategy_allocation.weighted_strategy_allocation +# strategy_weights: +# example: 100.0 +# +## Where do we save backtests +backtest_store_directory: "private.backtests" +backtest_max_age: 30 +backtest_compress: False +# +# And backups +csv_backup_directory: "data.backups_csv" +mongo_dump_directory: "data.mongo_dump" +# if mongo_dump_all is True, we dump all databases, otherwise just PST dbs +mongo_dump_all: True +echo_directory: "data.echos" +offsystem_backup_options: "-av" +# +# Interactive brokers +ib_ipaddress: 127.0.0.1 +ib_port: 4001 +ib_idoffset: 100 +# +# Broker class list resolution function +broker_factory_func: "sysbrokers.broker_factory.get_ib_class_list" +# +# Mongo DB +mongo_host: 127.0.0.1 +mongo_db: "production" +# DO NOT CHANGE THIS VALUE!!!! IT WILL SCREW UP ARCTIC. If you need to use a port other than 27017, use the url +# format for mongo_host, eg mongodb://127.0.0.1:27018 +mongo_port: 27017 +# +# Parquet store +parquet_store: "/home/me/data/parquet" +# +# Needs to be consistent with what you are using in crontab +echo_extension: ".txt" +# Spike checker +max_price_spike: 8.0 +# Default get_GMT_offset_hours +GMT_offset_hours: 0 +# Behaviour of price filtering +ignore_future_prices: True +ignore_prices_with_zero_volumes_intraday: True +ignore_prices_with_zero_volumes_daily: False +ignore_zero_prices: True +ignore_negative_prices: False +# +# Price frequency (we collect daily data, and separately this frequency +intraday_frequency: H +# +# Capital calculation +production_capital_method: "full" +# +stale_instruments: ["an_instrument_i_no_longer_trade_or_sample"] +# +stale_strategies: ["a strat we no longer run"] +# +# Roll status auto-update +roll_status_auto_update: + auto_roll_if_relative_volume_higher_than: 1.0 + min_relative_volume: 0.01 + min_absolute_volume: 100 + near_expiry_days: 10 + ## following has to be one of enum roll states in sysobjects/production/roll_state.py + ## eg Close, Force_Outright, Force + ## or Ask to request from user + default_roll_state_if_undecided: Ask + ## automatically roll if expired priced contract + auto_roll_expired: True +# +# Force certain instruments to use a specific execution algo +# Algos must be classes eg sysexecution.algos.algo_market.algoMarket +# Copy and paste this into private_config to modify +execution_algos: + default_algo: sysexecution.algos.algo_original_best.algoOriginalBest + market_algo: sysexecution.algos.algo_snaps.algoSnapMkt + limit_order_algo: sysexecution.algos.algo_limit_orders.algoLimit + best_algo: sysexecution.algos.algo_original_best.algoOriginalBest + algo_overrides: + some_market_name_eg_IRON: sysexecution.algos.algo_market.algoMarket +# +# BACKTESTING STUFF +# +# Raw data +# +volatility_calculation: + func: "estimators.vol.mixed_vol_calc" + name_returns_attr_in_rawdata: "daily_returns" + multiplier_to_get_daily_vol: 1.0 + days: 35 + min_periods: 10 + slow_vol_years: 10 + proportion_of_slow_vol: 0.3 + vol_abs_min: 0.0000000001 +# +# forecast capping and scaling +# fixed values +# +forecast_scalar: 1.0 +# +# with varying stuff +# +# +instruments_with_threshold: [] +use_forecast_scale_estimates: False +# +forecast_scalar_estimate: + pool_instruments: True + func: "estimators.forecast_scalar.forecast_scalar" + window: 250000 + min_periods: 500 + backfill: True +# +# capping +forecast_cap: 20.0 +# +average_absolute_forecast: 10.0 +# +# +# forecast combination +# +forecast_div_multiplier: 1.0 +# +use_forecast_div_mult_estimates: False +# +forecast_correlation_estimate: + pool_instruments: True + func: estimators.pooled_correlation.pooled_correlation_estimator + frequency: "W" + date_method: "expanding" + using_exponent: True + ew_lookback: 250 + min_periods: 20 + cleaning: True + rollyears: 20 + floor_at_zero: True + forward_fill_data: True +# +forecast_div_mult_estimate: + func: estimators.diversification_multipliers.diversification_multiplier_from_list + # this moving average is in business day space to smooth jumps + ewma_span: 125 + dm_max: 2.5 +# +use_forecast_weight_estimates: False +# +forecast_cost_estimates: + use_pooled_costs: False + use_pooled_turnover: True +# +# Smooth forecast weights; weights are in business days +forecast_weight_ewma_span: 125 +# +# Set this to 0.13 to enforce the speed limit even for fixed weights or +# regardless of what an estimator does +forecast_post_ceiling_cost_SR: 999 +# +forecast_weight_estimate: + func: optimisation.generic_optimiser.genericOptimiser + pool_gross_returns: True + equalise_gross: False + cost_multiplier: 2.0 + apply_cost_weight: False + ceiling_cost_SR: 9999 + frequency: "W" + date_method: "expanding" + rollyears: 20 + method: handcraft + cleaning: True + equalise_SR: False + ann_target_SR: 0.5 + equalise_vols: True + shrinkage_SR: 0.9 + shrinkage_corr: 0.5 + correlation_estimate: + func: estimators.correlation_estimator.correlationEstimator + using_exponent: True + ew_lookback: 50000 + min_periods: 10 + cleaning: False + floor_at_zero: False + mean_estimate: + func: estimators.mean_estimator.meanEstimator + using_exponent: True + ew_lookback: 50000 + min_periods: 5 + vol_estimate: + func: estimators.stdev_estimator.stdevEstimator + using_exponent: True + ew_lookback: 50000 + min_periods: 5 +# +# Capital correction +# +percentage_vol_target: 16.0 +notional_trading_capital: 1000000 +base_currency: "USD" +# other options are full_compounding and half_compounding, but see core.capital.py +capital_multiplier: + func: core.capital.fixed_capital +# +# add instrument codes here to make long only at position sizing +long_only_instruments: + - "an_instrument" + - "another_instrument" +# Portfolio creation +# +instrument_div_multiplier: 1.0 +# +use_instrument_div_mult_estimates: False +# +instrument_correlation_estimate: + func: estimators.correlation_over_time.correlation_over_time_for_returns + frequency: "W" + date_method: "expanding" + using_exponent: True + ew_lookback: 25 + min_periods: 20 + cleaning: True + rollyears: 20 + floor_at_zero: True + forward_fill_price_index: True +# +instrument_div_mult_estimate: + func: estimators.diversification_multipliers.diversification_multiplier_from_list + # smooth IDM in daily space + ewma_span: 125 + dm_max: 2.5 +# +use_instrument_weight_estimates: False +# +# smooth weights they will are in daily space +instrument_weight_ewma_span: 125 +# +instrument_weight_estimate: + func: optimisation.generic_optimiser.genericOptimiser + method: handcraft + frequency: "W" + equalise_gross: False + cost_multiplier: 1.0 + apply_cost_weight: False + date_method: "expanding" + rollyears: 20 + cleaning: True + equalise_SR: True + ann_target_SR: 0.5 + equalise_vols: True + shrinkage_mean: 1.00 + shrinkage_corr: 0.50 + monte_runs: 100 + correlation_estimate: + func: estimators.correlation_estimator.correlationEstimator + using_exponent: True + ew_lookback: 500000 + min_periods: 10 + floor_at_zero: True + cleaning: False + mean_estimate: + func: estimators.mean_estimator.meanEstimator + using_exponent: True + ew_lookback: 50000 + min_periods: 5 + vol_estimate: + func: estimators.stdev_estimator.stdevEstimator + using_exponent: True + ew_lookback: 50000 + min_periods: 5 +# +# buffering / position inertia +buffer_method: forecast +buffer_size: 0.10 +buffer_trade_to_edge: True +# costs and accounting +use_SR_costs: False +vol_normalise_currency_costs: True +multiply_roll_costs_by: 0.5 +# +# risk overlay - make these numbers and uncomment +#risk_overlay: +# max_risk_fraction_normal_risk: 99999 +# max_risk_fraction_stdev_risk: 99999 +# max_risk_limit_sum_abs_risk: 99999 +# max_risk_leverage: 99999 +# +instrument_returns_correlation: + func: estimators.correlation_over_time.correlation_over_time_for_returns + interval_frequency: "W" + frequency: "7D" + date_method: "rolling" + using_exponent: True + ew_lookback: 75 + min_periods: 10 + cleaning: True + offdiag: 0.0 + rollyears: 5 + floor_at_zero: False + forward_fill_price_index: True + clip: 0.99 +# small system optimisation +small_system: + shadow_cost: 50 + cost_multiplier: 1.0 + tracking_error_buffer: 0.0125 + shrink_instrument_returns_correlation: 0.5 +# +# duplicated/excluded instruments are ignored in backtests +# we still collect price data for them in production, do rolls etc +# this is to avoid double counting of basically the same instrument +# anything in the 'exclude' list is ignored in backtests +# we use the 'include section to match up for reporting when deciding which to trade +# Be careful when swapping between instruments, a bit dangerous if a position is held... +# You can override this by modifying the backtest config .yaml file used in either sim or production +# +# THe following example shows how you would select one instrument out of a choice of 3 +# +duplicate_instruments: + include: + things: "thing_we_want" + exclude: + things: ["bad_thing", "Another_thing"] +exclude_instrument_lists: + # ignore_instruments are ignored in backtests + # we still collect price data for them in production, do rolls etc + # You can override this by modifying the backtest config .yaml file used in either sim or production + # If you want to ignore because a duplicate market, use duplicate_instruments + # If you want to include in sim data, but not actually trade, use trading_restrictions + # + # BEST FOR INSTRUMENTS THAT LOOK EXPENSIVE, BUT WHERE WE HAVEN'T GOT ACCURATE DATA YET + # + # REVIEW REGULARLY AND CHECK THESE INSTRUMENTS AREN'T BETTER ELSEWHERE... + ignore_instruments: + - "EXAMPLE" + # + # Here we put stuff we can't trade, but for which we still want to bring in prices in sim + # For the dynamically optimised strategy this list will be used in sim as 'don't trade' + # For static strategies, you will probably want to set these to zero instrument weights + # In production, things here will be added to the 'reduce_only' list of instruments + # NOTE: This will be regionally biased, you will probably want to override this in private_config.yaml + # + trading_restrictions: + - RESTRICTED_EXAMPLE + # These are markets that are too expensive or illiquid to actually trade + # For the dynamically optimised strategy this list will be used in sim as 'don't trade' + # For static strategies, you will probably want to set these to zero instrument weights + # In production, things here will be added to the 'reduce_only' list of instruments + # Run interactive controls to get a list of suggested markets here + # To override this list, change in sim .yaml config (sim) and private_config.yaml (production) + bad_markets: + - BAD_EXAMPLE diff --git a/src/quantlib_st/config/fill_config_dict_with_defaults.py b/src/quantlib_st/config/fill_config_dict_with_defaults.py new file mode 100644 index 0000000..77b3c0a --- /dev/null +++ b/src/quantlib_st/config/fill_config_dict_with_defaults.py @@ -0,0 +1,25 @@ +def fill_config_dict_with_defaults(config_dict: dict, defaults_dict: dict) -> dict: + """ + >>> fill_config_dict_with_defaults({'a':2}, {'b':3}) + {'a': 2, 'b': 3} + """ + ## Substitute in default values from config_dict where missing from defaults_dict + ## Works at multiple levels + default_keys = list(defaults_dict.keys()) + config_keys = list(config_dict.keys()) + joint_keys = list(set(default_keys + config_keys)) + + for key in joint_keys: + config_value = config_dict.get(key, None) + default_value = defaults_dict.get(key, None) + if config_value is None: + config_value = default_value + elif default_value is None: + continue + elif type(config_value) is dict and type(default_value) is dict: + # sometimes, scalars in the default_dict are replaced with dicts + config_value = fill_config_dict_with_defaults(config_value, default_value) + + config_dict[key] = config_value + + return config_dict diff --git a/src/quantlib_st/config/instruments.py b/src/quantlib_st/config/instruments.py new file mode 100644 index 0000000..3be6f07 --- /dev/null +++ b/src/quantlib_st/config/instruments.py @@ -0,0 +1,103 @@ +from quantlib_st.config.configdata import Config + + +def get_list_of_bad_instruments_in_config(config: Config) -> list: + exclude_config = get_config_of_excluded_instruments(config) + bad_markets = exclude_config.get("bad_markets", []) + + return bad_markets + + +def get_list_of_untradeable_instruments_in_config(config: Config) -> list: + exclude_config = get_config_of_excluded_instruments(config) + trading_restrictions = exclude_config.get("trading_restrictions", []) + + return trading_restrictions + + +def get_list_of_ignored_instruments_in_config(config: Config) -> list: + exclude_config = get_config_of_excluded_instruments(config) + ignore_instruments = exclude_config.get("ignore_instruments", []) + + return ignore_instruments + + +def get_config_of_excluded_instruments(config: Config) -> dict: + exclude_instrument_lists = config.get_element_or_default( + "exclude_instrument_lists", {} + ) + return exclude_instrument_lists + + +def generate_matching_duplicate_dict(config: Config): + """ + Returns a dict, each element is a named set of duplicated instruments + Within each dict we have two elements: included, excluded + Each of these is a list + + For example: + dict(copper = dict(included = ["COPPER"], excluded = ["COPPER_mini"] + """ + + duplicate_instruments_config = config.get_element("duplicate_instruments") + exclude_dict = duplicate_instruments_config.get("exclude", {}) + include_dict = duplicate_instruments_config.get("include", {}) + + joint_keys = list(set(list(exclude_dict.keys()) + list(include_dict.keys()))) + + results_dict = dict( + [ + (key, get_duplicate_dict_entry(key, include_dict, exclude_dict)) + for key in joint_keys + ] + ) + + return results_dict + + +def get_duplicate_dict_entry(key: str, include_dict: dict, exclude_dict: dict) -> dict: + include_entry = get_entry_for_key_in_dict(key, include_dict, is_include_dict=True) + exclude_entry = get_entry_for_key_in_dict(key, exclude_dict, is_include_dict=False) + + return dict(included=include_entry, excluded=exclude_entry) + + +def get_entry_for_key_in_dict(key: str, check_dict: dict, is_include_dict: bool = True): + if key not in check_dict.keys(): + if is_include_dict: + print( + "%s is under duplicate_instruments['exclude_dict'] but not include_dict: should match" + % key + ) + else: + print( + "%s is under duplicate_instruments['include_dict'] but not exclude_dict: should match" + % key + ) + entry = [] + else: + entry = check_dict[key] + + if not type(entry) is list: + entry = [entry] + + return entry + + +def get_duplicate_list_of_instruments_to_remove_from_config(config: Config) -> list: + duplicate_instruments_config = config.get_element_or_default( + "duplicate_instruments", {} + ) + + exclude_dict = duplicate_instruments_config.get("exclude", {}) + list_of_duplicates = list(exclude_dict.values()) + + ## do this because can have multiple duplicates + duplicate_list_flattened = [] + for item in list_of_duplicates: + if type(item) is list: + duplicate_list_flattened = duplicate_list_flattened + item + else: + duplicate_list_flattened.append(item) + + return duplicate_list_flattened diff --git a/src/quantlib_st/config/private_config.py b/src/quantlib_st/config/private_config.py new file mode 100644 index 0000000..e0b9345 --- /dev/null +++ b/src/quantlib_st/config/private_config.py @@ -0,0 +1,29 @@ +from typing import Optional + +import yaml + +from quantlib_st.core.fileutils import ( + resolve_path_and_filename_for_package, + does_filename_exist, +) +from quantlib_st.config.private_directory import get_full_path_for_private_config + +PRIVATE_CONFIG_FILE = "private_config.yaml" + + +def get_private_config_as_dict(filename: Optional[str] = None) -> dict: + if filename is None: + filename = get_full_path_for_private_config(PRIVATE_CONFIG_FILE) + if not does_filename_exist(filename): + print( + "Private configuration %s does not exist; no problem if running in sim mode" + % filename + ) + + return {} + + private_file = resolve_path_and_filename_for_package(filename) + with open(private_file) as file_to_parse: + private_dict = yaml.load(file_to_parse, Loader=yaml.FullLoader) + + return private_dict diff --git a/src/quantlib_st/config/private_directory.py b/src/quantlib_st/config/private_directory.py new file mode 100644 index 0000000..a844f34 --- /dev/null +++ b/src/quantlib_st/config/private_directory.py @@ -0,0 +1,15 @@ +import os + +DEFAULT_PRIVATE_DIR = "private" +PRIVATE_CONFIG_DIR_ENV_VAR = "PYSYS_PRIVATE_CONFIG_DIR" + + +def get_full_path_for_private_config(filename: str): + ## FIXME: should use os path join instead of '/'? + + if os.getenv(PRIVATE_CONFIG_DIR_ENV_VAR): + private_config_path = f"{os.environ[PRIVATE_CONFIG_DIR_ENV_VAR]}/{filename}" + else: + private_config_path = f"{DEFAULT_PRIVATE_DIR}/{filename}" + + return private_config_path diff --git a/src/quantlib_st/core/constants.py b/src/quantlib_st/core/constants.py new file mode 100644 index 0000000..5192495 --- /dev/null +++ b/src/quantlib_st/core/constants.py @@ -0,0 +1,21 @@ +none_type = type(None) + + +class named_object: + def __init__(self, name): + self._name = str(name) + + def __repr__(self): + return self._name + + +arg_not_supplied = named_object("arg not supplied") +user_exit = named_object("exit") + + +class status(named_object): + pass + + +success = status("success") +failure = status("failure") diff --git a/src/quantlib_st/core/dateutils.py b/src/quantlib_st/core/dateutils.py index a1e9a4a..28493b3 100644 --- a/src/quantlib_st/core/dateutils.py +++ b/src/quantlib_st/core/dateutils.py @@ -4,6 +4,9 @@ """ +import datetime +import pandas as pd + from enum import Enum CALENDAR_DAYS_IN_YEAR = 365.25 @@ -22,13 +25,29 @@ SECONDS_PER_DAY = HOURS_PER_DAY * SECONDS_PER_HOUR SECONDS_IN_YEAR = CALENDAR_DAYS_IN_YEAR * SECONDS_PER_DAY +ARBITRARY_START = datetime.datetime(1900, 1, 1) + +NOTIONAL_CLOSING_TIME_AS_PD_OFFSET = pd.DateOffset( + hours=23, + minutes=0, + seconds=0, +) + +""" + + FREQUENCIES + +""" Frequency = Enum( "Frequency", - "Unknown Year Month Week BDay Day", + "Unknown Year Month Week BDay Day Hour Minutes_15 Minutes_5 Minute Seconds_10 Second Mixed", ) - DAILY_PRICE_FREQ = Frequency.Day +BUSINESS_DAY_FREQ = Frequency.BDay +HOURLY_FREQ = Frequency.Hour + +MIXED_FREQ = Frequency.Mixed def from_config_frequency_pandas_resample(freq: Frequency) -> str: @@ -71,3 +90,112 @@ def from_frequency_to_times_per_year(freq: Frequency) -> float: raise ValueError("Frequency %s is not supported" % freq) from exc return float(times_per_year) + + +""" + + EQUAL DATES WITHIN A YEAR + +""" + + +def generate_equal_dates_within_year( + year: int, number_of_dates: int, force_start_year_align: bool = False +) -> list[datetime.datetime]: + """ + Generate equally spaced datetimes within a given year + >>> generate_equal_dates_within_year(2022,3) + [datetime.datetime(2022, 3, 2, 0, 0), datetime.datetime(2022, 7, 1, 0, 0), datetime.datetime(2022, 10, 30, 0, 0)] + >>> generate_equal_dates_within_year(2022,1) + [datetime.datetime(2022, 7, 2, 0, 0)] + >>> generate_equal_dates_within_year(2021,2, force_start_year_align=True) + [datetime.datetime(2021, 1, 1, 0, 0), datetime.datetime(2021, 7, 2, 0, 0)] + """ + + days_between_periods = int(CALENDAR_DAYS_IN_YEAR / float(number_of_dates)) + first_date = _calculate_first_date_for_equal_dates( + year=year, + days_between_periods=days_between_periods, + force_start_year_align=force_start_year_align, + ) + delta_for_each_period = datetime.timedelta(days=days_between_periods) + + all_dates = [ + first_date + (delta_for_each_period * period_count) + for period_count in range(number_of_dates) + ] + + return all_dates + + +def _calculate_first_date_for_equal_dates( + year: int, days_between_periods: int, force_start_year_align: bool = False +) -> datetime.datetime: + start_of_year = datetime.datetime(year, 1, 1) + + if force_start_year_align: + ## more realistic for most rolling calendars + first_date = start_of_year + else: + half_period = int(days_between_periods / 2) + half_period_increment = datetime.timedelta(days=half_period) + first_date = start_of_year + half_period_increment + + return first_date + + +""" + + FUTURES MONTHS + +""" + +FUTURES_MONTH_LIST = ["F", "G", "H", "J", "K", "M", "N", "Q", "U", "V", "X", "Z"] + + +def month_from_contract_letter(contract_letter: str) -> int: + """ + Returns month number (1 is January) from contract letter + >>> month_from_contract_letter("F") + 1 + >>> month_from_contract_letter("Z") + 12 + >>> month_from_contract_letter("A") + Exception: Contract letter A is not a valid future month (must be one of ['F', 'G', 'H', 'J', 'K', 'M', 'N', 'Q', 'U', 'V', 'X', 'Z']) + + """ + + try: + month_number = FUTURES_MONTH_LIST.index(contract_letter) + except ValueError: + raise Exception( + "Contract letter %s is not a valid future month (must be one of %s)" + % (contract_letter, str(FUTURES_MONTH_LIST)) + ) + + return month_number + 1 + + +def contract_month_from_number(month_number: int) -> str: + """ + Returns standard month letters used in futures land + + >>> contract_month_from_number(1) + 'F' + >>> contract_month_from_number(12) + 'Z' + >>> contract_month_from_number(0) + Exception: Months have to be between 1 and 12 + >>> contract_month_from_number(13) + Exception: Months have to be between 1 and 12 + + :param month_number: int + :return: str + """ + + try: + assert month_number > 0 and month_number < 13 + except: + raise Exception("Months have to be between 1 and 12") + + return FUTURES_MONTH_LIST[month_number - 1] diff --git a/src/quantlib_st/core/exceptions.py b/src/quantlib_st/core/exceptions.py new file mode 100644 index 0000000..627ae47 --- /dev/null +++ b/src/quantlib_st/core/exceptions.py @@ -0,0 +1,39 @@ +""" +Custom exceptions +""" + + +class missingInstrument(Exception): + pass + + +class missingContract(Exception): + pass + + +class missingData(Exception): + pass + + +class missingFile(Exception): + pass + + +class marketClosed(Exception): + pass + + +class fillExceedsTrade(Exception): + pass + + +class existingData(Exception): + pass + + +class orderCannotBeModified(Exception): + pass + + +class ContractNotFound(Exception): + pass diff --git a/src/quantlib_st/core/fileutils.py b/src/quantlib_st/core/fileutils.py new file mode 100644 index 0000000..932dad1 --- /dev/null +++ b/src/quantlib_st/core/fileutils.py @@ -0,0 +1,195 @@ +import os +import glob +from importlib import import_module +from pathlib import Path +from typing import List, Tuple, Optional + +## something unlikely to occur naturally in a pathname +RESERVED_CHARACTERS = "&!*" + +""" +FILENAME RESOLUTION +""" + + +def resolve_path_and_filename_for_package( + path_and_filename: str, separate_filename: Optional[str] = None +) -> str: + """ + A way of resolving relative and absolute filenames, and dealing with awkward OS specific things + + >>> resolve_path_and_filename_for_package("/home/rodion/", "file.csv") + '/home/rodion/file.csv' + + >>> resolve_path_and_filename_for_package(".home.rodion", "file.csv") + '/home/rodion/file.csv' + + >>> resolve_path_and_filename_for_package("core.tests", "file.csv") + '/home/rodion/quantlib_st/core/tests/file.csv' + + >>> resolve_path_and_filename_for_package("core.tests.file.csv") + '/home/rodion/quantlib_st/core/tests/file.csv' + + """ + path_as_list = transform_path_into_list(path_and_filename) + if separate_filename is None: + path_as_list, separate_filename = ( + extract_filename_from_combined_path_and_filename_list(path_as_list) + ) + + resolved_pathname = get_pathname_from_list(path_as_list) + + # Use Path for clean joining + return str(Path(resolved_pathname) / separate_filename) + + +def transform_path_into_list(pathname: str) -> List[str]: + """ + Splits the pathname by '.', '/', and '\'. + Matches the original implementation's behavior for absolute and relative paths. + """ + # Normalize separators and split + # Leading / or . will resulting in a leading empty string in the list + normalized = pathname.replace(".", "/").replace("\\", "/") + path_as_list = normalized.split("/") + + # The original implementation used rsplit and popped a trailing empty string + if path_as_list and path_as_list[-1] == "" and len(path_as_list) > 1: + path_as_list.pop() + + return path_as_list + + +def add_reserved_characters_to_pathname(pathname: str) -> str: + """ + Kept for backward compatibility. + """ + return ( + pathname.replace(".", RESERVED_CHARACTERS) + .replace("/", RESERVED_CHARACTERS) + .replace("\\", RESERVED_CHARACTERS) + ) + + +def extract_filename_from_combined_path_and_filename_list( + path_and_filename_as_list: List[str], +) -> Tuple[List[str], str]: + """ + Splits out the filename and extension from the end of the list. + """ + extension = path_and_filename_as_list.pop() + filename = path_and_filename_as_list.pop() + + separate_filename = f"{filename}.{extension}" + return path_and_filename_as_list, separate_filename + + +def get_pathname_from_list(path_as_list: List[str]) -> str: + """ + Determines if the path is absolute (Linux/Windows) or relative/package-based. + """ + if not path_as_list: + return "." + + first_part = path_as_list[0] + + if first_part == "": + # path_type_absolute Linux (or start with dot) + return get_absolute_linux_pathname_from_list(path_as_list[1:]) + elif is_window_path_list(path_as_list): + # windoze absolute + return get_absolute_windows_pathname_from_list(path_as_list) + else: + # package or standard relative + return get_relative_pathname_from_list(path_as_list) + + +def get_absolute_linux_pathname_from_list(path_as_list: List[str]) -> str: + """ + Returns the absolute linux pathname from a list + """ + return os.path.sep + os.path.join(*path_as_list) if path_as_list else os.path.sep + + +def get_absolute_windows_pathname_from_list(path_as_list: List[str]) -> str: + """ + Returns the absolute windows pathname from a list + """ + drive_part = path_as_list[0] + if drive_part.endswith(":"): + # Ensure 'C:' becomes 'C:\' or 'C:/' depending on OS + drive_part += os.path.sep + + return os.path.join(drive_part, *path_as_list[1:]) + + +def get_relative_pathname_from_list(path_as_list: List[str]) -> str: + """ + Resolves the path starting with a package name component. + """ + if not path_as_list: + return "." + + package_name = path_as_list[0] + try: + module = import_module(package_name) + if hasattr(module, "__file__") and module.__file__: + directory_name_of_package = os.path.dirname(module.__file__) + return os.path.join(directory_name_of_package, *path_as_list[1:]) + except (ImportError, TypeError): + # Fallback to current directory join if it's not a package + pass + + return os.path.join(*path_as_list) + + +def is_window_path_list(path_as_list: List[str]) -> bool: + """ + Checks if the path is a Windows-style drive path. + """ + return bool(path_as_list and path_as_list[0].endswith(":")) + + +def does_filename_exist(filename: str) -> bool: + resolved_filename = resolve_path_and_filename_for_package(filename) + file_exists = does_resolved_filename_exist(resolved_filename) + return file_exists + + +def does_resolved_filename_exist(resolved_filename: str) -> bool: + file_exists = os.path.isfile(resolved_filename) + return file_exists + + +def get_resolved_pathname(pathname: str) -> str: + """ + Resolve a pathname that may be absolute or package-relative. + """ + path_as_list = transform_path_into_list(pathname) + return get_pathname_from_list(path_as_list) + + +def files_with_extension_in_pathname( + pathname: str, extension: str = ".csv" +) -> List[str]: + """ + Find all the files with a particular extension in a directory. + """ + resolved_pathname = get_resolved_pathname(pathname) + return files_with_extension_in_resolved_pathname( + resolved_pathname, extension=extension + ) + + +def files_with_extension_in_resolved_pathname( + resolved_pathname: str, extension: str = ".csv" +) -> List[str]: + """ + Find all the files with a particular extension in a resolved directory. + """ + file_list = [ + os.path.basename(f) for f in glob.glob(f"{resolved_pathname}/*{extension}") + ] + file_list_no_extension = [filename.split(".")[0] for filename in file_list] + + return file_list_no_extension diff --git a/src/quantlib_st/core/genutils.py b/src/quantlib_st/core/genutils.py new file mode 100644 index 0000000..0b218c3 --- /dev/null +++ b/src/quantlib_st/core/genutils.py @@ -0,0 +1,391 @@ +""" +Utilities I can't put anywhere else... +""" + +from typing import Union +import datetime +import functools +import math + +import numpy as np + +from collections import namedtuple +from math import copysign, gcd +from typing import Union, List + +Changes = namedtuple("Changes", ["new", "existing", "removing"]) + + +def new_removing_existing(original_list: list, new_list: list) -> Changes: + """ + >>> new_removing_existing([1,2],[3,4]) + Changes(new=[3, 4], existing=[], removing=[1, 2]) + >>> new_removing_existing([1,2,3],[3,4]) + Changes(new=[4], existing=[3], removing=[1, 2]) + >>> new_removing_existing([1,2,3],[1,2]) + Changes(new=[], existing=[1, 2], removing=[3]) + >>> new_removing_existing([1],[1,2]) + Changes(new=[2], existing=[1], removing=[]) + """ + existing = list_intersection(original_list, new_list) + new = list_difference(new_list, original_list) + removing = list_difference(original_list, new_list) + + return Changes(new=new, existing=existing, removing=removing) + + +def list_intersection(x: list, y: list) -> list: + """ + >>> list_intersection([2,1], [1]) + [1] + >>> list_intersection([2,1], [3]) + [] + """ + return list(set(x).intersection(set(y))) + + +def list_difference(x: list, y: list) -> list: + """ + >>> list_difference([2,1], [1]) + [2] + >>> list_difference([2,1], [3]) + [1, 2] + >>> list_difference([3], [1, 2]) + [3] + >>> list_difference([2,1], [1,2]) + [] + """ + return list(set(x).difference(set(y))) + + +def list_union(x: list, y: list) -> list: + """ + >>> list_union([1,2], [1]) + [1, 2] + >>> list_union([2,1], [3]) + [1, 2, 3] + """ + return list(set(x).union(set(y))) + + +def get_unique_list(somelist: list) -> list: + return list(set(somelist)) + + +def get_unique_list_slow(somelist): + uniquelist = [] + + for somevalue in somelist: + if somevalue not in uniquelist: + uniquelist.append(somevalue) + + return uniquelist + + +def round_significant_figures(x: float, figures: int = 3) -> float: + """ + >>> round_significant_figures(0.0234, 2) + 0.023 + """ + if x == 0: + return 0.0 + + return round(x, figures - int(math.floor(math.log10(abs(x)))) - 1) + + +def flatten_list(some_list: list) -> list: + """ + >>> flatten_list([[1],[2,3]]) + [1, 2, 3] + """ + flattened = [item for sublist in some_list for item in sublist] + + return flattened + + +def str2Bool(x: str) -> bool: + if isinstance(x, bool): + return x + if x.lower() in ("t", "true"): + return True + if x.lower() in ("f", "false"): + return False + raise Exception("%s can't be resolved as a bool" % x) + + +def str_of_int(x: int) -> str: + """ + Returns the string of int of x, handling nan's or whatever + + >>> str_of_int(34) + '34' + + >>> str_of_int(34.0) + '34' + + >>> import numpy as np + >>> str_of_int(np.nan) + '' + + """ + if isinstance(x, int): + return str(x) + try: + return str(int(x)) + except BaseException: + return "" + + +def same_sign(x, y): + return sign(x) == sign(y) + + +def sign(x: Union[int, float]) -> float: + """ + >>> sign(3) + 1.0 + + >>> sign(3.0) + 1.0 + + >>> sign(-3) + -1.0 + + >>> sign(0) + 1.0 + + + """ + return copysign(1, x) + + +def return_another_value_if_nan(x, return_value=None): + """ + If x is np.nan return return_value + else return x + + :param x: np.nan or other + :return: x or return_value + + >>> return_another_value_if_nan(np.nan) + + >>> return_another_value_if_nan(np.nan, -1) + -1 + + >>> return_another_value_if_nan("thing") + 'thing' + + >>> return_another_value_if_nan(42) + 42 + + """ + + try: + if np.isnan(x): + return return_value + else: + pass + # Not a nan will return x + except BaseException: + # Not something that can be compared to a nan + pass + + # Either wrong type, or not a nan + return x + + +def are_dicts_equal(d1: dict, d2: dict) -> bool: + """ + >>> are_dicts_equal({1: 'a', 2: 'b'}, {2: 'b', 1: 'a'}) + True + >>> are_dicts_equal({1: 'a', 2: 'b'}, {3: 'b', 1: 'a'}) + False + >>> are_dicts_equal({1: 'a', 2: 'b'}, {2: 'c', 1: 'a'}) + False + """ + d1_keys = set(d1.keys()) + d2_keys = set(d2.keys()) + intersect_keys = d1_keys.intersection(d2_keys) + if len(intersect_keys) != len(d1_keys): + return False + same_values = set(o for o in intersect_keys if d1[o] == d2[o]) + if len(same_values) != len(d1_keys): + return False + return True + + +class quickTimer(object): + def __init__(self, seconds: Union[float, int] = 60): + self._time_started = datetime.datetime.now() + self._time_limit = seconds + + @property + def unfinished(self) -> bool: + return not self.finished + + @property + def finished(self) -> bool: + time_now = datetime.datetime.now() + elapsed = time_now - self.time_started + is_it_finished = elapsed.seconds > self.time_limit + + return is_it_finished + + @property + def time_started(self) -> datetime.datetime: + return self._time_started + + @property + def time_limit(self) -> int: + return self._time_limit # type: ignore + + +# avoids encoding problems with mongo +EMPTY_STRING = "" + + +def if_empty_string_return_object(x, object): + if x == EMPTY_STRING: + return object + else: + return x + + +def if_object_matches_return_empty_string(x, object_to_match): + if x is object_to_match: + return EMPTY_STRING + else: + return x + + +""" + + NAMED TUPLES AND OBJECTS + +""" + + +def transfer_object_attributes(named_tuple_object, original_object): + kwargs = dict( + [ + (field_name, getattr(original_object, field_name)) + for field_name in named_tuple_object._fields + ] + ) + new_object = named_tuple_object(**kwargs) + + return new_object + + +""" + + COMMON FACTORS + +""" + + +def highest_common_factor_for_list(list_of_ints: List[int]) -> int: + """ + + >>> highest_common_factor_for_list([2,3,4]) + 1 + >>> highest_common_factor_for_list([2,6,4]) + 2 + """ + return functools.reduce(gcd, list_of_ints) + + +def divide_list_of_ints_by_highest_common_factor(list_of_ints: List[int]) -> list: + """ + + >>> divide_list_of_ints_by_highest_common_factor([1,2]) + [1, 2] + >>> divide_list_of_ints_by_highest_common_factor([2,4]) + [1, 2] + >>> divide_list_of_ints_by_highest_common_factor([1,2,3]) + [1, 2, 3] + >>> divide_list_of_ints_by_highest_common_factor([1]) + [1] + """ + + gcd_value = highest_common_factor_for_list(list_of_ints) + new_list = [int(float(x) / gcd_value) for x in list_of_ints] + return new_list + + +def list_of_ints_with_highest_common_factor_positive_first( + list_of_ints: List[int], +) -> list: + """ + Used to identify the unique version of a spread or fly contract + + :param list_of_ints: + :return: list + + >>> list_of_ints_with_highest_common_factor_positive_first([1]) + [1] + >>> list_of_ints_with_highest_common_factor_positive_first([-1]) + [1] + >>> list_of_ints_with_highest_common_factor_positive_first([1,-1]) + [1, -1] + >>> list_of_ints_with_highest_common_factor_positive_first([-1,1]) + [1, -1] + >>> list_of_ints_with_highest_common_factor_positive_first([-1,2]) + [1, -2] + >>> list_of_ints_with_highest_common_factor_positive_first([-2,2]) + [1, -1] + >>> list_of_ints_with_highest_common_factor_positive_first([2,-2]) + [1, -1] + >>> list_of_ints_with_highest_common_factor_positive_first([2,-4,2]) + [1, -2, 1] + >>> list_of_ints_with_highest_common_factor_positive_first([-2,4,-2]) + [1, -2, 1] + + """ + new_list = divide_list_of_ints_by_highest_common_factor(list_of_ints) + multiply_sign = sign(new_list[0]) + new_list = [int(x * multiply_sign) for x in new_list] + return new_list + + +def np_convert(val): + """ + Converts the passed numpy value to a native python type + + >>> val = np.int64(1) + >>> type(val) + + >>> type(np_convert(val)) + + """ + return val.item() if isinstance(val, np.generic) else val + + +def intersection_intervals(intervals: list) -> list: + """ + >>> intersection_intervals([[1,5], [2,3]]) + [2, 3] + >>> intersection_intervals([[1,5], [2,6]]) + [2, 5] + >>> intersection_intervals([[1,5], [1,5]]) + [1, 5] + >>> intersection_intervals([[3,7], [2,6]]) + [3, 6] + >>> intersection_intervals([[1,5], [7,9]]) + [] + """ + start, end = intervals.pop() + while intervals: + start_temp, end_temp = intervals.pop() + start = max(start, start_temp) + end = min(end, end_temp) + + if end < start: + return [] + return [start, end] + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/core/maths.py b/src/quantlib_st/core/maths.py new file mode 100644 index 0000000..f204f4b --- /dev/null +++ b/src/quantlib_st/core/maths.py @@ -0,0 +1,117 @@ +import math as maths + +import numpy as np + +from copy import copy + + +def calculate_weighted_average_with_nans( + weights: list, list_of_values: list, sum_of_weights_should_be: float = 1.0 +) -> float: + """ + Calculates a weighted average while robustly handling missing data (NaNs). + + This function is essential for portfolio math where some components (weights or values) + might be missing. It follows these steps: + 1. Identifies pairs where either the weight or the value is NaN. + 2. Zeroes out the weight for those invalid pairs. + 3. Renormalizes the remaining "good" weights so they sum to the target + (e.g., if one of two 0.5 weights is removed, the remaining becomes 1.0). + 4. Computes the final weighted average. + + This ensures that if 50% of your data is missing, the average is calculated + based on the remaining 50%, correctly scaled to the intended total weight. + + >>> calculate_weighted_average_with_nans([0.2, 0.2, np.nan, 0.4],[2, np.nan, 3, np.nan]) + 2.0 + >>> calculate_weighted_average_with_nans([np.nan, 0.2, np.nan, 0.4],[2, np.nan, 3, np.nan]) + 0.0 + + """ + ## easier to work in np space + np_weights = np.array(weights) + np_values = np.array(list_of_values) + + # get safe weights + np_weights_without_nans_in_weights_or_values = ( + calculate_np_weights_without_nans_in_weights_or_values( + np_weights=np_weights, np_values=np_values + ) + ) + + normalised_weights = renormalise_array_of_values_to_sum( + np_weights_without_nans_in_weights_or_values, + renormalise_sum_to=sum_of_weights_should_be, + ) + + weighted_value = calculate_weighted_average( + np_weights=normalised_weights, np_values=np_values + ) + + return weighted_value + + +def calculate_np_weights_without_nans_in_weights_or_values( + np_weights: np.ndarray, np_values: np.ndarray +) -> np.ndarray: + """ + Get set of weights where neithier the value or the weight is nan + >>> calculate_np_weights_without_nans_in_weights_or_values(np.array([0.2, 0.2, np.nan, 0.4]),np.array([2, np.nan, 3, np.nan])) + array([0.2, 0. , 0. , 0. ]) + """ + + weights_times_values_as_np = np_weights * np_values + empty_weights = np.isnan(weights_times_values_as_np) + + weights_without_nan = copy(np_weights) + weights_without_nan[empty_weights] = 0.0 + + return weights_without_nan + + +def renormalise_array_of_values_to_sum( + array_of_values: np.ndarray, renormalise_sum_to: float = 1.0 +) -> np.ndarray: + """ + Most commonly used to renormalise weights to 1.0 + + >>> renormalise_array_of_values_to_sum(np.array([0.125, 0.125, 0.25, 0.0]), 2.0) + array([0.5, 0.5, 1. , 0. ]) + + >>> renormalise_array_of_values_to_sum(np.array([0.0, 0.0, 0.0, 0.0]), 1.0) + array([0., 0., 0., 0.]) + + """ + sum_of_values = np.nansum(array_of_values) + if sum_of_values == 0: + return np.zeros(len(array_of_values)) + + renormalise_multiplier = renormalise_sum_to / sum_of_values + new_values = array_of_values * renormalise_multiplier + + return new_values + + +def calculate_weighted_average(np_weights: np.ndarray, np_values: np.ndarray) -> float: + """ + Implicit replaces nan with zeros + + >>> calculate_weighted_average(np.array([0.2, 0.2, 0, 0.4]),np.array([2, np.nan, 3, np.nan])) + 0.4 + """ + weights_times_values_as_np = np_weights * np_values + + return float(np.nansum(weights_times_values_as_np)) + + +def magnitude(x): + """ + Magnitude of a positive numeber. Used for calculating significant figures + """ + return int(maths.log10(x)) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/core/objects.py b/src/quantlib_st/core/objects.py new file mode 100644 index 0000000..3690443 --- /dev/null +++ b/src/quantlib_st/core/objects.py @@ -0,0 +1,118 @@ +import importlib + + +LIST_OF_RESERVED_METHOD_NAMES = ["log", "name", "parent", "description", "data"] + + +def get_methods(a_stage_object) -> list: + """ + Get methods from a stage object + """ + dir_list = dir(a_stage_object) + + # remove private "_" + dir_list = [method_name for method_name in dir_list if method_name[0] != "_"] + + # remove special + dir_list = [ + method_name + for method_name in dir_list + if method_name not in LIST_OF_RESERVED_METHOD_NAMES + ] + + return dir_list + + +def resolve_function(func_or_func_name): + """ + If func_or_func_name is a callable function, then return the function + + If it is a string containing '.' then it is a function in a module, return the function + + :param func_or_func_name: Name of a function including package.module, or a callable function + >>> resolve_function(str) + + + >>> resolve_function("sysquant.estimators.vol.robust_vol_calc").__name__ + 'robust_vol_calc' + + """ + + if hasattr(func_or_func_name, "__call__"): + # it's a function, just return it so can be used + # doesn't work for all functions, but we only care about callable ones + return func_or_func_name + + if not isinstance(func_or_func_name, str): + raise Exception( + "Called resolve_function with non string or callable object %s" + % str(func_or_func_name) + ) + + if "." in func_or_func_name: + # it's another module, have to get it + mod_name, func_name = func_or_func_name.rsplit(".", 1) + mod = importlib.import_module(mod_name) + func = getattr(mod, func_name, None) + + else: + raise Exception( + "Need full module file name string: %s isn't good enough" + % func_or_func_name + ) + + return func + + +def resolve_data_method(some_object, data_string: str): + """ + eg if data_string="data1.data2.method" then returns the method some_object.data1.data2.method + + :param some_object: The object with a method + :param data_string: method or attribute within object + + :returns: method in some_object + + >>> from sysdata.sim.csv_futures_sim_data import csvFuturesSimData + >>> + >>> data=csvFuturesSimData() + >>> resolve_data_method(data, "daily_prices") + + + """ + + list_to_parse = data_string.rsplit(".") + + return _recursively_get_attr_within_list(some_object, list_to_parse) + + +def _recursively_get_attr_within_list(an_object, list_to_parse: list[str]): + if len(list_to_parse) == 0: + return an_object + + next_attr = list_to_parse.pop(0) + sub_object = getattr(an_object, next_attr) + + return _recursively_get_attr_within_list(sub_object, list_to_parse) + + +def hasallattr(some_object, attrlist: list[str]): + """ + Check something has all the attributes you need + + :returns: bool + + >>> from sysdata.sim.sim_data import simData + >>> data=simData() + >>> setattr(data, "one", 1) + >>> setattr(data, "two", 2) + >>> hasallattr(data, ["one", "two"]) + True + >>> hasallattr(data, ["one", "two","three"]) + False + """ + return all([hasattr(some_object, attrname) for attrname in attrlist]) + + +def get_class_name(class_object) -> str: + return class_object.__name__ diff --git a/src/quantlib_st/core/pandas/find_data.py b/src/quantlib_st/core/pandas/find_data.py new file mode 100644 index 0000000..bfeae67 --- /dev/null +++ b/src/quantlib_st/core/pandas/find_data.py @@ -0,0 +1,81 @@ +import datetime +import pandas as pd + +from typing import Union + +from quantlib_st.core.constants import arg_not_supplied, none_type + + +def get_row_of_df_aligned_to_weights_as_dict( + df: pd.DataFrame, relevant_date: datetime.datetime = arg_not_supplied +) -> dict: + """ + >>> d = datetime.datetime + >>> date_index = [d(2000,1,1),d(2000,1,2),d(2000,1,3), d(2000,1,4)] + >>> df = pd.DataFrame(dict(a=[1, 2, 3,4], b=[4,5,6,7]), index=date_index) + >>> get_row_of_df_aligned_to_weights_as_dict(df, d(2000,1,3)) + {'a': 3, 'b': 6} + """ + + if relevant_date is arg_not_supplied: + data_at_date = df.iloc[-1] + else: + try: + data_at_date = df.loc[relevant_date] + except KeyError: + raise Exception("Date %s not found in data" % str(relevant_date)) + + return data_at_date.to_dict() + + +def get_row_of_series( + series: pd.Series, relevant_date: datetime.datetime = arg_not_supplied +): + """ + >>> d = datetime.datetime + >>> date_index1 = [d(2000,1,1),d(2000,1,2),d(2000,1,3)] + >>> s1 = pd.Series([1,2,3], index=date_index1) + >>> get_row_of_series(s1, d(2000,1,2)) + 2 + """ + + if relevant_date is arg_not_supplied: + data_at_date = series.values[-1] + else: + try: + data_at_date = series.loc[relevant_date] + except KeyError: + raise Exception("Date %s not found in data" % str(relevant_date)) + + return data_at_date + + +def get_row_of_series_before_date( + series: pd.Series, relevant_date: datetime.datetime = arg_not_supplied +): + """ + >>> d = datetime.datetime + >>> date_index1 = [d(2000,1,1),d(2000,1,2),d(2000,1,5)] + >>> s1 = pd.Series([1,2,3], index=date_index1) + >>> get_row_of_series_before_date(s1, d(2000,1,3)) + 2 + """ + + if relevant_date is arg_not_supplied: + data_at_date = series.values[-1] + else: + index_point = get_max_index_before_datetime(series.index, relevant_date) + data_at_date = series.values[index_point] + + return data_at_date + + +def get_max_index_before_datetime( + index: pd.core.indexes.datetimes.DatetimeIndex, date_point: datetime.datetime +) -> Union[int, none_type]: + matching_index_size = index[index < date_point].size + + if matching_index_size == 0: + return None + else: + return matching_index_size - 1 diff --git a/src/quantlib_st/core/pandas/frequency.py b/src/quantlib_st/core/pandas/frequency.py index 505f5fa..65935f9 100644 --- a/src/quantlib_st/core/pandas/frequency.py +++ b/src/quantlib_st/core/pandas/frequency.py @@ -1,5 +1,20 @@ +import datetime +import numpy as np import pandas as pd +from typing import Union + +from quantlib_st.core.constants import named_object +from quantlib_st.core.dateutils import ( + Frequency, + BUSINESS_DAY_FREQ, + BUSINESS_DAYS_IN_YEAR, + CALENDAR_DAYS_IN_YEAR, + HOURLY_FREQ, + HOURS_PER_DAY, + NOTIONAL_CLOSING_TIME_AS_PD_OFFSET, +) + def resample_prices_to_business_day_index(x: pd.Series) -> pd.Series: """Resample prices to a business-day index by taking the last available price. @@ -14,3 +29,129 @@ def resample_prices_to_business_day_index(x: pd.Series) -> pd.Series: all instruments. """ return x.resample("1B").last() + + +def get_intraday_pdf_at_frequency( + pd_object: Union[pd.DataFrame, pd.Series], + frequency: str = "H", + closing_time: pd.DateOffset = NOTIONAL_CLOSING_TIME_AS_PD_OFFSET, +) -> Union[pd.Series, pd.DataFrame]: + """ + >>> import datetime + >>> d = datetime.datetime + >>> date_index = [d(2000,1,1,15),d(2000,1,1,16),d(2000,1,1,23), d(2000,1,2,15)] + >>> df = pd.DataFrame(dict(a=[1, 2, 3,4], b=[4,5,6,7]), index=date_index) + >>> get_intraday_pdf_at_frequency(df,"2H") + a b + 2000-01-01 14:00:00 1.0 4.0 + 2000-01-01 16:00:00 2.0 5.0 + 2000-01-02 14:00:00 4.0 7.0 + """ + intraday_only_df = intraday_date_rows_in_pd_object( + pd_object, closing_time=closing_time + ) + intraday_df = intraday_only_df.resample(frequency).last() + intraday_df_clean = intraday_df.dropna() + + return intraday_df_clean + + +def intraday_date_rows_in_pd_object( + pd_object: Union[pd.DataFrame, pd.Series], + closing_time: pd.DateOffset = NOTIONAL_CLOSING_TIME_AS_PD_OFFSET, +) -> Union[pd.DataFrame, pd.Series]: + """ + >>> import datetime + >>> d = datetime.datetime + >>> date_index = [d(2000,1,1,15),d(2000,1,1,23), d(2000,1,2,15)] + >>> df = pd.DataFrame(dict(a=[1, 2, 3], b=[4 , 6, 5]), index=date_index) + >>> intraday_date_rows_in_pd_object(df) + a b + 2000-01-01 15:00:00 1 4 + 2000-01-02 15:00:00 3 5 + """ + + return pd_object[ + [ + not check_time_matches_closing_time_to_second( + index_entry=index_entry, closing_time=closing_time + ) + for index_entry in pd_object.index + ] + ] + + +def check_time_matches_closing_time_to_second( + index_entry: datetime.datetime, + closing_time: pd.DateOffset = NOTIONAL_CLOSING_TIME_AS_PD_OFFSET, +) -> bool: + """ + Check time matches at one second resolution (good enough) + + >>> check_time_matches_closing_time_to_second(datetime.datetime(2023, 1, 15, 6, 32)) + False + >>> check_time_matches_closing_time_to_second(datetime.datetime(2023, 1, 15, 23, 0)) + True + """ + + if ( + index_entry.hour == closing_time.hours # type: ignore + and index_entry.minute == closing_time.minutes # type: ignore + and index_entry.second == closing_time.seconds # type: ignore + ): + return True + else: + return False + + +def infer_frequency( + df_or_ts: Union[pd.DataFrame, pd.Series] | named_object, +) -> Frequency: + if df_or_ts is named_object: + raise Exception("Cannot infer frequency when args is not supplied") + + inferred = pd.infer_freq(df_or_ts.index) # type: ignore + if inferred is None: + assert df_or_ts is pd.DataFrame or df_or_ts is pd.Series + return infer_frequency_approx(df_or_ts) + if inferred == "B": + return BUSINESS_DAY_FREQ + if inferred == "H": + return HOURLY_FREQ + raise Exception("Frequency of time series unknown") + + +UPPER_BOUND_HOUR_FRACTION_OF_A_DAY = 1.0 / 2.0 +LOWER_BOUND_HOUR_FRACTION_OF_A_DAY = 1.0 / HOURS_PER_DAY +BUSINESS_CALENDAR_FRACTION = CALENDAR_DAYS_IN_YEAR / BUSINESS_DAYS_IN_YEAR + + +def infer_frequency_approx(df_or_ts: Union[pd.DataFrame, pd.Series]) -> Frequency: + avg_time_delta_in_days = average_time_delta_for_time_series(df_or_ts) + + if _probably_daily_freq(avg_time_delta_in_days): + return BUSINESS_DAY_FREQ + + if _probably_hourly_freq(avg_time_delta_in_days): + return HOURLY_FREQ + + raise Exception("Can't work out approximate frequency") + + +def _probably_daily_freq(avg_time_delta_in_days: float) -> bool: + return round(avg_time_delta_in_days, 1) == BUSINESS_CALENDAR_FRACTION + + +def _probably_hourly_freq(avg_time_delta_in_days: float) -> bool: + return (avg_time_delta_in_days < UPPER_BOUND_HOUR_FRACTION_OF_A_DAY) & ( + avg_time_delta_in_days >= LOWER_BOUND_HOUR_FRACTION_OF_A_DAY + ) + + +def average_time_delta_for_time_series( + df_or_ts: Union[pd.DataFrame, pd.Series], +) -> float: + avg_time_delta = abs(np.diff(df_or_ts.index)).mean() + avg_time_delta_in_days = avg_time_delta / np.timedelta64(1, "D") + + return avg_time_delta_in_days diff --git a/src/quantlib_st/core/pandas/full_merge_with_replacement.py b/src/quantlib_st/core/pandas/full_merge_with_replacement.py new file mode 100644 index 0000000..afb32a3 --- /dev/null +++ b/src/quantlib_st/core/pandas/full_merge_with_replacement.py @@ -0,0 +1,200 @@ +from copy import copy +from typing import Union + +import pandas as pd + +from quantlib_st.core.constants import arg_not_supplied, named_object +from quantlib_st.core.pandas.merge_data_keeping_past_data import ( + VERY_BIG_NUMBER, + spike_check_merged_data, + SPIKE_IN_DATA, + mergingDataWithStatus, + MERGED_DATA, +) +from quantlib_st.core.pandas.pdutils import is_a_series, is_a_dataframe + + +def full_merge_of_existing_data( + old_data: Union[pd.Series, pd.DataFrame], + new_data: Union[pd.Series, pd.DataFrame], + keep_older: bool = True, + check_for_spike: bool = False, + max_spike: float = VERY_BIG_NUMBER, + column_to_check_for_spike: str | named_object = arg_not_supplied, +): + """ + Merges old data with new data. + + Any Nan in the existing data will ALWAYS be replaced (be careful!) + + If keep_older is False, then any value in the existing data which has the same index + as in newer data will be replaced with newer data + + If you don't want to replace *any* existing data that occurs in time space before + the earliest date of the new data, use merge_newer_data + + check_for_spike, max_spike, column_to_check are for spike checking + """ + merged_data_with_status = full_merge_of_existing_data_no_checks( + old_data, new_data, keep_older=keep_older + ) + + # check for spike + if check_for_spike: + merged_data_with_status = spike_check_merged_data( + merged_data_with_status, + column_to_check_for_spike=column_to_check_for_spike, # type: ignore + max_spike=max_spike, + ) + if merged_data_with_status.spike_present: + return SPIKE_IN_DATA + + return merged_data_with_status.merged_data + + +def full_merge_of_existing_data_no_checks( + old_data: Union[pd.Series, pd.DataFrame], + new_data: Union[pd.Series, pd.DataFrame], + keep_older: bool = True, +) -> mergingDataWithStatus: + """ + Merges old data with new data. + Any Nan in the existing data will be replaced (be careful!) + + Any Nan in the existing data will ALWAYS be replaced (be careful!) + + If keep_older is False, then any value in the existing data which has the same index + as in newer data will be replaced with newer data + """ + if len(old_data.index) == 0: + return mergingDataWithStatus.only_new_data(new_data) + if len(new_data.index) == 0: + return mergingDataWithStatus.only_old_data(old_data) + + merged_data = full_merge_of_data_with_both_old_and_new( + old_data=old_data, new_data=new_data, keep_older=keep_older + ) + + # get the difference between merged dataset and old one to get the earliest change + actually_new_data = pd.concat([merged_data, old_data]).drop_duplicates(keep=False) + + if len(actually_new_data.index) == 0: + return mergingDataWithStatus.only_old_data(old_data) + + first_date_in_new_data = actually_new_data.index[0] + + return mergingDataWithStatus( + status=MERGED_DATA, + date_of_merge_join=first_date_in_new_data, + merged_data=merged_data, + ) + + +def full_merge_of_data_with_both_old_and_new( + old_data: Union[pd.Series, pd.DataFrame], + new_data: Union[pd.Series, pd.DataFrame], + keep_older: bool = True, +) -> Union[pd.Series, pd.DataFrame]: + if is_a_series(old_data): + assert is_a_series(new_data) + merged_data = full_merge_of_existing_series( + old_series=old_data, new_series=new_data, keep_older=keep_older # type: ignore + ) + else: + assert is_a_dataframe(new_data) + merged_data = full_merge_of_existing_dataframe( + old_data=old_data, new_data=new_data, keep_older=keep_older # type: ignore + ) + + merged_data = merged_data.sort_index() + + return merged_data + + +def full_merge_of_existing_dataframe( + old_data: Union[pd.Series, pd.DataFrame], + new_data: pd.DataFrame, + keep_older: bool = True, +) -> pd.DataFrame: + """ + >>> import numpy as np, datetime + >>> old_data = pd.DataFrame(dict(a=[1,np.nan,3,np.nan], b=[np.nan,np.nan,7,np.nan]), index=pd.date_range(datetime.datetime(2000,1,1), periods=4)) + >>> new_data = pd.DataFrame(dict(a=[2,5, np.nan,8]), index=pd.date_range(datetime.datetime(2000,1,1), periods=4)) + >>> full_merge_of_existing_dataframe(old_data, new_data) + a b + 2000-01-01 1.0 NaN + 2000-01-02 5.0 NaN + 2000-01-03 3.0 7.0 + 2000-01-04 8.0 NaN + >>> new_data = pd.DataFrame(dict(a=[2,5, np.nan,8], b=[1, np.nan, 8, 9]), index=pd.date_range(datetime.datetime(2000,1,1), periods=4)) + >>> full_merge_of_existing_dataframe(old_data, new_data, keep_older=False) + a b + 2000-01-01 2.0 1.0 + 2000-01-02 5.0 NaN + 2000-01-03 3.0 8.0 + 2000-01-04 8.0 9.0 + """ + old_columns = old_data.columns + + merged_data_as_dict = {} + for colname in old_columns: ## Ignore new columns + old_series = copy(old_data[colname]) + try: + new_series = copy(new_data[colname]) + except KeyError: + # missing from new data, so we just take the old + merged_data_as_dict[colname] = old_series + continue + + merged_series = full_merge_of_existing_series( + old_series=old_series, new_series=new_series, keep_older=keep_older + ) + merged_data_as_dict[colname] = merged_series + + merged_data = pd.DataFrame(merged_data_as_dict) + + return merged_data + + +def full_merge_of_existing_series( + old_series: pd.Series, new_series: pd.Series, keep_older: bool = True +) -> pd.Series: + """ + Merges old data with new data. + Any Nan in the existing data will be replaced (be careful!) + + >>> import numpy as np, datetime + >>> old_series = pd.Series([1,np.nan,3,np.nan], pd.date_range(datetime.datetime(2000,1,1), periods=4)) + >>> new_series = pd.Series([2,5, np.nan,8], pd.date_range(datetime.datetime(2000,1,1), periods=4)) + >>> full_merge_of_existing_series(old_series, new_series) + 2000-01-01 1.0 + 2000-01-02 5.0 + 2000-01-03 3.0 + 2000-01-04 8.0 + Freq: D, Name: original, dtype: float64 + >>> full_merge_of_existing_series(old_series, new_series, keep_older=False) + 2000-01-01 2.0 + 2000-01-02 5.0 + 2000-01-03 3.0 + 2000-01-04 8.0 + Freq: D, dtype: float64 + """ + if len(old_series) == 0: + return new_series + if len(new_series) == 0: + return old_series + + if keep_older: + joint_data = pd.concat([old_series, new_series], axis=1) + joint_data.columns = ["original", "new"] + + # fill to the left + # NA from the original series will be preserved + joint_data_filled_across = joint_data.bfill(axis=1) + merged_data = joint_data_filled_across["original"] + else: + # update older data with non-NA values from new data series + merged_data = old_series.copy() + merged_data.update(new_series) + + return merged_data diff --git a/src/quantlib_st/core/pandas/merge_data_keeping_past_data.py b/src/quantlib_st/core/pandas/merge_data_keeping_past_data.py new file mode 100644 index 0000000..bff32e7 --- /dev/null +++ b/src/quantlib_st/core/pandas/merge_data_keeping_past_data.py @@ -0,0 +1,318 @@ +## Merge series together +import datetime + +import pandas as pd + +from enum import Enum +from typing import Union + +from quantlib_st.core.dateutils import SECONDS_PER_DAY +from quantlib_st.core.constants import named_object, arg_not_supplied +from quantlib_st.core.pandas.pdutils import is_a_series + +VERY_BIG_NUMBER = 99999999.0 + + +def merge_newer_data( + old_data: Union[pd.Series, pd.DataFrame], + new_data: Union[pd.Series, pd.DataFrame], + check_for_spike: bool = True, + max_spike: float = VERY_BIG_NUMBER, + column_to_check_for_spike: str | named_object = arg_not_supplied, +): + """ + Merge new data, with old data. Any new data that is older than the newest old data will be ignored + If check_for_spike will return data_error if price moves too much on join point (checking column_to_check) + + """ + merged_data_with_status = merge_newer_data_no_checks(old_data, new_data) + + # check for spike + if check_for_spike: + merged_data_with_status = spike_check_merged_data( + merged_data_with_status, + column_to_check_for_spike=column_to_check_for_spike, + max_spike=max_spike, + ) + if merged_data_with_status.spike_present: + return SPIKE_IN_DATA + + merged_data = merged_data_with_status.merged_data + + return merged_data + + +class mergeStatus(Enum): + ONLY_OLD = 1 + ONLY_NEW = 2 + MERGED = 3 + + +OLD_DATA_ONLY = mergeStatus.ONLY_OLD +NEW_DATA_ONLY = mergeStatus.ONLY_NEW +MERGED_DATA = mergeStatus.MERGED + +NO_SPIKE = named_object("No spike in data") +SPIKE_IN_DATA = named_object("Spike in data") +NO_MERGE_DATE = named_object("No data") + + +class mergeError(Exception): + pass + + +class mergingDataWithStatus(object): + def __init__( + self, + status: mergeStatus, + date_of_merge_join: Union[datetime.datetime, named_object], + merged_data: Union[pd.DataFrame, pd.Series], + ): + self._status = status + self._date_of_merge_join = date_of_merge_join + self._merged_data = merged_data + + @classmethod + def only_old_data(cls, old_data): + return mergingDataWithStatus( + status=OLD_DATA_ONLY, date_of_merge_join=NO_MERGE_DATE, merged_data=old_data + ) + + @classmethod + def only_new_data(cls, new_data): + return mergingDataWithStatus( + status=NEW_DATA_ONLY, date_of_merge_join=NO_MERGE_DATE, merged_data=new_data + ) + + @property + def spike_present(self) -> bool: + spike_date = self.spike_date + if spike_date is NO_SPIKE: + return False + else: + return True + + @property + def spike_date(self) -> Union[datetime.datetime, named_object]: + spike_date = getattr(self, "_spike_date", NO_SPIKE) + return spike_date + + @spike_date.setter + def spike_date(self, spike_date: datetime.datetime): + self._spike_date = spike_date + + @property + def status(self) -> mergeStatus: + return self._status + + @property + def date_of_merge_join(self) -> Union[datetime.datetime, named_object]: + return self._date_of_merge_join + + @property + def merged_data(self): + return self._merged_data + + +def merge_newer_data_no_checks( + old_data: Union[pd.Series, pd.DataFrame], new_data: Union[pd.Series, pd.DataFrame] +) -> mergingDataWithStatus: + """ + Merge new data, with old data. Any new data that is older than the newest old data will be ignored + + Also returns status and possibly date of merge + + """ + + if len(new_data.index) == 0: + return mergingDataWithStatus.only_old_data(old_data) + if len(old_data.index) == 0: + return mergingDataWithStatus.only_new_data(new_data) + + merged_data_with_status = _merge_newer_data_no_checks_if_both_old_and_new( + old_data, new_data + ) + + return merged_data_with_status + + +def _merge_newer_data_no_checks_if_both_old_and_new( + old_data: Union[pd.Series, pd.DataFrame], new_data: Union[pd.Series, pd.DataFrame] +) -> mergingDataWithStatus: + last_date_in_old_data = old_data.index[-1] + new_data.sort_index() + actually_new_data = new_data[new_data.index > last_date_in_old_data] + + if len(actually_new_data) == 0: + # No additional data + return mergingDataWithStatus.only_old_data(old_data) + + first_date_in_new_data = actually_new_data.index[0] + + merged_data = pd.concat([old_data, actually_new_data], axis=0) + merged_data = merged_data.sort_index() + + # remove duplicates (shouldn't be any, but...) + merged_data = merged_data[~merged_data.index.duplicated(keep="first")] + + return mergingDataWithStatus( + status=MERGED_DATA, + date_of_merge_join=first_date_in_new_data, + merged_data=merged_data, + ) + + +def spike_check_merged_data( + merged_data_with_status: mergingDataWithStatus, + column_to_check_for_spike: str | named_object = arg_not_supplied, + max_spike: float = VERY_BIG_NUMBER, +) -> mergingDataWithStatus: + merge_status = merged_data_with_status.status + merged_data = merged_data_with_status.merged_data + + if merge_status is OLD_DATA_ONLY: + # No checking, just old data + return merged_data_with_status + + if merge_status is NEW_DATA_ONLY: + # check everything as there is no old data + first_date_in_new_data = NO_MERGE_DATE + else: + first_date_in_new_data = merged_data_with_status.date_of_merge_join + + spike_date = _find_first_spike_in_data( + merged_data, + first_date_in_new_data, + column_to_check_for_spike=column_to_check_for_spike, + max_spike=max_spike, + ) + + merged_data_with_status.spike_date = spike_date # type: ignore + + return merged_data_with_status + + +def _find_first_spike_in_data( + merged_data: Union[pd.Series, pd.DataFrame], + date_of_merge_join: Union[datetime.datetime, named_object] = NO_MERGE_DATE, + column_to_check_for_spike: str | named_object = arg_not_supplied, + max_spike: float = VERY_BIG_NUMBER, +): + """ + Checks to see if any data after last_date_in_old_data has spikes + """ + data_to_check = _get_data_to_check( + merged_data, column_to_check_for_spike=column_to_check_for_spike + ) + change_in_vol_normalised_units = _calculate_change_in_vol_normalised_units( + data_to_check # type: ignore + ) + relevant_change_in_vol_normalised_units = ( + _get_relevant_period_in_vol_normalised_units_to_check( + change_in_vol_normalised_units=change_in_vol_normalised_units, + date_of_merge_join=date_of_merge_join, + ) + ) + + first_spike = _check_for_spikes_in_change_in_vol_normalised_units( + relevant_change_in_vol_normalised_units=relevant_change_in_vol_normalised_units, + max_spike=max_spike, + ) + + return first_spike + + +def _get_data_to_check( + merged_data: Union[pd.Series, pd.DataFrame], + column_to_check_for_spike: str | named_object = arg_not_supplied, +) -> Union[pd.Series, pd.DataFrame]: + if is_a_series(merged_data): + # already a series + data_to_check = merged_data + else: + column_list = merged_data.columns + if column_to_check_for_spike is arg_not_supplied: + ## arbitrarily use first column + column_to_check_for_spike = column_list[0] + + data_to_check = merged_data[column_to_check_for_spike] # type: ignore + + return data_to_check + + +def _calculate_change_in_vol_normalised_units(data_to_check: pd.Series) -> pd.Series: + # Calculate the average change per day + change_per_day = _calculate_change_in_daily_units(data_to_check) + + # absolute is what matters + absolute_change_per_day = change_per_day.abs() + + # hard to know what span to use here as could be daily, intraday or a + # mixture... + average_absolute_change = absolute_change_per_day.ewm(span=500).mean() + + change_in_vol_normalised_units = absolute_change_per_day / average_absolute_change + + return change_in_vol_normalised_units + + +def _calculate_change_in_daily_units(data_to_check: pd.Series) -> pd.Series: + """ + Calculate the average change in daily units asssuming brownian motion + for example, a change of 0.5 over half a day would be equal to a change of 0.5/sqrt(0.5) = 0.7 over a day + a change of 2.0 over 5 days, would be equal to a change of 2/sqrt(5) = 0.89 a day + >>> data_to_check1 = pd.Series([1,1,2], pd.date_range(datetime.datetime(2000,1,1), periods=3)) + >>> data_to_check2 = pd.Series([4,1,8], pd.date_range(datetime.datetime(2000,1,6), periods=3, freq="H")) + >>> data_to_check = pd.concat([data_to_check1, data_to_check2], axis=0) + >>> _calculate_change_in_daily_units(data_to_check) + 2000-01-02 00:00:00 0.000000 + 2000-01-03 00:00:00 1.000000 + 2000-01-06 00:00:00 1.154701 + 2000-01-06 01:00:00 -14.696938 + 2000-01-06 02:00:00 34.292856 + dtype: float64 + """ + data_diff = data_to_check.diff()[1:] + index_diff = data_to_check.index[1:] - data_to_check.index[:-1] + index_diff_days = [diff.total_seconds() / SECONDS_PER_DAY for diff in index_diff] + + change_in_daily_units_as_list = [ + diff / (diff_days**0.5) + for diff, diff_days in zip(data_diff.values, index_diff_days) + ] + + change_in_daily_units = pd.Series( + change_in_daily_units_as_list, index=data_to_check.index[1:] + ) + + return change_in_daily_units + + +def _get_relevant_period_in_vol_normalised_units_to_check( + change_in_vol_normalised_units: pd.Series, + date_of_merge_join: Union[datetime.datetime, named_object] = NO_MERGE_DATE, +): + if date_of_merge_join is NO_MERGE_DATE: + # No merged data so we check it all + relevant_change_in_vol_normalised_units = change_in_vol_normalised_units + else: + # just check more recent data + relevant_change_in_vol_normalised_units = change_in_vol_normalised_units[ + date_of_merge_join: + ] + + return relevant_change_in_vol_normalised_units + + +def _check_for_spikes_in_change_in_vol_normalised_units( + relevant_change_in_vol_normalised_units: pd.Series, + max_spike: float = VERY_BIG_NUMBER, +) -> Union[datetime.datetime, named_object]: + if any(relevant_change_in_vol_normalised_units > max_spike): + first_spike = relevant_change_in_vol_normalised_units.index[ + relevant_change_in_vol_normalised_units > max_spike + ][0] + else: + first_spike = NO_SPIKE + + return first_spike diff --git a/src/quantlib_st/core/pandas/merge_data_with_label_column.py b/src/quantlib_st/core/pandas/merge_data_with_label_column.py new file mode 100644 index 0000000..58337bb --- /dev/null +++ b/src/quantlib_st/core/pandas/merge_data_with_label_column.py @@ -0,0 +1,420 @@ +import datetime +import pandas as pd + +from typing import Union, Tuple, List + +from quantlib_st.core.pandas.full_merge_with_replacement import ( + full_merge_of_existing_series, +) +from quantlib_st.core.constants import named_object + + +def merge_data_series_with_label_column( + original_data: pd.DataFrame, + new_data: pd.DataFrame, + data_column="PRICE", + label_column="PRICE_CONTRACT", +): + """ + For two pd.DataFrames with 2 columns, including a label column, update the data when the labels + start consistently matching + + >>> import numpy as np + >>> s1=pd.DataFrame(dict(PRICE=[1,2,3,np.nan], PRICE_CONTRACT = ["a", "a", "b", "b"]), index=['a1','a2','a3','a4']) + >>> s2=pd.DataFrame(dict(PRICE=[ 7,3,4], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 2.0 a + a3 3.0 b + a4 4.0 b + >>> s2=pd.DataFrame(dict(PRICE=[ 2,5,4], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 2.0 a + a3 3.0 b + a4 4.0 b + >>> s2=pd.DataFrame(dict(PRICE=[ 2,3,np.nan], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 2.0 a + a3 3.0 b + a4 NaN b + >>> s1=pd.DataFrame(dict(PRICE=[1,np.nan,3,np.nan], PRICE_CONTRACT = ["a", "a", "b", "b"]), index=['a1','a2','a3','a4']) + >>> s2=pd.DataFrame(dict(PRICE=[ 2, 3,4], PRICE_CONTRACT = [ "a", "b", "b"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 2.0 a + a3 3.0 b + a4 4.0 b + >>> s1=pd.DataFrame(dict(PRICE=[1,np.nan,np.nan], PRICE_CONTRACT = ["a", "a", "b"]), index=['a1','a2','a3']) + >>> s2=pd.DataFrame(dict(PRICE=[ 2, 3,4], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 NaN a + a3 3.0 b + a4 4.0 b + >>> s2=pd.DataFrame(dict(PRICE=[ 2, 3,4], PRICE_CONTRACT = [ "b", "c", "c"]), index=['a2','a3','a4']) + >>> merge_data_series_with_label_column(s1,s2) + PRICE PRICE_CONTRACT + a1 1.0 a + a2 NaN a + a3 NaN b + + + :param original_data: a pd.DataFrame with two columns, equal to col_names + :param new_data: a pd.DataFrame with the same two columns + + :return: pd.DataFrame with two columns + """ + + if len(new_data) == 0: + return original_data + + if len(original_data) == 0: + return new_data + + current_and_merged_data = ( + _merge_data_series_with_label_column_when_old_and_new_data_provided( + original_data=original_data, + new_data=new_data, + data_column=data_column, + label_column=label_column, + ) + ) + + return current_and_merged_data + + +ALL_LABELS_MATCH = named_object("all labels match") +MISMATCH_ON_LAST_DAY = named_object("MISMATCH_ON_LAST_DAY") +ORIGINAL_INDEX_MATCHES_NEW = named_object("original index matches new") + + +def _merge_data_series_with_label_column_when_old_and_new_data_provided( + original_data: pd.DataFrame, + new_data: pd.DataFrame, + data_column="PRICE", + label_column="PRICE_CONTRACT", +): + """ + For two pd.DataFrames with 2 columns, including a label column, update the data when the labels + start consistently matching + """ + + # From the date after this, can happily merge new and old data + match_dates = _find_dates_when_label_changes( + original_data, new_data, label_column=label_column + ) + + if match_dates is MISMATCH_ON_LAST_DAY: + # No matching is possible + return original_data + elif match_dates is ORIGINAL_INDEX_MATCHES_NEW: + first_date_after_series_mismatch = original_data.index[0] + last_date_when_series_mismatch = ORIGINAL_INDEX_MATCHES_NEW + else: + first_date_after_series_mismatch, last_date_when_series_mismatch = match_dates + + current_and_merged_data = _merge_data_given_matching_dates( + original_data=original_data, + new_data=new_data, + last_date_when_series_mismatch=last_date_when_series_mismatch, + first_date_after_series_mismatch=first_date_after_series_mismatch, + data_column=data_column, + label_column=label_column, + ) + + return current_and_merged_data + + +def _find_dates_when_label_changes( + original_data: pd.DataFrame, + new_data: pd.DataFrame, + label_column="PRICE_CONTRACT", +) -> Union[named_object, Tuple[datetime.datetime, datetime.datetime]]: + """ + For two pd.DataFrames with 2 columns, including a label column, find the date after which the labelling + is consistent across columns + + >>> import numpy as np + >>> s1=pd.DataFrame(dict(PRICE=[1,2,3,np.nan], PRICE_CONTRACT = ["a", "a", "b", "b"]), index=['a1','a2','a3','a4']) + >>> s2=pd.DataFrame(dict(PRICE=[ 2,3,4], PRICE_CONTRACT = [ "b", "b", "b"]), index=['a2','a3','a4']) + >>> _find_dates_when_label_changes(s1, s2) + ('a3', 'a2') + >>> s2=pd.DataFrame(dict(PRICE=[ 2,3,4], PRICE_CONTRACT = [ "a", "b", "b"]), index=['a2','a3','a4']) + >>> _find_dates_when_label_changes(s1, s2) + ('a2', 'a1') + >>> s2=pd.DataFrame(dict(PRICE=[ 2,3,4], PRICE_CONTRACT = [ "c", "c", "c"]), index=['a2','a3','a4']) + >>> _find_dates_when_label_changes(s1, s2) + MISMATCH_ON_LAST_DAY + >>> _find_dates_when_label_changes(s1, s1) + original index matches new + >>> s2=pd.DataFrame(dict(PRICE=[1, 2,3,4], PRICE_CONTRACT = ["a","c", "c", "c"]), index=['a1','a2','a3','a4']) + >>> _find_dates_when_label_changes(s1, s2) + MISMATCH_ON_LAST_DAY + + """ + + joint_labels = pd.concat( + [original_data[label_column], new_data[label_column]], axis=1 + ) + joint_labels.columns = ["current", "new"] + joint_labels = joint_labels.sort_index() + + new_data_start = new_data.index[0] + + existing_labels_in_new_period = joint_labels["current"][new_data_start:].ffill() + new_labels_in_new_period = joint_labels["new"][new_data_start:].ffill() + + match_dates = _find_dates_when_labels_change_given_label_data( + original_data=original_data, + new_data=new_data, + existing_labels_in_new_period=existing_labels_in_new_period, + new_labels_in_new_period=new_labels_in_new_period, + ) + + return match_dates + + +def _find_dates_when_labels_change_given_label_data( + original_data: pd.DataFrame, + new_data: pd.DataFrame, + existing_labels_in_new_period: pd.Series, + new_labels_in_new_period: pd.Series, +) -> Union[named_object, Tuple[datetime.datetime, datetime.datetime]]: + # Find the last date when the labels didn't match, and the first date + # after that + match_dates = _find_dates_when_series_starts_matching( + existing_labels_in_new_period, new_labels_in_new_period + ) + + if match_dates is MISMATCH_ON_LAST_DAY: + # Can't use any of new data + return MISMATCH_ON_LAST_DAY + + elif match_dates is ALL_LABELS_MATCH: + match_dates_for_matching_labels = ( + _match_dates_when_entire_series_of_labels_matches( + original_data=original_data, new_data=new_data + ) + ) + return match_dates_for_matching_labels + else: + return match_dates + + +def _find_dates_when_series_starts_matching( + series1: pd.Series, series2: pd.Series +) -> Union[named_object, Tuple[datetime.datetime, datetime.datetime]]: + """ + Find the last index value when series1 and series 2 didn't match, and the next index after that + + series must be matched for index and same length + + >>> s1=pd.Series(["a", "b", "b", "b"], index=[1,2,3,4]) + >>> s2=pd.Series(["c", "b", "b", "b"], index=[1,2,3,4]) + >>> _find_dates_when_series_starts_matching(s1, s2) + (2, 1) + >>> s2=pd.Series(["a", "a", "b", "b"], index=[1,2,3,4]) + >>> _find_dates_when_series_starts_matching(s1, s2) + (3, 2) + >>> s2=pd.Series(["a", "b", "a", "b"], index=[1,2,3,4]) + >>> _find_dates_when_series_starts_matching(s1, s2) + (4, 3) + >>> s2=pd.Series(["a", "b", "b", "b"], index=[1,2,3,4]) + >>> _find_dates_when_series_starts_matching(s1, s2) + all labels match + >>> s2=pd.Series(["a", "b", "b", "c"], index=[1,2,3,4]) + >>> _find_dates_when_series_starts_matching(s1, s2) + MISMATCH_ON_LAST_DAY + + """ + + # Data is same length, and timestamp matched, so equality of values is + # sufficient + period_equal = [x == y for x, y in zip(series1.values, series2.values)] + + if all(period_equal): + return ALL_LABELS_MATCH + + if not period_equal[-1]: + return MISMATCH_ON_LAST_DAY + + match_dates = _match_dates_for_labels_when_not_equal_or_mismatch( + series1=series1, period_equal=period_equal + ) + + return match_dates + + +def _match_dates_for_labels_when_not_equal_or_mismatch( + series1: pd.Series, period_equal: List[bool] +) -> Tuple[datetime.datetime, datetime.datetime]: + # Want last False value + period_equal.reverse() + first_false_in_reversed_list = period_equal.index(False) + + last_true_before_first_false_in_reversed_list = first_false_in_reversed_list - 1 + + reversed_time_index = series1.index[::-1] + last_true_before_first_false_in_reversed_list_date = reversed_time_index[ + last_true_before_first_false_in_reversed_list + ] + first_false_in_reversed_list_date = reversed_time_index[ + first_false_in_reversed_list + ] + + first_date_after_series_mismatch = ( + last_true_before_first_false_in_reversed_list_date + ) + last_date_when_series_mismatch = first_false_in_reversed_list_date + + return first_date_after_series_mismatch, last_date_when_series_mismatch + + +def _match_dates_when_entire_series_of_labels_matches( + original_data: pd.DataFrame, new_data: pd.DataFrame +) -> Union[named_object, Tuple[datetime.datetime, datetime.datetime]]: + # Can use entire series becuase all match + if new_data.index[0] == original_data.index[0]: + # They are same size, so have to use whole of original data + return ORIGINAL_INDEX_MATCHES_NEW + + # All the new data matches + new_data_start = new_data.index[0] + first_date_after_series_mismatch = new_data_start + last_date_when_series_mismatch = original_data.index[ + original_data.index < new_data_start + ][-1] + + return first_date_after_series_mismatch, last_date_when_series_mismatch + + +def _merge_data_given_matching_dates( + original_data: pd.DataFrame, + new_data: pd.DataFrame, + first_date_after_series_mismatch: datetime.datetime, + last_date_when_series_mismatch: Union[named_object, datetime.datetime], + data_column="PRICE", + label_column="PRICE_CONTRACT", +): + # Concat the two price series together, fill to the left + # This will replace any NA values in existing prices with new ones + + original_data_series_before_mismatch = original_data[data_column][ + original_data.index >= first_date_after_series_mismatch + ] + new_data_series_after_mismatch = new_data[data_column][ + new_data.index >= first_date_after_series_mismatch + ] + + merged_data_series = full_merge_of_existing_series( + old_series=original_data_series_before_mismatch, + new_series=new_data_series_after_mismatch, + keep_older=True, + ) + + current_and_merged_data = _stitch_merged_and_existing_data( + merged_data_series=merged_data_series, + original_data=original_data, + new_data=new_data, + first_date_after_series_mismatch=first_date_after_series_mismatch, + last_date_when_series_mismatch=last_date_when_series_mismatch, + data_column=data_column, + label_column=label_column, + ) + + return current_and_merged_data + + +def _stitch_merged_and_existing_data( + merged_data_series: pd.Series, + original_data: pd.DataFrame, + new_data: pd.DataFrame, + first_date_after_series_mismatch: datetime.datetime, + last_date_when_series_mismatch: Union[named_object, datetime.datetime], + data_column="PRICE", + label_column="PRICE_CONTRACT", +) -> pd.DataFrame: + labelled_merged_data = _get_labelled_merged_data( + merged_data_series=merged_data_series, + original_data=original_data, + new_data=new_data, + first_date_after_series_mismatch=first_date_after_series_mismatch, + last_date_when_series_mismatch=last_date_when_series_mismatch, + data_column=data_column, + label_column=label_column, + ) + + # indices match exactly, so use new merged data + if last_date_when_series_mismatch is ORIGINAL_INDEX_MATCHES_NEW: + current_and_merged_data = labelled_merged_data + else: + ## Will stitch together, so ignore older original data + original_data_to_use = original_data[:last_date_when_series_mismatch] + + # Merged data is the old data, and then the new data, stitched together + current_and_merged_data = pd.concat( + [original_data_to_use, labelled_merged_data], axis=0 + ) + + return current_and_merged_data + + +def _get_labelled_merged_data( + merged_data_series: pd.Series, + original_data: pd.DataFrame, + new_data: pd.DataFrame, + first_date_after_series_mismatch: datetime.datetime, + last_date_when_series_mismatch: Union[named_object, datetime.datetime], + data_column="PRICE", + label_column="PRICE_CONTRACT", +) -> pd.DataFrame: + labels_in_merged_data = _get_merged_label_data( + merged_data_series=merged_data_series, + original_data=original_data, + new_data=new_data, + first_date_after_series_mismatch=first_date_after_series_mismatch, + last_date_when_series_mismatch=last_date_when_series_mismatch, + label_column=label_column, + ) + labelled_merged_data = pd.concat( + [labels_in_merged_data, merged_data_series], axis=1 + ) + labelled_merged_data.columns = [label_column, data_column] + + return labelled_merged_data + + +def _get_merged_label_data( + merged_data_series: pd.Series, + original_data: pd.DataFrame, + new_data: pd.DataFrame, + first_date_after_series_mismatch: datetime.datetime, + last_date_when_series_mismatch: Union[named_object, datetime.datetime], + label_column="PRICE_CONTRACT", +) -> pd.Series: + label_series_in_new_data = new_data[last_date_when_series_mismatch:][label_column] + label_series_in_old_data = original_data[:first_date_after_series_mismatch][ + label_column + ] + labels_in_merged_data_original_index = pd.concat( + [label_series_in_old_data, label_series_in_new_data], axis=0 + ) + labels_in_merged_data_original_index_without_duplicates = ( + labels_in_merged_data_original_index.loc[ + ~labels_in_merged_data_original_index.index.duplicated(keep="first") + ] + ) + labels_in_merged_data = ( + labels_in_merged_data_original_index_without_duplicates.reindex( + merged_data_series.index + ) + ) + + return labels_in_merged_data diff --git a/src/quantlib_st/core/pandas/pdutils.py b/src/quantlib_st/core/pandas/pdutils.py new file mode 100755 index 0000000..3454df6 --- /dev/null +++ b/src/quantlib_st/core/pandas/pdutils.py @@ -0,0 +1,392 @@ +""" +Utilities to help with pandas +""" + +import numpy as np +import pandas as pd +import datetime + +from typing import Union, List + +from quantlib_st.core.constants import named_object, arg_not_supplied + +DEFAULT_DATE_FORMAT_FOR_CSV = "%Y-%m-%d %H:%M:%S" +EXPECTED_LENGTH_OF_DATE = 19 + +FALLBACK_DATE_FORMAT_FOR_CSV = "%Y-%m-%d" + + +def rolling_pairwise_correlation( + x: pd.DataFrame, periods: int, min_periods: int = 3 +) -> pd.Series: + assert len(x.columns) == 2 + + rolling_corr_df = x.rolling(periods, min_periods=min_periods).corr() + return rolling_corr_df.droplevel(1)[::2].iloc[:, 1] + + +def is_a_series(x: Union[pd.Series, pd.DataFrame]) -> bool: + columns = getattr(x, "columns", None) + return columns is None + + +def is_a_dataframe(x: Union[pd.Series, pd.DataFrame]) -> bool: + return not is_a_series(x) + + +def top_and_tail(x: pd.DataFrame, rows=5) -> pd.DataFrame: + return pd.concat([x[:rows], x[-rows:]], axis=0) + + +def sum_series(list_of_series: List[pd.Series], ffill=True) -> pd.Series: + list_of_series_as_df = pd.concat(list_of_series, axis=1) + if ffill: + list_of_series_as_df = list_of_series_as_df.ffill() + + sum_of_series = list_of_series_as_df.sum(axis=1) + + return sum_of_series + + +def uniquets(x: Union[pd.Series, pd.DataFrame]) -> Union[pd.Series, pd.DataFrame]: + """ + Makes x unique + """ + x = x.groupby(level=0).last() + return x + + +def get_column_names_in_df_with_at_least_one_value(df: pd.DataFrame) -> List[str]: + """ + + >>> df = pd.DataFrame(dict(a=[1,2], b=[np.nan, 3], c=[np.nan, np.nan]), index=pd.date_range(datetime.datetime(2000,1,1),periods=2)) + >>> get_column_names_in_df_with_at_least_one_value(df) + ['a', 'b'] + """ + list_of_must_have = get_index_of_columns_in_df_with_at_least_one_value(df) + columns = list(df.columns) + must_have_columns = [ + col for idx, col in enumerate(columns) if list_of_must_have[idx] + ] + return must_have_columns + + +def get_index_of_columns_in_df_with_at_least_one_value(df: pd.DataFrame) -> List[bool]: + """ + Returns the bool for columns of slice_data for which we have at least one non nan value + + >>> df = pd.DataFrame(dict(a=[1,2], b=[np.nan, 3], c=[np.nan, np.nan]), index=pd.date_range(datetime.datetime(2000,1,1),periods=2)) + >>> get_index_of_columns_in_df_with_at_least_one_value(df) + [True, True, False] + """ + + return list(~df.isna().all().values) # type: ignore + + +def pd_readcsv( + filename: str, + date_index_name: str = "DATETIME", + date_format: str = DEFAULT_DATE_FORMAT_FOR_CSV, + fallback_date_format: str = FALLBACK_DATE_FORMAT_FOR_CSV, + input_column_mapping: Union[dict, named_object] = arg_not_supplied, + skiprows: int = 0, + skipfooter: int = 0, +) -> pd.DataFrame: + """ + Reads a pandas data frame, with time index labelled + package_name(/path1/path2.., filename + + :param filename: Filename with extension + :param date_index_name: Column name of date index + :param date_format: usual stfrtime format + :param input_column_mapping: If supplied remaps column names in .csv file + :param skiprows, skipfooter: passed to pd.read_csv + + :returns: pd.DataFrame + + """ + + df = pd.read_csv(filename, skiprows=skiprows, skipfooter=skipfooter) + + ## Add time index as index + try: + df = add_datetime_index( + df=df, date_index_name=date_index_name, date_format=date_format + ) + except: + df = add_datetime_index( + df=df, date_index_name=date_index_name, date_format=fallback_date_format + ) + + if input_column_mapping is not arg_not_supplied: + df = remap_columns_in_pd(df, input_column_mapping) # type: ignore + + return df + + +def add_datetime_index( + df: pd.DataFrame, + date_index_name: str, + date_format: str = DEFAULT_DATE_FORMAT_FOR_CSV, + expected_length_of_date: int = EXPECTED_LENGTH_OF_DATE, +) -> pd.DataFrame: + date_index = df[date_index_name] + date_index = date_index.astype(str) + + def left(x: str, n): + return x[:n] + + date_index = date_index.apply(left, n=expected_length_of_date) + df.index = pd.to_datetime(date_index, format=date_format).values + del df[date_index_name] + df.index.name = None + + return df + + +def remap_columns_in_pd(df: pd.DataFrame, input_column_mapping: dict) -> pd.DataFrame: + """ + Returns the bool for columns of slice_data for which we have at least one non nan value + + >>> df = pd.DataFrame(dict(a=[1,2], b=[np.nan, 3]), index=pd.date_range(datetime.datetime(2000,1,1),periods=2)) + >>> remap_columns_in_pd(df, dict(b='a', a='b')) + b a + 2000-01-01 1 NaN + 2000-01-02 2 3.0 + """ + + new_df = pd.DataFrame(index=df.index) + for new_col_name, old_col_name in input_column_mapping.items(): + new_df[new_col_name] = df[old_col_name] + + return new_df + + +def from_dict_of_values_to_df( + data_dict: dict, + ts_index: pd.Index, + columns: List[str] | named_object = arg_not_supplied, +) -> pd.DataFrame: + """ + Turn a set of fixed values into a pd.DataFrame that spans the long index + + >>> from_dict_of_values_to_df({'a':2, 'b':1}, ts_index = pd.date_range(datetime.datetime(2000,1,1),periods=4)) + a b + 2000-01-01 2 1 + 2000-01-02 2 1 + 2000-01-03 2 1 + 2000-01-04 2 1 + >>> from_dict_of_values_to_df({'a':2, 'b':1}, columns = ['b', 'a'],ts_index = pd.date_range(datetime.datetime(2000,1,1),periods=4)) + b a + 2000-01-01 1 2 + 2000-01-02 1 2 + 2000-01-03 1 2 + 2000-01-04 1 2 + + """ + + if columns is not arg_not_supplied: + data_dict = {keyname: data_dict[keyname] for keyname in columns} # type: ignore + + pd_dataframe = pd.DataFrame(data_dict, ts_index) + + return pd_dataframe + + +def from_scalar_values_to_ts(scalar_value: float, ts_index: pd.Index) -> pd.Series: + """ + Turn a scalar value into a pd.Series that spans the long index + >>> from_scalar_values_to_ts(4, ts_index = pd.date_range(datetime.datetime(2000,1,1),periods=4)) + 2000-01-01 4 + 2000-01-02 4 + 2000-01-03 4 + 2000-01-04 4 + Freq: D, dtype: int64 + """ + + pd_series = pd.Series(scalar_value, index=ts_index) + + return pd_series + + +def create_arbitrary_pdseries( + data_list: list, date_start=datetime.datetime(1980, 1, 1), freq: str = "B" +) -> pd.Series: + """ + Return a pandas Series with an arbitrary date index + + >>> create_arbitrary_pdseries([1,2,3]) + 1980-01-01 1 + 1980-01-02 2 + 1980-01-03 3 + Freq: B, dtype: int64 + >>> create_arbitrary_pdseries([1,2,3], date_start = datetime.datetime(2000,1,1), freq="W") + 2000-01-02 1 + 2000-01-09 2 + 2000-01-16 3 + Freq: W-SUN, dtype: int64 + """ + + date_index = pd.date_range(start=date_start, periods=len(data_list), freq=freq) + pdseries = pd.Series(data_list, index=date_index) + + return pdseries + + +def dataframe_pad( + starting_df: pd.DataFrame, + target_column_list: List[str], + pad_with_value: float = 0.0, +) -> pd.DataFrame: + """ + Takes a dataframe and adds extra columns if necessary so we end up with columns named column_list + + >>> df = pd.DataFrame(dict(a=[1, 2, 3], b=[4 , 6, 5]), index=pd.date_range(datetime.datetime(2000,1,1),periods=3)) + >>> dataframe_pad(df, ['a','b','c'], pad_with_value=4.0) + a b c + 2000-01-01 1 4 4.0 + 2000-01-02 2 6 4.0 + 2000-01-03 3 5 4.0 + >>> dataframe_pad(df, ['a','c']) + a c + 2000-01-01 1 0.0 + 2000-01-02 2 0.0 + 2000-01-03 3 0.0 + """ + + def _pad_column(column_name: str, starting_df: pd.DataFrame, pad_with_value: float): + if column_name in starting_df.columns: + return starting_df[column_name] + else: + return pd.Series( + np.full(starting_df.shape[0], pad_with_value), starting_df.index + ) + + new_data = [ + _pad_column(column_name, starting_df, pad_with_value) + for column_name in target_column_list + ] + + new_df = pd.concat(new_data, axis=1) + new_df.columns = target_column_list + + return new_df + + +def check_df_equals(x, y): + try: + pd.testing.assert_frame_equal(x, y) + return True + except AssertionError: + return False + + +def check_ts_equals(x, y): + try: + pd.testing.assert_series_equal(x, y, check_names=False) + return True + except AssertionError: + return False + + +def make_df_from_list_of_named_tuple( + tuple_class, + list_of_tuples: list, + make_index: bool = True, + field_name_for_index: str | named_object = arg_not_supplied, +): + """ + Turn a list of named tuples into a dataframe + The first element in the tuple will become the index + + >>> T = namedtuple('T', 'name value_a value_b') + >>> t1 = T('X', 3,1) + >>> t2 = T('Y',1,2) + >>> t3 = T('Z', 4, 3) + >>> make_df_from_list_of_named_tuple(T, [t1, t2, t3]) # doctest: +SKIP + value_a value_b + ... + X 3 1 + Y 1 2 + Z 4 3 + """ + elements = tuple_class._fields + dict_of_elements = {} + for element_name in elements: + this_element_values = [ + getattr(list_entry, element_name) for list_entry in list_of_tuples + ] + dict_of_elements[element_name] = this_element_values + + pdf = pd.DataFrame(dict_of_elements) + + if make_index: + if field_name_for_index is arg_not_supplied: + field_name_for_index = elements[0] + pdf_index = list(pdf[field_name_for_index]) + pdf = pdf.drop(labels=field_name_for_index, axis=1) + pdf.index = pdf_index + + return pdf + + +def sort_df_ignoring_missing(df: pd.DataFrame, column: List[str]) -> pd.DataFrame: + # sorts df by column, with rows with missing data coming at the end + # Pandas treats NaN, NaT, and None as missing + return df.sort_values(column, na_position="last") + + +def apply_with_min_periods( + xcol: pd.Series, my_func=np.nanmean, min_periods: int = 0 +) -> float: + not_nan = sum(~np.isnan(xcol)) + + if not_nan >= min_periods: + return my_func(xcol) + else: + return np.nan + + +### unused but might be useful +def from_series_to_matching_df_frame( + pd_series: pd.Series, pd_df_to_match: pd.DataFrame, method="ffill" +) -> pd.DataFrame: + list_of_columns = list(pd_df_to_match.columns) + new_df = from_series_to_df_with_column_names(pd_series, list_of_columns) + new_df = new_df.reindex(pd_df_to_match.index, method=method) # type: ignore + + return new_df + + +def from_series_to_df_with_column_names( + pd_series: pd.Series, list_of_columns: list +) -> pd.DataFrame: + new_df = pd.concat([pd_series] * len(list_of_columns), axis=1) + new_df.columns = list_of_columns + + return new_df + + +def print_full(x): + """ + Prints out a pd dataframe with no truncation, then sets display options back to + their defaults + :param x: + :type x: + """ + pd.set_option("display.max_rows", None) + pd.set_option("display.max_columns", None) + pd.set_option("display.width", 2000) + pd.set_option("display.max_colwidth", None) + print(x) + pd.reset_option("display.max_rows") + pd.reset_option("display.max_columns") + pd.reset_option("display.width") + pd.reset_option("display.float_format") + pd.reset_option("display.max_colwidth") + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/core/pandas/strategy_functions.py b/src/quantlib_st/core/pandas/strategy_functions.py index 21465a8..289ee18 100644 --- a/src/quantlib_st/core/pandas/strategy_functions.py +++ b/src/quantlib_st/core/pandas/strategy_functions.py @@ -1,8 +1,50 @@ +from copy import copy from typing import Union import pandas as pd +import numpy as np -from quantlib_st.core.dateutils import SECONDS_IN_YEAR +from quantlib_st.core.dateutils import BUSINESS_DAYS_IN_YEAR, SECONDS_IN_YEAR + + +def turnover( + x: pd.Series, y: Union[pd.Series, float, int], smooth_y_days: int = 250 +) -> float: + """ + Calculates the annualized turnover of a series 'x', normalized by a scale 'y'. + + This is typically used to measure how much a signal or position changes over time, + independent of the absolute size of the position or the risk target. + + Process: + 1. Resamples 'x' to business days. + 2. Normalizes 'x' by dividing by 'y' (the 'scale'). If 'y' is a series, it is + smoothed with an EWMA to avoid capturing noise in the risk target itself. + 3. Calculates the average absolute daily change of this normalized series. + 4. Multiplies by BUSINESS_DAYS_IN_YEAR (256) to return an annualized figure. + + A turnover of 2.0 means the system trades twice its average position size + per year. + + :param x: The series to measure (e.g. position size or forecast) + :param y: The normalization factor (e.g. target risk or constant scalar) + :param smooth_y_days: Lookback for smoothing 'y' if it is a series. + :return: Annualized turnover (float) + """ + + daily_x = x.resample("1B").last() + if isinstance(y, float) or isinstance(y, int): + daily_y = pd.Series(np.full(daily_x.shape[0], float(y)), daily_x.index) + else: + daily_y = y.reindex(daily_x.index, method="ffill") + ## need to apply a drag to this or will give zero turnover for constant risk + daily_y = daily_y.ewm(smooth_y_days, min_periods=2).mean() + + x_normalised_for_y = daily_x / daily_y.ffill() + + avg_daily = float(x_normalised_for_y.diff().abs().mean()) + + return avg_daily * BUSINESS_DAYS_IN_YEAR def drawdown(x: Union[pd.DataFrame, pd.Series]) -> Union[pd.DataFrame, pd.Series]: @@ -24,3 +66,65 @@ def spread_out_annualised_return_over_periods(data_as_annual: pd.Series) -> pd.S data_per_period = data_as_annual * period_intervals_in_year_fractions return data_per_period + + +def replace_all_zeros_with_nan(pd_series: pd.Series) -> pd.Series: + """ + >>> import datetime + >>> d = datetime.datetime + >>> date_index1 = [d(2000,1,1,23),d(2000,1,2,23),d(2000,1,3,23)] + >>> s1 = pd.Series([0,5,6], index=date_index1) + >>> replace_all_zeros_with_nan(s1) + 2000-01-01 23:00:00 NaN + 2000-01-02 23:00:00 5.0 + 2000-01-03 23:00:00 6.0 + dtype: float64 + """ + copy_pd_series = copy(pd_series) + copy_pd_series[copy_pd_series == 0.0] = np.nan + + if all(copy_pd_series.isna()): + copy_pd_series[:] = np.nan + + return copy_pd_series + + +def calculate_cost_deflator(price: pd.Series) -> pd.Series: + daily_returns = price_to_daily_returns(price) + ## crude but doesn't matter + vol_price = daily_returns.rolling(180, min_periods=3).std().ffill() + final_vol = vol_price.iloc[-1] + + cost_scalar = vol_price / final_vol + + return cost_scalar + + +def price_to_daily_returns(price: pd.Series) -> pd.Series: + daily_price = price.resample("1B").ffill() + daily_returns = daily_price.ffill().diff() + + return daily_returns + + +def years_in_data(data: pd.Series) -> list[int]: + """ + >>> import datetime + >>> d = datetime.datetime + >>> date_index1 = [d(2000,1,1),d(2002,1,2),d(2003,1,5)] + >>> s1 = pd.Series([1,2,3], index=date_index1) + >>> years_in_data(s1) + [2000, 2002, 2003] + """ + + all_years = [x.year for x in data.index] + unique_years = list(set(all_years)) + unique_years.sort() + + return unique_years + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/core/text.py b/src/quantlib_st/core/text.py new file mode 100644 index 0000000..dc0b7a7 --- /dev/null +++ b/src/quantlib_st/core/text.py @@ -0,0 +1,123 @@ +from copy import copy + +import re + + +def sort_dict_by_underscore_length(some_dict: dict) -> list[dict]: + """ + Sort dict according to keys and presence of leading underscores + + :return: list of dict. First element is dict of all keys with no leading underscores. + Second element is dict of all; keys with 1 leading underscore...and so on + >>> some_dict = {'a': 2, '_b':3, '__c':4, 'd': 5} + >>> sort_dict_by_underscore_length(some_dict) + [{'a': 2, 'd': 5}, {'_b': 3}, {'__c': 4}] + """ + some_dict_keys = list(some_dict.keys()) + some_dict_keys_sorted = sort_keywords_by_underscore_length(some_dict_keys) + + sorted_list_of_dicts = [ + dict([(key, some_dict[key]) for key in list_of_args]) + for list_of_args in some_dict_keys_sorted + ] + + return sorted_list_of_dicts + + +def sort_keywords_by_underscore_length(arg_keys: list[str]) -> list[list[str]]: + """ + Sort other_arg_keys depending on their leading underscores + + Does not strip the underscores! + >>> sort_keywords_by_underscore_length(['a', '_b', '__c', 'd']) + [['a', 'd'], ['_b'], ['__c']] + """ + + dict_of_keys_and_underscore_counts = dict( + [ + (key_value, count_leading_underscores_in_string(key_value)) + for key_value in arg_keys + ] + ) + count_underscore_values = list(dict_of_keys_and_underscore_counts.values()) + max_number_of_underscores = max(count_underscore_values) + + sorted_keywords = [ + [ + key + for key, key_value in dict_of_keys_and_underscore_counts.items() + if key_value == key_len + ] + for key_len in range(max_number_of_underscores + 1) + ] + + return sorted_keywords + + +def count_leading_underscores_in_string(process_string: str) -> int: + """ + How many underscores at start of process string + + """ + if len(process_string) == 0: + raise Exception( + "Can't pass a parameter name consisting only of underscores or a zero length string" + ) + + if process_string[0] == "_": + return count_leading_underscores_in_string(process_string[1:]) + 1 + + return 0 + + +def strip_underscores_from_dict_keys(arg_dict: dict) -> dict: + """ + + :param arg_dict: dict, with key names that may have leading underscores in them of any length + :return: dict with underscores removed from keynames + """ + + new_dict = dict( + [ + (strip_leading_underscores_from_str(old_key), dict_value) + for old_key, dict_value in arg_dict.items() + ] + ) + + return new_dict + + +def strip_leading_underscores_from_str(process_string: str) -> str: + return re.sub("^[^A-Za-z]*", "", process_string) + + +def force_args_to_same_length(data_args_input: list, data: list, pad_with=None): + """ + Ensure data_args is same length as data list, padding out if required + """ + if pad_with is None: + pad_with = {} + padded_data_args = [] + data_args = copy(data_args_input) + while len(padded_data_args) < len(data): + if len(data_args) == 0: + # We have exhausted all the available data, need to pad with empty + # dicts + padded_data_args.append(pad_with) + else: + # We have more left + padded_data_args.append(data_args.pop(0)) + # If we are done then the data and the padded arguments will now be the same length + # The while condition will fail + + if len(data_args) > 0: + print( + "WARNING: some unused data arguments passed when creating trading rule (had %d leftover, only needed %d)" + % (len(data_args), len(data)) + ) + + return padded_data_args + + +def camel_case_split(some_str: str) -> list: + return re.split("(? int: + return self.trade.as_single_trade_qty_or_error() + + def replace_required_trade_size_only_use_for_unsubmitted_trades( + self, new_trade: tradeQuantity + ): + # ensure refactoring works + assert type(new_trade) is tradeQuantity + + try: + assert len(new_trade) == len(self.trade) + except: + raise Exception( + "Trying to replace trade of length %d with one of length %d" + % (len(self.trade), len(new_trade)) + ) + + new_order = copy(self) + new_order._trade = new_trade + + return new_order + + @property + def order_type(self): + return self._order_type + + @order_type.setter + def order_type(self, order_type: orderType): + self._order_type = order_type + + @property + def fill(self): + return tradeQuantity(self._fill) + + @property + def filled_price(self): + return self._filled_price + + @property + def fill_datetime(self): + return self._fill_datetime + + def fill_order( + self, + fill_qty: tradeQuantity, + filled_price: float, + fill_datetime: datetime.datetime = None, + ): + # Fill qty is cumulative, eg this is the new amount filled + try: + assert self.trade.fill_less_than_or_equal_to_desired_trade(fill_qty) + except: + raise overFilledOrder( + "Can't fill order with fill %s more than trade quantity %s " + % (str(fill_qty), str(self.trade)) + ) + + self._fill = fill_qty + self._filled_price = filled_price + + if fill_datetime is None: + fill_datetime = datetime.datetime.now() + + self._fill_datetime = fill_datetime + + def fill_equals_zero(self) -> bool: + return self.fill.equals_zero() + + def fill_equals_desired_trade(self) -> bool: + return self.fill == self.trade + + def is_zero_trade(self) -> bool: + return self.trade.equals_zero() + + @property + def order_id(self) -> int: + order_id = resolve_orderid(self._order_id) + return order_id + + @order_id.setter + def order_id(self, order_id: int): + assert isinstance(order_id, int) + current_id = getattr(self, "_order_id", no_order_id) + if current_id is no_order_id: + self._order_id = order_id + else: + raise Exception("Can't change order id once set") + + @property + def children(self) -> list: + return self._children + + @children.setter + def children(self, children): + if isinstance(children, int): + children = [children] + + if not self.no_children(): + raise Exception( + "Can't add children to order which already has them: use add another child" + ) + + self._children = children + + def remove_all_children(self): + self._children = no_children + + def no_children(self): + return self.children is no_children + + def add_a_list_of_children(self, list_of_new_children: list): + _ = [self.add_another_child(new_child) for new_child in list_of_new_children] + + def add_another_child(self, new_child: int): + if self.no_children(): + new_children = [new_child] + else: + new_children = self.children + [new_child] + + self._children = new_children + + @property + def remaining(self) -> tradeQuantity: + return self.trade - self.fill + + def create_order_with_unfilled_qty(self): + new_order = copy(self) + new_trade = self.remaining + new_order._trade = new_trade + new_order._fill = new_trade.zero_version() + new_order._filled_price = None + new_order._fill_datetime = None + + return new_order + + def change_trade_size_proportionally_to_meet_abs_qty_limit(self, max_abs_qty: int): + # if this is a single leg trade, does a straight replacement + # otherwise + + new_order = copy(self) + old_trade = new_order.trade + new_trade = old_trade.change_trade_size_proportionally_to_meet_abs_qty_limit( + max_abs_qty + ) + new_order = ( + new_order.replace_required_trade_size_only_use_for_unsubmitted_trades( + new_trade + ) + ) + + return new_order + + def reduce_trade_size_proportionally_so_smallest_leg_is_max_size( + self, max_size: int + ): + new_order = copy(self) + old_trade = new_order.trade + new_trade = ( + old_trade.reduce_trade_size_proportionally_so_smallest_leg_is_max_size( + max_size + ) + ) + new_order = ( + new_order.replace_required_trade_size_only_use_for_unsubmitted_trades( + new_trade + ) + ) + + return new_order + + def change_trade_qty_to_filled_qty(self): + self._trade = self._fill + + @property + def parent(self): + parent = resolve_parent(self._parent) + return parent + + @parent.setter + def parent(self, parent: int): + if self._parent == no_parent: + self._parent = int(parent) + else: + raise Exception("Can't add parent to order which already has them") + + @property + def active(self): + return bool(self._active) + + def deactivate(self): + # Once deactivated: filled or cancelled, we can never go back! + self._active = False + + def zero_out(self): + zero_version_of_trades = self.trade.zero_version() + self._fill = zero_version_of_trades + self.deactivate() + + def as_dict(self): + object_dict = dict(key=self.key) + object_dict["trade"] = list(self.trade) + object_dict["fill"] = list(self.fill) + object_dict["fill_datetime"] = self.fill_datetime + object_dict["filled_price"] = self.filled_price + object_dict["locked"] = self._locked + object_dict["order_id"] = if_object_matches_return_empty_string( + self.order_id, no_order_id + ) + object_dict["parent"] = if_object_matches_return_empty_string( + self.parent, no_parent + ) + object_dict["children"] = if_object_matches_return_empty_string( + self.children, no_children + ) + object_dict["active"] = self.active + object_dict["order_type"] = self.order_type.as_string() + for info_key, info_value in self.order_info.items(): + object_dict[info_key] = info_value + + return object_dict + + @classmethod + def from_dict(Order, order_as_dict): + # will need modifying in child classes + trade = order_as_dict.pop("trade") + object_name = order_as_dict.pop("key") + locked = order_as_dict.pop("locked") + fill = order_as_dict.pop("fill") + filled_price = order_as_dict.pop("filled_price") + fill_datetime = order_as_dict.pop("fill_datetime") + order_id = if_empty_string_return_object( + order_as_dict.pop("order_id"), no_order_id + ) + parent = if_empty_string_return_object(order_as_dict.pop("parent"), no_parent) + children = if_empty_string_return_object( + order_as_dict.pop("children"), no_children + ) + active = order_as_dict.pop("active") + order_type = orderType(order_as_dict.pop("order_type", None)) + + order_info = order_as_dict + + order = Order( + object_name, + trade, + fill=fill, + fill_datetime=fill_datetime, + filled_price=filled_price, + locked=locked, + order_id=order_id, + parent=parent, + children=children, + active=active, + order_type=order_type, + **order_info, + ) + + return order + + @property + def key(self): + return self.tradeable_object.key + + def is_order_locked(self): + return bool(self._locked) + + def lock_order(self): + self._locked = True + + def unlock_order(self): + self._locked = False + + def same_tradeable_object(self, other): + my_object = self.tradeable_object + other_object = other.tradeable_object + return my_object == other_object + + def same_trade_size(self, other): + my_trade = self.trade + other_trade = other.trade + + return my_trade == other_trade + + def __eq__(self, other): + same_tradeable_object = self.same_tradeable_object(other) + same_trade = self.same_trade_size(other) + + return same_tradeable_object and same_trade + + def log_attributes(self): + """ + Returns a dict of order log attributes + :return: dict + """ + + return {} + + +def resolve_inputs_to_order(trade, fill) -> (tradeQuantity, tradeQuantity): + resolved_trade = tradeQuantity(trade) + if fill is None: + resolved_fill = resolved_trade.zero_version() + else: + resolved_fill = tradeQuantity(fill) + + return resolved_trade, resolved_fill + + +def resolve_orderid(order_id: int): + if order_id is no_order_id: + return no_order_id + if order_id is None: + return no_order_id + order_id = int(order_id) + return order_id + + +def resolve_parent(parent: int): + if parent is no_parent: + return no_parent + if parent is None: + return no_parent + parent = int(parent) + + return parent + + +def resolve_multi_leg_price_to_single_price( + trade_list: tradeQuantity, price_list: list +) -> float: + if len(price_list) == 0: + ## This will be the case when an order is first created or has no fills + return None + + if len(price_list) == 1: + return price_list[0] + + assert len(price_list) == len(trade_list) + + trade_list_as_common_factor = ( + list_of_ints_with_highest_common_factor_positive_first(trade_list) + ) + fill_price = [x * y for x, y in zip(trade_list_as_common_factor, price_list)] + + fill_price = sum(fill_price) + if np.isnan(fill_price): + return None + + return fill_price diff --git a/src/quantlib_st/execution/orders/named_order_objects.py b/src/quantlib_st/execution/orders/named_order_objects.py new file mode 100644 index 0000000..34e9ea9 --- /dev/null +++ b/src/quantlib_st/execution/orders/named_order_objects.py @@ -0,0 +1,16 @@ +from quantlib_st.core.constants import named_object + + +missing_order = named_object("missing order") +locked_order = named_object("locked order") +duplicate_order = named_object("duplicate order") +zero_order = named_object("zero order") +order_is_in_status_finished = named_object("order status is modification close") +order_is_in_status_modified = named_object("order status is being modified") +order_is_in_status_not_modified = named_object("order status is not currently modified") +order_is_in_status_reject_modification = named_object( + "order status is modification rejected" +) +no_order_id = named_object("no order ID") +no_children = named_object("no_children") +no_parent = named_object("no parent") diff --git a/src/quantlib_st/execution/trade_qty.py b/src/quantlib_st/execution/trade_qty.py new file mode 100644 index 0000000..d449fb1 --- /dev/null +++ b/src/quantlib_st/execution/trade_qty.py @@ -0,0 +1,253 @@ +import numpy as np + +from quantlib_st.core.genutils import sign +from quantlib_st.execution.orders.named_order_objects import missing_order + + +class tradeQuantity(list): + def __init__(self, trade_or_fill_qty): + if trade_or_fill_qty is None: + trade_or_fill_qty = [] + elif isinstance(trade_or_fill_qty, tradeQuantity): + pass + elif isinstance(trade_or_fill_qty, int): + trade_or_fill_qty = [trade_or_fill_qty] + elif isinstance(trade_or_fill_qty, np.int64): + trade_or_fill_qty = [int(trade_or_fill_qty)] + elif isinstance(trade_or_fill_qty, float): + trade_or_fill_qty = [int(trade_or_fill_qty)] + else: + # must be a list + trade_or_fill_qty = [int(trade) for trade in trade_or_fill_qty] + + super().__init__(trade_or_fill_qty) + + def zero_version(self): + len_self = len(self) + return tradeQuantity([0] * len_self) + + def fill_less_than_or_equal_to_desired_trade(self, proposed_fill): + return all( + [abs(x) <= abs(y) and x * y >= 0 for x, y in zip(proposed_fill, self)] + ) + + def equals_zero(self): + return all([x == 0 for x in self]) + + def sign_of_single_trade(self): + qty = self.as_single_trade_qty_or_error() + return sign(qty) + + def sign_equal(self, other): + return all([sign(x) == sign(y) for x, y in zip(self, other)]) + + def __eq__(self, other): + return all([x == y for x, y in zip(self, other)]) + + def __sub__(self, other): + assert len(self) == len(other) + result = [x - y for x, y in zip(self, other)] + result = tradeQuantity(result) + return result + + def __add__(self, other): + assert len(self) == len(other) + result = [x + y for x, y in zip(self, other)] + result = tradeQuantity(result) + return result + + def __radd__(self, other): + ## required to make list adding work + if other == 0: + return self + else: + return self.__add__(other) + + def total_abs_qty(self): + abs_qty_list = [abs(x) for x in self] + return sum(abs_qty_list) + + def change_trade_size_proportionally_to_meet_abs_qty_limit(self, max_abs_qty: int): + new_qty = change_trade_size_proportionally_to_meet_abs_qty_limit( + self, max_abs_qty + ) + return tradeQuantity(new_qty) + + def sort_with_idx(self, idx_list: list): + unsorted = self + qty_sorted = [unsorted[idx] for idx in idx_list] + self = tradeQuantity(qty_sorted) + + def as_single_trade_qty_or_error(self) -> int: + if len(self) > 1: + return missing_order + return self[0] + + def reduce_trade_size_proportionally_to_abs_limit_per_leg(self, abs_list: list): + # for each item in _trade and abs_list, return the signed minimum of the zip + # eg if self._trade = [2,-2] and abs_list = [1,1], return [2,-2] + applied_list = reduce_trade_size_proportionally_to_abs_limit_per_leg( + self, abs_list + ) + + return tradeQuantity(applied_list) + + def reduce_trade_size_proportionally_so_smallest_leg_is_max_size( + self, max_size: int + ): + """ + Cut the trade down proportionally so the smallest leg is min_size + eg self = [2], min_size = 1 -> [1] + self = [-2,2], min_size = 1 -> [-1,1] + self = [-2,4,-2], min_size = 1 -> [-1,2,2] + self = [-3,4,-3], min_size = 1 -> [-3,4,-3] + + :param max_size: + :return: tradeQuantity + """ + + new_trade_list = reduce_trade_size_proportionally_so_smallest_leg_is_max_size( + self, max_size + ) + return tradeQuantity(new_trade_list) + + def buy_or_sell(self) -> int: + # sign of trade quantity + return sign(self[0]) + + +class listOfTradeQuantity(list): + def total_filled_qty(self) -> tradeQuantity: + total_filled_qty = sum(self) + return total_filled_qty + + +def change_trade_size_proportionally_to_meet_abs_qty_limit( + trade_list: tradeQuantity, max_abs_qty: int +) -> list: + """ + + :param trade_list: tradeQuantity + :param max_abs_qty: int + :return: list of ints + + + """ + original_qty = trade_list + current_abs_qty = trade_list.total_abs_qty() + if current_abs_qty == 0: + return original_qty + if max_abs_qty == 0: + return [0] * len(original_qty) + max_abs_qty = float(max_abs_qty) + ratio = max_abs_qty / current_abs_qty + if ratio >= 1.0: + return original_qty + + new_qty = [abs(int(np.floor(ratio * qty))) for qty in trade_list] + new_qty_adjusted = reduce_trade_size_proportionally_to_abs_limit_per_leg( + original_qty, new_qty + ) + + return new_qty_adjusted + + +def reduce_trade_size_proportionally_to_abs_limit_per_leg( + trade_list_qty: list, abs_list: list +) -> list: + """ + + :param trade_list_qty: + :param abs_list: + :return: list + + + """ + # for each item in _trade and abs_list, return the signed minimum of the zip + # eg if self._trade = [2,-2] and abs_list = [1,1], return [2,-2] + + assert all([x >= 0 for x in abs_list]) + assert len(trade_list_qty) == len(abs_list) + + abs_trade_list = [abs(x) for x in trade_list_qty] + smallest_abs_leg = min(abs_trade_list) + if smallest_abs_leg == 0: + # can't do this + return [0 for x in trade_list_qty] + + abs_size_ratio_list = [ + min([x, y]) / float(x) for x, y in zip(abs_trade_list, abs_list) + ] + min_abs_size_ratio = min(abs_size_ratio_list) + new_smallest_leg = np.floor(smallest_abs_leg * min_abs_size_ratio) + ratio_applied = new_smallest_leg / smallest_abs_leg + trade_list_with_ratio_as_float = [x * ratio_applied for x in trade_list_qty] + trade_list_with_ratio_as_int = [int(x) for x in trade_list_with_ratio_as_float] + diff = [ + abs(x - y) + for x, y in zip(trade_list_with_ratio_as_float, trade_list_with_ratio_as_int) + ] + largediff = any([x > 0.0001 for x in diff]) + if largediff: + trade_list_with_ratio_as_int = [0] * len(trade_list_qty) + + return trade_list_with_ratio_as_int + + +def reduce_trade_size_proportionally_so_smallest_leg_is_max_size( + trade_list_qty: list, max_size: int +): + """ + Cut the trade down proportionally so the smallest leg is min_size + + :param max_size: + :return: tradeQuantity + + + """ + + if max_size == 0: + return [0] * len(trade_list_qty) + + assert max_size > 0 + + abs_trade_list = [abs(x) for x in trade_list_qty] + smallest_abs_leg = min(abs_trade_list) + + if smallest_abs_leg == 0: + return trade_list_qty + + new_smallest_leg = max_size + ratio_applied = new_smallest_leg / smallest_abs_leg + if ratio_applied >= 1.0: + return trade_list_qty + + trade_list_with_ratio_as_float = [x * ratio_applied for x in trade_list_qty] + trade_list_with_ratio_as_int = [int(x) for x in trade_list_with_ratio_as_float] + diff = [ + abs(x - y) + for x, y in zip(trade_list_with_ratio_as_float, trade_list_with_ratio_as_int) + ] + largediff = any([x > 0.0001 for x in diff]) + if largediff: + return trade_list_qty + + return trade_list_with_ratio_as_int + + +def calculate_most_conservative_qty_from_list_of_qty_with_limits_applied( + position: int, list_of_trade_qty: listOfTradeQuantity +) -> tradeQuantity: + # only works with single legs + trade_qty_list_as_single_legs = [ + trade_qty.as_single_trade_qty_or_error() for trade_qty in list_of_trade_qty + ] + + if position >= 0: + most_conservative_trade = min(trade_qty_list_as_single_legs) + else: + most_conservative_trade = max(trade_qty_list_as_single_legs) + + new_trade_qty = tradeQuantity(most_conservative_trade) + + return new_trade_qty diff --git a/src/quantlib_st/init/__init__.py b/src/quantlib_st/init/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/init/futures/__init__.py b/src/quantlib_st/init/futures/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/init/futures/build_multiple_prices_from_raw_data.py b/src/quantlib_st/init/futures/build_multiple_prices_from_raw_data.py new file mode 100644 index 0000000..ca18507 --- /dev/null +++ b/src/quantlib_st/init/futures/build_multiple_prices_from_raw_data.py @@ -0,0 +1,337 @@ +from collections import namedtuple + +import numpy as np +import pandas as pd +from dataclasses import dataclass + +from quantlib_st.objects.dict_of_futures_per_contract_prices import ( + dictFuturesContractFinalPrices, +) +from quantlib_st.objects.dict_of_named_futures_per_contract_prices import ( + price_name, + forward_name, + carry_name, + contract_name_from_column_name, +) + +missing_row = object() + + +@dataclass +class rollCalendarWithRollIndex: + roll_calendar: None + rolling_row_index: int = 1 + already_added_data: bool = False + + def iterate_roll(self): + self.rolling_row_index = self.rolling_row_index + 1 + + def not_end_of_calendar(self): + return self.rolling_row_index < len(self.roll_calendar.index) + + def data_now_added(self): + self.already_added_data = True + + +rollDateInfo = namedtuple( + "rollDateInfo", + [ + "last_roll_date", + "next_roll_date", + "start_of_roll_period", + "end_of_roll_period", + "roll_calendar_with_roll_index", + ], +) +contractAndPriceInfo = namedtuple( + "ccontractAndPriceInfo", + [ + "current_contract", + "next_contract", + "carry_contract", + "current_contract_str", + "next_contract_str", + "carry_contract_str", + "dict_of_futures_contract_closing_prices", + ], +) + + +def create_multiple_price_stack_from_raw_data( + roll_calendar, + dict_of_futures_contract_closing_prices: dictFuturesContractFinalPrices, +): + """ + # NO TYPE CHECK FOR ROLL_CALENDAR AS WOULD CAUSE CIRCULAR IMPORT + + :param roll_calendar: rollCalendar + :param dict_of_futures_closing_contract_prices: dictFuturesContractPrices with only one column, keys are date_str + + :return: pd.DataFrame with the 6 columns PRICE, CARRY, FORWARD, PRICE_CONTRACT, CARRY_CONTRACT, FORWARD_CONTRACT + """ + + all_price_data_stack = [] + roll_calendar_with_roll_index = rollCalendarWithRollIndex(roll_calendar) + while roll_calendar_with_roll_index.not_end_of_calendar(): + all_price_data = _get_price_data_between_rolls( + roll_calendar_with_roll_index, dict_of_futures_contract_closing_prices + ) + if all_price_data is missing_row: + pass + else: + all_price_data_stack.append(all_price_data) + roll_calendar_with_roll_index.data_now_added() + + roll_calendar_with_roll_index.iterate_roll() + + # end of loop + all_price_data_stack = pd.concat(all_price_data_stack, axis=0) + + return all_price_data_stack + + +def _get_price_data_between_rolls( + roll_calendar_with_roll_index: rollCalendarWithRollIndex, + dict_of_futures_contract_closing_prices: dictFuturesContractFinalPrices, +): + # consider consolidating input args + + roll_date_info = _calc_roll_date_info(roll_calendar_with_roll_index) + contract_date_info = _calc_contract_date_info( + roll_date_info, dict_of_futures_contract_closing_prices + ) + + invalid = _invalid_current_contract(contract_date_info) + + if invalid: + # missing, this is okay if we haven't started properly yet + if not roll_calendar_with_roll_index.already_added_data: + print( + "Missing contracts at start of roll calendar not in price data, ignoring" + ) + return missing_row + else: + raise Exception( + "Missing contracts in middle of roll calendar %s, not in price data!" + % str(roll_date_info.next_roll_date) + ) + + all_price_data = _calculate_price_data_from_current_next_carry_data( + roll_date_info, contract_date_info + ) + + return all_price_data + + +def _calc_roll_date_info( + roll_calendar_with_roll_index: rollCalendarWithRollIndex, +) -> rollDateInfo: + # Between these dates is where we are populating prices + roll_calendar = roll_calendar_with_roll_index.roll_calendar + rolling_row_index = roll_calendar_with_roll_index.rolling_row_index + + last_roll_date = roll_calendar.index[rolling_row_index - 1] + next_roll_date = roll_calendar.index[rolling_row_index] + + end_of_roll_period = next_roll_date + start_of_roll_period = last_roll_date + pd.DateOffset( + seconds=1 + ) # to avoid overlaps + + roll_date_info = rollDateInfo( + last_roll_date, + next_roll_date, + start_of_roll_period, + end_of_roll_period, + roll_calendar_with_roll_index, + ) + + return roll_date_info + + +def _calc_contract_date_info( + roll_date_info: rollDateInfo, + dict_of_futures_contract_closing_prices: dictFuturesContractFinalPrices, +) -> contractAndPriceInfo: + roll_calendar = roll_date_info.roll_calendar_with_roll_index.roll_calendar + + contracts_now = roll_calendar.loc[roll_date_info.next_roll_date, :] + current_contract = contracts_now.current_contract + next_contract = contracts_now.next_contract + carry_contract = contracts_now.carry_contract + + current_contract_str = str(current_contract) + next_contract_str = str(next_contract) + carry_contract_str = str(carry_contract) + + contract_date_info = contractAndPriceInfo( + current_contract, + next_contract, + carry_contract, + current_contract_str, + next_contract_str, + carry_contract_str, + dict_of_futures_contract_closing_prices, + ) + + return contract_date_info + + +def _invalid_current_contract(contract_date_info: contractAndPriceInfo) -> bool: + dict_of_futures_contract_closing_prices = ( + contract_date_info.dict_of_futures_contract_closing_prices + ) + contract_keys = dict_of_futures_contract_closing_prices.keys() + + if contract_date_info.current_contract_str not in contract_keys: + return True + else: + return False + + +def _calculate_price_data_from_current_next_carry_data( + roll_date_info: rollDateInfo, contract_date_info: contractAndPriceInfo +): + set_of_price_data = _get_current_next_carry_data(roll_date_info, contract_date_info) + + all_price_data = _build_all_price_data(set_of_price_data, contract_date_info) + + return all_price_data + + +def _get_current_next_carry_data( + roll_date_info: rollDateInfo, contract_date_info: contractAndPriceInfo +): + dict_of_futures_contract_closing_prices = ( + contract_date_info.dict_of_futures_contract_closing_prices + ) + current_contract_data = dict_of_futures_contract_closing_prices[ + contract_date_info.current_contract_str + ] + last_date_in_data = current_contract_data.index[-1] + start_of_period = roll_date_info.start_of_roll_period + end_of_period = min([roll_date_info.end_of_roll_period, last_date_in_data]) + + current_contract_data = current_contract_data.sort_index() + current_price_data = current_contract_data[start_of_period:end_of_period] + + carry_price_data = _get_carry_price_data( + contract_date_info, current_price_data, roll_date_info + ) + + next_price_data = _get_next_price_data( + contract_date_info, current_price_data, roll_date_info + ) + + return current_price_data, next_price_data, carry_price_data + + +def _get_next_price_data( + contract_date_info: contractAndPriceInfo, + current_price_data: pd.Series, + roll_date_info: rollDateInfo, +): + dict_of_futures_contract_closing_prices = ( + contract_date_info.dict_of_futures_contract_closing_prices + ) + contract_keys = dict_of_futures_contract_closing_prices.keys() + next_contract_str = contract_date_info.next_contract_str + + if next_contract_str not in contract_keys: + if _last_row_in_roll_calendar(roll_date_info): + # Last entry, this is fine + print( + "Next contract %s missing in last row of roll calendar - this is okay" + % next_contract_str + ) + next_price_data = pd.Series(np.nan, current_price_data.index) + next_price_data.iloc[:] = np.nan + else: + raise Exception( + "Missing contract %s in middle of roll calendar on %s" + % (next_contract_str, str(roll_date_info.next_roll_date)) + ) + else: + next_price_data_for_contract = dict_of_futures_contract_closing_prices[ + next_contract_str + ] + + next_price_data_for_contract = next_price_data_for_contract.sort_index() + + next_price_data = next_price_data_for_contract[ + roll_date_info.start_of_roll_period : roll_date_info.end_of_roll_period + ] + + return next_price_data + + +def _get_carry_price_data( + contract_date_info: contractAndPriceInfo, + current_price_data: pd.Series, + roll_date_info: rollDateInfo, +): + dict_of_futures_contract_closing_prices = ( + contract_date_info.dict_of_futures_contract_closing_prices + ) + contract_keys = dict_of_futures_contract_closing_prices.keys() + carry_contract_str = contract_date_info.carry_contract_str + + if carry_contract_str not in contract_keys: + if _last_row_in_roll_calendar(roll_date_info): + # Last entry, this is fine + print( + "Carry contract %s missing in last row of roll calendar - this is okay" + % carry_contract_str + ) + carry_price_data = pd.Series(np.nan, current_price_data.index) + carry_price_data.iloc[:] = np.nan + else: + raise Exception( + "Missing contract %s in middle of roll calendar on %s" + % (carry_contract_str, str(roll_date_info.next_roll_date)) + ) + else: + carry_price_data_for_contract = dict_of_futures_contract_closing_prices[ + carry_contract_str + ] + carry_price_data_for_contract = carry_price_data_for_contract.sort_index() + carry_price_data = carry_price_data_for_contract[ + roll_date_info.start_of_roll_period : roll_date_info.end_of_roll_period + ] + + return carry_price_data + + +def _last_row_in_roll_calendar(roll_date_info: rollDateInfo): + roll_calendar_with_roll_index = roll_date_info.roll_calendar_with_roll_index + if ( + roll_calendar_with_roll_index.rolling_row_index + == len(roll_calendar_with_roll_index.roll_calendar.index) - 1 + ): + return True + else: + return False + + +def _build_all_price_data(set_of_price_data, contract_date_info): + current_price_data, next_price_data, carry_price_data = set_of_price_data + all_price_data = pd.concat( + [current_price_data, next_price_data, carry_price_data], axis=1 + ) + all_price_data.columns = [ + price_name, + forward_name, + carry_name, + ] + + all_price_data[contract_name_from_column_name(price_name)] = ( + contract_date_info.current_contract + ) + all_price_data[contract_name_from_column_name(forward_name)] = ( + contract_date_info.next_contract + ) + all_price_data[contract_name_from_column_name(carry_name)] = ( + contract_date_info.carry_contract + ) + + return all_price_data diff --git a/src/quantlib_st/logging/__init__.py b/src/quantlib_st/logging/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/logging/adaptor.py b/src/quantlib_st/logging/adaptor.py new file mode 100644 index 0000000..1d88043 --- /dev/null +++ b/src/quantlib_st/logging/adaptor.py @@ -0,0 +1,102 @@ +import logging + +COMPONENT_LOG_LABEL = "component" +TYPE_LOG_LABEL = "type" +STAGE_LOG_LABEL = "stage" +CLIENTID_LOG_LABEL = "clientid" +BROKER_LOG_LABEL = "broker" +STRATEGY_NAME_LOG_LABEL = "strategy_name" +CURRENCY_CODE_LOG_LABEL = "currency_code" +INSTRUMENT_CODE_LOG_LABEL = "instrument_code" +CONTRACT_DATE_LOG_LABEL = "contract_date" +ORDER_ID_LOG_LABEL = "order_id" +INSTRUMENT_ORDER_ID_LABEL = "instrument_order_id" +CONTRACT_ORDER_ID_LOG_LABEL = "contract_order_id" +BROKER_ORDER_ID_LOG_LABEL = "broker_order_id" + + +ALLOWED_LOG_ATTRIBUTES = [ + TYPE_LOG_LABEL, + COMPONENT_LOG_LABEL, + STAGE_LOG_LABEL, + CURRENCY_CODE_LOG_LABEL, + INSTRUMENT_CODE_LOG_LABEL, + CONTRACT_DATE_LOG_LABEL, + ORDER_ID_LOG_LABEL, + STRATEGY_NAME_LOG_LABEL, + INSTRUMENT_ORDER_ID_LABEL, + CONTRACT_ORDER_ID_LOG_LABEL, + BROKER_ORDER_ID_LOG_LABEL, + BROKER_LOG_LABEL, + CLIENTID_LOG_LABEL, +] + + +class DynamicAttributeLogger(logging.LoggerAdapter): + def __init__(self, logger, attributes) -> None: + self._check_attributes(attributes) + super().__init__(logger, attributes) + + def process(self, msg, kwargs): + attrs = dict() + new_kwargs = dict() + + method = kwargs.pop("method", "overwrite") + if method not in ["clear", "preserve", "overwrite", "temp"]: + raise ValueError(f"Invalid value for 'method': {method}") + + for k, v in kwargs.items(): + if k in ALLOWED_LOG_ATTRIBUTES: + attrs[k] = v + else: + new_kwargs[k] = v + + """ + Four possible ways to deal with attributes + 1. temp: passed values overwrite existing for one message, then discarded + 2. clear: clear existing, use passed values + 3. preserve: merge with existing values preserved + 4. overwrite: merge with existing values overwritten + """ + if method == "temp": + if self.extra: + return "%s %s" % ({**self.extra, **attrs}, msg), new_kwargs + else: + return "%s %s" % (attrs, msg), new_kwargs + else: + merged = self._merge_attributes(method, attrs) + new_kwargs["extra"] = merged + self.extra = merged + + if self.extra: + return "%s %s" % (self.extra, msg), new_kwargs + else: + return "%s" % msg, new_kwargs + + def _merge_attributes(self, method, attributes): + if not self.extra or method == "clear": + merged = attributes + elif method == "preserve": + merged = {**attributes, **self.extra} + else: + merged = {**self.extra, **attributes} + + return merged + + def _check_attributes(self, attributes: dict): + if attributes: + bad_attributes = get_list_of_disallowed_attributes(attributes) + if len(bad_attributes) > 0: + raise Exception( + "Attributes %s not allowed in log" % str(bad_attributes) + ) + + +def get_list_of_disallowed_attributes(attributes: dict) -> list: + attr_keys = list(attributes.keys()) + not_okay = [ + attribute_name + for attribute_name in attr_keys + if attribute_name not in ALLOWED_LOG_ATTRIBUTES + ] + return not_okay diff --git a/src/quantlib_st/logging/logger.py b/src/quantlib_st/logging/logger.py new file mode 100644 index 0000000..632faea --- /dev/null +++ b/src/quantlib_st/logging/logger.py @@ -0,0 +1,86 @@ +import logging +import sys + +from quantlib_st.logging.adaptor import * + +LOG_FORMAT = "%(asctime)s %(levelname)s %(name)s %(message)s" + +_logging_configured = False + + +def get_logger(name, attributes=None): + """ + # set up a logger with a name + log = get_logger("my_logger") + + # create a log message with a logging level + log.debug("debug message") + log.info("info message") + log.warning("warning message") + log.error("error message") + log.critical("critical message") + + # parameterise the message + log.info("Hello %s", "world") + log.info("Goodbye %s %s", "cruel", "world") + + # setting attributes on initialisation + log = get_logger("attributes", {"stage": "first"}) + + # setting attributes on message creation + log.info("logging call attributes", instrument_code="GOLD") + + # merging attributes: method 'overwrite' (default if no method supplied) + overwrite = get_logger("Overwrite", {"type": "first"}) + overwrite.info("overwrite, type 'first'") + overwrite.info( + "overwrite, type 'second', stage 'one'", + method="overwrite", + type="second", + stage="one", + ) + + # merging attributes: method 'preserve' + preserve = get_logger("Preserve", {"type": "first"}) + preserve.info("preserve, type 'first'") + preserve.info( + "preserve, type 'first', stage 'one'", method="preserve", type="second", stage="one" + ) + + # merging attributes: method 'clear' + clear = get_logger("Clear", {"type": "first", "stage": "one"}) + clear.info("clear, type 'first', stage 'one'") + clear.info("clear, type 'second', no stage", method="clear", type="second") + clear.info("clear, no attributes", method="clear") + + :param name: logger name + :type name: str + :param attributes: log attributes + :type attributes: dict + :return: logger instance + :rtype: DynamicAttributeLogger + """ + _configure_logging() + if not name: + if attributes and "type" in attributes: + name = attributes["type"] + return DynamicAttributeLogger(logging.getLogger(name), attributes) + + +def _configure_logging(): + global _logging_configured + if _logging_configured: + return + + handler = logging.StreamHandler(stream=sys.stdout) + handler.setLevel(logging.INFO) + logging.getLogger("ib_insync").setLevel(logging.WARNING) + logging.getLogger("arctic").setLevel(logging.INFO) + logging.getLogger("matplotlib").setLevel(logging.INFO) + logging.basicConfig( + handlers=[handler], + format=LOG_FORMAT, + datefmt="%Y-%m-%d %H:%M:%S", + level=logging.DEBUG, + ) + _logging_configured = True diff --git a/src/quantlib_st/objects/__init__.py b/src/quantlib_st/objects/__init__.py new file mode 100644 index 0000000..0486708 --- /dev/null +++ b/src/quantlib_st/objects/__init__.py @@ -0,0 +1,4 @@ +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices +from quantlib_st.objects.multiple_prices import futuresMultiplePrices + +__all__ = ["futuresAdjustedPrices", "futuresMultiplePrices"] diff --git a/src/quantlib_st/objects/adjusted_prices.py b/src/quantlib_st/objects/adjusted_prices.py new file mode 100644 index 0000000..e36e1f6 --- /dev/null +++ b/src/quantlib_st/objects/adjusted_prices.py @@ -0,0 +1,194 @@ +from copy import copy + +import numpy as np +import pandas as pd + +from quantlib_st.core.pandas.full_merge_with_replacement import ( + full_merge_of_existing_series, +) +from quantlib_st.objects.dict_of_named_futures_per_contract_prices import ( + price_name, + contract_name_from_column_name, +) +from quantlib_st.objects.multiple_prices import futuresMultiplePrices + + +class futuresAdjustedPrices(pd.Series): + """ + adjusted price information + """ + + def __init__(self, price_data): + price_data.index.name = "index" # arctic compatible + super().__init__(price_data) + + @classmethod + def create_empty(futuresContractPrices): + """ + Our graceful fail is to return an empty, but valid, dataframe + """ + + futures_contract_prices = futuresContractPrices(pd.Series(dtype="float64")) + + return futures_contract_prices + + @classmethod + def stitch_multiple_prices( + futuresAdjustedPrices, + multiple_prices: futuresMultiplePrices, + forward_fill: bool = False, + ): + """ + Do backstitching of multiple prices using panama method + + If you want to change then override this method + + :param multiple_prices: multiple prices object + :param forward_fill: forward fill prices and forwards before stitching + + :return: futuresAdjustedPrices + + """ + adjusted_prices = _panama_stitch(multiple_prices, forward_fill) + return futuresAdjustedPrices(adjusted_prices) + + def update_with_multiple_prices_no_roll( + self, updated_multiple_prices: futuresMultiplePrices + ): + """ + Update adjusted prices assuming no roll has happened + + :param updated_multiple_prices: futuresMultiplePrices + :return: updated adjusted prices + """ + + updated_adj = _update_adjusted_prices_from_multiple_no_roll( + self, updated_multiple_prices + ) + + return updated_adj + + +def _panama_stitch( + multiple_prices_input: futuresMultiplePrices, forward_fill: bool = False +) -> pd.Series: + """ + Do a panama stitch for adjusted prices + + :param multiple_prices: futuresMultiplePrices + :return: pd.Series of adjusted prices + """ + multiple_prices = copy(multiple_prices_input) + if forward_fill: + multiple_prices.ffill(inplace=True) + + if multiple_prices.empty: + raise Exception("Can't stitch an empty multiple prices object") + + previous_row = multiple_prices.iloc[0, :] + adjusted_prices_values = [previous_row.PRICE] + + for dateindex in multiple_prices.index[1:]: + current_row = multiple_prices.loc[dateindex, :] + + if current_row.PRICE_CONTRACT == previous_row.PRICE_CONTRACT: + # no roll has occurred + # we just append the price + adjusted_prices_values.append(current_row.PRICE) + else: + # A roll has occurred + adjusted_prices_values = _roll_in_panama( + adjusted_prices_values, previous_row, current_row + ) + + previous_row = current_row + + # it's ok to return a DataFrame since the calling object will change the + # type + adjusted_prices = pd.Series(adjusted_prices_values, index=multiple_prices.index) + + return adjusted_prices + + +def _roll_in_panama(adjusted_prices_values, previous_row, current_row): + # This is the sort of code you will need to change to adjust the roll logic + # The roll differential is from the previous_row + roll_differential = previous_row.FORWARD - previous_row.PRICE + if np.isnan(roll_differential): + raise Exception( + "On this day %s which should be a roll date we don't have prices for both %s and %s contracts" + % ( + str(current_row.name), + previous_row.PRICE_CONTRACT, + previous_row.FORWARD_CONTRACT, + ) + ) + + # We add the roll differential to all previous prices + adjusted_prices_values = [ + adj_price + roll_differential for adj_price in adjusted_prices_values + ] + + # note this includes the price for the previous row, which will now be equal to the forward price + # We now add todays price. This will be for the new contract + + adjusted_prices_values.append(current_row.PRICE) + + return adjusted_prices_values + + +no_update_roll_has_occurred = futuresAdjustedPrices.create_empty() + + +def _update_adjusted_prices_from_multiple_no_roll( + existing_adjusted_prices: futuresAdjustedPrices, + updated_multiple_prices: futuresMultiplePrices, +) -> futuresAdjustedPrices: + """ + Update adjusted prices assuming no roll has happened + + :param existing_adjusted_prices: futuresAdjustedPrices + :param updated_multiple_prices: futuresMultiplePrices + :return: updated adjusted prices + """ + new_multiple_price_data, last_contract_in_price_data = _calc_new_multiple_prices( + existing_adjusted_prices, updated_multiple_prices + ) + + no_roll_has_occurred = new_multiple_price_data.check_all_contracts_equal_to( + last_contract_in_price_data + ) + + if not no_roll_has_occurred: + return no_update_roll_has_occurred + + new_adjusted_prices = new_multiple_price_data[price_name] + new_adjusted_prices = new_adjusted_prices.dropna() + + merged_adjusted_prices = full_merge_of_existing_series( + existing_adjusted_prices, new_adjusted_prices + ) + merged_adjusted_prices = futuresAdjustedPrices(merged_adjusted_prices) + + return merged_adjusted_prices + + +def _calc_new_multiple_prices( + existing_adjusted_prices: futuresAdjustedPrices, + updated_multiple_prices: futuresMultiplePrices, +) -> (futuresMultiplePrices, str): + last_date_in_current_adj = existing_adjusted_prices.index[-1] + multiple_prices_as_dict = updated_multiple_prices.as_dict() + + prices_in_multiple_prices = multiple_prices_as_dict[price_name] + price_contract_column = contract_name_from_column_name(price_name) + + last_contract_in_price_data = prices_in_multiple_prices[price_contract_column][ + :last_date_in_current_adj + ].iloc[-1] + + new_multiple_price_data = prices_in_multiple_prices.prices_after_date( + last_date_in_current_adj + ) + + return new_multiple_price_data, last_contract_in_price_data diff --git a/src/quantlib_st/objects/contract_dates_and_expiries.py b/src/quantlib_st/objects/contract_dates_and_expiries.py new file mode 100644 index 0000000..8ecde19 --- /dev/null +++ b/src/quantlib_st/objects/contract_dates_and_expiries.py @@ -0,0 +1,622 @@ +""" +Represent contract dates and expiries +""" + +import datetime + +from quantlib_st.core.dateutils import ( + contract_month_from_number, + month_from_contract_letter, +) +from quantlib_st.core.genutils import np_convert + +NO_EXPIRY_DATE_PASSED = "" +NO_DAY_PASSED = object() + +YEAR_SLICE = slice(0, 4) +MONTH_SLICE = slice(4, 6) +DAY_SLICE = slice(6, 8) +YYYYMM_SLICE = slice(0, 6) + + +class needSingleLegDate(Exception): + pass + + +def from_contract_numbers_to_contract_string( + new_year_number, new_month_number, new_day_number=NO_DAY_PASSED +): + new_month_str = "{0:02d}".format(new_month_number) + new_year_str = str(new_year_number) + + if new_day_number is NO_DAY_PASSED or new_day_number == 0: + new_day_str = "" + else: + new_day_str = "{0:02d}".format(new_day_number) + + new_contract_date_as_string = new_year_str + new_month_str + new_day_str + + return new_contract_date_as_string + + +EXPIRY_DATE_FORMAT = "%Y%m%d" + + +class expiryDate(datetime.datetime): + def as_tuple(self): + return (self.year, self.month, self.day) + + @classmethod + def from_str(expiryDate, date_as_str: str): + try: + as_date = datetime.datetime.strptime(date_as_str, EXPIRY_DATE_FORMAT) + except: + raise Exception( + "Expiry date %s not in format %s" % (date_as_str, EXPIRY_DATE_FORMAT) + ) + + return expiryDate(year=as_date.year, month=as_date.month, day=as_date.day) + + def as_str(self) -> str: + return self.strftime(EXPIRY_DATE_FORMAT) + + +class singleContractDate(object): + """ + A single contract date; either in the form YYYYMM or YYYYMMDD + *or* specified as a list + + Use cases: + - normal contract eg 201712 and expiry date like 20171214 + - VIX where contract needs to be defined as 20171214 because of weekly expiries + - Gas where contract month and expiry date are in different months + + We store the expiry date separately + + Representation is either 20171200 or 20171214 so always yyyymmdd + + Either: + + - we know the expiry date precisely and it's passed when we create the object + - OR we have to approximate by using the 1st of the month when the object is created + - OR we can make a better approximation by applying an offset to the approximate date + """ + + def __init__( + self, + date_str: str, + expiry_date: expiryDate = NO_EXPIRY_DATE_PASSED, + approx_expiry_offset: int = 0, + ): + """ + + :param date_str: string of numbers length 6 or 8 eg '201008' or '201008515' + :param expiry_date: string of numbers length 8 be passed eg '20101218' + """ + + try: + assert isinstance(date_str, str) + assert int(date_str) + + if len(date_str) == 6: + self._init_with_yymm(date_str) + elif len(date_str) == 8: + self._init_with_yymmdd(date_str) + else: + raise Exception("Can't parse %s as YYYYMM or YYYYMMDD" % str(date_str)) + + except Exception: + raise Exception( + "contractDate(contract_date) needs to be defined as a str, yyyymm or yyyymmdd" + ) + + self._expiry_date = expiry_date + self._approx_expiry_offset = approx_expiry_offset + + def __repr__(self): + return self.date_str + + def __eq__(self, other): + return self.expiry_date == other.expiry_date + + @property + def date_str(self): + return self._date_str + + def _init_with_yymm(self, date_str: str): + """ + Initialise class with length 6 str eg '201901' + + :param date_str: str + :return: None + """ + + self._date_str = "".join([date_str, "00"]) + self._only_has_month = True + + def _init_with_yymmdd(self, date_str: str): + """ + Initialise class with length 8 str eg '20190115' + + :param date_str: str + :return: None + """ + + if date_str[DAY_SLICE] == "00": + self._init_with_yymm(date_str[YYYYMM_SLICE]) + else: + self._date_str = date_str + self._only_has_month = False + + def _get_expiry_date_from_approx_expiry(self, approx_expiry_offset): + # guess from the contract date - we can always correct this later + + approx_expiry_date = self.as_date() + new_expiry_date = approx_expiry_date + datetime.timedelta( + days=np_convert(approx_expiry_offset) + ) + expiry_date_tuple = ( + new_expiry_date.year, + new_expiry_date.month, + new_expiry_date.day, + ) + + expiry_date = expiryDate(*expiry_date_tuple) + + return expiry_date + + @property + def expiry_date(self): + expiry_date = self._expiry_date + if expiry_date is NO_EXPIRY_DATE_PASSED: + approx_expiry_offset = self._approx_expiry_offset + expiry_date = self._get_expiry_date_from_approx_expiry(approx_expiry_offset) + + return expiry_date + + @property + def only_has_month(self): + return self._only_has_month + + # not using a setter as shouldn't be done casually + def update_expiry_date(self, expiry_date: expiryDate): + self._expiry_date = expiry_date + + def update_expiry_date_with_new_offset(self, expiry_date_offset: int): + expiry_date = self._get_expiry_date_from_approx_expiry(expiry_date_offset) + self.update_expiry_date(expiry_date) + + def as_dict(self): + ## safe, db independent way of storing expiry dates + expiry_date = self.expiry_date.as_tuple() + + # we do this so that we can init the object again from this with the + # correct length of contract_date + contract_date = self._date_str_with_no_trailing_zeros() + + return dict( + expiry_date=expiry_date, + contract_date=contract_date, + ) + + @classmethod + def create_from_dict(contractDate, results_dict): + # needs to match output from as_dict + + expiry_date = results_dict.get("expiry_date", NO_EXPIRY_DATE_PASSED) + + if expiry_date is not NO_EXPIRY_DATE_PASSED: + expiry_date = expiryDate(*expiry_date) + + contract_id = results_dict["contract_date"] + + return contractDate(contract_id, expiry_date=expiry_date) + + def year(self): + return int(self.date_str[YEAR_SLICE]) + + def month(self): + return int(self.date_str[MONTH_SLICE]) + + def day(self): + if not self.is_day_defined(): + return 0 + + return int(self.date_str[DAY_SLICE]) + + def is_day_defined(self): + if self.only_has_month: + return False + else: + return True + + def letter_month(self): + return contract_month_from_number(self.month()) + + def date_str_to_year_month(self) -> (int, str): + current_month_str = self.letter_month() + current_year_int = self.year() + + return current_year_int, current_month_str + + def as_date(self): + tuple_of_dates = self._as_date_tuple() + + return datetime.datetime(*tuple_of_dates) + + def _as_date_tuple(self): + if self.only_has_month: + day = 1 + else: + day = self.day() + + return (self.year(), self.month(), day) + + def _date_str_with_no_trailing_zeros(self): + if self.only_has_month: + # remove trailing zeros + date_str = self.date_str[YYYYMM_SLICE] + else: + date_str = self.date_str + + return date_str + + +CONTRACT_DATE_LIST_ENTRY_KEY = "contract_list" + + +class listOfContractDateStr(list): + def sorted_date_str(self): + return listOfContractDateStr(sorted(self)) + + def final_date_str(self): + return self.sorted_date_str()[-1] + + +class contractDate(object): + """ + + + A single contract date; either in the form YYYYMM or YYYYMMDD + *or* a list of contract dates + + Typically + + Use cases: + - normal contract eg 201712 and expiry date like 20171214 + - VIX where contract needs to be defined as 20171214 because of weekly expiries + - Gas where contract month and expiry date are in different months + + We store the expiry date separately + + Representation is either 20171200 or 20171214 so always yyyymmdd + + Either: + + - we know the expiry date precisely and it's passed when we create the object + - OR we have to approximate by using the 1st of the month when the object is created + - OR we can make a better approximation by applying an offset to the approximate date + """ + + def __init__( + self, + date_str, + expiry_date=NO_EXPIRY_DATE_PASSED, + approx_expiry_offset=0, + simple=False, + ): + """ + Vanilla + contractDate("202003") + contractDate("20200300") + contractDate("20200302") + + contractDate("202003", expiry_date = expiryDate(2020,3,1)) # approx offset will be ignored + contractDate("202003", approx_expiry_offset = 2) + + Spreads + contractDate(["202003", "202006"]) + contractDate("202003_202006") + contractDate("202003_202006", approx_expiry_offset = 2) ## offset applied to everything + contractDate("202003_202006", expiry_date = [expiryDate(2020,3,1), expiryDate(2020,6,2)]) + contractDate(dict(contract_list = [dict(contract_date = '20230300', expiry_date = (2023,3,1)), + dict(contract_date = '20230600', expiry_date = (2023,6,2))])) + + :param date_str: string of numbers length 6 or 8 eg '201008' or '201008515', or a list of those, or underscore + :param expiry_date: expiryDate object, or list same length as contract date list + :param approx_expiry_offset: int (applied to all expiry dates if a spread) + """ + if simple: + contract_date_list = [singleContractDate(date_str)] + else: + contract_date_list = resolve_date_string_into_list_of_single_contract_dates( + date_str, + expiry_date=expiry_date, + approx_expiry_offset=approx_expiry_offset, + ) + self._list_of_single_contract_dates = contract_date_list + + def __repr__(self): + return self.key + + def __eq__(self, other): + my_list_of_single_contract_dates = self.list_of_single_contract_dates + other_list_of_single_contract_dates = other.list_of_single_contract_dates + if len(my_list_of_single_contract_dates) != len( + other_list_of_single_contract_dates + ): + return False + + equal_for_each_item = [ + item == other_item + for item, other_item in zip( + my_list_of_single_contract_dates, other_list_of_single_contract_dates + ) + ] + + return all(equal_for_each_item) + + @property + def list_of_single_contract_dates(self): + return self._list_of_single_contract_dates + + @property + def list_of_date_str(self): + list_of_contract_dates = self.list_of_single_contract_dates + list_of_date_str = [ + contract_date.date_str for contract_date in list_of_contract_dates + ] + + return list_of_date_str + + def index_of_sorted_contract_dates(self) -> list: + clist = self.list_of_date_str + return sorted(range(len(clist)), key=lambda k: clist[k]) + + def sort_with_idx(self, idx_list): + unsorted = self.list_of_single_contract_dates + sorted_dates = [unsorted[idx] for idx in idx_list] + self._list_of_single_contract_dates = sorted_dates + + @property + def key(self): + return self.date_str + + @property + def is_spread_contract(self): + if len(self.list_of_single_contract_dates) > 1: + return True + else: + return False + + @property + def first_contract_date(self) -> singleContractDate: + if self.is_spread_contract: + raise needSingleLegDate( + "Can't use this method or property with multiple leg contractDate %s" + % str(self) + ) + + return self.list_of_single_contract_dates[0] + + def nth_single_contract_as_contract_date(self, contract_index: int): + contract_as_single_contract = self.nth_contract_date(contract_index) + contract_as_single_contract_as_dict = { + CONTRACT_DATE_LIST_ENTRY_KEY: [contract_as_single_contract.as_dict()] + } + + return contractDate(contract_as_single_contract_as_dict) + + def nth_contract_date(self, contract_index: int) -> singleContractDate: + return self.list_of_single_contract_dates[contract_index] + + @property + def only_has_month(self): + return self.first_contract_date.only_has_month + + @property + def expiry_date(self): + return self.first_contract_date.expiry_date + + @property + def date_str(self): + return "_".join([x.date_str for x in self.list_of_single_contract_dates]) + + @property + def date_str_to_year_month(self): + return self.first_contract_date.date_str_to_year_month + + # not using a setter as shouldn't be done casually + def update_single_expiry_date(self, expiry_date: expiryDate): + # we don't call update_nth expiry as this will assert it's a single + assert not self.is_spread_contract + self.update_nth_expiry_date(0, expiry_date) + + def update_expiry_date_with_new_offset(self, approx_expiry_offset: int): + assert not self.is_spread_contract + single_contract_date = self.nth_contract_date(0) + single_contract_date.update_expiry_date_with_new_offset(approx_expiry_offset) + + def update_nth_expiry_date(self, contract_index: int, expiry_date: expiryDate): + single_contract_date = self.nth_contract_date(contract_index) + single_contract_date.update_expiry_date(expiry_date) + + def as_dict(self): + return { + CONTRACT_DATE_LIST_ENTRY_KEY: [ + contract_date.as_dict() + for contract_date in self.list_of_single_contract_dates + ] + } + + @classmethod + def create_from_dict(contractDate, results_dict): + # needs to match output from as_dict + # Have 'old style' storage for legacy data + if CONTRACT_DATE_LIST_ENTRY_KEY in results_dict.keys(): + ## new style + return contractDate(results_dict) + else: + ## old style + return create_contract_date_from_old_style_dict(contractDate, results_dict) + + def year(self): + return self.first_contract_date.year() + + def month(self): + return self.first_contract_date.month() + + def day(self): + return self.first_contract_date.day() + + def is_day_defined(self): + return self.first_contract_date.is_day_defined() + + def letter_month(self): + return self.first_contract_date.letter_month() + + def __len__(self): + return len(self.list_of_single_contract_dates) + + def __getitem__(self, item): + return self.list_of_single_contract_dates[item] + + def __setitem__(self, key: int, value: singleContractDate): + self.list_of_single_contract_dates[key] = value + + +def resolve_date_string_into_list_of_single_contract_dates( + date_str, expiry_date=NO_EXPIRY_DATE_PASSED, approx_expiry_offset=0 +) -> list: + if type(date_str) is dict: + contract_date_list = get_contract_date_object_list_from_dict(date_str) + else: + contract_date_list = ( + get_contract_date_object_list_from_date_str_and_expiry_date( + date_str, expiry_date, approx_expiry_offset=approx_expiry_offset + ) + ) + + return contract_date_list + + +def get_contract_date_object_list_from_dict(date_str: dict) -> list: + try: + contract_dates_as_list = date_str[CONTRACT_DATE_LIST_ENTRY_KEY] + except: + raise Exception( + "Need to pass dict with single key %s" % CONTRACT_DATE_LIST_ENTRY_KEY + ) + + contract_date_list = [ + singleContractDate.create_from_dict(dict_in_list) + for dict_in_list in contract_dates_as_list + ] + + return contract_date_list + + +def get_contract_date_object_list_from_date_str_and_expiry_date( + date_str, expiry_date, approx_expiry_offset=0 +) -> list: + date_str_list = resolve_date_string_into_list_of_date_str(date_str) + expiry_date_list = resolve_expiry_date_into_list_of_expiry_dates( + expiry_date, date_str_list + ) + contract_date_list = [ + singleContractDate( + date_str_this_date, + expiry_date=expiry_date_this_str, + approx_expiry_offset=approx_expiry_offset, + ) + for date_str_this_date, expiry_date_this_str in zip( + date_str_list, expiry_date_list + ) + ] + + return contract_date_list + + +def get_date_str_list_and_expiry_date_list_from_date_str_and_expiry_date( + date_str, expiry_date +): + date_str_list = resolve_date_string_into_list_of_date_str(date_str) + expiry_date_list = resolve_expiry_date_into_list_of_expiry_dates( + expiry_date, date_str_list + ) + + return date_str_list, expiry_date_list + + +def resolve_date_string_into_list_of_date_str(date_str) -> list: + """ + str with no underscores becomes [str] + + str with underscores becomes [str1, str2,...] + + list remains list + + :param date_str: str or list + :return: list + """ + if type(date_str) is list: + return date_str + + date_str_as_list = date_str.split("_") + return date_str_as_list + + +def resolve_expiry_date_into_list_of_expiry_dates(expiry_date, date_str_as_list): + if expiry_date is NO_EXPIRY_DATE_PASSED: + return [NO_EXPIRY_DATE_PASSED] * len(date_str_as_list) + + if type(expiry_date) is list: + try: + assert len(expiry_date) == len(date_str_as_list) + except: + raise Exception( + "Length of expiry date list has to match length of date strings" + ) + + return expiry_date + + if type(expiry_date) is expiryDate: + try: + assert len(date_str_as_list) == 1 + except: + raise Exception( + "Passing a single expiry date but there is more than one contract date" + ) + + return [expiry_date] + + raise Exception( + "Don't know how to handle expiry date %s of type %s" + % (str(expiry_date), str(type(expiry_date))) + ) + + +def create_contract_date_from_old_style_dict(contractDate, results_dict: dict): + ## for compatibility with original format + expiry_date = results_dict.get("expiry_date", NO_EXPIRY_DATE_PASSED) + + if expiry_date is not NO_EXPIRY_DATE_PASSED: + expiry_date = expiryDate(*expiry_date) + + contract_id = results_dict["contract_date"] + + return contractDate(contract_id, expiry_date=expiry_date) + + +def contract_given_tuple(contract_date: contractDate, year_value: int, month_str: str): + if contract_date.only_has_month: + new_day_number = 0 + else: + new_day_number = contract_date.day() + + month_int = month_from_contract_letter(month_str) + date_str = from_contract_numbers_to_contract_string( + year_value, month_int, new_day_number + ) + + return contractDate(date_str, simple=True) diff --git a/src/quantlib_st/objects/contracts.py b/src/quantlib_st/objects/contracts.py new file mode 100644 index 0000000..b4be59a --- /dev/null +++ b/src/quantlib_st/objects/contracts.py @@ -0,0 +1,388 @@ +from dataclasses import dataclass +from typing import Union + +import datetime + +from quantlib_st.core.constants import arg_not_supplied + +from quantlib_st.logging.logger import * + +from quantlib_st.objects.contract_dates_and_expiries import ( + contractDate, + expiryDate, + listOfContractDateStr, +) +from quantlib_st.objects.instruments import futuresInstrument + + +NO_ROLL_CYCLE_PASSED = object() + + +@dataclass +class parametersForFuturesContract: + sampling: bool = False + + def as_dict(self) -> dict: + keys = self.__dataclass_fields__.keys() + self_as_dict = dict([(key, getattr(self, key)) for key in keys]) + + return self_as_dict + + @classmethod + def from_dict(parametersForFuturesContract, input_dict): + keys = parametersForFuturesContract.__dataclass_fields__.keys() + args_list = [input_dict.get(key, None) for key in keys] + args_list = [value for value in args_list if value is not None] + + return parametersForFuturesContract(*args_list) + + +def contract_key_from_code_and_id(instrument_code, contract_id): + contract = contract_from_code_and_id(instrument_code, contract_id) + return contract.key + + +def contract_from_code_and_id(instrument_code, contract_id): + return futuresContract(instrument_code, contract_id) + + +def contract_from_key(contract_key): + instrument_code, contract_id = get_code_and_id_from_contract_key(contract_key) + + return contract_from_code_and_id(instrument_code, contract_id) + + +class futuresContract(object): + """ + Define an individual futures contract + + This is a combination of an instrument_object and contract_date object + + """ + + def __init__( + self, + instrument_object: Union[str, futuresInstrument], + contract_date_object: Union[str, contractDate], + parameter_object: parametersForFuturesContract = arg_not_supplied, + simple: bool = False, + ): + """ + futuresContract(futuresInstrument, contractDate) + OR + futuresContract("instrument_code", "yyyymm") + + :param instrument_object: str or futuresInstrument + :param contract_date_object: contractDate or contractDateWithRollParameters or str + """ + if not simple: + ( + instrument_object, + contract_date_object, + ) = _resolve_args_for_futures_contract( + instrument_object, contract_date_object + ) + + if parameter_object is arg_not_supplied: + parameter_object = parametersForFuturesContract() + + self._instrument = instrument_object + self._contract_date = contract_date_object + self._params = parameter_object + + def log_attributes(self): + """ + Returns a dict of futuresContract log attributes + + :return: dict + """ + return { + INSTRUMENT_CODE_LOG_LABEL: self.instrument_code, + CONTRACT_DATE_LOG_LABEL: self.date_str, + } + + @property + def instrument(self): + return self._instrument + + @property + def contract_date(self): + return self._contract_date + + @property + def params(self): + return self._params + + def __repr__(self): + return self.key + + def __eq__(self, other): + instruments_match = self.instrument == other.instrument + contracts_match = self.date_str == other.date_str + + if instruments_match and contracts_match: + return True + else: + return False + + @classmethod + def from_key(cls, key): + code_and_id = get_code_and_id_from_contract_key(key) + instrument_code = code_and_id[0] + contract_id = code_and_id[1] + + return cls(instrument_object=instrument_code, contract_date_object=contract_id) + + @property + def key(self): + return get_contract_key_from_code_and_id(self.instrument_code, self.date_str) + + @property + def currently_sampling(self): + return self.params.sampling + + def sampling_on(self): + self.params.sampling = True + + def sampling_off(self): + self.params.sampling = False + + def as_dict(self): + """ + Turn into a dict. We only include instrument_code from the instrument_object, the rest would be found elsewhere + plus we have all the results from as_dict on the contract_date + + :return: dict + """ + + contract_date_dict = self.contract_date.as_dict() + instrument_dict = self.instrument.as_dict() + contract_params_dict = self.params.as_dict() + + joint_dict = dict( + contract_date_dict=contract_date_dict, + instrument_dict=instrument_dict, + contract_params=contract_params_dict, + ) + + return joint_dict + + @classmethod + def create_from_dict(futuresContract, futures_contract_dict): + """ + + :param futures_contract_dict: The result of running .as_dict on a futuresContract. + :return: futuresContract object + """ + + contract_date_dict = futures_contract_dict["contract_date_dict"] + instrument_dict = futures_contract_dict["instrument_dict"] + contract_params_dict = futures_contract_dict["contract_params"] + + contract_date_object = contractDate.create_from_dict(contract_date_dict) + instrument_object = futuresInstrument.create_from_dict(instrument_dict) + parameter_object = parametersForFuturesContract.from_dict(contract_params_dict) + + return futuresContract( + instrument_object, contract_date_object, parameter_object=parameter_object + ) + + @property + def instrument_code(self): + return self.instrument.instrument_code + + @property + def date_str(self): + return self.contract_date.date_str + + @property + def date(self): + return self.contract_date.as_date + + @property + def expiry_date(self): + return self.contract_date.expiry_date + + def expired(self): + days_since_expiry = self.days_since_expiry() + if days_since_expiry > 0: + return True + else: + return False + + def days_since_expiry(self) -> float: + days_until_expiry = self.days_until_expiry() + days_since_expiry = -days_until_expiry + + return days_since_expiry + + def days_until_expiry(self) -> int: + expiry_date = self.expiry_date.date() + date_now = datetime.datetime.now().date() + timedelta = expiry_date - date_now + return timedelta.days + + def update_single_expiry_date(self, new_expiry_date: expiryDate): + self.contract_date.update_single_expiry_date(new_expiry_date) + + def is_spread_contract(self): + return self.contract_date.is_spread_contract + + def new_contract_with_replaced_instrument_object(self, new_instrument_object): + contract_date_object = self.contract_date + params = self.params + + return futuresContract( + new_instrument_object, contract_date_object, parameter_object=params + ) + + def update_expiry_dates_one_at_a_time_with_method(self, method, **kwargs): + as_list_of_individual_contracts = self.as_list_of_individual_contracts() + new_expiries = [ + method(single_contract, **kwargs) + for single_contract in as_list_of_individual_contracts + ] + + for contract_index, expiry_date in enumerate(new_expiries): + self.update_nth_expiry_date(contract_index, expiry_date) + + return self + + def update_nth_expiry_date(self, contract_index: int, expiry_date: expiryDate): + self.contract_date.update_nth_expiry_date(contract_index, expiry_date) + + def as_list_of_individual_contracts(self) -> list: + return [ + self.new_contract_with_nth_contract_date(contract_index) + for contract_index in range(len(self.list_of_single_contract_dates())) + ] + + def new_contract_with_first_contract_date(self): + return self.new_contract_with_nth_contract_date(0) + + def new_contract_with_nth_contract_date(self, contract_index: int): + new_contract_date_object = ( + self.contract_date.nth_single_contract_as_contract_date(contract_index) + ) + + return self.new_contract_with_replaced_contract_date_object( + new_contract_date_object + ) + + def new_contract_with_replaced_contract_date_object( + self, new_contract_date_object: contractDate + ): + instrument_object = self.instrument + params = self.params + + return futuresContract(instrument_object, new_contract_date_object, params) + + def list_of_single_contract_dates(self) -> list: + return self.contract_date.list_of_single_contract_dates + + +def _resolve_args_for_futures_contract( + instrument_object, contract_date_object +) -> tuple: + if isinstance(instrument_object, str): + instrument_object = futuresInstrument(instrument_object) + + if ( + isinstance(contract_date_object, list) + or isinstance(contract_date_object, str) + or isinstance(contract_date_object, dict) + ): + contract_date_object = contractDate(contract_date_object) + + return instrument_object, contract_date_object + + +def key_contains_instrument_code(contract_key, instrument_code): + key_instrument_code, contract_id = get_code_and_id_from_contract_key(contract_key) + if key_instrument_code == instrument_code: + return True + else: + return False + + +def get_contract_key_from_code_and_id(instrument_code, contract_id): + return instrument_code + "/" + contract_id + + +def get_code_and_id_from_contract_key(contract_key): + return contract_key.split("/") + + +class listOfFuturesContracts(list): + """ + List of futuresContracts + """ + + def unique_list_of_instrument_codes(self): + list_of_instruments = [contract.instrument_code for contract in self] + + # will contain duplicates, make unique + unique_list_of_instruments = list(set(list_of_instruments)) + + return unique_list_of_instruments + + def contract_date_str_for_contracts_in_list_for_instrument_code( + self, instrument_code: str + ) -> list: + list_of_contracts = self.contracts_in_list_for_instrument_code(instrument_code) + list_of_date_str = list_of_contracts.list_of_dates() + list_of_date_str = list(set(list_of_date_str)) + return list_of_date_str + + def contracts_in_list_for_instrument_code(self, instrument_code: str): + list_of_contracts = [ + contract for contract in self if contract.instrument_code == instrument_code + ] + + list_of_contracts = listOfFuturesContracts(list_of_contracts) + + return list_of_contracts + + def currently_sampling(self): + contracts_currently_sampling = [ + contract for contract in self if contract.currently_sampling + ] + + return listOfFuturesContracts(contracts_currently_sampling) + + def list_of_dates(self) -> listOfContractDateStr: + # Return list of contract_date identifiers + contract_dates = [contract.date_str for contract in self] + return contract_dates + + def as_dict(self) -> dict: + contract_dates_keys = self.list_of_dates() + contract_values = self + + contract_dict = dict( + [(key, value) for key, value in zip(contract_dates_keys, contract_values)] + ) + + return contract_dict + + def difference(self, another_contract_list): + self_contract_dates = set(self.list_of_dates()) + another_contract_list_dates = set(another_contract_list.list_of_dates()) + + list_of_differential_dates = self_contract_dates.difference( + another_contract_list_dates + ) + + list_of_contracts = self._subset_of_list_from_list_of_dates( + list_of_differential_dates + ) + + return list_of_contracts + + def _subset_of_list_from_list_of_dates(self, list_of_dates): + self_as_dict = self.as_dict() + list_of_contracts = [ + self_as_dict[contract_date] for contract_date in list_of_dates + ] + + return listOfFuturesContracts(list_of_contracts) diff --git a/src/quantlib_st/objects/dict_of_futures_per_contract_prices.py b/src/quantlib_st/objects/dict_of_futures_per_contract_prices.py new file mode 100644 index 0000000..5da23f8 --- /dev/null +++ b/src/quantlib_st/objects/dict_of_futures_per_contract_prices.py @@ -0,0 +1,151 @@ +import datetime +import numpy as np +import pandas as pd + +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.exceptions import missingData +from quantlib_st.objects.contract_dates_and_expiries import listOfContractDateStr + + +class dictFuturesContractFinalPrices(dict): + def __repr__(self): + object_repr = "Dict of final futures contract prices with %d contracts" % len( + self.keys() + ) + return object_repr + + def sorted_contract_date_str(self): + """ + Time sorted contract ids + :return: + """ + try: + all_contract_date_str_sorted = getattr( + self, "_all_contract_date_str_sorted" + ) + except AttributeError: + all_contract_date_str_sorted = self._get_and_set_sorted_contract_date_str() + + return all_contract_date_str_sorted + + def _get_and_set_sorted_contract_date_str(self): + all_contract_date_str = listOfContractDateStr(self.keys()) + all_contract_date_str_sorted = all_contract_date_str.sorted_date_str() + self._all_contract_date_str_sorted = all_contract_date_str_sorted + + return all_contract_date_str_sorted + + def last_contract_date_str(self): + all_contract_date_str_sorted = self.sorted_contract_date_str() + + return all_contract_date_str_sorted.final_date_str() + + def joint_data(self): + joint_data = [ + pd.Series(prices, name=contractid) for contractid, prices in self.items() + ] + joint_data = pd.concat(joint_data, axis=1) + + return joint_data + + def matched_prices(self, contracts_to_match=arg_not_supplied) -> pd.DataFrame: + # Return pd.DataFrame where we only have prices in all contracts + + if contracts_to_match is arg_not_supplied: + contracts_to_match = self.keys() + + joint_data = self.joint_data() + joint_data_to_match = joint_data[contracts_to_match] + + matched_data = joint_data_to_match.dropna() + + if len(matched_data) == 0: + # This will happen if there are no matches + raise missingData + + return matched_data + + +class dictFuturesContractVolumes(dictFuturesContractFinalPrices): + def __repr__(self): + object_repr = "Dict of futures contract volumes with %d contracts" % len( + self.keys() + ) + return object_repr + + +class dictFuturesContractPrices(dict): + """ + A dict of futures contract prices + + Keys are contract_objects + + We can use standard dict methods, but convenience methods are included + """ + + def __repr__(self): + object_repr = "Dict of futures contract prices with %d contracts" % len( + self.keys() + ) + return object_repr + + def final_prices(self) -> dictFuturesContractFinalPrices: + """ + + :return: dict of final prices + """ + + all_contract_ids = list(self.keys()) + final_price_dict_as_list = [] + for contract_id in all_contract_ids: + final_prices = self[contract_id].return_final_prices() + final_prices.name = contract_id + final_price_dict_as_list.append((contract_id, final_prices)) + + final_prices_dict = dictFuturesContractFinalPrices(final_price_dict_as_list) + + return final_prices_dict + + def daily_volumes(self) -> dictFuturesContractVolumes: + """ + + :return: dict of daily volumes + """ + + all_contract_ids = list(self.keys()) + volume_dict_as_list = [] + for contract_id in all_contract_ids: + volumes = self[contract_id].daily_volumes() + volumes.name = contract_id + volume_dict_as_list.append((contract_id, volumes)) + + volumes_dict = dictFuturesContractVolumes(volume_dict_as_list) + + return volumes_dict + + +def get_last_matched_date_and_prices_for_contract_list( + dict_of_prices: dictFuturesContractPrices, + contracts_to_match: list, + list_of_contract_date_str: list, +) -> (datetime.datetime, list): + dict_of_final_prices = dict_of_prices.final_prices() + + try: + matched_final_prices = dict_of_final_prices.matched_prices( + contracts_to_match=contracts_to_match + ) + except missingData: + # This will happen if there are no matching prices + # We just return the last row + matched_final_prices = dict_of_final_prices.joint_data() + + last_matched_prices = list(matched_final_prices.iloc[-1].values) + last_matched_date = matched_final_prices.index[-1] + + # pad with extra nan values + last_matched_prices = last_matched_prices + [np.nan] * ( + len(list_of_contract_date_str) - len(last_matched_prices) + ) + + return last_matched_date, last_matched_prices diff --git a/src/quantlib_st/objects/dict_of_named_futures_per_contract_prices.py b/src/quantlib_st/objects/dict_of_named_futures_per_contract_prices.py new file mode 100644 index 0000000..c9a0667 --- /dev/null +++ b/src/quantlib_st/objects/dict_of_named_futures_per_contract_prices.py @@ -0,0 +1,337 @@ +import pandas as pd + +from quantlib_st.core.pandas.merge_data_with_label_column import ( + merge_data_series_with_label_column, +) +from quantlib_st.objects.contract_dates_and_expiries import listOfContractDateStr + +## CHECK IF ALL THESE ARE NEEDED... + +price_name = "PRICE" +carry_name = "CARRY" +forward_name = "FORWARD" +price_column_names = dict(CARRY=carry_name, PRICE=price_name, FORWARD=forward_name) +list_of_price_column_names = list(price_column_names.values()) +list_of_price_column_names.sort() +contract_suffix = "_CONTRACT" + + +def contract_name_from_column_name(column_name): + return column_name + contract_suffix + + +contract_column_names = dict( + [(key, contract_name_from_column_name(key)) for key in list_of_price_column_names] +) +list_of_contract_column_names = list(contract_column_names.values()) + + +class dictNamedFuturesContractFinalPrices(dict): + ## keys are PRICE, CARRY, FORWARD + def __init__(self, entry_dict): + _check_key_list_is_valid_against_names(entry_dict) + super().__init__(entry_dict) + + def __repr__(self): + return "dictNamedFuturesContractFinalPrices with keys %s" % str(self.keys()) + + +def _check_key_list_is_valid_against_names(some_dict): + keys = list(some_dict.keys()) + keys.sort() + try: + assert keys == list_of_price_column_names + except: + raise Exception( + "Object %s should have keys %s!" + % (str(some_dict), str(list_of_price_column_names)) + ) + + +class futuresNamedContractFinalPricesWithContractID(pd.DataFrame): + """ + Just the final prices from a futures contract, plus + Columns are named 'NAME' and 'NAME_CONTRACT' + """ + + def __init__( + self, + ts_of_prices: pd.Series, + ts_of_contracts: pd.Series, + price_column_name: str = list_of_price_column_names[0], + ): + """ + + :param price_and_contract_data: pd.DataFrame with two columns + :param column_name: column name for price + """ + assert price_column_name in list_of_price_column_names + + price_and_contract_data = pd.concat([ts_of_prices, ts_of_contracts], axis=1) + + contract_column_name = price_column_name + contract_suffix + price_and_contract_data.columns = [price_column_name, contract_column_name] + + super().__init__(price_and_contract_data) + + self._price_column_name = price_column_name + self._contract_column_name = contract_column_name + + @property + def price_column_name(self) -> str: + return self._price_column_name + + @property + def contract_column_name(self) -> str: + return self._contract_column_name + + @property + def prices(self) -> pd.Series: + return self[self.price_column_name] + + @property + def ts_of_contract_str(self) -> pd.Series: + return self[self.contract_column_name] + + def as_pd(self): + return pd.DataFrame(self) + + @classmethod + def create_with_single_contractid( + futuresNamedContractFinalPricesWithContractID, + ts_of_prices: pd.Series, + contractid: str, + price_column_name=price_name, + ): + """ + + :param price_data: futuresContractFinalPrices + :param contractid: str YYYYMMDD + :param column_name: column name for price + :param contract_column_name_suffix: column name for contract + """ + + contract_data = [contractid] * len(ts_of_prices) + ts_of_contracts = pd.Series(contract_data, index=ts_of_prices.index) + + return futuresNamedContractFinalPricesWithContractID( + ts_of_prices, ts_of_contracts, price_column_name=price_column_name + ) + + def prices_after_date(self, date_slice): + prices = self.prices[date_slice:] + contracts = self.ts_of_contract_str[date_slice:] + return futuresNamedContractFinalPricesWithContractID( + prices, contracts, price_column_name=self.price_column_name + ) + + def final_contract(self) -> str: + """ + + :return: last value in contract id column + """ + contract_ids = self.ts_of_contract_str + + return contract_ids.iloc[-1] + + def check_all_contracts_equal_to(self, test_contractid: str) -> bool: + """ + Check to see if all contracts are the same as contractid + + :param contractid: str yyyymmdd + :return: bool + """ + + contract_ids = self.ts_of_contract_str + + check_equality = [ + str(int(cid)) == test_contractid for cid in contract_ids.values + ] + + return all(check_equality) + + def merge_data(self, new_data): + """ + Assuming that contracts all match, + + merge the data series together + + :param new_data: object of same type + :return: object of same type + """ + merged_data = _merge_futures_contract_final_prices_with_contract_id( + self, new_data + ) + + return merged_data + + +def _merge_futures_contract_final_prices_with_contract_id( + original_data: futuresNamedContractFinalPricesWithContractID, + new_data: futuresNamedContractFinalPricesWithContractID, +) -> futuresNamedContractFinalPricesWithContractID: + if len(new_data) == 0: + return original_data + + _assert_merge_is_valid(original_data, new_data) + + price_column_name = original_data.price_column_name + contract_column_name = original_data.contract_column_name + + merged_data_as_pd = merge_data_series_with_label_column( + original_data, + new_data, + data_column=price_column_name, + label_column=contract_column_name, + ) + + merged_data = futuresNamedContractFinalPricesWithContractID( + merged_data_as_pd[price_column_name], + merged_data_as_pd[contract_column_name], + price_column_name=original_data.price_column_name, + ) + + return merged_data + + +def _assert_merge_is_valid( + original_data: futuresNamedContractFinalPricesWithContractID, + new_data: futuresNamedContractFinalPricesWithContractID, +): + last_contract_in_original_data = original_data.final_contract() + + try: + assert new_data.check_all_contracts_equal_to(last_contract_in_original_data) + except BaseException: + raise Exception( + "When merging data, final contractid in original data must match all new data" + ) + try: + assert new_data.price_column_name == original_data.price_column_name + assert new_data.contract_column_name == original_data.contract_column_name + except BaseException: + raise Exception("When merging data, column names must match") + + +class setOfNamedContracts(dict): + def __init__(self, entry_dict): + super().__init__(entry_dict) + _check_key_list_is_valid_against_names(entry_dict) + + def __repr__(self): + return "setOfContracts %s" % str([(key, value) for key, value in self.items()]) + + @property + def list_of_date_str(self) -> listOfContractDateStr: + list_of_date_str = self.values() + list_of_date_str = listOfContractDateStr(list_of_date_str) + + return list_of_date_str + + @property + def price(self): + return self[price_name] + + @property + def carry(self): + return self[carry_name] + + @property + def forward(self): + return self[forward_name] + + def furthest_out_contract_date(self) -> str: + current_contract_list = list(self.values()) + furthest_out_contract_date = max(current_contract_list) + + return furthest_out_contract_date + + +class dictFuturesNamedContractFinalPricesWithContractID(dict): + # options for construction: + # - pulled out of multiple prices + # - from two dicts of prices, and contract ids + def __init__(self, dict_with_cids: dict): + """ + + :param dict_with_cids: dict, containing futuresNamedContractFinalPricesWithContractID + :return: object + """ + super().__init__(dict_with_cids) + _check_key_list_is_valid_against_names(dict_with_cids) + + def __repr__(self): + return "dictFuturesNamedContractFinalPricesWithContractID with keys %s" % str( + self.keys() + ) + + @classmethod + def create_from_two_dicts( + dictFuturesNamedContractFinalPricesWithContractID, + dict_of_final_prices: dictNamedFuturesContractFinalPrices, + dict_of_contract_ids: setOfNamedContracts, + ): + """ + + :param dict_of_final_prices: dict of futuresContractFinalPrices + :param dict_of_contract_ids: dict of str, yyyymmdd contract_ids. Keys must match + """ + + new_dict = {} + for key in list_of_price_column_names: + try: + price_series = dict_of_final_prices[key] + contract_id = dict_of_contract_ids[key] + except KeyError: + raise Exception( + "key value %s missing from dict_of_contract_ids or price series" + % key + ) + + prices_with_contractid = futuresNamedContractFinalPricesWithContractID.create_with_single_contractid( + price_series, contract_id, price_column_name=key + ) + new_dict[key] = prices_with_contractid + + return dictFuturesNamedContractFinalPricesWithContractID(new_dict) + + def merge_data(self, new_price_dict): + """ + Update this data with some new data, element by element + + :param new_price_dict: another of the same class. Keys and column names must match. Contract IDs must match + :return: merged price dict + """ + merged_data = _merge_dictFuturesContractFinalPricesWithContractID( + self, new_price_dict + ) + + return merged_data + + +def _merge_dictFuturesContractFinalPricesWithContractID( + first_dict: dictFuturesNamedContractFinalPricesWithContractID, + second_dict: dictFuturesNamedContractFinalPricesWithContractID, +) -> dictFuturesNamedContractFinalPricesWithContractID: + # for each element, run a merge + list_of_keys = list_of_price_column_names + merged_dict = {} + for key_name in list_of_keys: + current_data = first_dict[key_name] + try: + new_data = second_dict[key_name] + except KeyError: + raise Exception( + "Key name mismatch when merging price data, %s missing from new data" + % key_name + ) + try: + merged_data = current_data.merge_data(new_data) + except Exception as e: + raise e + + merged_dict[key_name] = merged_data + + merged_dict = dictFuturesNamedContractFinalPricesWithContractID(merged_dict) + + return merged_dict diff --git a/src/quantlib_st/objects/fills.py b/src/quantlib_st/objects/fills.py new file mode 100644 index 0000000..826a98e --- /dev/null +++ b/src/quantlib_st/objects/fills.py @@ -0,0 +1,124 @@ +from typing import Union +from dataclasses import dataclass + +import datetime +import pandas as pd +import numpy as np + +from quantlib_st.execution.orders.base_orders import Order +from quantlib_st.execution.orders.named_order_objects import missing_order, named_object + + +@dataclass +class Fill: + date: datetime.datetime + qty: int + price: float + price_requires_slippage_adjustment: bool = False + + @classmethod + def zero_fill(cls, date): + return cls(date=date, qty=0, price=np.nan) + + @property + def is_unfilled(self) -> bool: + return self.qty == 0 + + +def is_empty_fill(fill: Union[named_object, Fill]) -> bool: + if fill is missing_order: + return True + if fill.is_unfilled: + return True + + return False + + +def empty_fill(date: datetime.datetime) -> Fill: + return Fill.zero_fill(date) + + +class ListOfFills(list): + def __init__(self, list_of_fills): + list_of_fills = [fill for fill in list_of_fills if not is_empty_fill(fill)] + super().__init__(list_of_fills) + + def _as_dict_of_lists(self) -> dict: + qty_list = [fill.qty for fill in self] + price_list = [fill.price for fill in self] + date_list = [fill.date for fill in self] + + return dict(qty=qty_list, price=price_list, date=date_list) + + def as_pd_df(self) -> pd.DataFrame: + self_as_dict = self._as_dict_of_lists() + date_index = self_as_dict.pop("date") + df = pd.DataFrame(self_as_dict, index=date_index) + df = df.sort_index() + + return df + + @classmethod + def from_position_series_and_prices(cls, positions: pd.Series, price: pd.Series): + list_of_fills = _list_of_fills_from_position_series_and_prices( + positions=positions, price=price + ) + + return cls(list_of_fills) + + +def _list_of_fills_from_position_series_and_prices( + positions: pd.Series, price: pd.Series +) -> ListOfFills: + ( + trades_without_zeros, + prices_aligned_to_trades, + ) = _get_valid_trades_and_aligned_prices(positions=positions, price=price) + + trades_as_list = list(trades_without_zeros.values) + prices_as_list = list(prices_aligned_to_trades.values) + dates_as_list = list(prices_aligned_to_trades.index) + + list_of_fills_as_list = [ + Fill(date, qty, price, price_requires_slippage_adjustment=True) + for date, qty, price in zip(dates_as_list, trades_as_list, prices_as_list) + ] + + list_of_fills = ListOfFills(list_of_fills_as_list) + + return list_of_fills + + +def _get_valid_trades_and_aligned_prices( + positions: pd.Series, price: pd.Series +) -> tuple: + # No delaying done here so we assume positions are already delayed + trades = positions.diff() + trades_without_na = trades[~trades.isna()] + trades_without_zeros = trades_without_na[trades_without_na != 0] + + prices_aligned_to_trades = price.reindex(trades_without_zeros.index, method="ffill") + + return trades_without_zeros, prices_aligned_to_trades + + +def fill_from_order(order: Order) -> Fill: + try: + assert len(order.trade) == 1 + except: + raise Exception("Can't get fills from multi-leg orders") + + if order.fill_equals_zero(): + return missing_order + + fill_price = order.filled_price + fill_datetime = order.fill_datetime + fill_qty = order.fill[0] + + if fill_price is None: + return missing_order + + if fill_datetime is None: + return missing_order + + return Fill(fill_datetime, fill_qty, fill_price) diff --git a/src/quantlib_st/objects/instruments.py b/src/quantlib_st/objects/instruments.py new file mode 100644 index 0000000..eefff12 --- /dev/null +++ b/src/quantlib_st/objects/instruments.py @@ -0,0 +1,393 @@ +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.genutils import flatten_list + +from dataclasses import dataclass + +import numpy as np +import pandas as pd + +EMPTY_INSTRUMENT = "" + + +class futuresInstrument(object): + def __init__(self, instrument_code: str): + self._instrument_code = instrument_code + + @property + def instrument_code(self): + return self._instrument_code + + def empty(self): + return self.instrument_code == EMPTY_INSTRUMENT + + @classmethod + def create_from_dict(futuresInstrument, input_dict): + # Might seem pointless, but (a) is used in original code, (b) gives a nice consistent feel + return futuresInstrument(input_dict["instrument_code"]) + + def as_dict(self): + # Might seem pointless, but (a) is used in original code, (b) gives a nice consistent feel + return dict(instrument_code=self.instrument_code) + + def __eq__(self, other): + return self.instrument_code == other.instrument_code + + @property + def key(self): + return self.instrument_code + + def __repr__(self): + return str(self.instrument_code) + + +META_FIELD_LIST = [ + "Description", + "Pointsize", + "Currency", + "AssetClass", + "PerBlock", + "Percentage", + "PerTrade", + "Region", +] + + +def _zero_if_nan(x): + if np.isnan(x): + return 0 + else: + return x + + +NO_REGION = "NO_REGION" + + +def _string_if_nan(x, string=NO_REGION): + if np.isnan(x): + return string + else: + return x + + +class instrumentMetaData(object): + def __init__( + self, + Description: str = "", + Pointsize: float = 0.0, + Currency: str = "", + AssetClass: str = "", + PerBlock: float = 0.0, + Percentage: float = 0.0, + PerTrade: float = 0.0, + Region: str = "", + ): + self.Description = Description + self.Currency = Currency + self.Pointsize = _zero_if_nan(Pointsize) + self.AssetClass = AssetClass + self.PerBlock = _zero_if_nan(PerBlock) + self.Percentage = _zero_if_nan(Percentage) + self.PerTrade = _zero_if_nan(PerTrade) + self.Region = Region + + def as_dict(self) -> dict: + keys = META_FIELD_LIST + self_as_dict = dict([(key, getattr(self, key)) for key in keys]) + + return self_as_dict + + @classmethod + def from_dict(instrumentMetaData, input_dict): + keys = list(input_dict.keys()) + args_list = [input_dict[key] for key in keys] + + return instrumentMetaData(*args_list) + + def __eq__(self, other): + return self.as_dict() == other.as_dict() + + def __repr__(self): + return str(self.as_dict()) + + +@dataclass +class futuresInstrumentWithMetaData: + instrument: futuresInstrument + meta_data: instrumentMetaData + + @property + def instrument_code(self) -> str: + return self.instrument.instrument_code + + @property + def key(self) -> str: + return self.instrument_code + + def as_dict(self) -> dict: + meta_data_dict = self.meta_data.as_dict() + meta_data_dict["instrument_code"] = self.instrument_code + + return meta_data_dict + + @classmethod + def from_dict(futuresInstrumentWithMetaData, input_dict): + instrument_code = input_dict.pop("instrument_code") + instrument = futuresInstrument(instrument_code) + meta_data = instrumentMetaData.from_dict(input_dict) + + return futuresInstrumentWithMetaData(instrument, meta_data) + + @classmethod + def create_empty(futuresInstrumentWithMetaData): + instrument = futuresInstrument(EMPTY_INSTRUMENT) + meta_data = instrumentMetaData() + + instrument_with_metadata = futuresInstrumentWithMetaData(instrument, meta_data) + + return instrument_with_metadata + + def empty(self): + return self.instrument.empty() + + def __eq__(self, other): + instrument_matches = self.instrument == other.instrument + meta_data_matches = self.meta_data == other.meta_data + + return instrument_matches and meta_data_matches + + +class listOfFuturesInstrumentWithMetaData(list): + def as_df(self): + instrument_codes = [ + instrument_object.instrument_code for instrument_object in self + ] + meta_data_keys = [ + instrument_object.meta_data.as_dict().keys() for instrument_object in self + ] + meta_data_keys_flattened = flatten_list(meta_data_keys) + meta_data_keys_unique = list(set(meta_data_keys_flattened)) + + meta_data_as_lists = dict( + [ + ( + metadata_name, + [ + getattr(instrument_object.meta_data, metadata_name) + for instrument_object in self + ], + ) + for metadata_name in meta_data_keys_unique + ] + ) + + meta_data_as_dataframe = pd.DataFrame( + meta_data_as_lists, index=instrument_codes + ) + + return meta_data_as_dataframe + + +class assetClassesAndInstruments(dict): + def __repr__(self): + return str(self.as_pd()) + + def get_instrument_list(self) -> list: + return list(self.keys()) + + @classmethod + def from_pd_series(self, pd_series: pd.Series): + instruments = list(pd_series.index) + asset_classes = list(pd_series.values) + as_dict = dict( + [ + (instrument_code, asset_class) + for instrument_code, asset_class in zip(instruments, asset_classes) + ] + ) + + return assetClassesAndInstruments(as_dict) + + def all_asset_classes(self) -> list: + asset_classes = list(self.values()) + unique_asset_classes = list(set(asset_classes)) + unique_asset_classes.sort() + + return unique_asset_classes + + def as_pd(self) -> pd.Series: + instruments = [key for key in self.keys()] + asset_classes = [value for value in self.values()] + + return pd.Series(asset_classes, index=instruments) + + def all_instruments_in_asset_class( + self, asset_class: str, must_be_in=arg_not_supplied + ) -> list: + asset_class_instrument_list = [ + instrument + for instrument, item_asset_class in self.items() + if item_asset_class == asset_class + ] + + if must_be_in is arg_not_supplied: + return asset_class_instrument_list + + ## we need to filter + filtered_asset_class_instrument_list = [ + instrument + for instrument in asset_class_instrument_list + if instrument in must_be_in + ] + + return filtered_asset_class_instrument_list + + +class instrumentCosts(object): + def __init__( + self, + price_slippage: float = 0.0, + value_of_block_commission: float = 0.0, + percentage_cost: float = 0.0, + value_of_pertrade_commission: float = 0.0, + ): + self._price_slippage = price_slippage + self._value_of_block_commission = value_of_block_commission + self._percentage_cost = percentage_cost + self._value_of_pertrade_commission = value_of_pertrade_commission + + @classmethod + def from_meta_data_and_spread_cost( + instrumentCosts, meta_data: instrumentMetaData, spread_cost: float + ): + return instrumentCosts( + price_slippage=spread_cost, + value_of_block_commission=meta_data.PerBlock, + percentage_cost=meta_data.Percentage, + value_of_pertrade_commission=meta_data.PerTrade, + ) + + def __repr__(self): + return ( + "instrumentCosts slippage %f block_commission %f percentage cost %f per trade commission %f " + % ( + self.price_slippage, + self.value_of_block_commission, + self.percentage_cost, + self.value_of_pertrade_commission, + ) + ) + + def commission_only(self): + new_costs = instrumentCosts( + price_slippage=0.0, + value_of_block_commission=self.value_of_block_commission, + percentage_cost=self.percentage_cost, + value_of_pertrade_commission=self.value_of_pertrade_commission, + ) + + return new_costs + + def spread_only(self): + new_costs = instrumentCosts( + price_slippage=self.price_slippage, + value_of_block_commission=0, + percentage_cost=0, + value_of_pertrade_commission=0, + ) + + return new_costs + + @property + def price_slippage(self): + return self._price_slippage + + @property + def value_of_block_commission(self): + return self._value_of_block_commission + + @property + def percentage_cost(self): + return self._percentage_cost + + @property + def value_of_pertrade_commission(self): + return self._value_of_pertrade_commission + + def calculate_sr_cost( + self, + block_price_multiplier: float, + price: float, + ann_stdev_price_units: float, + blocks_traded: float = 1.0, + ) -> float: + cost_instrument_currency = self.calculate_cost_instrument_currency( + blocks_traded=blocks_traded, + block_price_multiplier=block_price_multiplier, + price=price, + ) + + ann_stdev_instrument_currency = ann_stdev_price_units * block_price_multiplier + + return cost_instrument_currency / ann_stdev_instrument_currency + + def calculate_cost_percentage_terms( + self, blocks_traded: float, block_price_multiplier: float, price: float + ) -> float: + cost_in_currency_terms = self.calculate_cost_instrument_currency( + blocks_traded, block_price_multiplier=block_price_multiplier, price=price + ) + + value_per_block = price * block_price_multiplier + total_value = blocks_traded * value_per_block + cost_in_percentage_terms = cost_in_currency_terms / total_value + + return cost_in_percentage_terms + + def calculate_cost_instrument_currency( + self, + blocks_traded: float, + block_price_multiplier: float, + price: float, + include_slippage: bool = True, + ) -> float: + value_per_block = price * block_price_multiplier + if include_slippage: + slippage = self.calculate_slippage_instrument_currency( + blocks_traded, block_price_multiplier=block_price_multiplier + ) + else: + slippage = 0 + + commission = self.calculate_total_commission( + blocks_traded, value_per_block=value_per_block + ) + + return slippage + commission + + def calculate_total_commission(self, blocks_traded: float, value_per_block: float): + ### YOU WILL NEED TO CHANGE THIS IF YOUR BROKER HAS A MORE COMPLEX STRUCTURE + per_trade_commission = self.calculate_per_trade_commission() + per_block_commission = self.calculate_cost_per_block_commission(blocks_traded) + percentage_commission = self.calculate_percentage_commission( + blocks_traded, value_per_block + ) + + return max([per_block_commission, per_trade_commission, percentage_commission]) + + def calculate_slippage_instrument_currency( + self, blocks_traded: float, block_price_multiplier: float + ) -> float: + return abs(blocks_traded) * self.price_slippage * block_price_multiplier + + def calculate_per_trade_commission(self): + return self.value_of_pertrade_commission + + def calculate_cost_per_block_commission(self, blocks_traded): + return abs(blocks_traded) * self.value_of_block_commission + + def calculate_percentage_commission(self, blocks_traded, price_per_block): + trade_value = self.calculate_trade_value(blocks_traded, price_per_block) + return self.percentage_cost * trade_value + + def calculate_trade_value(self, blocks_traded, value_per_block): + return abs(blocks_traded) * value_per_block diff --git a/src/quantlib_st/objects/multiple_prices.py b/src/quantlib_st/objects/multiple_prices.py new file mode 100644 index 0000000..e9f3892 --- /dev/null +++ b/src/quantlib_st/objects/multiple_prices.py @@ -0,0 +1,219 @@ +from copy import copy +from dataclasses import dataclass + +import datetime as datetime +import pandas as pd + +from quantlib_st.core.exceptions import missingData +from quantlib_st.init.futures.build_multiple_prices_from_raw_data import ( + create_multiple_price_stack_from_raw_data, +) +from quantlib_st.objects.dict_of_named_futures_per_contract_prices import ( + list_of_price_column_names, + list_of_contract_column_names, + contract_column_names, + setOfNamedContracts, + contract_name_from_column_name, + futuresNamedContractFinalPricesWithContractID, + dictFuturesNamedContractFinalPricesWithContractID, + price_column_names, + price_name, + carry_name, + forward_name, +) + +from quantlib_st.objects.dict_of_futures_per_contract_prices import ( + dictFuturesContractFinalPrices, +) + + +@dataclass +class singleRowMultiplePrices: + price: float = None + carry: float = None + forward: float = None + price_contract: str = None + carry_contract: str = None + forward_contract: str = None + + def concat_with_multiple_prices(self, multiple_prices, timedelta_seconds=1): + new_time_index = multiple_prices.index[-1] + datetime.timedelta( + seconds=timedelta_seconds + ) + new_df_row = self.as_aligned_pd_row(new_time_index) + + combined_df = pd.concat([pd.DataFrame(multiple_prices), new_df_row], axis=0) + + new_multiple_prices = futuresMultiplePrices(combined_df) + + return new_multiple_prices + + def as_aligned_pd_row(self, time_index: datetime.timedelta) -> pd.DataFrame: + new_dict = { + price_name: self.price, + carry_name: self.carry, + forward_name: self.forward, + contract_name_from_column_name(price_name): self.price_contract, + contract_name_from_column_name(carry_name): self.carry_contract, + contract_name_from_column_name(forward_name): self.forward_contract, + } + + new_dict_with_nones_removed = dict( + [(key, value) for key, value in new_dict.items() if value is not None] + ) + + new_df_row = pd.DataFrame(new_dict_with_nones_removed, index=[time_index]) + + return new_df_row + + +class futuresMultiplePrices(pd.DataFrame): + def __init__(self, data): + _check_valid_multiple_price_data(data) + super().__init__(data) + + data.index.name = "index" # arctic compatible + + @classmethod + ## NOT TYPE CHECKING OF ROLL_CALENDAR AS WOULD CAUSE CIRCULAR IMPORT + def create_from_raw_data( + futuresMultiplePrices, + roll_calendar, + dict_of_futures_contract_closing_prices: dictFuturesContractFinalPrices, + ): + """ + + :param roll_calendar: rollCalendar + :param dict_of_futures_closing_contract_prices: dictFuturesContractPrices with only one column + + :return: pd.DataFrame with the 6 columns PRICE, CARRY, FORWARD, PRICE_CONTRACT, CARRY_CONTRACT, FORWARD_CONTRACT + """ + + all_price_data_stack = create_multiple_price_stack_from_raw_data( + roll_calendar, dict_of_futures_contract_closing_prices + ) + + multiple_prices = futuresMultiplePrices(all_price_data_stack) + multiple_prices._is_empty = False + + return multiple_prices + + @classmethod + def create_empty(futuresMultiplePrices): + """ + Our graceful fail is to return an empty, but valid, dataframe + """ + + data = pd.DataFrame(columns=multiple_data_columns) + + multiple_prices = futuresMultiplePrices(data) + + return multiple_prices + + def inverse(self): + new_version = copy(self) + for colname in list_of_price_column_names: + new_version[colname] = 1 / self[colname] + + return futuresMultiplePrices(new_version) + + def add_offset_to_prices(self, offset: float): + new_version = copy(self) + for colname in list_of_price_column_names: + new_version[colname] = self[colname] + offset + + return futuresMultiplePrices(new_version) + + def current_contract_dict(self) -> setOfNamedContracts: + if len(self) == 0: + raise missingData + + final_row = self.iloc[-1] + contract_dict = dict( + [(key, final_row[value]) for key, value in contract_column_names.items()] + ) + contract_dict = setOfNamedContracts(contract_dict) + + return contract_dict + + def as_dict(self) -> dictFuturesNamedContractFinalPricesWithContractID: + """ + Split up and transform into dict + + :return: dictFuturesContractFinalPricesWithContractID, keys PRICE, FORWARD, CARRY + """ + + self_as_dict = {} + for price_column_name in list_of_price_column_names: + contract_column_name = contract_name_from_column_name(price_column_name) + self_as_dict[price_column_name] = ( + futuresNamedContractFinalPricesWithContractID( + self[price_column_name], + self[contract_column_name], + price_column_name=price_column_name, + ) + ) + + self_as_dict = dictFuturesNamedContractFinalPricesWithContractID(self_as_dict) + + return self_as_dict + + @classmethod + def from_merged_dict( + futuresMultiplePrices, + prices_dict: dictFuturesNamedContractFinalPricesWithContractID, + ): + """ + Re-create from dict, eg results of _as_dict + + :param prices_dict: dictFuturesContractFinalPricesWithContractID keys PRICE, CARRY, FORWARD + :return: object + """ + + multiple_prices_list = [] + for key_name in price_column_names.keys(): + try: + relevant_data = prices_dict[key_name] + + except KeyError: + raise Exception( + "Create multiple prices as dict needs %s as key" % key_name + ) + + multiple_prices_list.append(relevant_data.as_pd()) + + multiple_prices_data_frame = pd.concat(multiple_prices_list, axis=1) + + # Now it's possible we have more price data for some things than others + # so we forward fill contract_ids; not prices + multiple_prices_data_frame[list_of_contract_column_names] = ( + multiple_prices_data_frame[list_of_contract_column_names].ffill() + ) + + multiple_prices_object = futuresMultiplePrices(multiple_prices_data_frame) + + return multiple_prices_object + + def sort_index(self): + df = pd.DataFrame(self) + sorted_df = df.sort_index() + + return futuresMultiplePrices(sorted_df) + + +multiple_data_columns = list_of_price_column_names + list_of_contract_column_names + + +def _check_valid_multiple_price_data(df): + if len(df.columns) == 0: + return + + missing_columns = [ + colname for colname in multiple_data_columns if colname not in df.columns + ] + + if len(missing_columns) > 0: + raise Exception( + "futuresMultiplePrices requires columns %s but is missing %s" + % (str(multiple_data_columns), str(missing_columns)) + ) diff --git a/src/quantlib_st/objects/production/__init__.py b/src/quantlib_st/objects/production/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/objects/production/tradeable_object.py b/src/quantlib_st/objects/production/tradeable_object.py new file mode 100644 index 0000000..1a37023 --- /dev/null +++ b/src/quantlib_st/objects/production/tradeable_object.py @@ -0,0 +1,245 @@ +from quantlib_st.objects.instruments import futuresInstrument +from quantlib_st.objects.contracts import futuresContract +from quantlib_st.objects.contract_dates_and_expiries import contractDate + + +class tradeableObject(object): + """ + Anything we can trade + + Could be an instrument, or contract. This is the base class + """ + + def __init__(self, object_name): + # probably overridden with nicer entry + self._key = object_name + + def __repr__(self): + return self.key + + @classmethod + def from_key(tradeableObject, object_name): + return tradeableObject(object_name) + + def __eq__(self, other): + return self.key == other.key + + @property + def key(self): + # probably overridden + return self._key + + +class listOfInstrumentStrategies(list): + def unique_join_with_other_list(self, other): + return listOfInstrumentStrategies(set(list(self + other))) + + def get_list_of_strategies(self) -> list: + list_of_strategies = list( + set([instrument_strategy.strategy_name for instrument_strategy in self]) + ) + + return list_of_strategies + + def get_list_of_instruments_for_strategy(self, strategy_name: str) -> list: + list_of_instrument_strategies = ( + self.get_list_of_instrument_strategies_for_strategy(strategy_name) + ) + list_of_instruments = [ + instrument_strategy.instrument_code + for instrument_strategy in list_of_instrument_strategies + ] + + return list_of_instruments + + def get_list_of_instrument_strategies_for_strategy(self, strategy_name: str): + list_of_instrument_strategies = [ + instrument_strategy + for instrument_strategy in self + if instrument_strategy.strategy_name == strategy_name + ] + + return list_of_instrument_strategies + + def filter_to_remove_list_of_instruments(self, list_of_instruments_to_remove: list): + filtered_list = [ + instrument_strategy + for instrument_strategy in self + if instrument_strategy.instrument_code not in list_of_instruments_to_remove + ] + return listOfInstrumentStrategies(filtered_list) + + def filter_to_remove_list_of_strategies(self, list_of_strategies_to_remove: list): + filtered_list = [ + instrument_strategy + for instrument_strategy in self + if instrument_strategy.strategy_name not in list_of_strategies_to_remove + ] + return listOfInstrumentStrategies(filtered_list) + + +STRATEGY_NAME_KEY = "strategy_name" +INSTRUMENT_CODE_KEY = "instrument_code" + + +class instrumentStrategy(tradeableObject): + def __init__(self, strategy_name: str, instrument_code: str): + instrument_object = futuresInstrument(instrument_code) + self._instrument = instrument_object + self._strategy_name = strategy_name + + def __hash__(self): + return self.instrument_code.__hash__() + self.strategy_name.__hash__() + + def __repr__(self): + return self.key + + @property + def key(self): + return "%s %s" % (self.strategy_name, str(self.instrument)) + + @property + def old_key(self): + return self.strategy_name + "/" + str(self.instrument) + + def __eq__(self, other): + if self.instrument != other.instrument: + return False + + if self.strategy_name != other.strategy_name: + return False + + return True + + @property + def instrument(self): + return self._instrument + + @property + def instrument_code(self): + return self.instrument.instrument_code + + @property + def strategy_name(self): + return self._strategy_name + + def as_dict(self): + return { + STRATEGY_NAME_KEY: self.strategy_name, + INSTRUMENT_CODE_KEY: self.instrument_code, + } + + @classmethod + def from_dict(instrumentStrategy, attr_dict): + return instrumentStrategy( + attr_dict[STRATEGY_NAME_KEY], attr_dict[INSTRUMENT_CODE_KEY] + ) + + @classmethod + def from_key(instrumentStrategy, key): + if key.find("/") > -1: + strategy_name, instrument_code = key.split("/") + else: + strategy_name, instrument_code = key.split(" ") + return instrumentStrategy( + strategy_name=strategy_name, instrument_code=instrument_code + ) + + +class futuresContractStrategy(tradeableObject): + def __init__(self, strategy_name: str, instrument_code: str, contract_id): + """ + + :param strategy_name: str + :param instrument_code: str + :param contract_id: a single contract_order_id YYYYMM, or a list of contract IDS YYYYMM for a spread order + """ + self._contract_date = contractDate(contract_id) + self._instrument = futuresInstrument(instrument_code) + self._strategy_name = strategy_name + + @classmethod + def from_strategy_name_and_contract_object( + futuresContractStrategy, strategy_name: str, futures_contract: futuresContract + ): + return futuresContractStrategy( + strategy_name=strategy_name, + contract_id=futures_contract.date_str, + instrument_code=futures_contract.instrument_code, + ) + + def __eq__(self, other): + if self.instrument != other.instrument: + return False + + if self.strategy_name != other.strategy_name: + return False + + if self.contract_date != other.contract_date: + return False + + return True + + @property + def futures_contract(self) -> futuresContract: + return futuresContract(self.instrument, self.contract_date) + + @property + def instrument_strategy(self) -> instrumentStrategy: + return instrumentStrategy( + instrument_code=self.instrument_code, strategy_name=self.strategy_name + ) + + @classmethod + def from_key(instrumentTradeableObject, key): + strategy_name, instrument_code, contract_id_str = key.split("/") + + return instrumentTradeableObject( + strategy_name, instrument_code, contract_id_str + ) + + @property + def contract_date(self): + return self._contract_date + + @property + def contract_date_key(self): + return self.contract_date.key + + @property + def alt_contract_date_key(self): + if len(self.contract_date_key) == 6: + return self.contract_date_key + "00" + + if len(self.contract_date_key) == 8: + return self.contract_date_key[:6] + + @property + def strategy_name(self): + return self._strategy_name + + @property + def instrument_code(self): + return self.instrument.instrument_code + + @property + def instrument(self): + return self._instrument + + @property + def key(self): + return "/".join( + [self.strategy_name, self.instrument_code, self.contract_date_key] + ) + + @property + def alt_key(self): + return "/".join( + [self.strategy_name, self.instrument_code, self.alt_contract_date_key] + ) + + def sort_idx_for_contracts(self) -> list: + return self.contract_date.index_of_sorted_contract_dates() + + def sort_contracts_with_idx(self, idx_list: list): + self.contract_date.sort_with_idx(idx_list) diff --git a/src/quantlib_st/objects/rolls.py b/src/quantlib_st/objects/rolls.py new file mode 100644 index 0000000..067a5b7 --- /dev/null +++ b/src/quantlib_st/objects/rolls.py @@ -0,0 +1,451 @@ +import datetime +from copy import copy + +from quantlib_st.core.genutils import np_convert +from quantlib_st.core.dateutils import month_from_contract_letter, FUTURES_MONTH_LIST +from quantlib_st.objects.contract_dates_and_expiries import ( + contractDate, + contract_given_tuple, +) + +forward = 1 +backwards = -1 + + +class rollCycle(object): + """ + A cycle determining how one contract rolls to the next + + Only works with monthly contracts + """ + + def __init__(self, cyclestring: str): + assert isinstance(cyclestring, str) + + self._cyclestring = "".join(sorted(cyclestring)) + + def __repr__(self): + return self.cyclestring + + def __len__(self) -> int: + return len(self.cyclestring) + + def __eq__(self, other): + return self.cyclestring == other.cyclestring + + @property + def cyclestring(self): + return self._cyclestring + + def iterate_contract_date( + self, direction: int, contract_date: contractDate + ) -> contractDate: + year_value, month_str = contract_date.date_str_to_year_month() + if direction == forward: + new_year_value, new_month_str = self._next_year_month_given_tuple( + year_value, month_str + ) + elif direction == backwards: + new_year_value, new_month_str = self._previous_year_month_given_tuple( + year_value, month_str + ) + else: + raise Exception( + "Direction %d has to be %s or %s" % (direction, forward, backwards) + ) + + return contract_given_tuple(contract_date, new_year_value, new_month_str) + + def _previous_year_month_given_tuple( + self, year_value: int, month_str: str + ) -> (int, str): + """ + Returns a tuple (year, month: str) + + :param month_str: str + :param year_value: int + :return: tuple (int, str) + """ + + new_month_as_str = self._previous_month(month_str) + if self._month_is_first(month_str): + year_value = year_value - 1 + + return year_value, new_month_as_str + + def _next_year_month_given_tuple( + self, year_value: int, month_str: str + ) -> (int, str): + """ + Returns a tuple (year, month: str) + + :param month_str: str + :param year_value: int + :return: tuple (int, str) + """ + + new_month_as_str = self._next_month(month_str) + if self._month_is_last(month_str): + year_value = year_value + 1 + + return year_value, new_month_as_str + + def _next_month(self, current_month: str) -> str: + """ + Move one month forward in expiry cycle + + :param current_month: Current month as a str + :return: new month as str + """ + + return self._offset_month(current_month, 1) + + def _previous_month(self, current_month: str) -> str: + """ + Move one month back in expiry cycle + + :param current_month: Current month as a str + :return: new month as str + """ + + return self._offset_month(current_month, -1) + + def _offset_month(self, current_month: str, offset: int) -> str: + """ + Move a number of months in the expiry cycle + + :param current_month: Current month as a str + :param offset: number of months to go forwards or backwards + :return: new month as str + """ + + current_index = self._where_month(current_month) + len_cycle = len(self._cyclestring) + new_index = current_index + offset + cycled_index = new_index % len_cycle + + return self.cyclestring[cycled_index] + + def _where_month(self, current_month: str) -> int: + """ + Return the index value (0 is first) of month in expiry + + :param current_month: month as str + :return: int + """ + if not self.check_is_month_in_rollcycle(current_month): + raise Exception("%s not in cycle %s" % (current_month, self._cyclestring)) + + return self.cyclestring.index(current_month) + + def _month_is_first(self, current_month: str) -> bool: + """ + Is this the first month in the expiry cycle? + + :param current_month: month as str + :return: bool + """ + + return self._where_month(current_month) == 0 + + def _month_is_last(self, current_month: str) -> bool: + """ + Is this the last month in the expiry cycle? + + :param current_month: month as str + :return: bool + """ + + return self._where_month(current_month) == len(self._cyclestring) - 1 + + def _as_list(self) -> list: + """ + + :return: list with int values referring to month numbers eg January =12 etc + """ + return [ + month_from_contract_letter(contract_letter) + for contract_letter in self.cyclestring + ] + + def check_is_month_in_rollcycle(self, current_month: str) -> bool: + """ + Is current_month in our expiry cycle? + + :param current_month: month as str + :return: bool + """ + if current_month in self._cyclestring: + return True + else: + return False + + +GLOBAL_ROLLCYCLE = rollCycle("".join(FUTURES_MONTH_LIST)) + + +class rollParameters(object): + """ + A rollParameters object contains information about roll cycles and how we hold contracts + + When combined with a contractDate we get a rollWithData which we can use to manipulate the contractDate + according to the rules of rollParameters + + """ + + def __init__( + self, + hold_rollcycle: str, + priced_rollcycle: str, + roll_offset_day: int = 0, + carry_offset: int = -1, + approx_expiry_offset: int = 0, + ): + """ + + :param hold_rollcycle: The rollcycle which we actually want to hold, str + :param priced_rollcycle: The entire rollcycle for which prices are available, str + :param roll_offset_day: The day, relative to the expiry date, when we usually roll; int + :param carry_offset: The number of contracts forward or backwards we look for to define carry in the priced roll cycle; int + :param approx_expiry_offset: The offset, relative to the 1st of the contract month, when an expiry date usually occurs; int + + """ + + self._hold_rollcycle = rollCycle(hold_rollcycle) + self._priced_rollcycle = rollCycle(priced_rollcycle) + self._global_rollcycle = GLOBAL_ROLLCYCLE + + self._roll_offset_day = int(roll_offset_day) + self._carry_offset = int(carry_offset) + self._approx_expiry_offset = int(approx_expiry_offset) + + @property + def roll_offset_day(self): + return self._roll_offset_day + + @property + def carry_offset(self): + return self._carry_offset + + @property + def approx_expiry_offset(self): + return self._approx_expiry_offset + + def __eq__(self, other): + return ( + (self.hold_rollcycle == other.hold_rollcycle) + & (self.priced_rollcycle == self.priced_rollcycle) + & (self.global_rollcycle == other.global_rollcycle) + & (self.roll_offset_day == other.roll_offset_day) + & (self.carry_offset == other.carry_offset) + & (self.approx_expiry_offset == other.approx_expiry_offset) + ) + + def __repr__(self): + dict_rep = self.as_dict() + str_rep = ", ".join( + ["%s:%s" % (key, str(dict_rep[key])) for key in dict_rep.keys()] + ) + return "Rollcycle parameters " + str_rep + + @property + def priced_rollcycle(self) -> rollCycle: + return self._priced_rollcycle + + @property + def hold_rollcycle(self) -> rollCycle: + return self._hold_rollcycle + + @property + def global_rollcycle(self): + return self._global_rollcycle + + @classmethod + def create_from_dict(rollData, roll_data_dict: dict): + futures_instrument_roll_data = rollData(**roll_data_dict) + + return futures_instrument_roll_data + + def as_dict(self) -> dict: + return dict( + hold_rollcycle=self.hold_rollcycle.cyclestring, + priced_rollcycle=self.priced_rollcycle.cyclestring, + roll_offset_day=self.roll_offset_day, + carry_offset=self.carry_offset, + approx_expiry_offset=self.approx_expiry_offset, + ) + + def rolls_per_year_in_hold_cycle(self) -> int: + hold_cycle = self.hold_rollcycle + return len(hold_cycle) + + +class contractDateWithRollParameters(object): + """ """ + + def __init__(self, contract_date: contractDate, roll_parameters: rollParameters): + """ + Roll data plus a specific contract date means we can do things like iterate the roll cycle etc + """ + + self._roll_parameters = roll_parameters + self._contract_date = contract_date + + @property + def roll_parameters(self): + return self._roll_parameters + + @property + def contract_date(self): + return self._contract_date + + @property + def date_str(self): + return self.contract_date.date_str + + def __repr__(self): + return "%s with roll parameters %s" % ( + str(self.contract_date), + str(self.roll_parameters), + ) + + def next_priced_contract(self): + contract = self._closest_previous_valid_priced_contract() + return contract._iterate_contract(forward, "priced_rollcycle") + + def previous_priced_contract(self): + contract = self._closest_next_valid_priced_contract() + return contract._iterate_contract(backwards, "priced_rollcycle") + + def next_held_contract(self): + contract = self._closest_previous_valid_held_contract() + return contract._iterate_contract(forward, "hold_rollcycle") + + def previous_held_contract(self): + contract = self._closest_next_valid_held_contract() + return contract._iterate_contract(backwards, "hold_rollcycle") + + def _closest_next_valid_priced_contract(self): + # returns current contract if a valid priced contract, or next one in + # cycle that is + valid_contract_to_return = self + while not valid_contract_to_return._valid_date_in_priced_rollcycle(): + valid_contract_to_return = valid_contract_to_return._next_month_contract() + return valid_contract_to_return + + def _closest_previous_valid_priced_contract(self): + # returns current contract if a valid priced contract, or previous one + # in cycle that is + valid_contract_to_return = self + while not valid_contract_to_return._valid_date_in_priced_rollcycle(): + valid_contract_to_return = ( + valid_contract_to_return._previous_month_contract() + ) + return valid_contract_to_return + + def _closest_next_valid_held_contract(self): + # returns current contract if a valid held contract, or next one in + # cycle that is + valid_contract_to_return = self + while not valid_contract_to_return._valid_date_in_hold_rollcycle(): + valid_contract_to_return = valid_contract_to_return._next_month_contract() + return valid_contract_to_return + + def _closest_previous_valid_held_contract(self): + # returns current contract if a valid held contract, or previous one in + # cycle that is + valid_contract_to_return = self + while not valid_contract_to_return._valid_date_in_hold_rollcycle(): + valid_contract_to_return = ( + valid_contract_to_return._previous_month_contract() + ) + return valid_contract_to_return + + def _next_month_contract(self): + return self._iterate_contract(forward, "global_rollcycle") + + def _previous_month_contract(self): + return self._iterate_contract(backwards, "global_rollcycle") + + def _iterate_contract(self, direction: int, rollcycle_name: str): + """ + Used for going backward or forwards + + :param direction_function_name: str, attribute method of a roll cycle, either 'next_year_month' or 'previous_year_month' + :param rollcycle_name: str, attribute method of self.roll_parameters, either 'priced_rollcycle' or 'held_rollcycle' + :return: new contractDate object + """ + rollcycle_to_use = getattr(self.roll_parameters, rollcycle_name) + + try: + assert self._valid_date_in_named_rollcycle(rollcycle_name) is True + except BaseException: + raise Exception( + "ContractDate %s must be in %s %s" + % (str(self.contract_date), rollcycle_name, str(rollcycle_to_use)) + ) + + new_contract_date = rollcycle_to_use.iterate_contract_date( + direction, self.contract_date + ) + + existing_roll_parameters = self.roll_parameters + + new_contract_date_with_roll_data_object = contractDateWithRollParameters( + new_contract_date, existing_roll_parameters + ) + + return new_contract_date_with_roll_data_object + + def _valid_date_in_priced_rollcycle(self) -> bool: + return self._valid_date_in_named_rollcycle("priced_rollcycle") + + def _valid_date_in_hold_rollcycle(self) -> bool: + return self._valid_date_in_named_rollcycle("hold_rollcycle") + + def _valid_date_in_named_rollcycle(self, rollcycle_name: str) -> bool: + relevant_rollcycle = getattr(self.roll_parameters, rollcycle_name) + current_month = self.contract_date.letter_month() + + return relevant_rollcycle.check_is_month_in_rollcycle(current_month) + + def carry_contract(self): + if self.roll_parameters.carry_offset == -1: + return self.previous_priced_contract() + elif self.roll_parameters.carry_offset == 1: + return self.next_priced_contract() + else: + raise Exception("carry_offset needs to be +1 or -1") + + @property + def desired_roll_date(self) -> datetime.datetime: + return self.contract_date.expiry_date + datetime.timedelta( + days=np_convert(self.roll_parameters.roll_offset_day) + ) + + def get_contracts_from_recently_to_contract_date(self): + """ + Returns all the unexpired contracts between now and the contract date + + We go back 3 months in case of a mismatch between roll parameters and actual expiries when setting up data + + :return: list of contractDate + """ + + datetime_now = datetime.datetime.now() - datetime.timedelta(100) + contract_dates = [] + current_contract_date_with_roll_parameters = copy(self) + + while ( + current_contract_date_with_roll_parameters.contract_date.expiry_date + >= datetime_now + ): + current_contract_date = ( + current_contract_date_with_roll_parameters.contract_date + ) + contract_dates.append(current_contract_date) + current_contract_date_with_roll_parameters = ( + current_contract_date_with_roll_parameters.previous_priced_contract() + ) + + return contract_dates diff --git a/src/quantlib_st/objects/spot_fx_prices.py b/src/quantlib_st/objects/spot_fx_prices.py new file mode 100644 index 0000000..cb0f79b --- /dev/null +++ b/src/quantlib_st/objects/spot_fx_prices.py @@ -0,0 +1,110 @@ +from collections import namedtuple + +import pandas as pd + +from quantlib_st.core.pandas.merge_data_keeping_past_data import ( + merge_newer_data, + SPIKE_IN_DATA, +) +from quantlib_st.core.pandas.full_merge_with_replacement import ( + full_merge_of_existing_data, +) + + +class fxPrices(pd.Series): + """ + adjusted price information + """ + + def __init__(self, data): + super().__init__(data) # type: ignore + data.index.name = "index" + data.name = "" + + @classmethod + def create_empty(cls): + """ + Our graceful fail is to return an empty, but valid, dataframe + """ + + empty_data = pd.Series() + fx_prices = cls(empty_data) + + return fx_prices + + @classmethod + def from_data_frame(cls, data_frame): + return cls(data_frame.T.squeeze()) + + def merge_with_other_prices( + self, new_fx_prices, only_add_rows=True, check_for_spike=True + ): + """ + Merges self with new data. + If only_add_rows is True, + Otherwise: Any Nan in the existing data will be replaced (be careful!) + + :param new_fx_prices: + + :return: merged fx prices: doesn't update self + """ + if only_add_rows: + return self.add_rows_to_existing_data( + new_fx_prices, check_for_spike=check_for_spike + ) + else: + return self._full_merge_of_existing_data(new_fx_prices) + + def _full_merge_of_existing_data(self, new_fx_prices): + """ + Merges self with new data. + Any Nan in the existing data will be replaced (be careful!) + We make this private so not called accidentally + + :param new_fx_prices: the new data + :return: updated data, doesn't update self + """ + + merged_data = full_merge_of_existing_data(self, new_fx_prices) + + return fxPrices(merged_data) + + def add_rows_to_existing_data(self, new_fx_prices, check_for_spike=True): + """ + Merges self with new data. + Only newer data will be added + + :param new_fx_prices: + + :return: merged fxPrices + """ + + merged_fx_prices = merge_newer_data( + self, new_fx_prices, check_for_spike=check_for_spike + ) + if merged_fx_prices is SPIKE_IN_DATA: + return SPIKE_IN_DATA + + merged_fx_prices = fxPrices(merged_fx_prices) + + return merged_fx_prices + + +currencyValue = namedtuple("currencyValue", "currency, value") + + +class listOfCurrencyValues(list): + pass + + +# by convention we always get prices vs the dollar +DEFAULT_CURRENCY = "USD" + + +def get_fx_tuple_from_code(code): + assert len(code) == 6 + + currency1 = code[:3] + currency2 = code[3:] + + return currency1, currency2 diff --git a/src/quantlib_st/sysdata/__init__.py b/src/quantlib_st/sysdata/__init__.py new file mode 100644 index 0000000..738f56d --- /dev/null +++ b/src/quantlib_st/sysdata/__init__.py @@ -0,0 +1,3 @@ +from quantlib_st.sysdata.data_blob import dataBlob + +__all__ = ["dataBlob"] diff --git a/src/quantlib_st/sysdata/base_data.py b/src/quantlib_st/sysdata/base_data.py new file mode 100644 index 0000000..599a16a --- /dev/null +++ b/src/quantlib_st/sysdata/base_data.py @@ -0,0 +1,76 @@ +from quantlib_st.logging.logger import * + + +class baseData(object): + """ + Core data object - Base class + + simData objects are used to get data from a particular source, and give certain information about it + + The baseData class is highly generic + + Normally we'd inherit from this for specific implementations (eg simulation, production for different data types), + specific asset classes (eg carry data for futures), and then for a + specific source of data (eg csv files, databases, ...) + + The inheritance is: + + Base generic class: baseData + -> implementation specific eg simData for simulation + -> asset class specific eg futuresdata.FuturesData + -> source specific eg legacy.csvFuturesSimData + + """ + + def __init__(self, log=get_logger("baseData")): + """ + simData socket base class + + >>> data = baseData() + >>> data + simData object + """ + + self._log = log + + def __repr__(self): + return "baseData object" + + @property + def log(self): + return self._log + + def __getitem__(self, keyname): + """ + convenience method to get the price, make it look like a dict + + :param keyname: instrument to get prices for + :type keyname: str + + :returns: pd.DataFrame + """ + + raise Exception( + "__getitem__ not defined for baseData class: use a class where it has been overridden" + ) + + def keys(self): + """ + list of things in this data set (futures contracts, instruments...) + + :returns: list of str + + >>> data=baseData() + >>> data.keys() + [] + """ + + raise Exception( + "keys() not defined for baseData class: use a class where it has been overridden" + ) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/sysdata/csv/__init__.py b/src/quantlib_st/sysdata/csv/__init__.py new file mode 100644 index 0000000..246c54f --- /dev/null +++ b/src/quantlib_st/sysdata/csv/__init__.py @@ -0,0 +1,13 @@ +__all__ = [ + "csvFuturesMultiplePricesData", + "csvFuturesAdjustedPricesData", + "csvFxPricesData", + "csvFuturesInstrumentData", + "csvRollParametersData", +] + +from quantlib_st.sysdata.csv.csv_multiple_prices import csvFuturesMultiplePricesData +from quantlib_st.sysdata.csv.csv_adjusted_prices import csvFuturesAdjustedPricesData +from quantlib_st.sysdata.csv.csv_spot_fx import csvFxPricesData +from quantlib_st.sysdata.csv.csv_instrument_data import csvFuturesInstrumentData +from quantlib_st.sysdata.csv.csv_roll_parameters import csvRollParametersData diff --git a/src/quantlib_st/sysdata/csv/csv_adjusted_prices.py b/src/quantlib_st/sysdata/csv/csv_adjusted_prices.py new file mode 100644 index 0000000..2d4a9a7 --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_adjusted_prices.py @@ -0,0 +1,84 @@ +import pandas as pd + +from quantlib_st.sysdata.futures.adjusted_prices import futuresAdjustedPricesData +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices +from quantlib_st.core.fileutils import ( + resolve_path_and_filename_for_package, + files_with_extension_in_pathname, +) +from quantlib_st.core.pandas.pdutils import pd_readcsv +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.logging.logger import get_logger + +ADJUSTED_PRICES_DIRECTORY = "data.futures.adjusted_prices_csv" +DATE_INDEX_NAME = "DATETIME" + + +class csvFuturesAdjustedPricesData(futuresAdjustedPricesData): + """ + Class for adjusted prices read/write to CSV. + """ + + def __init__( + self, datapath=arg_not_supplied, log=get_logger("csvFuturesContractPriceData") + ): + super().__init__(log=log) + + if datapath is arg_not_supplied: + datapath = ADJUSTED_PRICES_DIRECTORY + + self._datapath = datapath + + def __repr__(self): + return "csvFuturesAdjustedPricesData accessing %s" % self._datapath + + @property + def datapath(self): + return self._datapath + + def get_list_of_instruments(self) -> list: + return files_with_extension_in_pathname(self.datapath, ".csv") + + def _get_adjusted_prices_without_checking( + self, instrument_code: str + ) -> futuresAdjustedPrices: + filename = self._filename_given_instrument_code(instrument_code) + + try: + instrpricedata = pd_readcsv(filename) + except OSError: + self.log.warning("Can't find adjusted price file %s" % filename) + return futuresAdjustedPrices.create_empty() + + instrpricedata.columns = ["price"] + instrpricedata = instrpricedata.groupby(level=0).last() + instrpricedata = pd.Series(instrpricedata.iloc[:, 0]) + + instrpricedata = futuresAdjustedPrices(instrpricedata) + + return instrpricedata + + def _delete_adjusted_prices_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError( + "You can't delete adjusted prices stored as a csv - Add to overwrite existing or delete file manually" + ) + + def _add_adjusted_prices_without_checking_for_existing_entry( + self, instrument_code: str, adjusted_price_data: futuresAdjustedPrices + ): + # Ensures the file will be written with a column header + adjusted_price_data_as_dataframe = pd.DataFrame(adjusted_price_data) + adjusted_price_data_as_dataframe.columns = ["price"] + + filename = self._filename_given_instrument_code(instrument_code) + adjusted_price_data_as_dataframe.to_csv(filename, index_label=DATE_INDEX_NAME) + + def _filename_given_instrument_code(self, instrument_code: str): + return resolve_path_and_filename_for_package( + self.datapath, "%s.csv" % (instrument_code) + ) + + +__all__ = ["csvFuturesAdjustedPricesData"] diff --git a/src/quantlib_st/sysdata/csv/csv_instrument_data.py b/src/quantlib_st/sysdata/csv/csv_instrument_data.py new file mode 100644 index 0000000..c1e347f --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_instrument_data.py @@ -0,0 +1,138 @@ +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package +from quantlib_st.sysdata.futures.instruments import futuresInstrumentData +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.objects.instruments import ( + futuresInstrument, + futuresInstrumentWithMetaData, + instrumentMetaData, + META_FIELD_LIST, +) +from quantlib_st.logging.logger import get_logger +import pandas as pd + +INSTRUMENT_CONFIG_PATH = "data.futures.csvconfig" +CONFIG_FILE_NAME = "instrumentconfig.csv" + + +class csvFuturesInstrumentData(futuresInstrumentData): + """ + Get data about instruments from a special configuration used for initialising the system. + """ + + def __init__( + self, + datapath=arg_not_supplied, + log=get_logger("csvFuturesInstrumentData"), + ): + super().__init__(log=log) + + if datapath is arg_not_supplied: + datapath = INSTRUMENT_CONFIG_PATH + config_file = resolve_path_and_filename_for_package(datapath, CONFIG_FILE_NAME) + self._config_file = config_file + + def get_list_of_instruments(self) -> list: + return list(self.get_all_instrument_data_as_df().index) + + def _get_instrument_data_without_checking( + self, instrument_code: str + ) -> futuresInstrumentWithMetaData: + all_instrument_data = self.get_all_instrument_data_as_df() + instrument_with_meta_data = get_instrument_with_meta_data_object( + all_instrument_data, instrument_code + ) + + return instrument_with_meta_data + + def _delete_instrument_data_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError( + "Can't overwrite part of .csv use write_all_instrument_data_from_df" + ) + + def _add_instrument_data_without_checking_for_existing_entry( + self, instrument_object: futuresInstrumentWithMetaData + ): + raise NotImplementedError( + "Can't overwrite part of .csv use write_all_instrument_data_from_df" + ) + + def write_all_instrument_data_from_df(self, instrument_data_as_df: pd.DataFrame): + instrument_data_as_df.to_csv( + self._config_file, + index_label="Instrument", + columns=[field for field in META_FIELD_LIST], + ) + + def get_all_instrument_data_as_df(self) -> pd.DataFrame: + """ + Get configuration information as a dataframe. + """ + config_data = self._instrument_csv_as_df() + return config_data + + def _instrument_csv_as_df(self) -> pd.DataFrame: + config_data = getattr(self, "_instrument_df", None) + if config_data is None: + config_data = self._load_and_store_instrument_csv_as_df() + + return config_data + + def _load_and_store_instrument_csv_as_df(self) -> pd.DataFrame: + try: + config_data = pd.read_csv(self.config_file) + except BaseException: + raise Exception("Can't read file %s" % self.config_file) + + try: + config_data.index = config_data.Instrument + config_data.drop(labels="Instrument", axis=1, inplace=True) + + except BaseException: + raise Exception("Badly configured file %s" % (self._config_file)) + + self._config_data = config_data + + return config_data + + @property + def config_file(self): + return self._config_file + + def __repr__(self): + return "Instruments data from %s" % self._config_file + + +def get_instrument_with_meta_data_object( + all_instrument_data: pd.DataFrame, instrument_code: str +) -> futuresInstrumentWithMetaData: + config_for_this_instrument = all_instrument_data.loc[instrument_code] + config_items = all_instrument_data.columns + + meta_data_dict = get_meta_data_dict_for_instrument( + config_for_this_instrument, config_items + ) + + instrument = futuresInstrument(instrument_code) + meta_data = instrumentMetaData.from_dict(meta_data_dict) + + instrument_with_meta_data = futuresInstrumentWithMetaData(instrument, meta_data) + + return instrument_with_meta_data + + +def get_meta_data_dict_for_instrument( + config_for_this_instrument: pd.DataFrame, config_items: list +): + meta_data = dict( + [ + (item_name, getattr(config_for_this_instrument, item_name)) + for item_name in config_items + ] + ) + + return meta_data + + +__all__ = ["csvFuturesInstrumentData"] diff --git a/src/quantlib_st/sysdata/csv/csv_multiple_prices.py b/src/quantlib_st/sysdata/csv/csv_multiple_prices.py new file mode 100644 index 0000000..abcffe1 --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_multiple_prices.py @@ -0,0 +1,100 @@ +import pandas as pd + +from quantlib_st.sysdata.futures.multiple_prices import futuresMultiplePricesData +from quantlib_st.objects.multiple_prices import ( + futuresMultiplePrices, + list_of_contract_column_names, +) + +from quantlib_st.core.fileutils import ( + resolve_path_and_filename_for_package, + files_with_extension_in_pathname, +) +from quantlib_st.core.pandas.pdutils import pd_readcsv +from quantlib_st.core.genutils import str_of_int +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.logging.logger import get_logger + +CSV_MULTIPLE_PRICE_DIRECTORY = "data.futures.multiple_prices_csv" +DATE_INDEX_NAME = "DATETIME" + + +class csvFuturesMultiplePricesData(futuresMultiplePricesData): + """ + Class for roll calendars read/write to CSV. + """ + + def __init__( + self, + datapath: str = arg_not_supplied, + log=get_logger("csvFuturesMultiplePricesData"), + ): + super().__init__(log=log) + + if datapath is arg_not_supplied: + datapath = CSV_MULTIPLE_PRICE_DIRECTORY + + self._datapath = datapath + + def __repr__(self): + return "csvFuturesMultiplePricesData accessing %s" % self.datapath + + @property + def datapath(self): + return self._datapath + + def get_list_of_instruments(self): + return files_with_extension_in_pathname(self.datapath, ".csv") + + def _get_multiple_prices_without_checking( + self, instrument_code: str + ) -> futuresMultiplePrices: + instr_all_price_data = self._read_instrument_prices(instrument_code) + for contract_col_name in list_of_contract_column_names: + instr_all_price_data[contract_col_name] = instr_all_price_data[ + contract_col_name + ].apply(str_of_int) + + return futuresMultiplePrices(instr_all_price_data) + + def _delete_multiple_prices_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError( + "You can't delete multiple prices stored as a csv - Add to overwrite existing or delete file manually" + ) + + def _add_multiple_prices_without_checking_for_existing_entry( + self, instrument_code: str, multiple_price_data: futuresMultiplePrices + ): + filename = self._filename_given_instrument_code(instrument_code) + multiple_price_data.to_csv(filename, index_label=DATE_INDEX_NAME) + + self.log.debug( + "Written multiple prices for %s to %s" % (instrument_code, filename), + instrument_code=instrument_code, + ) + + def _read_instrument_prices(self, instrument_code: str) -> pd.DataFrame: + filename = self._filename_given_instrument_code(instrument_code) + + try: + instr_all_price_data = pd_readcsv(filename, date_index_name=DATE_INDEX_NAME) + except OSError: + self.log.warning( + "Can't find multiple price file %s or error reading" % filename, + instrument_code=instrument_code, + ) + return futuresMultiplePrices.create_empty() + + return instr_all_price_data + + def _filename_given_instrument_code(self, instrument_code: str): + filename = resolve_path_and_filename_for_package( + self.datapath, "%s.csv" % (instrument_code) + ) + + return filename + + +__all__ = ["csvFuturesMultiplePricesData"] diff --git a/src/quantlib_st/sysdata/csv/csv_roll_parameters.py b/src/quantlib_st/sysdata/csv/csv_roll_parameters.py new file mode 100644 index 0000000..646b08a --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_roll_parameters.py @@ -0,0 +1,120 @@ +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package +from quantlib_st.sysdata.futures.rolls_parameters import rollParametersData +from quantlib_st.objects.rolls import rollParameters +from quantlib_st.logging.logger import get_logger + +import pandas as pd + +ROLLS_DATAPATH = "data.futures.csvconfig" +ROLLS_CONFIG_FILE = "rollconfig.csv" + + +class allRollParameters(pd.DataFrame): + @classmethod + def read_from_file(allRollParameters, filename): + try: + roll_data = pd.read_csv(filename) + except BaseException: + raise Exception("Can't read file %s" % filename) + + try: + roll_data.index = roll_data.Instrument + roll_data.drop(labels="Instrument", axis=1, inplace=True) + + except BaseException: + raise Exception("Badly configured file %s" % (filename)) + + return allRollParameters(roll_data) + + def get_list_of_instruments(self) -> list: + return list(self.index) + + def get_roll_parameters_for_instrument( + self, instrument_code: str + ) -> rollParameters: + config_for_this_instrument = self.loc[instrument_code] + roll_parameters_object = rollParameters( + hold_rollcycle=config_for_this_instrument.HoldRollCycle, + roll_offset_day=config_for_this_instrument.RollOffsetDays, + carry_offset=config_for_this_instrument.CarryOffset, + priced_rollcycle=config_for_this_instrument.PricedRollCycle, + approx_expiry_offset=config_for_this_instrument.ExpiryOffset, + ) + + return roll_parameters_object + + def update_roll_parameters_for_instrument( + self, instrument_code: str, roll_parameters: rollParameters + ): + self.at[instrument_code, "HoldRollCycle"] = roll_parameters.hold_rollcycle + self.at[instrument_code, "RollOffsetDays"] = roll_parameters.roll_offset_day + self.at[instrument_code, "CarryOffset"] = roll_parameters.carry_offset + self.at[instrument_code, "PricedRollCycle"] = roll_parameters.priced_rollcycle + self.at[instrument_code, "ExpiryOffset"] = roll_parameters.approx_expiry_offset + + def write_to_file(self, filename: str): + self.to_csv(filename, index_label="Instrument") + + +class csvRollParametersData(rollParametersData): + """ + Get roll parameters from CSV config used for initialising the system. + """ + + def __init__( + self, log=get_logger("csvRollParametersData"), datapath=arg_not_supplied + ): + super().__init__(log=log) + if datapath is arg_not_supplied: + datapath = ROLLS_DATAPATH + config_file = resolve_path_and_filename_for_package(datapath, ROLLS_CONFIG_FILE) + + self._config_file = config_file + + def __repr__(self): + return "Roll data for initialising system config" + + def get_list_of_instruments(self) -> list: + all_roll_parameters = self.get_roll_parameters_all_instruments() + return all_roll_parameters.get_list_of_instruments() + + def _get_roll_parameters_without_checking( + self, instrument_code: str + ) -> rollParameters: + all_parameters = self.get_roll_parameters_all_instruments() + return all_parameters.get_roll_parameters_for_instrument(instrument_code) + + def _delete_roll_parameters_data_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError("csv is read only") + + def _add_roll_parameters_without_checking_for_existing_entry( + self, instrument_code: str, roll_parameters: rollParameters + ): + # We don't normally allow this, but a special case as we safe modify + all_parameters = self.get_roll_parameters_all_instruments() + all_parameters.update_roll_parameters_for_instrument( + instrument_code, roll_parameters + ) + all_parameters.write_to_file(self.config_file) + + self.log.warning( + "*** WRITTEN NEW ROLL PARAMETERS TO %s - copy to /data/futures/csvconfig/rollconfig.csv NOW ***" + % self.config_file + ) + + def write_all_roll_parameters_data(self, roll_parameters_df: pd.DataFrame): + all_roll_parameters = allRollParameters(roll_parameters_df) + all_roll_parameters.write_to_file(self.config_file) + + def get_roll_parameters_all_instruments(self) -> allRollParameters: + return allRollParameters.read_from_file(self.config_file) + + @property + def config_file(self): + return self._config_file + + +__all__ = ["csvRollParametersData", "allRollParameters"] diff --git a/src/quantlib_st/sysdata/csv/csv_spot_fx.py b/src/quantlib_st/sysdata/csv/csv_spot_fx.py new file mode 100644 index 0000000..81db2bd --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_spot_fx.py @@ -0,0 +1,126 @@ +from dataclasses import dataclass +import pandas as pd + +from quantlib_st.sysdata.fx.spotfx import fxPricesData +from quantlib_st.objects.spot_fx_prices import fxPrices +from quantlib_st.core.fileutils import ( + resolve_path_and_filename_for_package, + files_with_extension_in_pathname, +) +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.pandas.pdutils import pd_readcsv, DEFAULT_DATE_FORMAT_FOR_CSV +from quantlib_st.logging.logger import get_logger +from quantlib_st.logging.adaptor import CURRENCY_CODE_LOG_LABEL + +FX_PRICES_DIRECTORY = "data.futures.fx_prices_csv" + + +@dataclass +class ConfigCsvFXPrices: + """ + :param price_column: Column where spot FX prices are + :param date_column: Column where dates are + :param date_format: Format for dates https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior + """ + + date_column: str = "DATETIME" + date_format: str = DEFAULT_DATE_FORMAT_FOR_CSV + price_column: str = "PRICE" + + +class csvFxPricesData(fxPricesData): + """ + Class for fx prices read/write to CSV. + """ + + def __init__( + self, + datapath=arg_not_supplied, + log=get_logger("csvFxPricesData"), + config: ConfigCsvFXPrices = arg_not_supplied, + ): + """ + Get FX data from a .csv file + + :param datapath: Path where csv files are located + :param log: logging object + """ + + super().__init__(log=log) + + if datapath is arg_not_supplied: + datapath = FX_PRICES_DIRECTORY + + if config is arg_not_supplied: + config = ConfigCsvFXPrices() + + self._datapath = datapath + self._config = config + + def __repr__(self): + return "csvFxPricesData accessing %s" % self._datapath + + @property + def datapath(self): + return self._datapath + + @property + def config(self): + return self._config + + def get_list_of_fxcodes(self) -> list: + return files_with_extension_in_pathname(self._datapath, ".csv") + + def _get_fx_prices_without_checking(self, code: str) -> fxPrices: + filename = self._filename_given_fx_code(code) + config = self.config + price_column = config.price_column + date_column = config.date_column + date_format = config.date_format + + try: + fx_data = pd_readcsv( + filename, date_format=date_format, date_index_name=date_column + ) + except OSError: + self.log.warning( + "Can't find currency price file %s" % filename, + **{CURRENCY_CODE_LOG_LABEL: code}, + ) + return fxPrices.create_empty() + + fx_data = pd.Series(fx_data[price_column]) + + fx_data = fxPrices(fx_data.sort_index()) + + return fx_data + + def _delete_fx_prices_without_any_warning_be_careful(self, code: str): + raise NotImplementedError( + "You can't delete adjusted prices stored as a csv - Add to overwrite existing or delete file manually" + ) + + def _add_fx_prices_without_checking_for_existing_entry( + self, code: str, fx_price_data: fxPrices + ): + filename = self._filename_given_fx_code(code) + config = self.config + price_column = config.price_column + date_column = config.date_column + date_format = config.date_format + + fx_price_data.name = price_column + fx_price_data.to_csv( + filename, index_label=date_column, date_format=date_format, header=True + ) + + self.log.debug( + "Wrote currency prices to %s for %s" % (filename, code), + **{CURRENCY_CODE_LOG_LABEL: code}, + ) + + def _filename_given_fx_code(self, code: str): + return resolve_path_and_filename_for_package(self._datapath, "%s.csv" % (code)) + + +__all__ = ["csvFxPricesData", "ConfigCsvFXPrices"] diff --git a/src/quantlib_st/sysdata/data_blob.py b/src/quantlib_st/sysdata/data_blob.py new file mode 100644 index 0000000..6ba5fb0 --- /dev/null +++ b/src/quantlib_st/sysdata/data_blob.py @@ -0,0 +1,415 @@ +from copy import copy +from typing import Any, Optional, TYPE_CHECKING + +from quantlib_st.core.objects import get_class_name +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.fileutils import get_resolved_pathname +from quantlib_st.core.text import camel_case_split +from quantlib_st.logging.logger import get_logger +from quantlib_st.logging.adaptor import COMPONENT_LOG_LABEL + +if TYPE_CHECKING: + from quantlib_st.sysdata.production_config import Config # pragma: no cover + from quantlib_st.sysdata.mongodb.mongo_connection import mongoDb # pragma: no cover + from quantlib_st.sysdata.parquet.parquet_access import ( + ParquetAccess, + ) # pragma: no cover + from quantlib_st.sysdata.mongodb.mongo_IB_client_id import ( + mongoIbBrokerClientIdData, # pragma: no cover + ) + from quantlib_st.brokers.ib.ib_connection import connectionIB # pragma: no cover + + +class dataBlob(object): + def __init__( + self, + class_list: list = arg_not_supplied, + log_name: str = "", + csv_data_paths: dict = arg_not_supplied, + parquet_store_path: str = arg_not_supplied, + ib_conn: Any = arg_not_supplied, + mongo_db: Any = arg_not_supplied, + log=arg_not_supplied, + keep_original_prefix: bool = False, + ): + """ + Set up a data pipeline with standard attribute names, logging, links to DB etc. + + Class names we know how to handle are: + 'ib*', 'mongo*', 'arctic*', 'csv*', 'parquet*' + """ + + self._mongo_db = mongo_db + self._ib_conn = ib_conn + self._log = log + self._log_name = log_name + self._csv_data_paths = csv_data_paths + self._keep_original_prefix = keep_original_prefix + self._parquet_store_path = parquet_store_path + + self._attr_list = [] + + if class_list is arg_not_supplied: + # can set up dynamically later + pass + else: + self.add_class_list(class_list) + + self._original_data = copy(self) + + def __repr__(self): + return "dataBlob with elements: %s" % ",".join(self._attr_list) + + def add_class_list(self, class_list: list, use_prefix: str = arg_not_supplied): + for class_object in class_list: + self.add_class_object(class_object, use_prefix=use_prefix) + + def add_class_object(self, class_object, use_prefix: str = arg_not_supplied): + class_name = get_class_name(class_object) + new_name = self._get_new_name(class_name, use_prefix=use_prefix) + if not self._already_existing_class_name(new_name): + resolved_instance = self._get_resolved_instance_of_class(class_object) + self._add_new_class_with_new_name( + resolved_instance=resolved_instance, attr_name=new_name + ) + + def _get_resolved_instance_of_class(self, class_object): + class_adding_method = self._get_class_adding_method(class_object) + resolved_instance = class_adding_method(class_object) + + return resolved_instance + + def _get_class_adding_method(self, class_object): + prefix = self._get_class_prefix(class_object) + class_dict = dict( + ib=self._add_ib_class, + csv=self._add_csv_class, + arctic=self._add_arctic_class, + mongo=self._add_mongo_class, + parquet=self._add_parquet_class, + ) + + method_to_add_with = class_dict.get(prefix, None) + if method_to_add_with is None: + error_msg = "Don't know how to handle object named %s" % get_class_name( + class_object + ) + self._raise_and_log_error(error_msg) + + return method_to_add_with + + def _get_class_prefix(self, class_object) -> str: + class_name = get_class_name(class_object) + split_up_name = camel_case_split(class_name) + prefix = split_up_name[0] + + return prefix + + def _add_ib_class(self, class_object): + log = self._get_specific_logger(class_object) + try: + resolved_instance = class_object(self.ib_conn, self, log=log) + except Exception as e: + class_name = get_class_name(class_object) + msg = ( + "Error %s couldn't evaluate %s(self.ib_conn, self) This might be because (a) IB gateway not running, or (b) import is missing\ + or (c) arguments don't follow pattern" + % (str(e), class_name) + ) + self._raise_and_log_error(msg) + + return resolved_instance + + def _add_mongo_class(self, class_object): + log = self._get_specific_logger(class_object) + try: + resolved_instance = class_object(mongo_db=self.mongo_db, log=log) + except Exception as e: + class_name = get_class_name(class_object) + msg = ( + "Error '%s' couldn't evaluate %s(mongo_db=self.mongo_db) \ + This might be because import is missing\ + or arguments don't follow pattern" + % (str(e), class_name) + ) + self._raise_and_log_error(msg) + + return resolved_instance + + def _add_arctic_class(self, class_object): + log = self._get_specific_logger(class_object) + try: + resolved_instance = class_object(mongo_db=self.mongo_db, log=log) + except Exception as e: + class_name = get_class_name(class_object) + msg = ( + "Error %s couldn't evaluate %s(mongo_db=self.mongo_db) \ + This might be because import is missing\ + or arguments don't follow pattern" + % (str(e), class_name) + ) + self._raise_and_log_error(msg) + + return resolved_instance + + def _add_parquet_class(self, class_object): + log = self._get_specific_logger(class_object) + try: + resolved_instance = class_object( + parquet_access=self.parquet_access, log=log + ) + except Exception as e: + class_name = get_class_name(class_object) + msg = ( + "Error '%s' couldn't evaluate %s(parquet_access = self.parquet_access) \ + This might be because import is missing\ + or arguments don't follow pattern or parquet_store is undefined" + % (str(e), class_name) + ) + self._raise_and_log_error(msg) + + return resolved_instance + + def _add_csv_class(self, class_object): + datapath = self._get_csv_paths_for_class(class_object) + log = self._get_specific_logger(class_object) + + try: + resolved_instance = class_object(datapath=datapath, log=log) + except Exception as e: + class_name = get_class_name(class_object) + msg = ( + "Error %s couldn't evaluate %s(datapath = datapath) \ + This might be because import is missing\ + or arguments don't follow pattern" + % (str(e), class_name) + ) + self._raise_and_log_error(msg) + + return resolved_instance + + def _get_csv_paths_for_class(self, class_object) -> str: + class_name = get_class_name(class_object) + csv_data_paths = self.csv_data_paths + if csv_data_paths is arg_not_supplied: + return arg_not_supplied + + datapath = csv_data_paths.get(class_name, "") + if datapath == "": + self.log.warning( + "No key for %s in csv_data_paths, will use defaults (may break in production, should be fine in sim)" + % class_name + ) + return arg_not_supplied + + return datapath + + @property + def csv_data_paths(self) -> dict: + csv_data_paths = getattr(self, "_csv_data_paths", arg_not_supplied) + + return csv_data_paths + + def _get_specific_logger(self, class_object): + class_name = get_class_name(class_object) + log = get_logger(self.log.name, {COMPONENT_LOG_LABEL: class_name}) + + return log + + def _get_new_name(self, class_name: str, use_prefix: str = arg_not_supplied) -> str: + split_up_name = camel_case_split(class_name) + attr_name = identifying_name( + split_up_name, + keep_original_prefix=self._keep_original_prefix, + use_prefix=use_prefix, + ) + + return attr_name + + def _add_new_class_with_new_name(self, resolved_instance, attr_name: str): + already_exists = self._already_existing_class_name(attr_name) + if already_exists: + # not uncommon, don't log or would be a sea of spam + pass + else: + setattr(self, attr_name, resolved_instance) + self._add_attr_to_list(attr_name) + + def _already_existing_class_name(self, attr_name: str): + existing_attr = getattr(self, attr_name, None) + if existing_attr is None: + return False + else: + return True + + def _add_attr_to_list(self, new_attr: str): + self._attr_list.append(new_attr) + + def update_log(self, new_log): + self._log = new_log + + """ + Following two methods implement context manager + """ + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.close() + + def close(self): + if self._ib_conn is not arg_not_supplied: + self.ib_conn.close_connection() + self.db_ib_broker_client_id.release_clientid(self.ib_conn.client_id()) + + # No need to explicitly close Mongo connections; handled by Python garbage collection + + @property + def ib_conn(self): + ib_conn = getattr(self, "_ib_conn", arg_not_supplied) + if ib_conn is arg_not_supplied: + ib_conn = self._get_new_ib_connection() + self._ib_conn = ib_conn + + return ib_conn + + def _get_new_ib_connection(self): + # Try this 5 times... + attempts = 0 + failed_ids = [] + client_id = self._get_next_client_id_for_ib() + while True: + try: + from quantlib_st.brokers.ib.ib_connection import connectionIB + + ib_conn = connectionIB(client_id, log_name=self.log_name) + for id in failed_ids: + self.db_ib_broker_client_id.release_clientid(id) + return ib_conn + except Exception as e: + failed_ids.append(client_id) + client_id = self._get_next_client_id_for_ib() + attempts += 1 + if attempts > 5: + for id in failed_ids: + self.db_ib_broker_client_id.release_clientid(id) + raise e + + def _get_next_client_id_for_ib(self) -> int: + # default to tracking ID through mongo change if required + from quantlib_st.sysdata.mongodb.mongo_IB_client_id import ( + mongoIbBrokerClientIdData, + ) + + self.add_class_object(mongoIbBrokerClientIdData) + client_id = self.db_ib_broker_client_id.return_valid_client_id() + + return int(client_id) + + @property + def mongo_db(self): + mongo_db = getattr(self, "_mongo_db", arg_not_supplied) + if mongo_db is arg_not_supplied: + mongo_db = self._get_new_mongo_db() + self._mongo_db = mongo_db + + return mongo_db + + @property + def parquet_access(self): + from quantlib_st.sysdata.parquet.parquet_access import ParquetAccess + + return ParquetAccess(self.parquet_root_directory) + + @property + def parquet_root_directory(self) -> str: + path = self._parquet_store_path + if path is arg_not_supplied: + try: + path = get_parquet_root_directory(self.config) + except Exception: + raise Exception("Need to define parquet_store in config to use parquet") + + return path + + def _get_new_mongo_db(self): + from quantlib_st.sysdata.mongodb.mongo_connection import mongoDb + + mongo_db = mongoDb() + + return mongo_db + + @property + def config(self): + config = getattr(self, "_config", None) + if config is None: + from quantlib_st.sysdata.production_config import get_production_config + + config = self._config = get_production_config() + + return config + + def _raise_and_log_error(self, error_msg: str): + self.log.critical(error_msg) + raise Exception(error_msg) + + @property + def log(self): + log = getattr(self, "_log", arg_not_supplied) + if log is arg_not_supplied: + log = get_logger(self.log_name) + self._log = log + + return log + + @property + def log_name(self) -> str: + log_name = getattr(self, "_log_name", "") + return log_name + + +source_dict = dict(arctic="db", mongo="db", csv="db", parquet="db", ib="broker") + + +def get_parquet_root_directory(config): + path = config.get_element("parquet_store") + return get_resolved_pathname(path) + + +def identifying_name( + split_up_name: list, + keep_original_prefix: bool = False, + use_prefix: str = arg_not_supplied, +) -> str: + """ + Turns sourceClassNameData into broker_class_name or db_class_name + """ + lower_split_up_name = [x.lower() for x in split_up_name] + data_label = lower_split_up_name.pop(-1) # always 'data' + original_source_label = lower_split_up_name.pop(0) # source eg csv, ib, mongo + + try: + assert data_label == "data" + except BaseException: + raise Exception("Get_data strings only work if class name ends in ...Data") + + if use_prefix is not arg_not_supplied: + source_label = use_prefix + elif keep_original_prefix: + source_label = original_source_label + else: + try: + source_label = source_dict[original_source_label] + except BaseException: + raise Exception( + "Only works with classes that begin with one of %s" + % str(source_dict.keys()) + ) + + lower_split_up_name = [source_label] + lower_split_up_name + + return "_".join(lower_split_up_name) + + +__all__ = ["dataBlob", "identifying_name", "get_parquet_root_directory"] diff --git a/src/quantlib_st/sysdata/futures/__init__.py b/src/quantlib_st/sysdata/futures/__init__.py new file mode 100644 index 0000000..52d8883 --- /dev/null +++ b/src/quantlib_st/sysdata/futures/__init__.py @@ -0,0 +1,13 @@ +__all__ = [ + "futuresAdjustedPricesData", + "futuresMultiplePricesData", + "futuresInstrumentData", + "rollParametersData", + "spreadCostData", +] + +from quantlib_st.sysdata.futures.adjusted_prices import futuresAdjustedPricesData +from quantlib_st.sysdata.futures.multiple_prices import futuresMultiplePricesData +from quantlib_st.sysdata.futures.instruments import futuresInstrumentData +from quantlib_st.sysdata.futures.rolls_parameters import rollParametersData +from quantlib_st.sysdata.futures.spread_costs import spreadCostData diff --git a/src/quantlib_st/sysdata/futures/adjusted_prices.py b/src/quantlib_st/sysdata/futures/adjusted_prices.py new file mode 100644 index 0000000..26d7d48 --- /dev/null +++ b/src/quantlib_st/sysdata/futures/adjusted_prices.py @@ -0,0 +1,115 @@ +""" +Adjusted prices: + +- back-adjustor +- just adjusted prices + +""" + +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices + +USE_CHILD_CLASS_ERROR = "You need to use a child class of futuresAdjustedPricesData" + + +class futuresAdjustedPricesData(baseData): + """ + Read and write data class to get adjusted prices + + We'd inherit from this class for a specific implementation + """ + + def __repr__(self): + return USE_CHILD_CLASS_ERROR + + def keys(self): + return self.get_list_of_instruments() + + def get_adjusted_prices(self, instrument_code: str) -> futuresAdjustedPrices: + if self.is_code_in_data(instrument_code): + adjusted_prices = self._get_adjusted_prices_without_checking( + instrument_code + ) + else: + adjusted_prices = futuresAdjustedPrices.create_empty() + + return adjusted_prices + + def __getitem__(self, instrument_code: str) -> futuresAdjustedPrices: + return self.get_adjusted_prices(instrument_code) + + def delete_adjusted_prices(self, instrument_code: str, are_you_sure: bool = False): + if are_you_sure: + if self.is_code_in_data(instrument_code): + self._delete_adjusted_prices_without_any_warning_be_careful( + instrument_code + ) + self.log.info( + "Deleted adjusted price data for %s" % instrument_code, + instrument_code=instrument_code, + ) + + else: + # doesn't exist anyway + self.log.warning( + "Tried to delete non existent adjusted prices for %s" + % instrument_code, + instrument_code=instrument_code, + ) + else: + self.log.error( + "You need to call delete_adjusted_prices with a flag to be sure", + instrument_code=instrument_code, + ) + + def is_code_in_data(self, instrument_code: str) -> bool: + if instrument_code in self.get_list_of_instruments(): + return True + else: + return False + + def add_adjusted_prices( + self, + instrument_code: str, + adjusted_price_data: futuresAdjustedPrices, + ignore_duplication: bool = False, + ): + if self.is_code_in_data(instrument_code): + if ignore_duplication: + pass + else: + self.log.error( + "There is already %s in the data, you have to delete it first" + % instrument_code, + instrument_code=instrument_code, + ) + + self._add_adjusted_prices_without_checking_for_existing_entry( + instrument_code, adjusted_price_data + ) + + self.log.info( + "Added data for instrument %s" % instrument_code, + instrument_code=instrument_code, + ) + + def _add_adjusted_prices_without_checking_for_existing_entry( + self, instrument_code: str, adjusted_price_data: futuresAdjustedPrices + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def get_list_of_instruments(self) -> list: + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _delete_adjusted_prices_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _get_adjusted_prices_without_checking( + self, instrument_code: str + ) -> futuresAdjustedPrices: + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + +__all__ = ["futuresAdjustedPricesData"] diff --git a/src/quantlib_st/sysdata/futures/instruments.py b/src/quantlib_st/sysdata/futures/instruments.py new file mode 100644 index 0000000..75d2bc2 --- /dev/null +++ b/src/quantlib_st/sysdata/futures/instruments.py @@ -0,0 +1,157 @@ +""" +Read / write and represent instrument data +""" + +import pandas as pd + +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.objects.instruments import ( + futuresInstrumentWithMetaData, + listOfFuturesInstrumentWithMetaData, +) +from quantlib_st.logging.logger import get_logger + +USE_CHILD_CLASS_ERROR = "You need to use a child class of futuresInstrumentData" + + +class futuresInstrumentData(baseData): + """ + Read and write data class to get instrument data + + We'd inherit from this class for a specific implementation + """ + + def __repr__(self): + return "futuresInstrumentData base class - DO NOT USE" + + def __init__(self, log=get_logger("futuresInstrumentData")): + super().__init__(log=log) + + def keys(self) -> list: + return self.get_list_of_instruments() + + def __getitem__(self, instrument_code: str): + return self.get_instrument_data(instrument_code) + + def update_slippage_costs(self, instrument_code: str, new_slippage: float): + self.update_meta_data( + instrument_code, meta_name="Slippage", new_value=new_slippage + ) + + def update_meta_data(self, instrument_code: str, meta_name: str, new_value): + instrument_object = self.get_instrument_data(instrument_code) + existing_meta_data = instrument_object.meta_data + try: + existing_meta_data_value = getattr(existing_meta_data, meta_name) + except AttributeError: + raise Exception( + "Meta data %s does not exist for instrument %s" + % (meta_name, instrument_code) + ) + setattr(existing_meta_data, meta_name, new_value) + self.add_instrument_data(instrument_object, ignore_duplication=True) + self.log.debug( + "Updated %s for %s from %s to %s" + % (meta_name, instrument_code, existing_meta_data_value, new_value) + ) + + def get_all_instrument_data_as_list_of_instrument_objects( + self, + ) -> listOfFuturesInstrumentWithMetaData: + all_instrument_codes = self.get_list_of_instruments() + all_instrument_objects = [ + self.get_instrument_data(instrument_code) + for instrument_code in all_instrument_codes + ] + list_of_instrument_objects = listOfFuturesInstrumentWithMetaData( + all_instrument_objects + ) + + return list_of_instrument_objects + + def get_all_instrument_data_as_df(self) -> pd.DataFrame: + """ + Gets information about all instruments + + Returns dataframe of meta data, indexed by instrument code + """ + + list_of_instrument_objects = ( + self.get_all_instrument_data_as_list_of_instrument_objects() + ) + list_as_df = list_of_instrument_objects.as_df() + + return list_as_df + + def get_instrument_data( + self, instrument_code: str + ) -> futuresInstrumentWithMetaData: + if self.is_code_in_data(instrument_code): + return self._get_instrument_data_without_checking(instrument_code) + else: + return futuresInstrumentWithMetaData.create_empty() + + def delete_instrument_data(self, instrument_code: str, are_you_sure: bool = False): + self.log.debug("Updating log attributes", instrument_code=instrument_code) + + if are_you_sure: + if self.is_code_in_data(instrument_code): + self._delete_instrument_data_without_any_warning_be_careful( + instrument_code + ) + self.log.info("Deleted instrument object %s" % instrument_code) + + else: + # doesn't exist anyway + self.log.warning("Tried to delete non existent instrument") + else: + self.log.error( + "You need to call delete_instrument_data with a flag to be sure" + ) + + def is_code_in_data(self, instrument_code: str) -> bool: + if instrument_code in self.get_list_of_instruments(): + return True + else: + return False + + def add_instrument_data( + self, + instrument_object: futuresInstrumentWithMetaData, + ignore_duplication: bool = False, + ): + instrument_code = instrument_object.instrument_code + + self.log.debug("Updating log attributes", instrument_code=instrument_code) + + if self.is_code_in_data(instrument_code): + if ignore_duplication: + pass + else: + self.log.error( + "There is already %s in the data, you have to delete it first" + % instrument_code + ) + + self._add_instrument_data_without_checking_for_existing_entry(instrument_object) + + self.log.info("Added instrument object %s" % instrument_object.instrument_code) + + def get_list_of_instruments(self): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _get_instrument_data_without_checking(self, instrument_code: str): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _delete_instrument_data_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _add_instrument_data_without_checking_for_existing_entry( + self, instrument_object: futuresInstrumentWithMetaData + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + +__all__ = ["futuresInstrumentData"] diff --git a/src/quantlib_st/sysdata/futures/multiple_prices.py b/src/quantlib_st/sysdata/futures/multiple_prices.py new file mode 100644 index 0000000..31351e3 --- /dev/null +++ b/src/quantlib_st/sysdata/futures/multiple_prices.py @@ -0,0 +1,126 @@ +""" +A multiple price object is a: + +pd. dataframe with the 6 columns PRICE, CARRY, PRICE_CONTRACT, CARRY_CONTRACT, FORWARD, FORWARD_CONTRACT + +All contracts are in yyyymm format + +We require these to calculate back adjusted prices and also to work out carry + +They can be stored, or worked out 'on the fly' +""" + +from quantlib_st.core.exceptions import existingData +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.logging.logger import get_logger + +# These are used when inferring prices in an incomplete series +from quantlib_st.objects.multiple_prices import futuresMultiplePrices +from quantlib_st.logging.adaptor import INSTRUMENT_CODE_LOG_LABEL + +USE_CHILD_CLASS_ERROR = "You need to use a child class of futuresMultiplePricesData" + + +class futuresMultiplePricesData(baseData): + """ + Read and write data class to get multiple prices for a specific future + + We'd inherit from this class for a specific implementation + """ + + def __repr__(self): + return "futuresMultiplePricesData base class - DO NOT USE" + + def keys(self): + return self.get_list_of_instruments() + + def __getitem__(self, instrument_code: str) -> futuresMultiplePrices: + return self.get_multiple_prices(instrument_code) + + def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: + if self.is_code_in_data(instrument_code): + multiple_prices = self._get_multiple_prices_without_checking( + instrument_code + ) + else: + multiple_prices = futuresMultiplePrices.create_empty() + + return multiple_prices + + def delete_multiple_prices(self, instrument_code: str, are_you_sure=False): + log_attrs = {INSTRUMENT_CODE_LOG_LABEL: instrument_code, "method": "temp"} + + if are_you_sure: + if self.is_code_in_data(instrument_code): + self._delete_multiple_prices_without_any_warning_be_careful( + instrument_code + ) + self.log.info( + "Deleted multiple price data for %s" % instrument_code, + **log_attrs, + ) + + else: + # doesn't exist anyway + self.log.warning( + "Tried to delete non existent multiple prices for %s" + % instrument_code, + **log_attrs, + ) + else: + self.log.error( + "You need to call delete_multiple_prices with a flag to be sure", + **log_attrs, + ) + raise Exception("You need to be sure!") + + def is_code_in_data(self, instrument_code: str) -> bool: + if instrument_code in self.get_list_of_instruments(): + return True + else: + return False + + def add_multiple_prices( + self, + instrument_code: str, + multiple_price_data: futuresMultiplePrices, + ignore_duplication=False, + ): + log_attrs = {INSTRUMENT_CODE_LOG_LABEL: instrument_code, "method": "temp"} + if self.is_code_in_data(instrument_code): + if ignore_duplication: + pass + else: + self.log.error( + "There is already %s in the data, you have to delete it first" + % instrument_code, + **log_attrs, + ) + raise existingData + + self._add_multiple_prices_without_checking_for_existing_entry( + instrument_code, multiple_price_data + ) + + self.log.info("Added data for instrument %s" % instrument_code, **log_attrs) + + def _add_multiple_prices_without_checking_for_existing_entry( + self, instrument_code: str, multiple_price_data: futuresMultiplePrices + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def get_list_of_instruments(self) -> list: + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _get_multiple_prices_without_checking( + self, instrument_code: str + ) -> futuresMultiplePrices: + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _delete_multiple_prices_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + +__all__ = ["futuresMultiplePricesData"] diff --git a/src/quantlib_st/sysdata/futures/rolls_parameters.py b/src/quantlib_st/sysdata/futures/rolls_parameters.py new file mode 100644 index 0000000..d8b0db7 --- /dev/null +++ b/src/quantlib_st/sysdata/futures/rolls_parameters.py @@ -0,0 +1,111 @@ +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.logging.logger import get_logger +from quantlib_st.objects.rolls import rollParameters + +USE_CHILD_CLASS_ROLL_PARAMS_ERROR = ( + "You need to use a child class of rollParametersData" +) + + +class rollParametersMissing(Exception): + pass + + +class rollParametersData(baseData): + """ + Read and write data class to get roll data for a given instrument + + We'd inherit from this class for a specific implementation + + """ + + def __init__(self, log=get_logger("futuresInstrumentData")): + super().__init__(log=log) + + def __repr__(self): + return "rollParametersData base class - DO NOT USE" + + def keys(self): + return self.get_list_of_instruments() + + def __getitem__(self, instrument_code: str) -> rollParameters: + return self.get_roll_parameters(instrument_code) + + def get_roll_parameters(self, instrument_code: str) -> rollParameters: + if self.is_code_in_data(instrument_code): + return self._get_roll_parameters_without_checking(instrument_code) + else: + raise rollParametersMissing( + "Don't have parameters for %s" % instrument_code + ) + + def delete_roll_parameters(self, instrument_code: str, are_you_sure: bool = False): + self.log.debug("Updating log attributes", instrument_code=instrument_code) + + if are_you_sure: + if self.is_code_in_data(instrument_code): + self._delete_roll_parameters_data_without_any_warning_be_careful( + instrument_code + ) + self.log.info("Deleted roll parameters for %s" % instrument_code) + + else: + # doesn't exist anyway + self.log.warning( + "Tried to delete roll parameters for non existent instrument code %s" + % instrument_code + ) + else: + self.log.error( + "You need to call delete_roll_parameters with a flag to be sure" + ) + + def add_roll_parameters( + self, + instrument_code: str, + roll_parameters: rollParameters, + ignore_duplication: bool = False, + ): + self.log.debug("Updating log attributes", instrument_code=instrument_code) + + if self.is_code_in_data(instrument_code): + if ignore_duplication: + pass + else: + raise self.log.warning( + "There is already %s in the data, you have to delete it first" + % instrument_code + ) + + self._add_roll_parameters_without_checking_for_existing_entry( + instrument_code, roll_parameters + ) + + self.log.info("Added roll parameters for instrument %s" % instrument_code) + + def is_code_in_data(self, instrument_code: str) -> bool: + if instrument_code in self.get_list_of_instruments(): + return True + else: + return False + + def _delete_roll_parameters_data_without_any_warning_be_careful( + self, instrument_code: str + ): + raise NotImplementedError(USE_CHILD_CLASS_ROLL_PARAMS_ERROR) + + def _add_roll_parameters_without_checking_for_existing_entry( + self, instrument_code: str, roll_parameters: rollParameters + ): + raise NotImplementedError(USE_CHILD_CLASS_ROLL_PARAMS_ERROR) + + def get_list_of_instruments(self) -> list: + raise NotImplementedError(USE_CHILD_CLASS_ROLL_PARAMS_ERROR) + + def _get_roll_parameters_without_checking( + self, instrument_code: str + ) -> rollParameters: + raise NotImplementedError(USE_CHILD_CLASS_ROLL_PARAMS_ERROR) + + +__all__ = ["rollParametersData", "rollParametersMissing"] diff --git a/src/quantlib_st/sysdata/futures/spread_costs.py b/src/quantlib_st/sysdata/futures/spread_costs.py new file mode 100644 index 0000000..86f5dbc --- /dev/null +++ b/src/quantlib_st/sysdata/futures/spread_costs.py @@ -0,0 +1,44 @@ +## Expected slippage eg half bid-ask spread +## Used to be in instrument config, now separate +import pandas as pd +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.logging.logger import get_logger + + +class spreadCostData(baseData): + def __init__(self, log=get_logger("SpreadCosts")): + super().__init__(log=log) + + def delete_spread_cost(self, instrument_code: str): + raise NotImplementedError + + def update_spread_cost(self, instrument_code: str, spread_cost: float): + raise NotImplementedError + + def get_list_of_instruments(self) -> list: + raise NotImplementedError + + def get_spread_cost(self, instrument_code: str) -> float: + raise NotImplementedError + + def get_spread_costs_as_series(self) -> pd.Series: + raise NotImplementedError + + def _get_spread_cost_if_series_provided(self, instrument_code: str) -> float: + all_data = self.get_spread_costs_as_series() + costs = all_data.get(instrument_code, 0) + return costs + + def _get_spread_costs_as_series_if_individual_spreads_provided(self) -> pd.Series: + all_instruments = self.get_list_of_instruments() + spread_costs_as_series = pd.Series( + [ + self.get_spread_cost(instrument_code) + for instrument_code in all_instruments + ], + index=all_instruments, + ) + return spread_costs_as_series + + +__all__ = ["spreadCostData"] diff --git a/src/quantlib_st/sysdata/fx/__init__.py b/src/quantlib_st/sysdata/fx/__init__.py new file mode 100644 index 0000000..9d9bce9 --- /dev/null +++ b/src/quantlib_st/sysdata/fx/__init__.py @@ -0,0 +1,3 @@ +__all__ = ["fxPricesData"] + +from quantlib_st.sysdata.fx.spotfx import fxPricesData diff --git a/src/quantlib_st/sysdata/fx/spotfx.py b/src/quantlib_st/sysdata/fx/spotfx.py new file mode 100644 index 0000000..4e09660 --- /dev/null +++ b/src/quantlib_st/sysdata/fx/spotfx.py @@ -0,0 +1,242 @@ +""" +Spot fx prices +""" + +import numpy as np +import pandas as pd +import datetime + +from quantlib_st.sysdata.base_data import baseData +from quantlib_st.core.pandas.merge_data_keeping_past_data import SPIKE_IN_DATA + +from quantlib_st.logging.logger import get_logger +from quantlib_st.logging.adaptor import CURRENCY_CODE_LOG_LABEL +from quantlib_st.objects.spot_fx_prices import ( + fxPrices, + get_fx_tuple_from_code, + DEFAULT_CURRENCY, +) + +DEFAULT_DATES = pd.date_range( + start=datetime.datetime(1970, 1, 1), freq="B", end=datetime.datetime.now() +) +DEFAULT_RATE_SERIES = pd.Series(np.full(len(DEFAULT_DATES), 1.0), index=DEFAULT_DATES) + +USE_CHILD_CLASS_ERROR = "You need to use a child class of fxPricesData" + + +class fxPricesData(baseData): + """ + Read and write data class to get fx prices + + We'd inherit from this class for a specific implementation + """ + + def __repr__(self): + return USE_CHILD_CLASS_ERROR + + def keys(self): + return self.get_list_of_fxcodes() + + def __getitem__(self, code): + return self.get_fx_prices(code) + + def get_fx_prices(self, fx_code: str) -> fxPrices: + """ + Get a historical series of FX prices + + :param fx_code: currency code, in the form EURUSD + :return: fxData object + """ + try: + currency1, currency2 = get_fx_tuple_from_code(fx_code) + except Exception: + self.log.warning("Currency can't be located for %s using USD" % fx_code) + return DEFAULT_RATE_SERIES + + if currency1 == currency2: + # Trivial, just a bunch of 1's + fx_data = DEFAULT_RATE_SERIES + + elif currency2 == DEFAULT_CURRENCY: + # We ought to have data + fx_data = self._get_standard_fx_prices(fx_code) + + elif currency1 == DEFAULT_CURRENCY: + # inversion + fx_data = self._get_fx_prices_for_inversion(fx_code) + + else: + # Try a cross rate + fx_data = self._get_fx_cross(fx_code) + + return fx_data + + def _get_standard_fx_prices(self, fx_code: str) -> fxPrices: + currency1, currency2 = get_fx_tuple_from_code(fx_code) + assert currency2 == DEFAULT_CURRENCY + fx_data = self._get_fx_prices_vs_default(currency1) + + return fx_data + + def _get_fx_prices_for_inversion(self, fx_code: str) -> fxPrices: + """ + Get a historical series of FX prices, must be USDXXX + """ + currency1, currency2 = get_fx_tuple_from_code(fx_code) + assert currency1 == DEFAULT_CURRENCY + + raw_fx_data = self._get_fx_prices_vs_default(currency2) + if raw_fx_data.empty: + self.log.warning( + "Data for %s is missing, needed to calculate %s" + % (currency2 + DEFAULT_CURRENCY, DEFAULT_CURRENCY + currency2), + **{CURRENCY_CODE_LOG_LABEL: fx_code, "method": "temp"}, + ) + return raw_fx_data + + inverted_fx_data = 1.0 / raw_fx_data + + return inverted_fx_data + + def _get_fx_cross(self, fx_code: str) -> fxPrices: + """ + Get a currency cross rate XXXYYY, eg not XXXUSD or USDXXX or XXXXXX + """ + currency1, currency2 = get_fx_tuple_from_code(fx_code) + currency1_vs_default = self._get_fx_prices_vs_default(currency1) + currency2_vs_default = self._get_fx_prices_vs_default(currency2) + + if currency1_vs_default.empty or currency2_vs_default.empty: + return fxPrices.create_empty() + + (aligned_c1, aligned_c2) = currency1_vs_default.align( + currency2_vs_default, join="outer" + ) + + fx_rate_series = aligned_c1.ffill() / aligned_c2.ffill() + + return fx_rate_series + + def _get_fx_prices_vs_default(self, currency1: str) -> fxPrices: + """ + Get a historical series of FX prices, must be XXXUSD + """ + code = currency1 + DEFAULT_CURRENCY + fx_data = self._get_fx_prices(code) + + return fx_data + + def _get_fx_prices(self, code: str) -> fxPrices: + if not self.is_code_in_data(code): + self.log.warning( + "Currency %s is missing from list of FX data" % code, + **{CURRENCY_CODE_LOG_LABEL: code, "method": "temp"}, + ) + + return fxPrices.create_empty() + + data = self._get_fx_prices_without_checking(code) + + return data + + def delete_fx_prices(self, code: str, are_you_sure=False): + log_attrs = {CURRENCY_CODE_LOG_LABEL: code, "method": "temp"} + + if are_you_sure: + if self.is_code_in_data(code): + self._delete_fx_prices_without_any_warning_be_careful(code) + self.log.info( + "Deleted fx price data for %s" % code, + **log_attrs, + ) + + else: + # doesn't exist anyway + self.log.warning( + "Tried to delete non existent fx prices for %s" % code, + **log_attrs, + ) + else: + self.log.warning( + "You need to call delete_fx_prices with a flag to be sure", + **log_attrs, + ) + + def is_code_in_data(self, code: str) -> bool: + if code in self.get_list_of_fxcodes(): + return True + else: + return False + + def add_fx_prices( + self, code: str, fx_price_data: fxPrices, ignore_duplication: bool = False + ): + log_attrs = {CURRENCY_CODE_LOG_LABEL: code, "method": "temp"} + if self.is_code_in_data(code): + if ignore_duplication: + pass + else: + self.log.warning( + "There is already %s in the data, you have to delete it first, or " + "set ignore_duplication=True, or use update_fx_prices" % code, + **log_attrs, + ) + return None + + self._add_fx_prices_without_checking_for_existing_entry(code, fx_price_data) + self.log.info("Added fx data for code %s" % code, **log_attrs) + + def update_fx_prices( + self, code: str, new_fx_prices: fxPrices, check_for_spike=True + ) -> int: + """ + Checks existing data, adds any new data with a timestamp greater than the existing data + """ + log_attrs = {CURRENCY_CODE_LOG_LABEL: code, "method": "temp"} + + old_fx_prices = self.get_fx_prices(code) + merged_fx_prices = old_fx_prices.add_rows_to_existing_data( + new_fx_prices, check_for_spike=check_for_spike + ) + + if merged_fx_prices is SPIKE_IN_DATA: + return SPIKE_IN_DATA + + rows_added = len(merged_fx_prices) - len(old_fx_prices) + + if rows_added == 0: + if len(old_fx_prices) == 0: + self.log.debug("No new or old prices for %s" % code, **log_attrs) + + else: + self.log.debug( + "No additional data since %s for %s" + % (str(old_fx_prices.index[-1]), code), + **log_attrs, + ) + return 0 + + self.add_fx_prices(code, merged_fx_prices, ignore_duplication=True) + + self.log.debug( + "Added %d additional rows for %s" % (rows_added, code), + **log_attrs, + ) + + return rows_added + + def get_list_of_fxcodes(self): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _add_fx_prices_without_checking_for_existing_entry(self, code, fx_price_data): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _delete_fx_prices_without_any_warning_be_careful(self, code): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + def _get_fx_prices_without_checking(self, code): + raise NotImplementedError(USE_CHILD_CLASS_ERROR) + + +__all__ = ["fxPricesData", "DEFAULT_RATE_SERIES", "DEFAULT_DATES"] diff --git a/src/quantlib_st/sysdata/production_config.py b/src/quantlib_st/sysdata/production_config.py new file mode 100644 index 0000000..fdab3ec --- /dev/null +++ b/src/quantlib_st/sysdata/production_config.py @@ -0,0 +1,8 @@ +from quantlib_st.config.configdata import Config + + +def get_production_config() -> Config: + ## For use outside of backtesting part of code + ## Just returns a default config, which will include the private.yaml stuff + + return Config.default_config() diff --git a/src/quantlib_st/sysdata/sim/__init__.py b/src/quantlib_st/sysdata/sim/__init__.py new file mode 100644 index 0000000..956d319 --- /dev/null +++ b/src/quantlib_st/sysdata/sim/__init__.py @@ -0,0 +1,15 @@ +from quantlib_st.sysdata.sim.base_data import baseData +from quantlib_st.sysdata.sim.sim_data import simData +from quantlib_st.sysdata.sim.futures_sim_data import futuresSimData +from quantlib_st.sysdata.sim.futures_sim_data_with_data_blob import ( + genericBlobUsingFuturesSimData, +) +from quantlib_st.sysdata.sim.csv_futures_sim_test_data import CsvFuturesSimTestData + +__all__ = [ + "baseData", + "simData", + "futuresSimData", + "genericBlobUsingFuturesSimData", + "CsvFuturesSimTestData", +] diff --git a/src/quantlib_st/sysdata/sim/base_data.py b/src/quantlib_st/sysdata/sim/base_data.py new file mode 100644 index 0000000..8e2d31b --- /dev/null +++ b/src/quantlib_st/sysdata/sim/base_data.py @@ -0,0 +1,3 @@ +from quantlib_st.sysdata.base_data import baseData + +__all__ = ["baseData"] diff --git a/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py b/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py new file mode 100644 index 0000000..c28e737 --- /dev/null +++ b/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py @@ -0,0 +1,113 @@ +import pandas as pd +from datetime import datetime + +from quantlib_st.sysdata.csv.csv_multiple_prices import csvFuturesMultiplePricesData +from quantlib_st.sysdata.csv.csv_adjusted_prices import csvFuturesAdjustedPricesData +from quantlib_st.sysdata.csv.csv_spot_fx import csvFxPricesData +from quantlib_st.sysdata.csv.csv_instrument_data import csvFuturesInstrumentData +from quantlib_st.sysdata.csv.csv_roll_parameters import csvRollParametersData + +from quantlib_st.sysdata.data_blob import dataBlob +from quantlib_st.objects.spot_fx_prices import fxPrices +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices +from quantlib_st.objects.multiple_prices import futuresMultiplePrices + +from quantlib_st.sysdata.sim.futures_sim_data_with_data_blob import ( + genericBlobUsingFuturesSimData, +) +from quantlib_st.logging.logger import get_logger +from quantlib_st.core.dateutils import ARBITRARY_START + + +class CsvFuturesSimTestData(genericBlobUsingFuturesSimData): + """ + Specialisation of futuresSimData that allows start and end dates to be configured. + Useful for unit tests, so that new data added to the CSV price files doesn't mess with + assertions, or if a test is needed at a certain date/time or period. + """ + + DATE_FORMAT = "%Y-%m-%d %H:%M:%S" + + # Latest in CSV at time of writing + DEFAULT_END_DATE = datetime.strptime("2021-03-08 20:00:00", DATE_FORMAT) + + def __init__( + self, start_date=None, end_date=None, log=get_logger("csvFuturesSimTestData") + ): + data = dataBlob( + log=log, + csv_data_paths=dict( + csvFuturesAdjustedPricesData="data.test.adjusted_prices_csv", + csvFuturesInstrumentData="data.test.csvconfig", + ), + class_list=[ + csvFuturesAdjustedPricesData, + csvFuturesMultiplePricesData, + csvFuturesInstrumentData, + csvFxPricesData, + csvRollParametersData, + ], + ) + + super().__init__(data=data) + + if start_date is not None: + self._start_date = start_date + else: + self._start_date = ARBITRARY_START + + if end_date is not None: + self._end_date = end_date + else: + self._end_date = self.DEFAULT_END_DATE + + def __repr__(self): + return ( + f"csvFuturesSimTestData with {self.get_instrument_list()} instruments, " + f"start date {self.start_date.strftime(self.DATE_FORMAT)}, " + f"end date {self.end_date.strftime(self.DATE_FORMAT)}" + ) + + @property + def start_date(self): + return self._start_date + + @property + def end_date(self): + return self._end_date + + def get_backadjusted_futures_price( + self, instrument_code: str + ) -> futuresAdjustedPrices: + data = super().db_futures_adjusted_prices_data.get_adjusted_prices( + instrument_code + ) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted + + def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: + data = super().get_multiple_prices(instrument_code) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted + + def get_fx_for_instrument( + self, instrument_code: str, base_currency: str + ) -> fxPrices: + data = super().get_fx_for_instrument(instrument_code, base_currency) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted + + def daily_prices(self, instrument_code: str) -> pd.Series: + data = super().daily_prices(instrument_code) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted + + def get_instrument_raw_carry_data(self, instrument_code: str) -> pd.DataFrame: + data = super().get_instrument_raw_carry_data(instrument_code) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted + + def get_current_and_forward_price_data(self, instrument_code: str) -> pd.DataFrame: + data = super().get_current_and_forward_price_data(instrument_code) + date_adjusted = data[self.start_date : self.end_date] + return date_adjusted diff --git a/src/quantlib_st/sysdata/sim/futures_sim_data.py b/src/quantlib_st/sysdata/sim/futures_sim_data.py new file mode 100644 index 0000000..85acca2 --- /dev/null +++ b/src/quantlib_st/sysdata/sim/futures_sim_data.py @@ -0,0 +1,266 @@ +import pandas as pd + +from quantlib_st.core.exceptions import missingInstrument +from quantlib_st.sysdata.sim.sim_data import simData + +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices +from quantlib_st.objects.instruments import ( + assetClassesAndInstruments, + instrumentCosts, + futuresInstrumentWithMetaData, +) +from quantlib_st.objects.multiple_prices import futuresMultiplePrices +from quantlib_st.objects.dict_of_named_futures_per_contract_prices import ( + price_name, + carry_name, + forward_name, + contract_name_from_column_name, +) +from quantlib_st.objects.rolls import rollParameters + +price_contract_name = contract_name_from_column_name(price_name) +carry_contract_name = contract_name_from_column_name(carry_name) +forward_contract_name = contract_name_from_column_name(forward_name) + + +class futuresSimData(simData): + def __repr__(self): + return "futuresSimData object with %d instruments" % len( + self.get_instrument_list() + ) + + def all_asset_classes(self) -> list: + asset_class_data = self.get_instrument_asset_classes() + + return asset_class_data.all_asset_classes() + + def all_instruments_in_asset_class(self, asset_class: str) -> list: + """ + Return all the instruments in a given asset class + + :param asset_class: str + :return: list of instrument codes + """ + asset_class_data = self.get_instrument_asset_classes() + list_of_instrument_codes = self.get_instrument_list() + asset_class_instrument_list = asset_class_data.all_instruments_in_asset_class( + asset_class, must_be_in=list_of_instrument_codes + ) + + return asset_class_instrument_list + + def asset_class_for_instrument(self, instrument_code: str) -> str: + """ + Which asset class is some instrument in? + + :param instrument_code: + :return: str + """ + + asset_class_data = self.get_instrument_asset_classes() + asset_class = asset_class_data[instrument_code] + + return asset_class + + def length_of_history_in_days_for_instrument(self, instrument_code: str) -> int: + return len(self.daily_prices(instrument_code)) + + def get_raw_price_from_start_date( + self, instrument_code: str, start_date + ) -> pd.Series: + """ + For futures the price is the backadjusted price + + :param instrument_code: + :return: price + """ + price = self.get_backadjusted_futures_price(instrument_code) + if len(price) == 0: + raise Exception("Instrument code %s has no data!" % instrument_code) + + return price[start_date:] + + def get_instrument_raw_carry_data(self, instrument_code: str) -> pd.DataFrame: + """ + Returns a pd. dataframe with the 4 columns PRICE, CARRY, PRICE_CONTRACT, CARRY_CONTRACT + + These are specifically needed for futures trading + + We'd inherit from this method for a specific data source + + :param instrument_code: instrument to get carry data for + :type instrument_code: str + + :returns: pd.DataFrame + + """ + + all_price_data = self.get_multiple_prices(instrument_code) + carry_data = all_price_data[ + [price_name, carry_name, price_contract_name, carry_contract_name] + ] + + return carry_data + + def get_current_and_forward_price_data(self, instrument_code: str) -> pd.DataFrame: + """ + Returns a pd. dataframe with the 4 columns PRICE, PRICE_CONTRACT, FORWARD_, FORWARD_CONTRACT + + These are required if we want to backadjust from scratch + + We'd inherit from this method for a specific data source + + :param instrument_code: instrument to get carry data for + :type instrument_code: str + + :returns: pd.DataFrame + + """ + + all_price_data = self.get_multiple_prices(instrument_code) + + return all_price_data[ + [price_name, forward_name, price_contract_name, forward_contract_name] + ] + + def get_rolls_per_year(self, instrument_code: str) -> int: + roll_parameters = self.get_roll_parameters(instrument_code) + rolls_per_year = roll_parameters.rolls_per_year_in_hold_cycle() + + return rolls_per_year + + def get_raw_cost_data(self, instrument_code: str) -> instrumentCosts: + """ + Gets cost data for an instrument + + Get cost data + + Execution slippage [half spread] price units + Commission (local currency) per block + Commission - percentage of value (0.01 is 1%) + Commission (local currency) per block + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: dict of floats + + """ + + try: + cost_data_object = self.get_instrument_object_with_meta_data( + instrument_code + ) + except missingInstrument: + self.log.warning( + "Cost data missing for %s will use zero costs" % instrument_code + ) + return instrumentCosts() + + spread_cost = self.get_spread_cost(instrument_code) + + instrument_meta_data = cost_data_object.meta_data + instrument_costs = instrumentCosts.from_meta_data_and_spread_cost( + instrument_meta_data, spread_cost=spread_cost + ) + + return instrument_costs + + def get_value_of_block_price_move(self, instrument_code: str) -> float: + """ + How much is a $1 move worth in value terms? + + :param instrument_code: instrument to get value for + :type instrument_code: str + + :returns: float + + """ + + instr_object = self.get_instrument_object_with_meta_data(instrument_code) + meta_data = instr_object.meta_data + block_move_value = meta_data.Pointsize + + return block_move_value + + def get_instrument_currency(self, instrument_code: str) -> str: + """ + What is the currency that this instrument is priced in? + + :param instrument_code: instrument to get value for + :type instrument_code: str + + :returns: str + + """ + instr_object = self.get_instrument_object_with_meta_data(instrument_code) + meta_data = instr_object.meta_data + currency = meta_data.Currency + + return currency + + def get_instrument_asset_classes(self) -> assetClassesAndInstruments: + """ + + :return: A pd.Series, row names are instruments, content is asset class + """ + + raise NotImplementedError() + + def get_spread_cost(self, instrument_code: str) -> float: + raise NotImplementedError + + def get_backadjusted_futures_price( + self, instrument_code: str + ) -> futuresAdjustedPrices: + """ + + :param instrument_code: + :return: + """ + + raise NotImplementedError() + + def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: + start_date = self.start_date_for_data() + + return self.get_multiple_prices_from_start_date( + instrument_code, start_date=start_date + ) + + def get_multiple_prices_from_start_date( + self, instrument_code: str, start_date + ) -> futuresMultiplePrices: + raise NotImplementedError() + + def get_instrument_meta_data( + self, instrument_code + ) -> futuresInstrumentWithMetaData: + """ + Get a futures instrument where the meta data is cost data + + :returns: futuresInstrument + + """ + raise NotImplementedError() + + def get_roll_parameters(self, instrument_code: str) -> rollParameters: + raise NotImplementedError + + def get_instrument_object_with_meta_data( + self, instrument_code: str + ) -> futuresInstrumentWithMetaData: + """ + Get data about an instrument, as a futuresInstrument + + :param instrument_code: + :return: futuresInstrument object + """ + + raise NotImplementedError() + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/sysdata/sim/futures_sim_data_with_data_blob.py b/src/quantlib_st/sysdata/sim/futures_sim_data_with_data_blob.py new file mode 100644 index 0000000..3754d65 --- /dev/null +++ b/src/quantlib_st/sysdata/sim/futures_sim_data_with_data_blob.py @@ -0,0 +1,130 @@ +from quantlib_st.sysdata.sim.futures_sim_data import futuresSimData + +from quantlib_st.sysdata.futures.adjusted_prices import futuresAdjustedPricesData +from quantlib_st.sysdata.fx.spotfx import fxPricesData +from quantlib_st.sysdata.futures.instruments import futuresInstrumentData +from quantlib_st.sysdata.futures.multiple_prices import futuresMultiplePricesData +from quantlib_st.sysdata.futures.rolls_parameters import rollParametersData +from quantlib_st.sysdata.futures.spread_costs import spreadCostData +from quantlib_st.sysdata.data_blob import dataBlob + +from quantlib_st.objects.instruments import ( + assetClassesAndInstruments, + futuresInstrumentWithMetaData, +) +from quantlib_st.core.exceptions import missingData +from quantlib_st.objects.spot_fx_prices import fxPrices +from quantlib_st.objects.adjusted_prices import futuresAdjustedPrices +from quantlib_st.objects.multiple_prices import futuresMultiplePrices +from quantlib_st.objects.rolls import rollParameters + + +class genericBlobUsingFuturesSimData(futuresSimData): + """ + dataBlob must have the appropriate classes added or it won't work + """ + + def __init__(self, data: dataBlob): + super().__init__(log=data.log) + self._data = data + + def get_instrument_list(self): + return self.db_futures_adjusted_prices_data.get_list_of_instruments() + + def _get_fx_data_from_start_date( + self, currency1: str, currency2: str, start_date + ) -> fxPrices: + fx_code = currency1 + currency2 + data = self.db_fx_prices_data.get_fx_prices(fx_code) + + data_after_start = data[start_date:] + + return data_after_start + + def get_instrument_asset_classes(self) -> assetClassesAndInstruments: + all_instrument_data = self.get_all_instrument_data_as_df() + asset_classes = all_instrument_data["AssetClass"] + asset_class_data = assetClassesAndInstruments.from_pd_series(asset_classes) + + return asset_class_data + + def get_all_instrument_data_as_df(self): + all_instrument_data = ( + self.db_futures_instrument_data.get_all_instrument_data_as_df() + ) + instrument_list = self.get_instrument_list() + all_instrument_data = all_instrument_data[ + all_instrument_data.index.isin(instrument_list) + ] + + return all_instrument_data + + def get_backadjusted_futures_price( + self, instrument_code: str + ) -> futuresAdjustedPrices: + data = self.db_futures_adjusted_prices_data.get_adjusted_prices(instrument_code) + + return data + + def get_multiple_prices_from_start_date( + self, instrument_code: str, start_date + ) -> futuresMultiplePrices: + data = self.db_futures_multiple_prices_data.get_multiple_prices(instrument_code) + if len(data) == 0: + raise missingData( + "Data for %s not found! Remove from instrument list, or add to config.ignore_instruments" + % instrument_code + ) + + return data[start_date:] + + def get_instrument_meta_data( + self, instrument_code: str + ) -> futuresInstrumentWithMetaData: + ## cost and other meta data stored in the same place + return self.get_instrument_object_with_meta_data(instrument_code) + + def get_instrument_object_with_meta_data( + self, instrument_code: str + ) -> futuresInstrumentWithMetaData: + instrument = self.db_futures_instrument_data.get_instrument_data( + instrument_code + ) + + return instrument + + def get_roll_parameters(self, instrument_code: str) -> rollParameters: + roll_parameters = self.db_roll_parameters.get_roll_parameters(instrument_code) + + return roll_parameters + + def get_spread_cost(self, instrument_code: str) -> float: + return self.db_spread_cost_data.get_spread_cost(instrument_code) + + @property + def data(self): + return self._data + + @property + def db_fx_prices_data(self) -> fxPricesData: + return self.data.db_fx_prices + + @property + def db_futures_adjusted_prices_data(self) -> futuresAdjustedPricesData: + return self.data.db_futures_adjusted_prices + + @property + def db_futures_instrument_data(self) -> futuresInstrumentData: + return self.data.db_futures_instrument + + @property + def db_futures_multiple_prices_data(self) -> futuresMultiplePricesData: + return self.data.db_futures_multiple_prices + + @property + def db_roll_parameters(self) -> rollParametersData: + return self.data.db_roll_parameters + + @property + def db_spread_cost_data(self) -> spreadCostData: + return self.data.db_spread_cost diff --git a/src/quantlib_st/sysdata/sim/sim_data.py b/src/quantlib_st/sysdata/sim/sim_data.py new file mode 100644 index 0000000..2ce0d20 --- /dev/null +++ b/src/quantlib_st/sysdata/sim/sim_data.py @@ -0,0 +1,325 @@ +import pandas as pd +import datetime + +from quantlib_st.core.exceptions import missingData +from quantlib_st.core.objects import get_methods +from quantlib_st.core.dateutils import ARBITRARY_START +from quantlib_st.core.pandas.frequency import ( + get_intraday_pdf_at_frequency, + resample_prices_to_business_day_index, +) +from quantlib_st.sysdata.sim.base_data import baseData +from quantlib_st.logging.logger import * +from quantlib_st.objects.spot_fx_prices import fxPrices +from quantlib_st.objects.instruments import instrumentCosts + + +class simData(baseData): + """ + Core data object - Base class for simulation + + simData objects are used to get a collection of data + + The bare simData class isn't much good and holds only price and fx data + + Normally we'd inherit from this for specific asset classes (eg carry data for futures), and then for a + specific source of data (eg csv files, databases, ...) + + The inheritance is: + + -> asset class specific eg futuresdata.FuturesData + -> source specific eg legacy.csvFuturesSimData + + We can plug in different sources if desired + + """ + + def __repr__(self): # type: ignore + return "simData object with %d instruments" % len(self.get_instrument_list()) + + def __getitem__(self, keyname: str): + """ + convenience method to get the price, make it look like a dict + + :param keyname: instrument to get prices for + :type keyname: str + + :returns: pd.DataFrame + """ + price = self.get_raw_price(keyname) + + return price + + def keys(self) -> list: + """ + list of instruments in this data set + + :returns: list of str + + >>> data=simData() + >>> data.keys() + [] + """ + return self.get_instrument_list() + + def system_init(self, base_system: "System"): + """ + This is run when added to a base system + + :param base_system + :return: nothing + """ + + # inherit the log + self._log = get_logger("base_system", {STAGE_LOG_LABEL: "data"}) + self._parent = base_system + + @property + def parent(self): + return self._parent + + def start_date_for_data(self): + try: + start_date = getattr(self, "_start_date_for_data_from_config") + except AttributeError: + start_date = self._get_and_set_start_date_for_data_from_config() + return start_date + + def _get_and_set_start_date_for_data_from_config(self) -> datetime: + start_date = _resolve_start_date(self) + self._start_date_for_data_from_config = start_date + + return start_date + + def methods(self) -> list: + # included for user API computability with SystemStage + return get_methods(self) + + def daily_prices(self, instrument_code: str) -> pd.Series: + """ + Gets daily prices + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.Series + + """ + return self._get_daily_prices_for_directional_instrument(instrument_code) + + def _get_daily_prices_for_directional_instrument( + self, instrument_code: str + ) -> pd.Series: + """ + Gets daily prices + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.Series + + """ + instrprice = self.get_raw_price(instrument_code) + if len(instrprice) == 0: + raise Exception("No adjusted daily prices for %s" % instrument_code) + dailyprice = resample_prices_to_business_day_index(instrprice) + + return dailyprice + + def hourly_prices(self, instrument_code: str) -> pd.Series: + return self._get_hourly_prices_for_directional_instrument(instrument_code) + + def _get_hourly_prices_for_directional_instrument( + self, instrument_code: str + ) -> pd.Series: + instrprice = self.get_raw_price(instrument_code) + if len(instrprice) == 0: + raise Exception("No adjusted hourly prices for %s" % instrument_code) + + # ignore type warning - series or data frame both work + hourly_prices = get_intraday_pdf_at_frequency(instrprice) + + return hourly_prices + + def get_fx_for_instrument( + self, instrument_code: str, base_currency: str + ) -> fxPrices: + """ + Get the FX rate between the FX rate for the instrument and the base (account) currency + + :param instrument_code: instrument to value for + :type instrument_code: str + + :param base_currency: instrument to value for + :type instrument_code: str + + :returns: Tx1 pd.Series + + >>> data=simData() + >>> data.get_fx_for_instrument("wibble", "USD").tail(5) + 2040-12-04 1.0 + 2040-12-05 1.0 + 2040-12-06 1.0 + 2040-12-07 1.0 + 2040-12-10 1.0 + Freq: B, dtype: float64 + """ + + instrument_currency = self.get_instrument_currency(instrument_code) + fx_rate_series = self._get_fx_data(instrument_currency, base_currency) + + return fx_rate_series + + def get_raw_price(self, instrument_code: str) -> pd.Series: + """ + Default method to get instrument price at 'natural' frequency + + Will usually be overridden when inherited with specific data source + + :param instrument_code: instrument to get prices for + :type instrument_code: str + + :returns: pd.Series + + """ + start_date = self.start_date_for_data() + + return self.get_raw_price_from_start_date( + instrument_code, start_date=start_date + ) + + def get_raw_price_from_start_date( + self, instrument_code: str, start_date: datetime.datetime + ) -> pd.Series: + """ + Default method to get instrument price at 'natural' frequency + + Will usually be overridden when inherited with specific data source + + :param instrument_code: instrument to get prices for + :type instrument_code: str + + :returns: pd.Series + + """ + raise NotImplementedError("Need to inherit from simData") + + def get_instrument_list(self) -> list: + """ + list of instruments in this data set + + :returns: list of str + + """ + raise NotImplementedError("Need to inherit from simData") + + def get_value_of_block_price_move(self, instrument_code: str) -> float: + """ + How much does a $1 (or whatever) move in the price of an instrument block affect its value? + eg 100.0 for 100 shares + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: float + + """ + self.log.warning( + "Using base method of simData, value of block price move may not be accurate" + ) + + return 1.0 + + def get_raw_cost_data(self, instrument_code: str) -> instrumentCosts: + """ + Get cost data + + Execution slippage [half spread] price units + Commission (local currency) per block + Commission - percentage of value (0.01 is 1%) + Commission (local currency) per block + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: dict of floats + + """ + + self.log.warning("Using base method of simData, using zero costs") + + return instrumentCosts() + + def get_instrument_currency(self, instrument_code: str) -> str: + """ + Get the currency for a particular instrument + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: str + + """ + + raise NotImplementedError( + "Need to inherit from base class for specific data source" + ) + + def _get_fx_data(self, currency1: str, currency2: str) -> fxPrices: + """ + Get the FX rate currency1/currency2 between two currencies + Or return None if not available + + (Normally we'd override this with a specific source) + + + """ + start_date = self.start_date_for_data() + + return self._get_fx_data_from_start_date( + currency1, currency2, start_date=start_date + ) + + def _get_fx_data_from_start_date( + self, currency1: str, currency2: str, start_date: datetime.datetime + ) -> fxPrices: + """ + Get the FX rate currency1/currency2 between two currencies + Or return None if not available + + (Normally we'd override this with a specific source) + + + """ + raise NotImplementedError("Need to inherit for a specific data source") + + +def _resolve_start_date(sim_data: simData): + try: + config = _resolve_config(sim_data) + except missingData: + start_date = ARBITRARY_START + else: + start_date = getattr(config, "start_date", ARBITRARY_START) + + if isinstance(start_date, datetime.date): + # yaml parses unquoted date like 2000-01-01 to datetime.date + start_date = datetime.datetime.combine(start_date, datetime.datetime.min.time()) + elif not isinstance(start_date, datetime.datetime): + try: + start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d") + except: + raise Exception( + "Parameter start_date %s in config file does not conform to pattern 2020-03-19" + % str(start_date) + ) + + return start_date + + +def _resolve_config(sim_data: simData): + try: + config = sim_data.parent.config + return config + except AttributeError: + raise missingData diff --git a/src/quantlib_st/systems/accounts/account_buffering_subsystem.py b/src/quantlib_st/systems/accounts/account_buffering_subsystem.py new file mode 100644 index 0000000..395904a --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_buffering_subsystem.py @@ -0,0 +1,208 @@ +import numpy as np +import pandas as pd + +from quantlib_st.core.pandas.strategy_functions import turnover +from quantlib_st.systems.system_cache import diagnostic + +from quantlib_st.systems.accounts.account_costs import accountCosts + + +class accountBufferingSubSystemLevel(accountCosts): + @diagnostic() + def subsystem_turnover(self, instrument_code: str) -> float: + positions = self.get_subsystem_position(instrument_code) + + average_position_for_turnover = self.get_average_position_at_subsystem_level( + instrument_code + ) + + subsystem_turnover = turnover(positions, average_position_for_turnover) + + return subsystem_turnover + + @diagnostic() + def get_buffered_subsystem_position( + self, instrument_code: str, roundpositions: bool = True + ) -> pd.Series: + """ + Get the buffered position + + :param instrument_code: instrument to get + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: Tx1 pd.DataFrame + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.get_buffered_position("EDOLLAR").tail(3) + position + 2015-12-09 1 + 2015-12-10 1 + 2015-12-11 1 + """ + + optimal_position = self.get_subsystem_position(instrument_code) + + buffer_method = self.config.get_element_or_default("buffer_method", "none") + if buffer_method == "none": + if roundpositions: + return optimal_position.round() + else: + return optimal_position + + pos_buffers = self.get_buffers_for_subsystem_position(instrument_code) + + buffered_position = ( + self._get_buffered_subsystem_position_given_optimal_position_and_buffers( + optimal_position=optimal_position, + pos_buffers=pos_buffers, + roundpositions=roundpositions, + ) + ) + + return buffered_position + + def _get_buffered_subsystem_position_given_optimal_position_and_buffers( + self, + optimal_position: pd.Series, + pos_buffers: pd.DataFrame, + roundpositions: bool = True, + ) -> pd.Series: + self.log.debug("Calculating buffered subsystem positions") + trade_to_edge = self.config.buffer_trade_to_edge # type: ignore + + buffered_position = apply_buffer( + optimal_position, + pos_buffers, + trade_to_edge=trade_to_edge, + roundpositions=roundpositions, + ) + + return buffered_position + + @diagnostic() + def get_buffers_for_subsystem_position(self, instrument_code: str) -> pd.DataFrame: + """ + Get the buffered position from a previous module + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :returns: Tx2 pd.DataFrame: columns top_pos, bot_pos + + KEY INPUT + """ + + return self.parent.positionSize.get_buffers_for_subsystem_position( # type: ignore + instrument_code + ) + + +def apply_buffer( + optimal_position: pd.Series, + pos_buffers: pd.DataFrame, + trade_to_edge: bool = False, + roundpositions: bool = False, +) -> pd.Series: + """ + Apply a buffer to a position + + If position is outside the buffer, we either trade to the edge of the + buffer, or to the optimal + + If we're rounding positions, then we floor and ceiling the buffers. + + :param optimal_position: optimal position + :type optimal_position: pd.Series + + :param pos_buffers: + :type pos_buffers: Tx2 pd.dataframe, top_pos and bot_pos + + :param trade_to_edge: Trade to the edge (True) or the optimal (False) + :type trade_to_edge: bool + + :param roundpositions: Produce rounded positions + :type roundpositions: bool + + :returns: pd.Series + """ + + pos_buffers = pos_buffers.ffill() + use_optimal_position = optimal_position.ffill() + + top_pos = pos_buffers.top_pos + bot_pos = pos_buffers.bot_pos + + if roundpositions: + use_optimal_position = use_optimal_position.round() + top_pos = top_pos.round() + bot_pos = bot_pos.round() + + current_position = use_optimal_position.values[0] + if np.isnan(current_position): + current_position = 0.0 + + buffered_position_list = [current_position] + + for idx in range(len(optimal_position.index))[1:]: + current_position = apply_buffer_single_period( + current_position, + float(use_optimal_position.values[idx]), + float(top_pos.values[idx]), + float(bot_pos.values[idx]), + trade_to_edge=trade_to_edge, + ) + buffered_position_list.append(current_position) + + buffered_position = pd.Series(buffered_position_list, index=optimal_position.index) + + return buffered_position + + +def apply_buffer_single_period( + last_position, optimal_position, top_pos, bot_pos, trade_to_edge +): + """ + Apply a buffer to a position, single period + + If position is outside the buffer, we either trade to the edge of the + buffer, or to the optimal + + :param last_position: last position we had + :type last_position: float + + :param optimal_position: ideal position + :type optimal_position: float + + :param top_pos: top of buffer + :type top_pos: float + + :param bot_pos: bottom of buffer + :type bot_pos: float + + :param trade_to_edge: Trade to the edge (True) or the optimal (False) + :type trade_to_edge: bool + + :returns: float + """ + + if np.isnan(top_pos) or np.isnan(bot_pos) or np.isnan(optimal_position): + return last_position + + if last_position > top_pos: + if trade_to_edge: + return top_pos + else: + return optimal_position + elif last_position < bot_pos: + if trade_to_edge: + return bot_pos + else: + return optimal_position + else: + return last_position diff --git a/src/quantlib_st/systems/accounts/account_buffering_system.py b/src/quantlib_st/systems/accounts/account_buffering_system.py new file mode 100644 index 0000000..2fa6384 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_buffering_system.py @@ -0,0 +1,116 @@ +import pandas as pd + +from quantlib_st.systems.accounts.account_buffering_subsystem import apply_buffer +from quantlib_st.core.pandas.strategy_functions import turnover +from quantlib_st.systems.system_cache import diagnostic + +from quantlib_st.systems.accounts.account_inputs import accountInputs + + +class accountBufferingSystemLevel(accountInputs): + @diagnostic() + def instrument_turnover( + self, instrument_code: str, roundpositions: bool = True + ) -> float: + """ + Get the annualised turnover for an instrument + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :returns: float + + + """ + average_position_for_turnover = ( + self.get_average_position_for_instrument_at_portfolio_level(instrument_code) + ) + + positions = self.get_buffered_position( + instrument_code, roundpositions=roundpositions + ) + + return turnover(positions, average_position_for_turnover) + + @diagnostic() + def get_buffers_for_position(self, instrument_code: str) -> pd.DataFrame: + """ + Get the buffered position from a previous module + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :returns: Tx2 pd.DataFrame: columns top_pos, bot_pos + + KEY INPUT + """ + + return self.parent.portfolio.get_buffers_for_position(instrument_code) # type: ignore + + @diagnostic() + def get_buffered_position( + self, instrument_code: str, roundpositions: bool = True + ) -> pd.Series: + """ + Get the buffered position + + :param instrument_code: instrument to get + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: Tx1 pd.DataFrame + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.get_buffered_position("EDOLLAR").tail(3) + position + 2015-12-09 1 + 2015-12-10 1 + 2015-12-11 1 + """ + + optimal_position = self.get_notional_position(instrument_code) + + buffer_method = self.config.get_element_or_default("buffer_method", "none") + if buffer_method == "none": + if roundpositions: + return optimal_position.round() + else: + return optimal_position + + pos_buffers = self.get_buffers_for_position(instrument_code) + + buffered_position = ( + self._get_buffered_position_given_optimal_position_and_buffers( + optimal_position=optimal_position, + pos_buffers=pos_buffers, + roundpositions=roundpositions, + ) + ) + + return buffered_position + + def _get_buffered_position_given_optimal_position_and_buffers( + self, + optimal_position: pd.Series, + pos_buffers: pd.DataFrame, + roundpositions: bool = True, + ) -> pd.Series: + self.log.debug("Calculating buffered positions") + trade_to_edge = self.config.buffer_trade_to_edge # type: ignore + + buffered_position = apply_buffer( + optimal_position, + pos_buffers, + trade_to_edge=trade_to_edge, + roundpositions=roundpositions, + ) + + return buffered_position diff --git a/src/quantlib_st/systems/accounts/account_costs.py b/src/quantlib_st/systems/accounts/account_costs.py new file mode 100644 index 0000000..35c0737 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_costs.py @@ -0,0 +1,366 @@ +import pandas as pd + +from quantlib_st.core.maths import calculate_weighted_average_with_nans +from quantlib_st.core.genutils import str2Bool +from quantlib_st.core.dateutils import ROOT_BDAYS_INYEAR +from quantlib_st.core.pandas.strategy_functions import turnover + +from quantlib_st.estimators.turnover import turnoverDataForTradingRule + +from quantlib_st.systems.accounts.account_inputs import accountInputs +from quantlib_st.systems.system_cache import diagnostic, input + + +class accountCosts(accountInputs): + @diagnostic() + def get_SR_cost_for_instrument_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> float: + """ + Get the SR cost for a forecast/rule combination + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :returns: float + + KEY OUTPUT + """ + ## Calculate holding and transaction separately, as the former could be pooled + transaction_cost = self.get_SR_transaction_cost_for_instrument_forecast( + instrument_code=instrument_code, rule_variation_name=rule_variation_name + ) + holding_cost = self.get_SR_holding_cost_only(instrument_code) + + return transaction_cost + holding_cost + + @diagnostic() + def get_SR_transaction_cost_for_instrument_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> float: + """ + Get the SR cost for a forecast/rule combination + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :returns: float + + KEY OUTPUT + """ + + use_pooled_costs = str2Bool( + self.config.forecast_cost_estimates["use_pooled_costs"] + ) + + if use_pooled_costs: + SR_cost = self._get_SR_transaction_costs_for_rule_with_pooled_costs( + instrument_code, rule_variation_name + ) + + else: + SR_cost = self._get_SR_transaction_cost_of_rule_for_individual_instrument( + instrument_code, rule_variation_name + ) + return SR_cost + + @input + def _get_SR_transaction_costs_for_rule_with_pooled_costs( + self, instrument_code: str, rule_variation_name: str + ) -> float: + instrument_code_list = self.has_same_rules_as_code(instrument_code) + SR_cost = self._get_SR_transaction_cost_instr_forecast_for_list( + instrument_code_list, rule_variation_name + ) + + return SR_cost + + @diagnostic() + def _get_SR_transaction_cost_instr_forecast_for_list( + self, instrument_code_list: list, rule_variation_name: str + ) -> float: + """ + Get the SR cost for a forecast/rule combination, averaged across multiple instruments + + :param instrument_code_list: instrument to get values for + :type instrument_code: str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :returns: float + + + """ + + list_of_SR_cost = [ + self._get_SR_transaction_cost_of_rule_for_individual_instrument( + instrument_code, rule_variation_name + ) + for instrument_code in instrument_code_list + ] + + # weight by length + cost_weightings = self._get_forecast_length_weighting_for_list_of_instruments( + instrument_code_list, rule_variation_name + ) + + weighted_SR_costs = [ + SR_cost * weight + for SR_cost, weight in zip(list_of_SR_cost, cost_weightings) + ] + + avg_SR_cost = sum(weighted_SR_costs) + + return avg_SR_cost + + @diagnostic() + def _get_forecast_length_weighting_for_list_of_instruments( + self, instrument_code_list: list, rule_variation_name: str + ) -> list: + forecast_lengths = [ + self._get_forecast_length_for_instrument_rule( + instrument_code, rule_variation_name + ) + for instrument_code in instrument_code_list + ] + total_length = float(sum(forecast_lengths)) + + weights = [ + forecast_length / total_length for forecast_length in forecast_lengths + ] + + return weights + + @diagnostic() + def _get_forecast_length_for_instrument_rule( + self, instrument_code: str, rule_variation_name: str + ) -> int: + forecast = self.get_capped_forecast(instrument_code, rule_variation_name) + return len(forecast) + + @diagnostic() + def _get_SR_transaction_cost_of_rule_for_individual_instrument( + self, instrument_code: str, rule_variation_name: str + ) -> float: + # note the turnover may still be pooled.. + turnover = self.forecast_turnover(instrument_code, rule_variation_name) + + # holding costs calculated elsewhere + SR_cost_trading = self.get_SR_trading_cost_only_given_turnover( + instrument_code, turnover + ) + + return SR_cost_trading + + @diagnostic() + def get_SR_cost_given_turnover( + self, instrument_code: str, turnover: float + ) -> float: + SR_cost_trading = self.get_SR_trading_cost_only_given_turnover( + instrument_code, turnover + ) + SR_cost_holding = self.get_SR_holding_cost_only(instrument_code) + SR_cost = SR_cost_holding + SR_cost_trading + + return SR_cost + + def get_SR_trading_cost_only_given_turnover( + self, instrument_code: str, turnover: float + ) -> float: + cost_per_trade = self.get_SR_cost_per_trade_for_instrument(instrument_code) + + SR_cost_trading = turnover * cost_per_trade + + return SR_cost_trading + + def get_SR_holding_cost_only(self, instrument_code: str) -> float: + cost_per_trade = self.get_SR_cost_per_trade_for_instrument(instrument_code) + hold_turnovers = self.get_rolls_per_year(instrument_code) * 2.0 + + ## Assumes no benefit from spread trades i.e. do two separate trades + SR_cost_holding = hold_turnovers * cost_per_trade + + return SR_cost_holding + + @diagnostic() + def get_turnover_for_forecast_combination( + self, codes_to_use: list, rule_variation_name: str + ) -> turnoverDataForTradingRule: + turnover_as_list = self._forecast_turnover_for_list_by_instrument( + codes_to_use, rule_variation_name=rule_variation_name + ) + turnover_as_dict = dict( + [ + (instrument_code, turnover) + for (instrument_code, turnover) in zip(codes_to_use, turnover_as_list) + ] + ) + + turnover_data_for_trading_rule = turnoverDataForTradingRule(turnover_as_dict) + + return turnover_data_for_trading_rule + + @diagnostic() + def forecast_turnover( + self, instrument_code: str, rule_variation_name: str + ) -> float: + use_pooled_turnover = str2Bool( + self.config.forecast_cost_estimates["use_pooled_turnover"] + ) + + if use_pooled_turnover: + turnover = self._forecast_turnover_pooled( + instrument_code, rule_variation_name + ) + else: + turnover = self._forecast_turnover_for_individual_instrument( + instrument_code, rule_variation_name + ) + + return turnover + + @diagnostic() + def _forecast_turnover_pooled( + self, instrument_code: str, rule_variation_name: str + ) -> float: + instrument_code_list = self.has_same_rules_as_code(instrument_code) + turnover_for_SR = self._forecast_turnover_for_list( + instrument_code_list, rule_variation_name=rule_variation_name + ) + + return turnover_for_SR + + @diagnostic() + def _forecast_turnover_for_list( + self, instrument_code_list: list, rule_variation_name: str + ) -> float: + """ + Get the average turnover for a rule, over instrument_code_list + + :param instrument_code_list: instruments to get values for + :type instrument_code_list: list of str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :returns: float + + """ + + turnovers = self._forecast_turnover_for_list_by_instrument( + codes_to_use=instrument_code_list, rule_variation_name=rule_variation_name + ) + + # weight by length + weights = self._get_forecast_length_weighting_for_list_of_instruments( + instrument_code_list, rule_variation_name + ) + + avg_turnover = calculate_weighted_average_with_nans(weights, turnovers) + + return avg_turnover + + @diagnostic() + def _forecast_turnover_for_list_by_instrument( + self, codes_to_use: list, rule_variation_name: str + ) -> list: + turnovers = [ + self._forecast_turnover_for_individual_instrument( + instrument_code, rule_variation_name + ) + for instrument_code in codes_to_use + ] + + return turnovers + + @diagnostic() + def _forecast_turnover_for_individual_instrument( + self, instrument_code: str, rule_variation_name: str + ) -> float: + forecast = self.get_capped_forecast(instrument_code, rule_variation_name) + + average_forecast_for_turnover = self.average_forecast() + + annual_turnover_for_forecast = turnover(forecast, average_forecast_for_turnover) + + return annual_turnover_for_forecast + + @diagnostic() + def get_SR_cost_per_trade_for_instrument(self, instrument_code: str) -> float: + """ + Get the vol normalised SR costs for an instrument + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: float + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.get_SR_cost_per_trade_for_instrument("EDOLLAR") + 0.0065584086244069775 + """ + + raw_costs = self.get_raw_cost_data(instrument_code) + block_price_multiplier = self.get_value_of_block_price_move(instrument_code) + notional_blocks_traded = 1 + average_price = self._recent_average_price(instrument_code) + ann_stdev_price_units = self._recent_average_annual_price_vol(instrument_code) + + SR_cost = raw_costs.calculate_sr_cost( + block_price_multiplier=block_price_multiplier, + ann_stdev_price_units=ann_stdev_price_units, + blocks_traded=notional_blocks_traded, + price=average_price, + ) + + return SR_cost + + @diagnostic() + def _recent_average_annual_price_vol(self, instrument_code: str) -> float: + average_vol = self._recent_average_daily_vol(instrument_code) + + avg_annual_vol = average_vol * ROOT_BDAYS_INYEAR + + return avg_annual_vol + + @diagnostic() + def _recent_average_daily_vol(self, instrument_code: str) -> float: + daily_vol = self.get_daily_returns_volatility(instrument_code) + start_date = self._date_one_year_before_end_of_price_index(instrument_code) + average_vol = float(daily_vol[start_date:].mean()) + + return average_vol + + @diagnostic() + def _recent_average_price(self, instrument_code: str) -> float: + daily_price = self.get_daily_prices(instrument_code) + start_date = self._date_one_year_before_end_of_price_index(instrument_code) + average_price = float(daily_price[start_date:].mean()) + + return average_price + + @diagnostic() + def _date_one_year_before_end_of_price_index(self, instrument_code: str): + daily_price = self.get_instrument_prices_for_position_or_forecast( + instrument_code + ) + + last_date = daily_price.index[-1] + start_date = last_date - pd.DateOffset(years=1) + + return start_date + + @property + def use_SR_costs(self) -> bool: + return str2Bool(self.config.use_SR_costs) diff --git a/src/quantlib_st/systems/accounts/account_forecast.py b/src/quantlib_st/systems/accounts/account_forecast.py index 6a23c6b..5328639 100644 --- a/src/quantlib_st/systems/accounts/account_forecast.py +++ b/src/quantlib_st/systems/accounts/account_forecast.py @@ -3,12 +3,15 @@ from typing import Optional from quantlib_st.core.dateutils import ROOT_BDAYS_INYEAR +from quantlib_st.core.pandas.pdutils import sum_series +from quantlib_st.estimators.vol import robust_daily_vol_given_price + +from quantlib_st.systems.accounts.account_costs import accountCosts from quantlib_st.systems.accounts.curves.account_curve import accountCurve from quantlib_st.systems.accounts.pandl_calculators.pandl_SR_cost import ( pandlCalculationWithSRCosts, ) - -from quantlib_st.estimators.vol import robust_daily_vol_given_price +from quantlib_st.systems.system_cache import diagnostic ARBITRARY_FORECAST_CAPITAL = 100 ARBITRARY_FORECAST_ANNUAL_RISK_TARGET_PERCENTAGE = 0.16 @@ -16,6 +19,212 @@ ARBITRARY_VALUE_OF_PRICE_POINT = 1.0 +class accountForecast(accountCosts): + @diagnostic(not_pickable=True) + def pandl_for_instrument_forecast_weighted_within_trading_rule( + self, instrument_code: str, rule_variation_name: str, delayfill: bool = True + ) -> accountCurve: + pandl_for_instrument_forecast = self.pandl_for_instrument_forecast( + instrument_code, rule_variation_name, delayfill=delayfill + ) + + weight = ( + self._normalised_weight_for_forecast_and_instrument_within_trading_rule( + instrument_code, rule_variation_name=rule_variation_name + ) + ) + + weighted_pandl = pandl_for_instrument_forecast.weight(weight) + + return weighted_pandl + + @diagnostic(not_pickable=True) + def pandl_for_instrument_forecast_weighted( + self, instrument_code: str, rule_variation_name: str, delayfill: bool = True + ) -> accountCurve: + pandl_for_instrument_forecast = self.pandl_for_instrument_forecast( + instrument_code, rule_variation_name, delayfill=delayfill + ) + + weight = self._normalised_weight_for_forecast_and_instrument( + instrument_code, rule_variation_name=rule_variation_name + ) + + weighted_pandl = pandl_for_instrument_forecast.weight(weight) + + return weighted_pandl + + @diagnostic() + def _normalised_weight_for_forecast_and_instrument_within_trading_rule( + self, + instrument_code: str, + rule_variation_name: str, + ) -> pd.Series: + weight = self._unnormalised_weight_for_forecast_and_instrument( + instrument_code=instrument_code, rule_variation_name=rule_variation_name + ) + + total_weight_for_rule = self._total_unnormalised_weight_for_trading_rule( + rule_variation_name + ) + total_weight_aligned = total_weight_for_rule.reindex(weight.index).ffill() + + normalised_weight = weight / total_weight_aligned + + return normalised_weight + + @diagnostic() + def _total_unnormalised_weight_for_trading_rule( + self, rule_variation_name: str + ) -> pd.Series: + list_of_instruments = self.get_instrument_list() + + list_of_weights = [ + self._unnormalised_weight_for_forecast_and_instrument( + instrument_code, rule_variation_name=rule_variation_name + ) + for instrument_code in list_of_instruments + ] + + sum_weights = sum_series(list_of_weights) + + return sum_weights + + @diagnostic() + def _normalised_weight_for_forecast_and_instrument( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + weight = self._unnormalised_weight_for_forecast_and_instrument( + instrument_code=instrument_code, rule_variation_name=rule_variation_name + ) + total_weight = ( + self._total_unnormalised_weight_across_all_instruments_and_forecasts() + ) + total_weight_aligned = total_weight.reindex(weight.index).ffill() + + normalised_weight = weight / total_weight_aligned + + return normalised_weight + + @diagnostic() + def _total_unnormalised_weight_across_all_instruments_and_forecasts( + self, + ) -> pd.Series: + list_of_instruments = self.get_instrument_list() + list_of_weights = [ + self._total_unnormalised_weight_for_instrument(instrument_code) + for instrument_code in list_of_instruments + ] + + sum_of_weights = sum_series(list_of_weights) + + return sum_of_weights + + @diagnostic() + def _total_unnormalised_weight_for_instrument( + self, instrument_code: str + ) -> pd.Series: + list_of_rules = self.list_of_rules_for_code(instrument_code) + list_of_weights = [ + self._unnormalised_weight_for_forecast_and_instrument( + instrument_code, rule_variation_name + ) + for rule_variation_name in list_of_rules + ] + + sum_of_weights = sum_series(list_of_weights) + + return sum_of_weights + + @diagnostic() + def _unnormalised_weight_for_forecast_and_instrument( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + idm = self.instrument_diversification_multiplier() + fdm = self.forecast_diversification_multiplier(instrument_code) + instrument_weight = self.specific_instrument_weight(instrument_code) + forecast_weight = self.forecast_weight( + instrument_code=instrument_code, rule_variation_name=rule_variation_name + ) + + weighting_df = pd.concat([idm, fdm, instrument_weight, forecast_weight], axis=1) + weighting_df = weighting_df.ffill() + weighting_df = weighting_df.bfill() + joint_weight = weighting_df.product(axis=1) + + return joint_weight + + @diagnostic(not_pickable=True) + def pandl_for_instrument_forecast( + self, instrument_code: str, rule_variation_name: str, delayfill: bool = True + ) -> accountCurve: + """ + Get the p&l for one instrument and forecast; as % of arbitrary capital + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param rule_variation_name: rule to get values for + :type rule_variation_name: str + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :returns: accountCurve + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.pandl_for_instrument_forecast("EDOLLAR", "ewmac8").ann_std() + 0.20270495775586916 + + """ + + self.log.debug( + "Calculating pandl for instrument forecast for %s %s" + % (instrument_code, rule_variation_name), + instrument_code=instrument_code, + ) + + forecast = self.get_capped_forecast(instrument_code, rule_variation_name) + + price = self.get_instrument_prices_for_position_or_forecast( + instrument_code=instrument_code, position_or_forecast=forecast + ) + + daily_returns_volatility = self.get_daily_returns_volatility(instrument_code) + + # We NEVER use cash costs for forecasts ... + SR_cost = self.get_SR_cost_for_instrument_forecast( + instrument_code, rule_variation_name + ) + + target_abs_forecast = self.target_abs_forecast() + + capital = self.get_notional_capital() + risk_target = self.get_annual_risk_target() + value_per_point = self.get_value_of_block_price_move(instrument_code) + + fx = self.get_fx_rate(instrument_code) + + pandl_fcast = pandl_for_instrument_forecast( + forecast, + price=price, + capital=capital, + fx=fx, + risk_target=risk_target, + daily_returns_volatility=daily_returns_volatility, + target_abs_forecast=target_abs_forecast, + SR_cost=SR_cost, + delayfill=delayfill, + value_per_point=value_per_point, + ) + + return pandl_fcast + + def pandl_for_instrument_forecast( forecast: pd.Series, price: pd.Series, diff --git a/src/quantlib_st/systems/accounts/account_inputs.py b/src/quantlib_st/systems/accounts/account_inputs.py new file mode 100644 index 0000000..e08daea --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_inputs.py @@ -0,0 +1,317 @@ +import pandas as pd + +from quantlib_st.core.dateutils import BUSINESS_DAY_FREQ, HOURLY_FREQ +from quantlib_st.core.pandas.frequency import infer_frequency +from quantlib_st.core.constants import arg_not_supplied, named_object +from quantlib_st.core.pandas.pdutils import from_scalar_values_to_ts +from quantlib_st.objects.instruments import instrumentCosts + +from quantlib_st.systems.stage import SystemStage +from quantlib_st.systems.system_cache import diagnostic + + +class accountInputs(SystemStage): + def get_raw_price(self, instrument_code: str) -> pd.Series: + return self.parent.data.get_raw_price(instrument_code) + + def get_instrument_prices_for_position_or_forecast( + self, + instrument_code: str, + position_or_forecast: pd.Series | named_object = arg_not_supplied, + ) -> pd.Series: + if position_or_forecast is arg_not_supplied: + return self.get_daily_prices(instrument_code) + + instrument_prices = ( + self.instrument_prices_for_position_or_forecast_infer_frequency( + instrument_code=instrument_code, + position_or_forecast=position_or_forecast, + ) + ) + instrument_prices_reindexed = instrument_prices.reindex( + position_or_forecast.index, method="ffill" # type: ignore + ) + + return instrument_prices_reindexed + + def instrument_prices_for_position_or_forecast_infer_frequency( + self, + instrument_code: str, + position_or_forecast: pd.Series | named_object = arg_not_supplied, + ) -> pd.Series: + try: + frequency = infer_frequency(position_or_forecast) + if frequency is BUSINESS_DAY_FREQ: + instrument_prices = self.get_daily_prices(instrument_code) + elif frequency is HOURLY_FREQ: + instrument_prices = self.get_hourly_prices(instrument_code) + else: + raise Exception( + "Frequency %s does not have prices for %s should be hourly or daily" + % (str(frequency), instrument_code) + ) + except: + self.log.warning( + "Going to index hourly prices for %s to position_or_forecast might result in phantoms" + % instrument_code + ) + hourly_prices = self.get_hourly_prices(instrument_code) + + instrument_prices = hourly_prices.reindex( + position_or_forecast.index, method="ffill" + ) + + return instrument_prices + + def get_daily_prices(self, instrument_code: str) -> pd.Series: + return self.parent.rawdata.get_daily_prices(instrument_code) + + def get_hourly_prices(self, instrument_code: str) -> pd.Series: + return self.parent.rawdata.get_hourly_prices(instrument_code) + + def get_capped_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + return self.parent.forecastScaleCap.get_capped_forecast( + instrument_code, rule_variation_name + ) + + @diagnostic() + def get_daily_returns_volatility(self, instrument_code: str) -> pd.Series: + system = self.parent + returns_vol = system.rawdata.daily_returns_volatility(instrument_code) + + return returns_vol + + def get_daily_percentage_volatility(self, instrument_code: str) -> pd.Series: + daily_perc_vol = self.parent.rawdata.get_daily_percentage_volatility( + instrument_code + ) + + return daily_perc_vol + + def has_same_rules_as_code(self, instrument_code): + """ + Return instruments with same trading rules as this instrument + + KEY INPUT + + :param instrument_code: + :type str: + + :returns: list of str + + """ + return self.parent.combForecast.has_same_rules_as_code(instrument_code) + + def target_abs_forecast(self) -> float: + return self.parent.forecastScaleCap.target_abs_forecast() + + def average_forecast(self) -> float: + return self.config.average_absolute_forecast + + def forecast_cap(self) -> float: + return self.config.forecast_cap + + def get_raw_cost_data(self, instrument_code: str) -> instrumentCosts: + return self.parent.rawdata.get_raw_cost_data(instrument_code) + + def get_rolls_per_year(self, instrument_code: str) -> int: + rolls_per_year = self.parent.rawdata.rolls_per_year(instrument_code) + + return rolls_per_year + + def get_value_of_block_price_move(self, instrument_code: str) -> float: + return self.parent.rawdata.get_value_of_block_price_move(instrument_code) + + def get_fx_rate(self, instrument_code: str) -> pd.Series: + return self.parent.positionSize.get_fx_rate(instrument_code) + + def get_subsystem_position(self, instrument_code: str) -> pd.Series: + return self.parent.positionSize.get_subsystem_position(instrument_code) + + def get_notional_capital(self) -> float: + """ + Get notional capital from the previous module + + KEY INPUT + + :returns: float + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.get_notional_capital() + 100000.0 + """ + return self.parent.positionSize.get_vol_target_dict()[ + "notional_trading_capital" + ] + + def get_annual_risk_target(self) -> float: + """ + Get annual risk target from the previous module + + KEY INPUT + + :returns: float + """ + return ( + self.parent.positionSize.get_vol_target_dict()["percentage_vol_target"] + / 100.0 + ) + + def get_average_position_for_instrument_at_portfolio_level( + self, instrument_code: str + ) -> pd.Series: + average_position_for_subsystem = self.get_average_position_at_subsystem_level( + instrument_code + ) + scaling_factor = self.get_instrument_scaling_factor(instrument_code) + scaling_factor_aligned = scaling_factor.reindex( + average_position_for_subsystem.index, method="ffill" + ) + average_position = scaling_factor_aligned * average_position_for_subsystem + + return average_position + + def get_average_position_at_subsystem_level( + self, instrument_code: str + ) -> pd.Series: + """ + Get the volatility scalar (position with forecast of +10 using all capital) + + KEY INPUT + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + """ + + return self.parent.positionSize.get_average_position_at_subsystem_level( + instrument_code + ) + + def get_notional_position(self, instrument_code: str) -> pd.Series: + """ + Get the notional position from a previous module + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + KEY INPUT + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + """ + return self.parent.portfolio.get_notional_position(instrument_code) + + @diagnostic() + def get_instrument_scaling_factor(self, instrument_code: str) -> pd.Series: + """ + Get instrument weight * IDM + + The number we multiply subsystem by to get position + + Used to calculate SR costs + + :param instrument_code: instrument to value for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + """ + idm = self.get_instrument_diversification_multiplier() + instr_weights = self.get_instrument_weights() + + inst_weight_this_code = instr_weights[instrument_code] + + multiplier = inst_weight_this_code * idm + + return multiplier + + def get_instrument_diversification_multiplier(self): + """ + Get instrument div mult + + :returns: Tx1 pd.DataFrame + + KEY INPUT + + """ + + return self.parent.portfolio.get_instrument_diversification_multiplier() + + def get_instrument_weights(self): + """ + Get instrument weights + + KEY INPUT + + :returns: Tx1 pd.DataFrame + + + """ + + return self.parent.portfolio.get_instrument_weights() + + def get_instrument_list(self) -> list: + return self.parent.get_instrument_list() + + @property + def config(self): + return self.parent.config + + def instrument_diversification_multiplier(self) -> pd.Series: + return self.parent.portfolio.get_instrument_diversification_multiplier() + + def forecast_diversification_multiplier(self, instrument_code: str) -> pd.Series: + return self.parent.combForecast.get_forecast_diversification_multiplier( + instrument_code + ) + + def specific_instrument_weight(self, instrument_code: str) -> pd.Series: + instrument_weights = self.instrument_weights() + + return instrument_weights[instrument_code] + + def instrument_weights(self) -> pd.DataFrame: + return self.parent.portfolio.get_instrument_weights() + + def forecast_weight( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + forecast_weights_for_instrument = self.forecast_weights_for_instrument( + instrument_code + ) + if rule_variation_name not in forecast_weights_for_instrument.columns: + price_series = self.get_raw_price(instrument_code) + zero_weights = from_scalar_values_to_ts(0.0, price_series.index) + + return zero_weights + else: + return forecast_weights_for_instrument[rule_variation_name] + + def forecast_weights_for_instrument(self, instrument_code: str) -> pd.DataFrame: + return self.parent.combForecast.get_forecast_weights(instrument_code) + + def list_of_rules_for_code(self, instrument_code: str) -> list: + return self.parent.combForecast.get_trading_rule_list(instrument_code) + + def list_of_trading_rules(self) -> list: + return self.parent.rules.trading_rules() + + def get_actual_position(self, instrument_code: str) -> pd.Series: + return self.parent.portfolio.get_actual_position(instrument_code) + + def get_actual_buffers_for_position(self, instrument_code: str) -> pd.DataFrame: + return self.parent.portfolio.get_actual_buffers_for_position(instrument_code) diff --git a/src/quantlib_st/systems/accounts/account_instruments.py b/src/quantlib_st/systems/accounts/account_instruments.py new file mode 100644 index 0000000..c1431e5 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_instruments.py @@ -0,0 +1,225 @@ +import pandas as pd + +from quantlib_st.core.pandas.strategy_functions import turnover + +from quantlib_st.systems.system_cache import diagnostic, dont_cache +from quantlib_st.systems.accounts.account_costs import accountCosts +from quantlib_st.systems.accounts.account_buffering_system import ( + accountBufferingSystemLevel, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_SR_cost import ( + pandlCalculationWithSRCosts, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_cash_costs import ( + pandlCalculationWithCashCostsAndFills, +) +from quantlib_st.systems.accounts.curves.account_curve import accountCurve + + +class accountInstruments(accountCosts, accountBufferingSystemLevel): + # don't cache: just a switch method + @dont_cache + def pandl_for_instrument( + self, instrument_code: str, delayfill: bool = True, roundpositions: bool = True + ) -> accountCurve: + """ + Get the p&l for one instrument + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: accountCurve + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> system.accounts.pandl_for_instrument("US10").ann_std() + 0.13908407620762306 + """ + + self.log.debug( + "Calculating pandl for instrument for %s" % instrument_code, + instrument_code=instrument_code, + ) + + positions = self.get_buffered_position( + instrument_code, roundpositions=roundpositions + ) + + instrument_pandl = self._pandl_for_instrument_with_positions( + instrument_code, + positions=positions, + delayfill=delayfill, + roundpositions=roundpositions, + ) + + return instrument_pandl + + @dont_cache + def _pandl_for_instrument_with_positions( + self, + instrument_code: str, + positions: pd.Series, + delayfill: bool = True, + roundpositions: bool = True, + ) -> accountCurve: + """ + Get the p&l for one instrument + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: accountCurve + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> system.accounts.pandl_for_instrument("US10").ann_std() + 0.13908407620762306 + """ + + self.log.debug( + "Calculating pandl for instrument for %s" % instrument_code, + instrument_code=instrument_code, + ) + + use_SR_costs = self.use_SR_costs + + if use_SR_costs: + instrument_pandl = self._pandl_for_instrument_with_SR_costs( + instrument_code, + positions=positions, + roundpositions=roundpositions, + delayfill=delayfill, + ) + else: + instrument_pandl = self._pandl_for_instrument_with_cash_costs( + instrument_code, + positions=positions, + roundpositions=roundpositions, + delayfill=delayfill, + ) + + return instrument_pandl + + @diagnostic(not_pickable=True) + def _pandl_for_instrument_with_SR_costs( + self, + instrument_code: str, + positions: pd.Series, + delayfill: bool = True, + roundpositions: bool = True, + ) -> accountCurve: + price = self.get_instrument_prices_for_position_or_forecast( + instrument_code, position_or_forecast=positions + ) + fx = self.get_fx_rate(instrument_code) + value_of_price_point = self.get_value_of_block_price_move(instrument_code) + daily_returns_volatility = self.get_daily_returns_volatility(instrument_code) + + capital = self.get_notional_capital() + + instrument_turnover = self.instrument_turnover( + instrument_code, roundpositions=roundpositions + ) + annualised_SR_cost = self.get_SR_cost_given_turnover( + instrument_code, instrument_turnover + ) + + average_position = self.get_average_position_for_instrument_at_portfolio_level( + instrument_code + ) + + pandl_calculator = pandlCalculationWithSRCosts( + price, + SR_cost=annualised_SR_cost, + positions=positions, + average_position=average_position, + daily_returns_volatility=daily_returns_volatility, + capital=capital, + value_per_point=value_of_price_point, + delayfill=delayfill, + fx=fx, + roundpositions=roundpositions, + ) + + account_curve = accountCurve(pandl_calculator, weighted=True) # type: ignore + + return account_curve + + @diagnostic() + def turnover_at_portfolio_level( + self, instrument_code: str, roundpositions: bool = True + ) -> float: + ## assumes we use all capital + average_position_for_turnover = self.get_average_position_at_subsystem_level( + instrument_code + ) + + ## Using actual capital + positions = self.get_buffered_position( + instrument_code, roundpositions=roundpositions + ) + + ## Turnover will be at portfolio level, so a small number, but meaningful when added up + return turnover(positions, average_position_for_turnover) + + @diagnostic(not_pickable=True) + def _pandl_for_instrument_with_cash_costs( + self, + instrument_code: str, + positions: pd.Series, + delayfill: bool = True, + roundpositions: bool = True, + ) -> accountCurve: + if not roundpositions: + self.log.warning( + "Using roundpositions=False with cash costs may lead to inaccurate " + "costs (fixed costs, eg commissions will be overstated!!!)" + ) + + raw_costs = self.get_raw_cost_data(instrument_code) + + price = self.get_instrument_prices_for_position_or_forecast( + instrument_code, position_or_forecast=positions + ) + fx = self.get_fx_rate(instrument_code) + value_of_price_point = self.get_value_of_block_price_move(instrument_code) + + capital = self.get_notional_capital() + + vol_normalise_currency_costs = self.config.vol_normalise_currency_costs # type: ignore + rolls_per_year = self.get_rolls_per_year(instrument_code) + multiply_roll_costs_by = self.config.multiply_roll_costs_by # type: ignore + + pandl_calculator = pandlCalculationWithCashCostsAndFills( + price, + raw_costs=raw_costs, + positions=positions, + capital=capital, + value_per_point=value_of_price_point, + delayfill=delayfill, + fx=fx, + roundpositions=roundpositions, + vol_normalise_currency_costs=vol_normalise_currency_costs, + rolls_per_year=rolls_per_year, + multiply_roll_costs_by=multiply_roll_costs_by, + ) + + account_curve = accountCurve(pandl_calculator, weighted=True) # type: ignore + + return account_curve diff --git a/src/quantlib_st/systems/accounts/account_portfolio.py b/src/quantlib_st/systems/accounts/account_portfolio.py new file mode 100644 index 0000000..fdab865 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_portfolio.py @@ -0,0 +1,71 @@ +import numpy as np + +from quantlib_st.systems.accounts.account_instruments import accountInstruments +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + dictOfAccountCurves, +) +from quantlib_st.systems.accounts.curves.account_curve_group import accountCurveGroup +from quantlib_st.systems.system_cache import output + + +class accountPortfolio(accountInstruments): + @output(not_pickable=True) + def portfolio(self, delayfill=True, roundpositions=True): + """ + Get the p&l for entire portfolio + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: accountCurve + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.portfolio().ann_std() + 0.2638225179274214 + """ + + self.log.info("Calculating pandl for portfolio") + capital = self.get_notional_capital() + instruments = self.get_instrument_list() + dict_of_pandl_by_instrument = dict( + [ + ( + instrument_code, + self.pandl_for_instrument( + instrument_code, + delayfill=delayfill, + roundpositions=roundpositions, + ), + ) + for instrument_code in instruments + ] + ) + + dict_of_pandl_by_instrument = dictOfAccountCurves(dict_of_pandl_by_instrument) + + account_curve = accountCurveGroup( + dict_of_pandl_by_instrument, capital=capital, weighted=True + ) # type: ignore + + return account_curve + + @output() + def total_portfolio_level_turnover(self, roundpositions=True): + list_of_instruments = self.get_instrument_list() + list_of_turnovers_at_portfolio_level = [ + self.turnover_at_portfolio_level( + instrument_code, roundpositions=roundpositions + ) + for instrument_code in list_of_instruments + ] + + total_turnover = np.nansum(list_of_turnovers_at_portfolio_level) + + return total_turnover diff --git a/src/quantlib_st/systems/accounts/account_subsystem.py b/src/quantlib_st/systems/accounts/account_subsystem.py new file mode 100644 index 0000000..09b4d97 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_subsystem.py @@ -0,0 +1,206 @@ +from quantlib_st.systems.system_cache import diagnostic, dont_cache +from quantlib_st.systems.accounts.account_buffering_subsystem import ( + accountBufferingSubSystemLevel, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_SR_cost import ( + pandlCalculationWithSRCosts, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_cash_costs import ( + pandlCalculationWithCashCostsAndFills, +) +from quantlib_st.systems.accounts.curves.account_curve import accountCurve +from quantlib_st.systems.accounts.curves.account_curve_group import accountCurveGroup +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + dictOfAccountCurves, +) + + +class accountSubsystem(accountBufferingSubSystemLevel): + @diagnostic(not_pickable=True) + def pandl_across_subsystems( + self, delayfill=True, roundpositions=False + ) -> accountCurveGroup: + instrument_list = self.get_instrument_list() + + pandl_across_subsystems = self.pandl_across_subsystems_given_instrument_list( + instrument_list, delayfill=delayfill, roundpositions=roundpositions + ) + + return pandl_across_subsystems + + @diagnostic(not_pickable=True) + def pandl_across_subsystems_given_instrument_list( + self, instrument_list: list, delayfill=True, roundpositions=False + ) -> accountCurveGroup: + dict_of_pandl_across_subsystems = dict( + [ + ( + instrument_code, + self.pandl_for_subsystem( + instrument_code, + delayfill=delayfill, + roundpositions=roundpositions, + ), + ) + for instrument_code in instrument_list + ] + ) + + dict_of_pandl_across_subsystems = dictOfAccountCurves( + dict_of_pandl_across_subsystems + ) + + capital = self.get_notional_capital() + + pandl_across_subsystems = accountCurveGroup( + dict_of_pandl_across_subsystems, capital=capital, weighted=False + ) # type: ignore + + return pandl_across_subsystems + + # dont cache switch statement + @dont_cache + def pandl_for_subsystem( + self, instrument_code, delayfill=True, roundpositions=False + ) -> accountCurve: + """ + Get the p&l for one instrument + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: accountCurve + + >>> from systems.basesystem import System + >>> from systems.tests.testdata import get_test_object_futures_with_portfolios + >>> (portfolio, posobject, combobject, capobject, rules, rawdata, data, config)=get_test_object_futures_with_portfolios() + >>> system=System([portfolio, posobject, combobject, capobject, rules, rawdata, Account()], data, config) + >>> + >>> system.accounts.pandl_for_subsystem("US10", percentage=True).ann_std() + 0.23422378634127036 + """ + + self.log.debug( + "Calculating pandl for subsystem for instrument %s" % instrument_code, + instrument_code=instrument_code, + ) + + use_SR_cost = self.use_SR_costs + + if use_SR_cost: + pandl = self._pandl_for_subsystem_with_SR_costs( + instrument_code, delayfill=delayfill, roundpositions=roundpositions + ) + else: + pandl = self._pandl_for_subsystem_with_cash_costs( + instrument_code, delayfill=delayfill, roundpositions=roundpositions + ) + + return pandl + + @diagnostic(not_pickable=True) + def _pandl_for_subsystem_with_SR_costs( + self, instrument_code, delayfill=True, roundpositions=False + ) -> accountCurve: + pandl_calculator = self._pandl_calculator_for_subsystem_with_SR_costs( + instrument_code=instrument_code, + delayfill=delayfill, + roundpositions=roundpositions, + ) + + account_curve = accountCurve(pandl_calculator) + + return account_curve + + @diagnostic(not_pickable=True) + def _pandl_calculator_for_subsystem_with_SR_costs( + self, instrument_code, delayfill=True, roundpositions=False + ) -> pandlCalculationWithSRCosts: + positions = self.get_buffered_subsystem_position(instrument_code) + price = self.get_instrument_prices_for_position_or_forecast( + instrument_code, position_or_forecast=positions + ) + + fx = self.get_fx_rate(instrument_code) + + value_of_price_point = self.get_value_of_block_price_move(instrument_code) + daily_returns_volatility = self.get_daily_returns_volatility(instrument_code) + + ## following doesn't include IDM or instrument weight + average_position = self.get_average_position_at_subsystem_level(instrument_code) + + subsystem_turnover = self.subsystem_turnover(instrument_code) + annualised_SR_cost = self.get_SR_cost_given_turnover( + instrument_code, subsystem_turnover + ) + + capital = self.get_notional_capital() + + pandl_calculator = pandlCalculationWithSRCosts( + price, + SR_cost=annualised_SR_cost, + positions=positions, + average_position=average_position, + daily_returns_volatility=daily_returns_volatility, + capital=capital, + value_per_point=value_of_price_point, + delayfill=delayfill, + fx=fx, + roundpositions=roundpositions, + ) + + return pandl_calculator + + @diagnostic(not_pickable=True) + def _pandl_for_subsystem_with_cash_costs( + self, instrument_code, delayfill=True, roundpositions=True + ) -> accountCurve: + pandl_calculator = self._pandl_calculator_for_subsystem_with_cash_costs( + instrument_code=instrument_code, + delayfill=delayfill, + roundpositions=roundpositions, + ) + + account_curve = accountCurve(pandl_calculator) + + return account_curve + + @diagnostic(not_pickable=True) + def _pandl_calculator_for_subsystem_with_cash_costs( + self, instrument_code, delayfill=True, roundpositions=True + ) -> pandlCalculationWithCashCostsAndFills: + raw_costs = self.get_raw_cost_data(instrument_code) + positions = self.get_buffered_subsystem_position(instrument_code) + price = self.get_instrument_prices_for_position_or_forecast( + instrument_code, position_or_forecast=positions + ) ### here! + + fx = self.get_fx_rate(instrument_code) + + value_of_price_point = self.get_value_of_block_price_move(instrument_code) + + capital = self.get_notional_capital() + + vol_normalise_currency_costs = self.config.vol_normalise_currency_costs # type: ignore + rolls_per_year = self.get_rolls_per_year(instrument_code) + + pandl_calculator = pandlCalculationWithCashCostsAndFills( + price, + raw_costs=raw_costs, + positions=positions, + capital=capital, + value_per_point=value_of_price_point, + delayfill=delayfill, + fx=fx, + roundpositions=roundpositions, + vol_normalise_currency_costs=vol_normalise_currency_costs, + rolls_per_year=rolls_per_year, + ) + + return pandl_calculator diff --git a/src/quantlib_st/systems/accounts/account_trading_rules.py b/src/quantlib_st/systems/accounts/account_trading_rules.py new file mode 100644 index 0000000..3c8021e --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_trading_rules.py @@ -0,0 +1,216 @@ +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.systems.system_cache import diagnostic +from quantlib_st.systems.accounts.account_forecast import accountForecast +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + dictOfAccountCurves, + nestedDictOfAccountCurves, +) +from quantlib_st.systems.accounts.curves.account_curve_group import accountCurveGroup +from quantlib_st.systems.accounts.curves.nested_account_curve_group import ( + nestedAccountCurveGroup, +) + + +class accountTradingRules(accountForecast): + @diagnostic(not_pickable=True) + def pandl_for_trading_rule_weighted( + self, rule_variation_name: str, delayfill: bool = True + ) -> accountCurveGroup: + list_of_instruments = self.get_instrument_list() + dict_of_pandl_by_instrument = dict( + [ + ( + instrument_code, + self.pandl_for_instrument_forecast_weighted( + instrument_code=instrument_code, + rule_variation_name=rule_variation_name, + delayfill=delayfill, + ), + ) + for instrument_code in list_of_instruments + ] + ) + + dict_of_pandl_by_instrument = dictOfAccountCurves(dict_of_pandl_by_instrument) + + capital = self.get_notional_capital() + + account_curve = accountCurveGroup( + dict_of_pandl_by_instrument, capital=capital, weighted=True + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_trading_rule_unweighted( + self, rule_variation_name: str, delayfill: bool = True + ) -> accountCurveGroup: + list_of_instruments = self.get_instrument_list() + dict_of_pandl_by_instrument = dict( + [ + ( + instrument_code, + self.pandl_for_instrument_forecast( + instrument_code=instrument_code, + rule_variation_name=rule_variation_name, + delayfill=delayfill, + ), + ) + for instrument_code in list_of_instruments + ] + ) + + dict_of_pandl_by_instrument = dictOfAccountCurves(dict_of_pandl_by_instrument) + + capital = self.get_notional_capital() + + account_curve = accountCurveGroup( + dict_of_pandl_by_instrument, capital=capital, weighted=False + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_trading_rule( + self, rule_variation_name: str, delayfill: bool = True + ) -> accountCurveGroup: + # If I want the performance of a given trading rule across individual + # instruments in isolation, then I need to take pandl_for_trading_rule_weighted + # and normalise it so that the returns are as a proportion of the sum of + # all the relevant + # forecast weight * FDM * instrument weight * IDM; + # this is equivalent to the rules risk contribution within the system + + # flag as weighted but actually semi-weighted + list_of_instruments = self.get_instrument_list() + dict_of_pandl_by_instrument = dict( + [ + ( + instrument_code, + self.pandl_for_instrument_forecast_weighted_within_trading_rule( + instrument_code, + rule_variation_name=rule_variation_name, + delayfill=delayfill, + ), + ) + for instrument_code in list_of_instruments + ] + ) + + dict_of_pandl_by_instrument = dictOfAccountCurves(dict_of_pandl_by_instrument) + + capital = self.get_notional_capital() + + account_curve = accountCurveGroup( + dict_of_pandl_by_instrument, capital=capital, weighted=True + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_all_trading_rules( + self, delayfill: bool = True + ) -> nestedAccountCurveGroup: + ## group of pandl_for_trading_rule_weighted + list_of_rules = self.list_of_trading_rules() + + dict_of_pandl_by_rule = dict( + [ + (rule, self.pandl_for_trading_rule_weighted(rule, delayfill=delayfill)) + for rule in list_of_rules + ] + ) + + dict_of_pandl_by_rule = nestedDictOfAccountCurves(dict_of_pandl_by_rule) + capital = self.get_notional_capital() + + account_curve = nestedAccountCurveGroup( + dict_of_pandl_by_rule, capital=capital, weighted=True + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_all_trading_rules_unweighted(self, delayfill: bool = True): + # group of pandl_for_trading_rule + list_of_rules = self.list_of_trading_rules() + + dict_of_pandl_by_rule = dict( + [ + (rule, self.pandl_for_trading_rule(rule, delayfill=delayfill)) + for rule in list_of_rules + ] + ) + + dict_of_pandl_by_rule = nestedDictOfAccountCurves(dict_of_pandl_by_rule) + capital = self.get_notional_capital() + + account_curve = nestedAccountCurveGroup( + dict_of_pandl_by_rule, capital=capital, weighted=False + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_instrument_rules( + self, instrument_code: str, delayfill: bool = True + ) -> accountCurveGroup: + # how all trading rules have done for a particular instrument, weighted + list_of_rules = self.list_of_rules_for_code(instrument_code) + dict_of_pandl_by_rule = dict( + [ + ( + rule_variation_name, + self.pandl_for_instrument_forecast_weighted( + instrument_code=instrument_code, + rule_variation_name=rule_variation_name, + delayfill=delayfill, + ), + ) + for rule_variation_name in list_of_rules + ] + ) + + dict_of_pandl_by_rule = dictOfAccountCurves(dict_of_pandl_by_rule) + + capital = self.get_notional_capital() + + account_curve = accountCurveGroup( + dict_of_pandl_by_rule, capital=capital, weighted=True + ) + + return account_curve + + @diagnostic(not_pickable=True) + def pandl_for_instrument_rules_unweighted( + self, + instrument_code: str, + trading_rule_list=arg_not_supplied, + delayfill: bool = True, + ) -> accountCurveGroup: + # (unweighted group - elements are pandl_for_instrument_forecast across trading rules) + if trading_rule_list is arg_not_supplied: + trading_rule_list = self.list_of_rules_for_code(instrument_code) + dict_of_pandl_by_rule = dict( + [ + ( + rule_variation_name, + self.pandl_for_instrument_forecast( + instrument_code=instrument_code, + rule_variation_name=rule_variation_name, + delayfill=delayfill, + ), + ) + for rule_variation_name in trading_rule_list + ] + ) + + dict_of_pandl_by_rule = dictOfAccountCurves(dict_of_pandl_by_rule) + + capital = self.get_notional_capital() + + account_curve = accountCurveGroup( + dict_of_pandl_by_rule, capital=capital, weighted=False + ) + + return account_curve diff --git a/src/quantlib_st/systems/accounts/account_with_multiplier.py b/src/quantlib_st/systems/accounts/account_with_multiplier.py new file mode 100644 index 0000000..8acd163 --- /dev/null +++ b/src/quantlib_st/systems/accounts/account_with_multiplier.py @@ -0,0 +1,163 @@ +from copy import copy + +import pandas as pd + +from quantlib_st.core.objects import resolve_function +from quantlib_st.core.pandas.pdutils import from_scalar_values_to_ts + +from quantlib_st.systems.system_cache import output, diagnostic +from quantlib_st.systems.accounts.account_portfolio import accountPortfolio +from quantlib_st.systems.accounts.account_buffering_system import ( + accountBufferingSystemLevel, +) + +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + dictOfAccountCurves, +) +from quantlib_st.systems.accounts.curves.account_curve_group import accountCurveGroup +from quantlib_st.systems.accounts.curves.account_curve import accountCurve + + +class accountWithMultiplier(accountPortfolio, accountBufferingSystemLevel): + @output(not_pickable=True) + def portfolio_with_multiplier(self, delayfill=True, roundpositions=True): + self.log.info("Calculating pandl for portfolio with multiplier") + capital = self.get_actual_capital() + instruments = self.get_instrument_list() + port_pandl = [ + ( + instrument_code, + self.pandl_for_instrument_with_multiplier( + instrument_code, delayfill=delayfill, roundpositions=roundpositions + ), + ) + for instrument_code in instruments + ] + + port_pandl = dictOfAccountCurves(port_pandl) + + port_pandl = accountCurveGroup(port_pandl, capital=capital, weighted=True) + + return port_pandl + + @output(not_pickable=True) + def pandl_for_instrument_with_multiplier( + self, instrument_code: str, delayfill=True, roundpositions=True + ) -> accountCurve: + """ + Get the p&l for one instrument, using variable capital + + :param instrument_code: instrument to get values for + :type instrument_code: str + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: accountCurve + + """ + + self.log.debug( + "Calculating pandl for instrument for %s with capital multiplier" + % instrument_code, + instrument_code=instrument_code, + ) + + positions = self.get_buffered_position_with_multiplier( + instrument_code, roundpositions=roundpositions + ) + + instrument_pandl = self._pandl_for_instrument_with_positions( + instrument_code, + positions=positions, + delayfill=delayfill, + roundpositions=roundpositions, + ) + + return instrument_pandl + + @diagnostic() + def get_buffered_position_with_multiplier( + self, instrument_code: str, roundpositions: bool = True + ) -> pd.Series: + """ + Get the buffered position + + :param instrument_code: instrument to get + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: Tx1 pd.DataFrame + + """ + + optimal_position = self.get_actual_position(instrument_code) + pos_buffers = self.get_actual_buffers_for_position(instrument_code) + + buffered_position = ( + self._get_buffered_position_given_optimal_position_and_buffers( + optimal_position=optimal_position, + pos_buffers=pos_buffers, + roundpositions=roundpositions, + ) + ) + + return buffered_position + + @diagnostic() + def get_actual_capital(self) -> pd.Series: + """ + Get a capital multiplier multiplied by notional capital + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: pd.Series + + """ + + capmult = self.capital_multiplier() + notional = self.get_notional_capital() + + if type(notional) is int or type(notional) is float: + notional_ts = from_scalar_values_to_ts(notional, capmult.index) + else: + notional_ts = notional.reindex(capmult.index).ffill() + + capital = capmult * notional_ts + capital = capital.shift(1) + + return capital + + @diagnostic() + def capital_multiplier(self) -> pd.Series: + """ + Get a capital multiplier + + :param delayfill: Lag fills by one day + :type delayfill: bool + + :param roundpositions: Round positions to whole contracts + :type roundpositions: bool + + :returns: pd.Series + + """ + config = self.config + system = self.parent + + capmult_params = copy(config.capital_multiplier) + capmult_func = resolve_function(capmult_params.pop("func")) + + capmult = capmult_func(system, **capmult_params) + + capmult = capmult.reindex(self.portfolio().index).ffill() + + return capmult diff --git a/src/quantlib_st/systems/accounts/accounts_stage.py b/src/quantlib_st/systems/accounts/accounts_stage.py new file mode 100644 index 0000000..cedf660 --- /dev/null +++ b/src/quantlib_st/systems/accounts/accounts_stage.py @@ -0,0 +1,12 @@ +## We don't need to inherit from accountForecast, accountInputs, accountBufferingSystemLevel, accountInstruments +## as we get those via these other objects + +from quantlib_st.systems.accounts.account_with_multiplier import accountWithMultiplier +from quantlib_st.systems.accounts.account_subsystem import accountSubsystem +from quantlib_st.systems.accounts.account_trading_rules import accountTradingRules + + +class Account(accountTradingRules, accountWithMultiplier, accountSubsystem): + @property + def name(self): # type: ignore + return "accounts" diff --git a/src/quantlib_st/systems/accounts/curves/account_curve_group.py b/src/quantlib_st/systems/accounts/curves/account_curve_group.py new file mode 100644 index 0000000..ca1cabf --- /dev/null +++ b/src/quantlib_st/systems/accounts/curves/account_curve_group.py @@ -0,0 +1,169 @@ +from copy import copy +import pandas as pd + +from quantlib_st.core.dateutils import Frequency + +from quantlib_st.systems.accounts.curves.account_curve import accountCurve +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + dictOfAccountCurves, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_generic_costs import ( + GROSS_CURVE, + NET_CURVE, + COSTS_CURVE, +) +from quantlib_st.systems.accounts.curves.stats_dict import statsDict + + +class accountCurveGroup(accountCurve): + def __init__(self, dict_of_account_curves: dictOfAccountCurves, capital, **kwargs): + total_pandl_calculator = dict_of_account_curves.summed_pandl_calculator( + capital=capital + ) + super().__init__(total_pandl_calculator, **kwargs) + + self._dict_of_account_curves = dict_of_account_curves + self._kwargs = _kwargs_with_defaults(kwargs) + + @property + def asset_columns(self): + return list(self.dict_of_account_curves.keys()) + + def __getitem__(self, item): + kwargs = self.kwargs + return accountCurve(self.get_pandl_calculator_for_item(item), **kwargs) + + def get_pandl_calculator_for_item(self, item: str): + return self.dict_of_account_curves[item].pandl_calculator_with_costs + + def to_frame(self) -> pd.DataFrame: + asset_columns = self.asset_columns + data_as_list = [self[asset_name] for asset_name in asset_columns] + data_as_pd = pd.concat(data_as_list, axis=1) + data_as_pd.columns = asset_columns + + return data_as_pd + + def get_stats( + self, stat_name: str, curve_type: str = "net", freq: str = "daily" + ) -> statsDict: + return statsDict(self, item=stat_name, freq=freq, curve_type=curve_type) + + ## TO RETURN A 'NEW' ACCOUNT CURVE GROUP + @property + def gross(self): + kwargs = self.kwargs_without_item("curve_type") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + curve_type=GROSS_CURVE, + **kwargs, + ) + + @property + def net(self): + kwargs = self.kwargs_without_item("curve_type") + + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + curve_type=NET_CURVE, + **kwargs, + ) + + @property + def costs(self): + kwargs = self.kwargs_without_item("curve_type") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + curve_type=COSTS_CURVE, + **kwargs, + ) + + @property + def daily(self): + kwargs = self.kwargs_without_item("frequency") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + frequency=Frequency.BDay, + **kwargs, + ) + + @property + def weekly(self): + kwargs = self.kwargs_without_item("frequency") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + frequency=Frequency.Week, + **kwargs, + ) + + @property + def monthly(self): + kwargs = self.kwargs_without_item("frequency") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + frequency=Frequency.Month, + **kwargs, + ) + + @property + def annual(self): + kwargs = self.kwargs_without_item("frequency") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + frequency=Frequency.Year, + **kwargs, + ) + + @property + def percent(self): + kwargs = self.kwargs_without_item("is_percentage") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + is_percentage=True, + **kwargs, + ) + + @property + def value_terms(self): + kwargs = self.kwargs_without_item("is_percentage") + return accountCurveGroup( + self.dict_of_account_curves, + capital=self.capital, + is_percentage=False, + **kwargs, + ) + + @property + def dict_of_account_curves(self) -> dictOfAccountCurves: + return self._dict_of_account_curves + + def kwargs_without_item(self, itemname): + kwargs = copy(self.kwargs) + kwargs.pop(itemname) + + return kwargs + + @property + def kwargs(self) -> dict: + return self._kwargs + + +def _kwargs_with_defaults(kwargs: dict) -> dict: + if "frequency" not in kwargs: + kwargs["frequency"] = Frequency.BDay + if "curve_type" not in kwargs: + kwargs["curve_type"] = NET_CURVE + if "is_percentage" not in kwargs: + kwargs["is_percentage"] = False + if "weighted" not in kwargs: + kwargs["weighted"] = False + + return kwargs diff --git a/src/quantlib_st/systems/accounts/curves/dict_of_account_curves.py b/src/quantlib_st/systems/accounts/curves/dict_of_account_curves.py new file mode 100644 index 0000000..6aca735 --- /dev/null +++ b/src/quantlib_st/systems/accounts/curves/dict_of_account_curves.py @@ -0,0 +1,41 @@ +from quantlib_st.systems.accounts.pandl_calculators.pandl_calculation_dict import ( + dictOfPandlCalculatorsWithGenericCosts, + pandlCalculationWithoutPositions, +) + + +class dictOfAccountCurves(dict): + def summed_pandl_calculator(self, capital) -> pandlCalculationWithoutPositions: + dict_of_pandl_calculators = self.dict_of_pandl_calculators() + summed_pandl_calculator = dict_of_pandl_calculators.sum(capital=capital) + + return summed_pandl_calculator + + def dict_of_pandl_calculators(self) -> dictOfPandlCalculatorsWithGenericCosts: + dict_of_pandl_calculators = dict( + [ + (asset_name, account_curve.pandl_calculator_with_costs) + for asset_name, account_curve in self.items() + ] + ) + + return dictOfPandlCalculatorsWithGenericCosts(dict_of_pandl_calculators) + + @property + def asset_columns(self) -> list: + return list(self.keys()) + + @property + def weighted(self) -> bool: + weighted_list = [acurve.weighted for acurve in self.values()] + if all(weighted_list): + return True + if all(~weighted_list): # type: ignore + return False + raise Exception( + "Can't have a dict of account curves where some are weighted and some are unweighted" + ) + + +class nestedDictOfAccountCurves(dictOfAccountCurves): + pass diff --git a/src/quantlib_st/systems/accounts/curves/nested_account_curve_group.py b/src/quantlib_st/systems/accounts/curves/nested_account_curve_group.py new file mode 100644 index 0000000..09d8ae6 --- /dev/null +++ b/src/quantlib_st/systems/accounts/curves/nested_account_curve_group.py @@ -0,0 +1,125 @@ +from quantlib_st.systems.accounts.curves.account_curve_group import accountCurveGroup +from quantlib_st.systems.accounts.curves.dict_of_account_curves import ( + nestedDictOfAccountCurves, +) + +from quantlib_st.core.dateutils import Frequency + +from quantlib_st.systems.accounts.pandl_calculators.pandl_generic_costs import ( + NET_CURVE, + GROSS_CURVE, + COSTS_CURVE, +) + + +class nestedAccountCurveGroup(accountCurveGroup): + def __init__( + self, + nested_dict_of_account_curves: nestedDictOfAccountCurves, + capital, + **kwargs, + ): + super().__init__(nested_dict_of_account_curves, capital=capital, **kwargs) + + self._nested_dict_of_account_curves = nested_dict_of_account_curves + + def __getitem__(self, item) -> accountCurveGroup: + return self.get_account_curve_dict_for_item(item) + + def get_account_curve_dict_for_item(self, item: str) -> accountCurveGroup: + return self.nested_account_curves[item] + + ## TO RETURN A 'NEW' ACCOUNT CURVE GROUP + @property + def gross(self): + kwargs = self.kwargs_without_item("curve_type") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + curve_type=GROSS_CURVE, + **kwargs, + ) + + @property + def net(self): + kwargs = self.kwargs_without_item("curve_type") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + curve_type=NET_CURVE, + **kwargs, + ) + + @property + def costs(self): + kwargs = self.kwargs_without_item("curve_type") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + curve_type=COSTS_CURVE, + **kwargs, + ) + + @property + def daily(self): + kwargs = self.kwargs_without_item("frequency") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + frequency=Frequency.BDay, + **kwargs, + ) + + @property + def weekly(self): + kwargs = self.kwargs_without_item("frequency") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + frequency=Frequency.Week, + **kwargs, + ) + + @property + def monthly(self): + kwargs = self.kwargs_without_item("frequency") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + frequency=Frequency.Month, + **kwargs, + ) + + @property + def annual(self): + kwargs = self.kwargs_without_item("frequency") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + frequency=Frequency.Year, + **kwargs, + ) + + @property + def percent(self): + kwargs = self.kwargs_without_item("is_percentage") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + is_percentage=True, + **kwargs, + ) + + @property + def value_terms(self): + kwargs = self.kwargs_without_item("is_percentage") + return nestedAccountCurveGroup( + self.nested_account_curves, + capital=self.capital, + is_percentage=False, + **kwargs, + ) + + @property + def nested_account_curves(self) -> nestedDictOfAccountCurves: + return self._nested_dict_of_account_curves diff --git a/src/quantlib_st/systems/accounts/curves/stats_dict.py b/src/quantlib_st/systems/accounts/curves/stats_dict.py new file mode 100644 index 0000000..8dbe81e --- /dev/null +++ b/src/quantlib_st/systems/accounts/curves/stats_dict.py @@ -0,0 +1,254 @@ +from scipy.stats import ttest_1samp + +import numpy as np + +from quantlib_st.core.dateutils import Frequency +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.systems.accounts.curves.account_curve import accountCurve +from quantlib_st.systems.accounts.pandl_calculators.pandl_generic_costs import ( + GROSS_CURVE, + NET_CURVE, + COSTS_CURVE, +) + + +def _from_freq_str_to_frequency(freq_str): + LOOKUP_DICT = dict( + daily=Frequency.BDay, + weekly=Frequency.Week, + monthly=Frequency.Month, + annual=Frequency.Year, + ) + + return LOOKUP_DICT[freq_str] + + +def _from_curve_str_to_curve_type(curve_type_str): + LOOKUP_DICT = dict(gross=GROSS_CURVE, net=NET_CURVE, costs=COSTS_CURVE) + + return LOOKUP_DICT[curve_type_str] + + +class statsDict(dict): + def __init__( + self, + account_curve_group: "accountCurveGroup", + item="sharpe", + freq="daily", + curve_type="net", + percent=True, + ): + dict_of_results_by_stat = self.dict_of_results_by_asset_name( + item=item, + account_curve_group=account_curve_group, + freq_str=freq, + curve_type_str=curve_type, + is_percentage=percent, + ) + + super().__init__(dict_of_results_by_stat) + + self._account_curve_group = account_curve_group + self._item = item + + def dict_of_results_by_asset_name_equal_weighted( + self, + item: str = arg_not_supplied, + account_curve_group: "accountCurveGroup" = arg_not_supplied, + freq_str: str = "daily", + curve_type_str="net", + is_percentage=True, + ): + weight = 1.0 / len(self.keys()) + unweighted_results = self.dict_of_results_by_asset_name( + item=item, + account_curve_group=account_curve_group, + freq_str=freq_str, + curve_type_str=curve_type_str, + is_percentage=is_percentage, + ) + + weighted_results = dict( + [ + (asset_name, weight * unweighted_results[asset_name]) + for asset_name in unweighted_results.keys() + ] + ) + + return weighted_results + + def dict_of_results_by_asset_name_timeweighted( + self, + item: str = arg_not_supplied, + account_curve_group: "accountCurveGroup" = arg_not_supplied, + freq_str: str = "daily", + curve_type_str="net", + is_percentage=True, + ): + time_weights_dict = self._time_weights() + unweighted_results = self.dict_of_results_by_asset_name( + item=item, + account_curve_group=account_curve_group, + freq_str=freq_str, + curve_type_str=curve_type_str, + is_percentage=is_percentage, + ) + + weighted_results = dict( + [ + ( + asset_name, + time_weights_dict[asset_name] * unweighted_results[asset_name], + ) + for asset_name in unweighted_results.keys() + ] + ) + + return weighted_results + + def dict_of_results_by_asset_name( + self, + item: str = arg_not_supplied, + account_curve_group: "accountCurveGroup" = arg_not_supplied, + freq_str: str = "daily", + curve_type_str="net", + is_percentage=True, + ): + if account_curve_group is arg_not_supplied: + account_curve_group = self.account_curve_group + + dict_of_results_by_stat = dict( + [ + ( + asset_name, + self.statresult_for_item( + asset_name=asset_name, + item=item, + account_curve_group=account_curve_group, + freq_str=freq_str, + curve_type_str=curve_type_str, + is_percentage=is_percentage, + ), + ) + for asset_name in account_curve_group.asset_columns + ] + ) + + return dict_of_results_by_stat + + def statresult_for_item( + self, + asset_name: str, + item: str = arg_not_supplied, + account_curve_group: "accountCurveGroup" = arg_not_supplied, + freq_str: str = "daily", + curve_type_str="net", + is_percentage=True, + ): + if item is arg_not_supplied: + item = self.item + + account_curve = self.account_curve_for_asset( + account_curve_group=account_curve_group, + asset_name=asset_name, + freq_str=freq_str, + curve_type_str=curve_type_str, + is_percentage=is_percentage, + ) + + method_to_call = getattr(account_curve, item) + + return method_to_call() + + def account_curve_for_asset( + self, + asset_name: str, + account_curve_group: "accountCurveGroup" = arg_not_supplied, + freq_str: str = "daily", + curve_type_str="net", + is_percentage=True, + ) -> accountCurve: + if account_curve_group is arg_not_supplied: + account_curve_group = self.account_curve_group + + frequency = _from_freq_str_to_frequency(freq_str) + curve_type = _from_curve_str_to_curve_type(curve_type_str) + + account_curve = accountCurve( + account_curve_group.get_pandl_calculator_for_item(asset_name), + curve_type=curve_type, + is_percentage=is_percentage, + frequency=frequency, + ) + + return account_curve + + def mean(self, timeweighted=True): + if timeweighted: + results = self.dict_of_results_by_asset_name_timeweighted() + else: + results = self.dict_of_results_by_asset_name_equal_weighted() + + results_values = list(results.values()) + + return np.nanmean(results_values) + + def std(self, timeweighted=True): + if timeweighted: + results = self.dict_of_results_by_asset_name_timeweighted() + else: + results = self.dict_of_results_by_asset_name_equal_weighted() + + results_values = list(results.values()) + + return np.nanstd(results_values) + + def tstat(self, timeweighted=True): + if timeweighted: + results = self.dict_of_results_by_asset_name_timeweighted() + else: + results = self.dict_of_results_by_asset_name_equal_weighted() + + results_values = list(results.values()) + + return ttest_1samp(results_values, 0.0).statistic + + def pvalue(self, timeweighted=True): + if timeweighted: + results = self.dict_of_results_by_asset_name_timeweighted() + else: + results = self.dict_of_results_by_asset_name_equal_weighted() + + results_values = list(results.values()) + + return ttest_1samp(results_values, 0.0).pvalue + + def _time_weights(self) -> dict: + dict_of_time_lengths = dict( + [ + ( + asset_name, + self.account_curve_for_asset( + asset_name=asset_name + ).length_in_months, + ) + for asset_name in self.keys() + ] + ) + average_length = np.mean(list(dict_of_time_lengths.values())) + dict_of_time_weights = dict( + [ + (asset_name, dict_of_time_lengths[asset_name] / average_length) + for asset_name in dict_of_time_lengths.keys() + ] + ) + + return dict_of_time_weights + + @property + def account_curve_group(self): + return self._account_curve_group + + @property + def item(self): + return self._item diff --git a/src/quantlib_st/systems/accounts/pandl_calculators/pandl_calculation_dict.py b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_calculation_dict.py new file mode 100644 index 0000000..f5b4c2d --- /dev/null +++ b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_calculation_dict.py @@ -0,0 +1,106 @@ +import pandas as pd + +from quantlib_st.systems.accounts.pandl_calculators.pandl_generic_costs import ( + pandlCalculationWithGenericCosts, +) + + +class pandlCalculationWithoutPositions(pandlCalculationWithGenericCosts): + def __init__( + self, + pandl_in_base_currency: pd.Series, + costs_pandl_in_base_currency: pd.Series, + capital: pd.Series, + ): + super().__init__(price=pd.Series(dtype="float64"), capital=capital) + + self._pandl_in_base_currency = pandl_in_base_currency + self._costs_pandl_in_base_currency = costs_pandl_in_base_currency + + ## used for summing account curves + def pandl_in_base_currency(self) -> pd.Series: + return self._pandl_in_base_currency + + def costs_pandl_in_base_currency(self) -> pd.Series: + return self._costs_pandl_in_base_currency + + def net_pandl_in_instrument_currency(self): + raise NotImplementedError + + def pandl_in_instrument_currency(self): + raise NotImplementedError + + def costs_pandl_in_instrument_currency(self): + raise NotImplementedError + + def net_pandl_in_points(self): + raise NotImplementedError + + def pandl_in_points(self): + raise NotImplementedError + + def costs_pandl_in_points(self): + raise NotImplementedError + + @property + def price(self): + raise NotImplementedError + + @property + def positions(self): + raise NotImplementedError + + @property + def value_per_point(self): + raise NotImplementedError + + @property + def fx(self): + raise NotImplementedError + + @property + def _index_to_align_capital_to(self): + return self.pandl_in_base_currency().index + + +class dictOfPandlCalculatorsWithGenericCosts(dict): + def sum(self, capital) -> pandlCalculationWithoutPositions: + pandl_in_base_currency = self.sum_of_pandl_in_base_currency() + costs_pandl_in_base_currency = self.sum_of_costs_pandl_in_base_currency() + + pandl_calculator = pandlCalculationWithoutPositions( + pandl_in_base_currency=pandl_in_base_currency, + costs_pandl_in_base_currency=costs_pandl_in_base_currency, + capital=capital, + ) + + return pandl_calculator + + def sum_of_pandl_in_base_currency(self) -> pd.Series: + return sum_list_of_pandl_curves(self.list_of_pandl_in_base_currency) + + def sum_of_costs_pandl_in_base_currency(self) -> pd.Series: + return sum_list_of_pandl_curves(self.list_of_costs_pandl_in_base_currency) + + @property + def list_of_pandl_in_base_currency(self) -> list: + return self._list_of_attr("pandl_in_base_currency") + + @property + def list_of_costs_pandl_in_base_currency(self) -> list: + return self._list_of_attr("costs_pandl_in_base_currency") + + def _list_of_attr(self, attr_name) -> list: + list_of_methods = [ + getattr(pandl_item, attr_name) for pandl_item in self.values() + ] + list_of_attr = [x() for x in list_of_methods] + + return list_of_attr + + +def sum_list_of_pandl_curves(list_of_pandl_curves: list): + df_of_pandl_curves = pd.concat(list_of_pandl_curves, axis=1, sort=True) + summed_pandl_curve = df_of_pandl_curves.sum(axis=1) + + return summed_pandl_curve diff --git a/src/quantlib_st/systems/accounts/pandl_calculators/pandl_cash_costs.py b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_cash_costs.py new file mode 100644 index 0000000..e5e7386 --- /dev/null +++ b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_cash_costs.py @@ -0,0 +1,262 @@ +import datetime +import numpy as np +import pandas as pd + +from quantlib_st.core.pandas.find_data import get_row_of_series_before_date +from quantlib_st.core.pandas.strategy_functions import ( + calculate_cost_deflator, + years_in_data, +) +from quantlib_st.core.dateutils import generate_equal_dates_within_year +from quantlib_st.core.genutils import flatten_list + +from quantlib_st.systems.accounts.pandl_calculators.pandl_generic_costs import ( + pandlCalculationWithGenericCosts, +) +from quantlib_st.systems.accounts.pandl_calculators.pandl_using_fills import ( + pandlCalculationWithFills, +) + +from quantlib_st.objects.instruments import instrumentCosts +from quantlib_st.objects.fills import Fill + + +class pandlCalculationWithCashCostsAndFills( # type: ignore + pandlCalculationWithGenericCosts, pandlCalculationWithFills +): + def __init__( + self, + *args, + raw_costs: instrumentCosts, + rolls_per_year: int, + vol_normalise_currency_costs: bool = True, + multiply_roll_costs_by: float = 1.0, + **kwargs, + ): + super().__init__(*args, **kwargs) + self._raw_costs = raw_costs + self._vol_normalise_currency_costs = vol_normalise_currency_costs + self._rolls_per_year = rolls_per_year + self._multiply_roll_costs_by = multiply_roll_costs_by + + def calculations_df(self): # type: ignore + #### TEMPORARY + costs = self.costs_from_trading_in_instrument_currency_as_series() + pandl = self.pandl_in_instrument_currency() + net = self.net_pandl_in_instrument_currency() + + calculations_df = pd.concat([pandl, costs, net], axis=1) + calculations_df.index = ["gross", "costs", "net"] + + return calculations_df + + def costs_pandl_in_points(self) -> pd.Series: + ## We work backwards since the cost calculator returns a currency cost + costs_pandl_in_instrument_currency = self.costs_pandl_in_instrument_currency() + block_price_multiplier = self.value_per_point + costs_pandl_in_points = ( + costs_pandl_in_instrument_currency / block_price_multiplier + ) + + return costs_pandl_in_points + + def costs_pandl_in_instrument_currency(self) -> pd.Series: + costs_as_pd_series = self.costs_from_trading_in_instrument_currency_as_series() + normalised_costs = self.normalise_costs_in_instrument_currency( + costs_as_pd_series + ) + + return normalised_costs + + def costs_from_trading_in_instrument_currency_as_series(self) -> pd.Series: + instrument_currency_costs_as_list = ( + self.costs_from_trading_in_instrument_currency_as_list() + ) + date_index = self.date_index_for_all_fills() + costs_as_pd_series = pd.Series( + instrument_currency_costs_as_list, date_index, dtype="float64" + ) + costs_as_pd_series = costs_as_pd_series.sort_index() + costs_as_pd_series = costs_as_pd_series.groupby(costs_as_pd_series.index).sum() + + return costs_as_pd_series + + def costs_from_trading_in_instrument_currency_as_list(self) -> list: + list_of_fills = self.list_of_all_fills() + + instrument_currency_costs = [ + -self.calculate_cost_instrument_currency_for_a_fill(fill) + for fill in list_of_fills + ] + + return instrument_currency_costs + + def date_index_for_all_fills(self) -> list: + list_of_all_fills = self.list_of_all_fills() + date_index = [fill.date for fill in list_of_all_fills] + + return date_index + + def list_of_all_fills(self) -> list: + list_of_holding_fills = self.pseudo_fills_from_holding + list_of_trading_fills = self.fills + + return list_of_trading_fills + list_of_holding_fills + + @property + def pseudo_fills_from_holding(self) -> list: + pseudo_fills = getattr(self, "_pseudo_fills", None) + if pseudo_fills is None: + self._pseudo_fills = pseudo_fills = self._calculate_pseudo_fills() + + return pseudo_fills + + def _calculate_pseudo_fills(self) -> list: + list_of_years_in_data = years_in_data(self.positions) + fills_by_year = [ + self._pseudo_fills_for_year(year) for year in list_of_years_in_data + ] + + fills_as_single_list = flatten_list(fills_by_year) + + return fills_as_single_list + + def _pseudo_fills_for_year(self, year: int) -> list: + rolls_per_year = self.rolls_per_year + + if rolls_per_year == 0: + return [] + + date_list = generate_equal_dates_within_year(year, rolls_per_year) + average_holding_by_period = self._average_holdings_within_year( + year, rolls_per_year + ) + price_series = self.price.ffill() + last_date_with_positions = self.last_date_with_positions + multiply_roll_costs_by = self.multiply_roll_costs_by + + ## We multiply the quantity rather than the actual costs, as the later + ## cost calculation doesn't distinguish between rolls and other trades + + opening_fills_this_year = [ + Fill( + date=date, + qty=qty * multiply_roll_costs_by, + price=get_row_of_series_before_date(price_series, date), + price_requires_slippage_adjustment=True, + ) + for date, qty in zip(date_list, average_holding_by_period) + if date <= last_date_with_positions and abs(qty) > 0 + ] + + closing_fills_this_year = [ + Fill( + date=fill.date, + qty=-fill.qty, + price=fill.price, + ) + for fill in opening_fills_this_year + ] + + fills_this_year = opening_fills_this_year + closing_fills_this_year + + return fills_this_year + + def _average_holdings_within_year(self, year: int, rolls_per_year: int) -> list: + first_date = generate_equal_dates_within_year(year - 1, rolls_per_year)[-1] + subsequent_dates = generate_equal_dates_within_year(year, rolls_per_year) + all_dates = [first_date] + subsequent_dates + + list_of_average_holdings = [] + for date_index in range(len(subsequent_dates)): + end_date = all_dates[date_index + 1] + previous_date = all_dates[date_index] + avg_holding = self._average_holding_for_period(previous_date, end_date) + list_of_average_holdings.append(avg_holding) + + return list_of_average_holdings + + def _average_holding_for_period( + self, previous_date: datetime.datetime, end_date: datetime.datetime + ): + positions = self.positions + positions_in_period = positions[previous_date:end_date] + avg_position = positions_in_period.abs().mean() + if np.isnan(avg_position): + return 0.0 + else: + return avg_position + + @property + def last_date_with_positions(self) -> datetime.datetime: + return self.positions.index[-1] + + def normalise_costs_in_instrument_currency(self, costs_as_pd_series) -> pd.Series: + dont_normalise_currency_costs = not self.vol_normalise_currency_costs + if dont_normalise_currency_costs: + return costs_as_pd_series + + cost_deflator = self.cost_deflator() + reindexed_deflator = cost_deflator.reindex( + costs_as_pd_series.index, method="ffill" + ) + + normalised_costs = reindexed_deflator * costs_as_pd_series + + return normalised_costs + + def calculate_cost_instrument_currency_for_a_fill(self, fill: Fill) -> float: + cost_for_trade = calculate_cost_from_fill_with_cost_object( + fill=fill, value_per_point=self.value_per_point, raw_costs=self.raw_costs + ) + + return cost_for_trade + + def cost_deflator(self): + cost_deflator = getattr(self, "_cost_deflator", None) + if cost_deflator is None: + self._cost_deflator = cost_deflator = self._calculate_cost_deflator() + + return cost_deflator + + def _calculate_cost_deflator(self) -> pd.Series: + ## adjusts costs according to price vol + price = self.price + + cost_scalar = calculate_cost_deflator(price) + + return cost_scalar + + @property + def raw_costs(self) -> instrumentCosts: + return self._raw_costs + + @property + def vol_normalise_currency_costs(self) -> bool: + return self._vol_normalise_currency_costs + + @property + def rolls_per_year(self) -> int: + return self._rolls_per_year + + @property + def multiply_roll_costs_by(self) -> float: + return self._multiply_roll_costs_by + + +def calculate_cost_from_fill_with_cost_object( + fill: Fill, value_per_point: float, raw_costs: instrumentCosts +) -> float: + trade = fill.qty + price = fill.price + include_slippage = fill.price_requires_slippage_adjustment + + block_price_multiplier = value_per_point + cost_for_trade = raw_costs.calculate_cost_instrument_currency( + blocks_traded=trade, + price=price, + block_price_multiplier=block_price_multiplier, + include_slippage=include_slippage, + ) + + return cost_for_trade diff --git a/src/quantlib_st/systems/accounts/pandl_calculators/pandl_using_fills.py b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_using_fills.py new file mode 100644 index 0000000..b365a94 --- /dev/null +++ b/src/quantlib_st/systems/accounts/pandl_calculators/pandl_using_fills.py @@ -0,0 +1,173 @@ +import numpy as np +import pandas as pd + +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.systems.accounts.pandl_calculators.pandl_calculation import ( + pandlCalculation, + apply_weighting, +) + +from quantlib_st.objects.fills import ListOfFills + + +class pandlCalculationWithFills(pandlCalculation): + def __init__(self, *args, fills: ListOfFills = arg_not_supplied, **kwargs): # type: ignore + # if fills aren't supplied, can be inferred from positions + super().__init__(*args, **kwargs) + self._fills = fills + # This attribute is not used + self._calculated_price = None + + def weight(self, weight: pd.Series): + ## we don't weight fills, instead will be inferred from positions + weighted_capital = apply_weighting(weight, self.capital) + weighted_positions = apply_weighting(weight, self.positions) + + return pandlCalculationWithFills( + self.price, + positions=weighted_positions, + fx=self.fx, + capital=weighted_capital, + value_per_point=self.value_per_point, + roundpositions=self.roundpositions, + delayfill=self.delayfill, + ) + + @classmethod + def using_positions_and_prices_merged_from_fills( + pandlCalculation, + price: pd.Series, + positions: pd.Series, + fills: ListOfFills, + **kwargs, + ): + merged_prices = merge_fill_prices_with_prices(price, fills) + + return pandlCalculation(price=merged_prices, positions=positions, **kwargs) + + @property + def fills(self) -> ListOfFills: + fills = self._fills + if fills is arg_not_supplied: + # Infer from positions + # positions will have delayfill and round applied to them already + fills = self._infer_fills_from_position() + self._fills = fills + + ## Fills passed in directly are expected to be precise, so we don't round or delay them + + return fills + + def _infer_fills_from_position(self) -> ListOfFills: + # positions will have delayfill and round applied to them already + positions = self.positions + if positions is arg_not_supplied: + raise Exception("Need to pass fills or positions") + + fills = ListOfFills.from_position_series_and_prices( + positions=positions, price=self.price + ) + return fills + + @property + def positions(self) -> pd.Series: + positions = self._get_passed_positions() + if positions is arg_not_supplied: + ## fills will already have delay and round positions applied + positions = self._infer_position_from_fills() + self._positions = positions + return positions + else: + positions_to_use = self._process_positions(positions) # type: ignore + return positions_to_use + + def _infer_position_from_fills(self) -> pd.Series: + fills = self._fills + if fills is arg_not_supplied: + raise Exception("Need to pass fills or positions") + + positions = infer_positions_from_fills(fills) + + return positions + + +def merge_fill_prices_with_prices( + prices: pd.Series, list_of_fills: ListOfFills +) -> pd.Series: + list_of_trades_as_pd_df = list_of_fills.as_pd_df() + unique_trades_as_pd_df = unique_trades_df(list_of_trades_as_pd_df) + + prices_to_use = pd.concat( + [prices, unique_trades_as_pd_df.price], axis=1, join="outer" + ) + prices_to_use.columns = ["price", "fill_price"] + prices_to_use.index = pd.to_datetime(prices_to_use.index) + + # Where no fill price available, use price + prices_to_use = prices_to_use.ffill(axis=1) + + prices_to_use = prices_to_use.fill_price + + prices_to_use = prices_to_use.replace([np.inf, -np.inf], np.nan) + prices_to_use = prices_to_use.dropna() + + return prices_to_use + + +def unique_trades_df(trade_df: pd.DataFrame) -> pd.DataFrame: + cash_flow = trade_df.qty * trade_df.price + trade_df["cash_flow"] = cash_flow + new_df = trade_df.groupby(trade_df.index).sum() + # qty and cash_flow will be correct, price won't be + new_price = new_df.cash_flow / new_df.qty + new_df["price"] = new_price + new_df = new_df.drop(labels="cash_flow", axis=1) + + return new_df + + +def infer_positions_from_fills(fills: ListOfFills) -> pd.Series: + date_index = [fill.date for fill in fills] + qty_trade = [fill.qty for fill in fills] + trade_series = pd.Series(qty_trade, index=date_index) + trade_series = trade_series.sort_index() + position_series = trade_series.cumsum() + + return position_series + + +""" +Can have other class methods in future that allow you to just pass fills, trades, or something else + + + @classmethod + def using_fills(pandlCalculation, price: pd.Series, + fills: ListOfFills, + **kwargs): + + positions = from_fills_to_positions(fills) + + merged_prices = merge_fill_prices_with_prices(price, + fills) + + return pandlCalculation(price=price, + positions=positions, + **kwargs) + + @classmethod + def using_trades_inferring_fill_prices(pandlCalculation, price: pd.Series, + trades: pd.Series, + **kwargs): + + fills = from_trades_to_fills(trades, price) + positions = from_fills_to_positions(fills) + + merged_prices = merge_fill_prices_with_prices(price, + fills) + + return pandlCalculation(price = price, + positions=positions, + **kwargs) + + +""" diff --git a/src/quantlib_st/systems/accounts/tests/test_accounts.py b/src/quantlib_st/systems/accounts/tests/test_accounts.py new file mode 100644 index 0000000..36400c4 --- /dev/null +++ b/src/quantlib_st/systems/accounts/tests/test_accounts.py @@ -0,0 +1,26 @@ +import pytest + +from quantlib_st.sysdata.sim.csv_futures_sim_test_data import CsvFuturesSimTestData +from quantlib_st.config.configdata import Config +from quantlib_st.systems.provided.futures_chapter15.basesystem import futures_system + + +@pytest.fixture(scope="module") +def system(): + """Minimal system fixture using Account stage only.""" + system = futures_system( + data=CsvFuturesSimTestData(), + config=Config("systems.provided.config.test_account_config.yaml"), + ) + return system + + +def test_account_costs_minimal(system): + """Minimal smoke test for Account stage costs using two instruments.""" + edollar_cost = system.accounts.get_SR_cost_per_trade_for_instrument("EDOLLAR") + us10_cost = system.accounts.get_SR_cost_per_trade_for_instrument("US10") + + assert isinstance(edollar_cost, float) + assert isinstance(us10_cost, float) + assert edollar_cost >= 0.0 + assert us10_cost >= 0.0 diff --git a/src/quantlib_st/systems/basesystem.py b/src/quantlib_st/systems/basesystem.py new file mode 100644 index 0000000..eb33571 --- /dev/null +++ b/src/quantlib_st/systems/basesystem.py @@ -0,0 +1,428 @@ +from typing import Optional + +from quantlib_st.config.configdata import Config +from quantlib_st.config.instruments import ( + get_duplicate_list_of_instruments_to_remove_from_config, + get_list_of_bad_instruments_in_config, + get_list_of_ignored_instruments_in_config, + get_list_of_untradeable_instruments_in_config, +) +from quantlib_st.sysdata.sim.sim_data import simData +from quantlib_st.logging.logger import * +from quantlib_st.systems.system_cache import systemCache, base_system_cache + +from quantlib_st.systems.tools.autogroup import ( + calculate_autogroup_weights_given_parameters, + config_is_auto_group, + resolve_config_into_parameters_and_weights_for_autogrouping, +) + +""" +This is used for items which affect an entire system, not just one instrument +""" +ALL_KEYNAME = "all" + + +class System(object): + """ + system objects are used for signal processing in a 'tree' like framework + + + This is the base class which all systems inherit + + Systems are: + + made up of stages + + take a data, and optionally a config object + + The system only has one method 'of its own' which is get_instrument_list + + """ + + def __init__( + self, + stage_list: list, + data: simData, + config: Optional[Config] = None, + log=get_logger("base_system"), + ): + """ + Create a system object for doing simulations or live trading + + :param stage_list: A list of stages + :type stage_list: list of systems.stage.SystemStage (or anything that inherits from it) + + :param data: data for doing simulations + :type data: quantlib_st.sysdata.sim.sim_data.simData (or anything that inherits from that) + + :param config: Optional configuration + :type config: sysdata.configdata.Config + + :returns: new system object + + >>> from systems.stage import SystemStage + >>> stage=SystemStage() + >>> from sysdata.sim.csv_futures_sim_data import csvFuturesSimData + >>> data=csvFuturesSimData() + >>> System([stage], data) + System base_system with .config, .data, and .stages: Need to replace method when inheriting + + """ + + if config is None: + # Default - for very dull systems this is sufficient + config = Config() + + self._data = data + self._config = config + self._log = log + + self.config.system_init(self) + self.data.system_init(self) + self._setup_stages(stage_list) + self._cache = systemCache(self) + + def _setup_stages(self, stage_list: list): + stage_names = [] + + try: + iter(stage_list) + except AssertionError: + raise Exception( + "You didn't pass a list into this System instance; even just one stage should be System([stage_instance])" + ) + + for stage in stage_list: + """ + This is where we put the methods to store various stages of the process + + """ + + # Stages have names, which are also how we find them in the system + # attributes + current_stage_name = stage.name + + # Each stage has a link back to the parent system + # This init sets this, and also passes the system logging object + stage.system_init(self) + + if current_stage_name in stage_names: + raise Exception( + "You have duplicate subsystems with the name %s. Remove " + "one of them, or change a name." % current_stage_name + ) + + setattr(self, current_stage_name, stage) + stage_names.append(current_stage_name) + + self._stage_names = stage_names + + def __repr__(self): + sslist = ", ".join(self.stage_names) + description = "System %s with .config, .data, and .stages: " % self.name + + return description + sslist + + @property + def log(self): + return self._log + + @property + def data(self): + return self._data + + @property + def config(self): + return self._config + + @property + def name(self): + return "base_system" + + @property + def cache(self): + return self._cache + + @property + def stage_names(self): + return self._stage_names + + # note we have to use this special cache here, or we get recursion problems + @base_system_cache() + def get_instrument_list( + self, + remove_duplicates=True, + remove_ignored=True, + remove_trading_restrictions=False, + remove_bad_markets=False, + remove_short_history=False, + days_required=750, + force_to_passed_list=None, + ) -> list: + """ + Get the instrument list + + :returns: list of instrument_code str + """ + if force_to_passed_list is not None: + instrument_list = force_to_passed_list + else: + instrument_list = self._get_instrument_list_from_config( + remove_duplicates=remove_duplicates, + remove_short_history=remove_short_history, + remove_bad_markets=remove_bad_markets, + remove_ignored=remove_ignored, + remove_trading_restrictions=remove_trading_restrictions, + days_required=days_required, + ) + + return instrument_list + + def _get_instrument_list_from_config( + self, + remove_duplicates=True, + remove_ignored=True, + remove_trading_restrictions=False, + remove_bad_markets=False, + remove_short_history=False, + days_required=750, + ) -> list: + instrument_list = self._get_raw_instrument_list_from_config() + instrument_list = self._remove_instruments_from_instrument_list( + instrument_list, + remove_duplicates=remove_duplicates, + remove_short_history=remove_short_history, + remove_bad_markets=remove_bad_markets, + remove_ignored=remove_ignored, + remove_trading_restrictions=remove_trading_restrictions, + days_required=days_required, + ) + + instrument_list = sorted(set(list(instrument_list))) + + return instrument_list + + def _get_raw_instrument_list_from_config(self) -> list: + config = self.config + try: + # if instrument weights specified in config ... + instrument_weights = get_instrument_weights_from_config(config) + instrument_list = list(instrument_weights.keys()) + except: + try: + # alternative place if no instrument weights + instrument_list = config.instruments # type: ignore + except: + try: + # okay maybe not, must be in data + instrument_list = self.data.get_instrument_list() + except: + raise Exception("Can't find instrument_list anywhere!") + + return instrument_list + + def _remove_instruments_from_instrument_list( + self, + instrument_list, + remove_duplicates=True, + remove_ignored=True, + remove_trading_restrictions=False, + remove_bad_markets=False, + remove_short_history=False, + days_required: int = 750, + ): + list_of_instruments_to_remove = self.get_list_of_instruments_to_remove( + remove_duplicates=remove_duplicates, + remove_short_history=remove_short_history, + remove_bad_markets=remove_bad_markets, + remove_ignored=remove_ignored, + remove_trading_restrictions=remove_trading_restrictions, + days_required=days_required, + ) + + self.log.debug( + "Following instruments removed entirely from sim: %s" + % str(list_of_instruments_to_remove) + ) + + instrument_list = [ + instrument + for instrument in instrument_list + if instrument not in list_of_instruments_to_remove + ] + + return instrument_list + + @base_system_cache() + def get_list_of_markets_not_trading_but_with_data( + self, + remove_duplicates=True, + remove_ignored=True, + remove_trading_restrictions=True, + remove_bad_markets=True, + remove_short_history=False, + days_required=750, + ) -> list: + not_trading = self.get_list_of_instruments_to_remove( + remove_duplicates=remove_duplicates, + remove_short_history=remove_short_history, + remove_bad_markets=remove_bad_markets, + remove_ignored=remove_ignored, + remove_trading_restrictions=remove_trading_restrictions, + days_required=days_required, + ) + + instrument_list = self.get_instrument_list() + + ## don't bother if already removed + not_trading_in_instrument_list = list( + set(instrument_list).intersection(set(not_trading)) + ) + + self.log.debug( + "Following instruments marked as not trading %s" + % str(not_trading_in_instrument_list) + ) + + return not_trading_in_instrument_list + + def get_list_of_instruments_to_remove( + self, + remove_duplicates=True, + remove_ignored=True, + remove_trading_restrictions=False, + remove_bad_markets=False, + remove_short_history=False, + days_required=750, + ) -> list: + list_to_remove = [] + if remove_duplicates: + list_of_duplicates = self.get_list_of_duplicate_instruments_to_remove() + list_to_remove.extend(list_of_duplicates) + + if remove_ignored: + list_of_ignored = self.get_list_of_ignored_instruments_to_remove() + list_to_remove.extend(list_of_ignored) + + if remove_bad_markets: + list_of_bad = self.get_list_of_bad_markets() + list_to_remove.extend(list_of_bad) + + if remove_trading_restrictions: + list_of_restricted = self.get_list_of_markets_with_trading_restrictions() + list_to_remove.extend(list_of_restricted) + + if remove_short_history: + list_of_short = self.get_list_of_short_history(days_required=days_required) + list_to_remove.extend(list_of_short) + + list_to_remove = list(set(list_to_remove)) + list_to_remove.sort() + + return list_to_remove + + def get_list_of_duplicate_instruments_to_remove(self): + duplicate_list = get_duplicate_list_of_instruments_to_remove_from_config( + self.config + ) + duplicate_list.sort() + if len(duplicate_list) > 0: + self.log.debug( + "Following instruments are 'duplicate_markets' %s " + % str(duplicate_list) + ) + + return duplicate_list + + def get_list_of_ignored_instruments_to_remove(self) -> list: + ignore_instruments = get_list_of_ignored_instruments_in_config(self.config) + ignore_instruments.sort() + if len(ignore_instruments) > 0: + self.log.debug( + "Following instruments are marked as 'ignore_instruments': not included: %s" + % str(ignore_instruments) + ) + + return ignore_instruments + + def get_list_of_markets_with_trading_restrictions(self) -> list: + trading_restrictions = get_list_of_untradeable_instruments_in_config( + self.config + ) + trading_restrictions.sort() + if len(trading_restrictions) > 0: + ## will only log once as cached + self.log.debug( + "Following instruments have restricted trading: %s " + % str(trading_restrictions) + ) + return trading_restrictions + + def get_list_of_bad_markets(self) -> list: + bad_markets = get_list_of_bad_instruments_in_config(self.config) + bad_markets.sort() + if len(bad_markets) > 0: + ## will only log once as cached + self.log.debug( + "Following instruments are marked as 'bad_markets': %s" + % str(bad_markets) + ) + + return bad_markets + + def get_list_of_short_history(self, days_required: int = 750) -> list: + instrument_list = self.data.get_instrument_list() + + too_short = [ + instrument_code + for instrument_code in instrument_list + if self.data.length_of_history_in_days_for_instrument(instrument_code) # type: ignore + < days_required + ] + + too_short.sort() + + if len(too_short) > 0: + self.log.debug( + "Following instruments have insufficient history: %s" % str(too_short) + ) + + return too_short + + +def get_instrument_weights_from_config(config: Config) -> dict: + instrument_weights_config = getattr(config, "instrument_weights", None) + if instrument_weights_config is None: + raise Exception("Instrument config not available") + + if config_is_auto_group(instrument_weights_config): + instrument_weights_dict = _get_instrument_weights_with_autogrouping( + instrument_weights_config + ) + else: + instrument_weights_dict = instrument_weights_config + + return instrument_weights_dict + + +def _get_instrument_weights_with_autogrouping(instrument_weights_config: dict) -> dict: + ( + auto_group_parameters, + auto_group_weights, + ) = resolve_config_into_parameters_and_weights_for_autogrouping( + instrument_weights_config + ) + + group_weights = calculate_autogroup_weights_given_parameters( + auto_group_weights=auto_group_weights, + auto_group_parameters=auto_group_parameters, + ) + + return group_weights + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/systems/forecasting.py b/src/quantlib_st/systems/forecasting.py new file mode 100644 index 0000000..8c5e75d --- /dev/null +++ b/src/quantlib_st/systems/forecasting.py @@ -0,0 +1,248 @@ +import pandas as pd + +from quantlib_st.systems.stage import SystemStage +from quantlib_st.systems.system_cache import output, dont_cache +from quantlib_st.systems.trading_rules import TradingRule + + +class Rules(SystemStage): + """ + Construct the forecasting stage + + Ways we can do this: + + a) We do this by passing a list of trading rules + + forecasting([trading_rule1, trading_rule2, ..]) + + Note that trading rules can be created using the TradingRule class, or you + can just pass in the name of a function, or a function. + + We can also use a generate_variations method to create a list of multiple + rules + + b) or we can create from a system config + + + KEY INPUT: Depends on trading rule(s) data argument + KEY OUTPUT: system.rules.get_raw_forecast(instrument_code, rule_variation_name) + system.rules.trading_rules() + + Name: rules + """ + + def __init__(self, trading_rules=None): + """ + Create a SystemStage for forecasting + + We either pass a dict or a list of trading rules (functions, strings + specifying a function, or objects of class TradingRule) + ... or we'll get it from the overall system config + (trading_rules=None) + + :param trading_rules: Set of trading rules + :type trading_rules: None (rules will be inherited from self.parent + system) TradingRule, str, callable function, or tuple (single rule) + list or dict (multiple rules) + :param pre_calc_rules: bool, if True then the first call to get a rule will calculate the values for all markets + + :returns: Rules object + + """ + super().__init__() + + # We won't have trading rules we can use until we've parsed them + self._trading_rules = None + + # ... store the ones we've been passed for now + self._passed_trading_rules = trading_rules + + @property + def name(self): # type: ignore + return "rules" + + @property + def passed_trading_rules(self): + return self._passed_trading_rules + + def __repr__(self): + trading_rules = self.trading_rules() + + rule_names = ", ".join(trading_rules.keys()) + + return "Rules object with rules " + rule_names + + @output() + def get_raw_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + """ + Does what it says on the tin - pulls the forecast for the trading rule + + This forecast will need scaling and capping later + + KEY OUTPUT + + """ + + system = self.parent + + self.log.debug( + "Calculating raw forecast %s for %s" + % (instrument_code, rule_variation_name), + instrument_code=instrument_code, + ) + # this will process all the rules, if not already done + trading_rule_dict = self.trading_rules() + trading_rule = trading_rule_dict[rule_variation_name] + + result = trading_rule.call(system, instrument_code) + result = pd.Series(result) + + return result + + @dont_cache + def trading_rules(self): + """ + Ensure self.trading_rules is actually a properly specified list of trading rules + + We can't do this when we __init__ because we might not have a parent yet + + :returns: List of TradingRule objects + + """ + + current_rules = self._trading_rules + + # We have already parsed the trading rules for this object, just return + # them + if current_rules is not None: + return current_rules + + trading_rules = self._get_trading_rules_from_passed_rules() + self._trading_rules = trading_rules + + return trading_rules + + def _get_trading_rules_from_passed_rules(self): + # What where we passed when object was created? + passed_rules = self.passed_trading_rules + + if passed_rules is None: + passed_rules = self._get_rules_from_parent_or_raise_errors() + + new_rules = process_trading_rules(passed_rules) + + return new_rules + + @dont_cache + def _get_rules_from_parent_or_raise_errors(self): + """ + We weren't passed anything in the command lines so need to inherit from the system config + """ + error_msg = None + if not hasattr(self, "parent"): + error_msg = "A Rules stage needs to be part of a System to identify trading rules, unless rules are passed when object created" + + elif not hasattr(self.parent, "config"): + error_msg = "A system needs to include a config with trading_rules, unless rules are passed when object created" + + elif not hasattr(self.parent.config, "trading_rules"): # type: ignore + error_msg = "A system config needs to include trading_rules, unless rules are passed when object created" + + if error_msg is not None: + self.log.critical(error_msg) + raise Exception(error_msg) + + # self.parent.config.tradingrules will already be in dictionary + # form + forecasting_config_rules = self.parent.config.trading_rules # type: ignore + + return forecasting_config_rules + + +def process_trading_rules(passed_rules) -> dict: + """ + + There are a number of ways to specify a set of trading rules. This function processes them all, + and returns a dict of TradingRule objects. + + data types handled: + dict - parse each element of the dict and use the names [unless has one or more of keynames: function, data, args] + list - parse each element of the list and give them arbitrary names + anything else is assumed to be something we can pass to TradingRule (string, function, tuple, (dict with keynames function, data, args), or TradingRule object) + + + :param passed_rules: Set of trading rules + + :type passed_rules: Single rule: + dict(function=str, optionally: args=dict(), optionally: data=list()), + TradingRule, str, callable function, or tuple + + Multiple rules: + list, dict without 'function' keyname + + :returns: dict of Tradingrule objects + + """ + if isinstance(passed_rules, list): + # Give some arbitrary name + processed_rules = _process_trading_rules_in_list(passed_rules) + + elif _is_a_single_trading_rule_in_a_dict(passed_rules): + processed_rules = _process_single_trading_rule(passed_rules) + + elif _is_a_dict_of_multiple_trading_rules(passed_rules): + processed_rules = _process_dict_of_trading_rules(passed_rules) + + else: + # Must be an individual rule (string, function, dict with 'function' or + # tuple) + processed_rules = _process_single_trading_rule(passed_rules) + + return processed_rules + + +def _process_trading_rules_in_list(trading_rules: list): + return dict( + [ + ("rule%d" % ruleid, TradingRule(rule)) + for (ruleid, rule) in enumerate(trading_rules) + ] + ) + + +def _is_a_single_trading_rule_in_a_dict(trading_rules: dict): + if isinstance(trading_rules, dict): + if "function" in trading_rules: + return True + + return False + + +def _is_a_dict_of_multiple_trading_rules(trading_rules: dict): + if isinstance(trading_rules, dict): + if "function" not in trading_rules: + return True + + else: + return False + + +def _process_dict_of_trading_rules(trading_rules: dict): + processed_rules = dict( + [(keyname, TradingRule(trading_rules[keyname])) for keyname in trading_rules] + ) + return processed_rules + + +def _process_single_trading_rule(trading_rule): + list_of_rules = [trading_rule] + processed_rules = _process_trading_rules_in_list(list_of_rules) + return processed_rules + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/systems/provided/config/__init__.py b/src/quantlib_st/systems/provided/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/systems/provided/config/test_account_config.yaml b/src/quantlib_st/systems/provided/config/test_account_config.yaml new file mode 100644 index 0000000..dd8fae9 --- /dev/null +++ b/src/quantlib_st/systems/provided/config/test_account_config.yaml @@ -0,0 +1,26 @@ +#YAML +# +# Raw data +# +# +# Trading rules +# +# +percentage_vol_target: 20.0 +notional_trading_capital: 250000 +base_currency: "USD" + +# Portfolio creation (minimal) +instrument_weights: + EDOLLAR: 0.50 + US10: 0.50 +instrument_div_multiplier: 1.00 + +countries: + US: + - EDOLLAR + - US10 +asset_classes: + Rates: + - EDOLLAR + - US10 diff --git a/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py b/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py new file mode 100644 index 0000000..a364465 --- /dev/null +++ b/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py @@ -0,0 +1,47 @@ +""" +Minimal futures system for account-stage testing. + +This system wires only the Account stage to keep things fast and focused. +""" + +from quantlib_st.config.configdata import Config +from quantlib_st.sysdata.sim.csv_futures_sim_test_data import CsvFuturesSimTestData +from quantlib_st.systems.basesystem import System +from quantlib_st.systems.accounts.accounts_stage import Account + + +def futures_system( + data=None, + config=None, +): + """ + Minimal system with only Account stage. + + :param data: data object (defaults to CsvFuturesSimTestData) + :type data: quantlib_st.sysdata.sim.sim_data.simData or compatible + + :param config: Configuration object (defaults to test_account_config.yaml) + :type config: quantlib_st.config.configdata.Config + """ + + if data is None: + data = CsvFuturesSimTestData() + + if config is None: + config = Config("systems.provided.config.test_account_config.yaml") + + system = System( + [ + Account(), + ], + data, + config, + ) + + return system + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/systems/stage.py b/src/quantlib_st/systems/stage.py new file mode 100644 index 0000000..9359184 --- /dev/null +++ b/src/quantlib_st/systems/stage.py @@ -0,0 +1,53 @@ +from quantlib_st.core.objects import get_methods +from quantlib_st.logging.logger import * +from quantlib_st.systems.basesystem import System + + +class SystemStage(object): + """ + Default stage object: we inherit from this, rather than use 'in the raw' + + Here is the standard header to use for stages: + + Create a SystemStage for doing something + + + KEY INPUT: system.....method(args) + found in self.method(args) + + KEY OUTPUT: system.stage_name.method(args) + + Name: stage_name + """ + + @property + def name(self): + return "Need to replace method when inheriting" + + def __repr__(self): + return "SystemStage '%s' Try %s.methods()" % ( + self.name, + self.name, + ) + + def methods(self): + return get_methods(self) + + def system_init(self, system: System): + # method called once we have a system + self._parent = system + + # and a log + self._log = get_logger("base_system", {STAGE_LOG_LABEL: self.name}) + + @property + def log(self): + log = getattr( + self, "_log", get_logger("base_system", {STAGE_LOG_LABEL: self.name}) + ) + return log + + @property + def parent(self) -> System: + parent = getattr(self, "_parent", None) + return parent # type: ignore diff --git a/src/quantlib_st/systems/system_cache.py b/src/quantlib_st/systems/system_cache.py new file mode 100644 index 0000000..0dc7086 --- /dev/null +++ b/src/quantlib_st/systems/system_cache.py @@ -0,0 +1,793 @@ +### THIS IS PRETTY UGLY CODE BUT NOT CRITICAL SO NOT TOUCHING FOR NOW... + +""" +A cache lives inside each system object, storing preliminary results + +It's a dict, with keys that are tuples (stage name, item name) + +There are 3 kinds of things in a cache with different levels of persistence: + - anything that isn't special + - things that have an 'all' key - + - _protected - that wouldn't normally be deleted + +""" + +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package + +import pickle +import bz2 +from functools import wraps + +""" +This is used for items which affect an entire system, not just one instrument +""" +ALL_KEYNAME = "All_instruments" + +# more useful flags +EMPTY_KEYNAME = object() +MISSING_FROM_CACHE = object() + + +class cacheRef(object): + """ + References to use within caches + + """ + + def __init__( + self, stage_name, itemname, instrument_code=ALL_KEYNAME, flags="", keyname="" + ): + self.stage_name = stage_name + self.itemname = itemname + self.instrument_code = instrument_code + self.flags = flags + self.keyname = keyname + + def __repr__(self): + if self.keyname == "": + keystring = "" + else: + keystring = "[%s] " % self.keyname + + return "%s in %s for instrument %s %s%s" % ( + self.itemname, + self.stage_name, + self.instrument_code, + keystring, + self.flags, + ) + + # following code is to make keys hashable and suitable for dict keys + def __key(self): + return tuple(v for k, v in sorted(self.__dict__.items())) + + def __eq__(self, other): + return isinstance(other, self.__class__) and self.__key() == other.__key() + + def __hash__(self): + return hash(self.__key()) + + +class listOfCacheRefs(list): + """ + a list of cacheRef + + only used to apply filters, or summarise + + can apply filters successively if desired + """ + + def filter_by_stage_name(self, stage_name): + new_list = [ + cache_ref for cache_ref in self if cache_ref.stage_name == stage_name + ] + + return listOfCacheRefs(new_list) + + def filter_by_itemname(self, itemname): + new_list = [cache_ref for cache_ref in self if cache_ref.itemname == itemname] + + return listOfCacheRefs(new_list) + + def filter_by_instrument_code(self, instrument_code): + new_list = [ + cache_ref + for cache_ref in self + if cache_ref.instrument_code == instrument_code + ] + + return listOfCacheRefs(new_list) + + def filter_by_keyname(self, keyname): + new_list = [cache_ref for cache_ref in self if cache_ref.keyname == keyname] + + return listOfCacheRefs(new_list) + + def unique_list_of_stage_names(self): + return list(set([cache_ref.stage_name for cache_ref in self])) + + def unique_list_of_item_names(self): + return list(set([cache_ref.itemname for cache_ref in self])) + + def unique_list_of_instrument_codes(self): + return list(set([cache_ref.instrument_code for cache_ref in self])) + + def unique_list_of_keynames(self): + return list(set([cache_ref.keyname for cache_ref in self])) + + +class cacheElement(object): + """ + Each cache element consists of a value, and some bool values telling us what we can do with it + """ + + def __init__(self, value, protected=False, not_pickable=False): + self._value = value + self._protected = protected + self._not_pickable = not_pickable + + def __repr__(self): + return str(self._value) + + def value(self): + return self._value + + def not_pickable(self): + return self._not_pickable + + def protected(self): + return self._protected + + def can_be_pickled(self): + return not self._not_pickable + + +class systemCache(dict): + def __init__(self, parent_system): + super().__init__() + self._parent = parent_system # so we can access the instrument list + self.set_caching_on() + + @property + def parent(self): + return self._parent + + def set_caching_on(self): + self._caching_on = True + + def set_caching_off(self): + self._caching_on = False + + def are_we_caching(self): + return self._caching_on + + def __repr__(self): + if self.are_we_caching(): + list_of_elements = ", ".join( + [str(cache_ref) for cache_ref in self.get_items_with_data()] + ) + return "Cache, elements: " + list_of_elements + else: + return "Not using cache" + + def get_items_with_data(self): + """ + Return items in the cache with data (or at least key values set) + :returns: list of cacheRef objects + """ + + return listOfCacheRefs(list(self.keys())) + + def partial_cache(self, cache_ref_list): + """ + Returns the cache with only some items included + + :param cache_ref_list: the items to include + :type cache_ref_list: list of cacheRef instances + + :returns: systemCache object + """ + + new_cache = systemCache(self.parent) + for cache_ref in cache_ref_list: + new_cache[cache_ref] = self[cache_ref] + + return new_cache + + def pickle(self, relativefilename): + """ + Save everything in the cache to a pickle + + EXCEPT 'not picklable' items + + :param relativefilename: cache location filename in 'dot' format eg 'systems.basesystem.py' is this file + :type relativefilename: str + + :returns: None + + """ + + filename = resolve_path_and_filename_for_package(relativefilename) + + pickable_cache_refs = self._get_pickable_items() + + cache_to_pickle = self.partial_cache(pickable_cache_refs) + cache_to_pickle_as_dict = cache_to_pickle.as_dict() + + if self._parent.config.get_element("backtest_compress"): + with bz2.open(filename, "wb") as fhandle: + pickle.dump(cache_to_pickle_as_dict, fhandle) + else: + with open(filename, "wb+") as fhandle: + pickle.dump(cache_to_pickle_as_dict, fhandle) + + def as_dict(self): + self_as_dict = {} + for ref_name in self.get_items_with_data(): + self_as_dict[ref_name] = self[ref_name] + + return self_as_dict + + def unpickle(self, relativefilename, clearcache=True): + """ + Loads the saved cache + + Note that certain elements (accountCurve objects and optimisation) won't + be pickled, and so won't be loaded. You will need to regenerate + these. + + If clearcache is True then we clear the entire cache first. Otherwise + we end up with a 'mix' + - not advised so do at your peril + + :param filename: cache location + :type filename: filename in 'dot' format eg 'systems.basesystem.py' is this file + + :param clearcache: Clear the entire cache, or overwrite what we have? + :type clearcache: bool + + :returns: None + + """ + + filename = resolve_path_and_filename_for_package(relativefilename) + + if self._parent.config.get_element("backtest_compress"): + with bz2.open(filename, "rb") as fhandle: + cache_from_pickled = pickle.load(fhandle) + else: + with open(filename, "rb") as fhandle: + cache_from_pickled = pickle.load(fhandle) + + if clearcache: + self.clear() + + for itemname in cache_from_pickled.keys(): + self[itemname] = cache_from_pickled[itemname] + + def _get_protected_items(self): + """ + Return items in the cache which are protected + + :returns: list cache keys + """ + + protected_cache_refs = [ + cache_ref + for cache_ref in self.get_items_with_data() + if self[cache_ref].protected() + ] + + return protected_cache_refs + + def _get_pickable_items(self): + """ + Return keys of items in the cache which can be pickled + + :returns: list cache keys + """ + + pickable_cache_refs = [ + cache_ref + for cache_ref in self.get_items_with_data() + if self[cache_ref].can_be_pickled() + ] + + return pickable_cache_refs + + def get_cache_refs_for_instrument(self, instrument_code): + """ + return cache refs for a particular instrument code + + :param instrument_code: + :return: list of cache refs + """ + + cache_ref_list = self.get_items_with_data() + cache_ref_list = cache_ref_list.filter_by_instrument_code(instrument_code) + + return cache_ref_list + + def get_cacherefs_for_stage(self, stage_name): + """ + Returns cache itemnames relevant to a particular stage + + :param stage_name: stage name eg rawdata + :type stage_name: str + + :returns: list of cache refs + + """ + + cache_ref_list = self.get_items_with_data() + cache_ref_list = cache_ref_list.filter_by_stage_name(stage_name) + + return cache_ref_list + + def get_itemnames_for_stage(self, stage_name): + """ + Returns cache itemnames relevant to a particular stage + + :param stage_name: stage name eg rawdata + :type stage_name: str + + :returns: list of itemnames + + """ + + cache_ref_list = self.get_cacherefs_for_stage(stage_name) + itemnames = cache_ref_list.unique_list_of_item_names() + + return itemnames + + def get_cache_refs_across_system(self): + """ + Returns cross market cache cache refs + + :returns: list of cache refs + + """ + + return self.get_cache_refs_for_instrument(ALL_KEYNAME) + + def cache_ref_list_with_protected_removed(self, cache_ref_list): + """ + + :param cache_ref_list: A list of cache refs + :return: A list of cache refs, with anything protected removed + """ + + cache_ref_list = [ + cache_ref for cache_ref in cache_ref_list if not self[cache_ref].protected() + ] + + return listOfCacheRefs(cache_ref_list) + + def delete_items_for_stage(self, stage_name, delete_protected=False): + """ + Delete all items with data for a particular stage + + :param stage_name: stage name eg rawdata + :type stage_name: str + + :param deleted_protected: Delete everything, even stuff in self.protected? + :type delete_protected: bool + + :returns: deleted data + + """ + + cache_ref_list = self.get_cacherefs_for_stage(stage_name) + self.delete_elements_in_cache_ref_list( + cache_ref_list, delete_protected=delete_protected + ) + + def delete_items_for_instrument(self, instrument_code, delete_protected=False): + """ + Delete everything in the system relating to a particular instrument_code + + :param instrument_code: Instrument to delete + :type instrument_code: str + + :param deleted_protected: Delete everything, even stuff that is protected + :type delete_protected: bool + + + Example of usage: When working with a live system we listen to a message bus, if a new + price is received then we delete things in the cache + + This means when we ask for self.optimal_positions(instrument_code) it + has to recalc all intermediate steps as the cached + + However we ignore anything in self._protected This is normally cross + sectional data which we only want to calculate periodically + + if delete_protected is True then we delete that stuff as well + (this is roughly equivalent to creating the systems object from scratch) + + """ + + cache_ref_list = self.get_cache_refs_for_instrument(instrument_code) + self.delete_elements_in_cache_ref_list( + cache_ref_list, delete_protected=delete_protected + ) + + def delete_items_across_system(self, delete_protected=False): + """ + Deletes cross market cache itemnames (i.e. anything with instrument_code ALL_KEYNAME, normally 'all' + + :param deleted_protected: Delete everything, even stuff in self.protected? + :type delete_protected: bool + + :returns: nothing + + """ + + self.delete_items_for_instrument(ALL_KEYNAME, delete_protected=delete_protected) + + def delete_all_items(self, delete_protected=False): + """ + Delete everything in the cache + + :param deleted_protected: Delete everything, even stuff in self.protected? + :type delete_protected: bool + + :returns: deleted data + + """ + + cache_ref_list = self.get_items_with_data() + self.delete_elements_in_cache_ref_list( + cache_ref_list, delete_protected=delete_protected + ) + + def delete_elements_in_cache_ref_list(self, cache_ref_list, delete_protected=False): + """ + Delete everything in the cache + + :param deleted_protected: Delete everything, even stuff in self.protected? + :type delete_protected: bool + + :returns: nothing + + """ + + if not delete_protected: + cache_ref_list = self.cache_ref_list_with_protected_removed(cache_ref_list) + + self._delete_elements_in_cache_ref_list_dangerous(cache_ref_list) + + def _delete_elements_in_cache_ref_list_dangerous(self, cache_ref_list): + """ + Remove all elements in the given list from the cache + DO NOT CALL DIRECTLY - doesn't check for protected status + + :param cache_ref_list: + :return: nothing + """ + + ans = [ + self._delete_single_element_from_cache_dangerous(cache_ref) + for cache_ref in cache_ref_list + ] + + return None + + def _delete_single_element_from_cache_dangerous(self, cache_ref): + """ + Remove the relevant ref from the cache + DO NOT CALL DIRECTLY - doesn't check for protected status + + :param cache_ref: cacheRef instance + :return: nothing + """ + if cache_ref in self: + del self[cache_ref] + + def set_item_in_cache(self, value, cache_ref, protected=False, not_pickable=False): + """ + Set an item in a cache to a specific value. + + :param value: The value to set to + :type value: Anything (normally pd.frames or floats) + + :param protected: is the item protected from deletion? + :param nopickle: is the item not capable of pickling? + + :param cache_ref: The item to set + :type cache_ref: cacheRef + + + :returns: nothing + """ + + self[cache_ref] = cacheElement( + value, protected=protected, not_pickable=not_pickable + ) + + def _get_item_from_cache(self, cache_ref): + """ + Get the value of an item from the cache self._cache + Never called directly, use calc_or_cache instead + + :param cache_ref: The item to get + :type cache_ref: cacheRef + + :returns: MISSING_FROM_CACHE or item value + """ + cache_element = self.get(cache_ref, MISSING_FROM_CACHE) + + if cache_element is MISSING_FROM_CACHE: + return MISSING_FROM_CACHE + + return cache_element.value() + + def get_instrument_list(self): + return self.parent.get_instrument_list() + + def calc_or_cache( + self, + func, + this_stage, + *args, + protected=False, + not_pickable=False, + instrument_classify=True, + use_arg_names=True, + **kwargs, + ): + """ + Assumes that self._cache has an attribute itemname, and that is a dict + + If cached item exists return it. Else call + func with *args and **kwargs; if the latter updates the cache + + :param func: function to call if missing from cache + :type func: function + + :param this_stage: stage within system that is calling us + :type this_stage: system stage + + :param args, kwargs: also passed to func if called + + :param instrument_classify: if True then we find an argument that is an instrument code, and add as a cache key + + :param protected: status flag; only set if the value is missing from the dict + :param nopickle: status flag; only set if the value is missing from the dict + + :returns: contents of cache or result of calling function + + + """ + if not self.are_we_caching(): + # not caching, just return the value + value = func(this_stage, *args, **kwargs) + return value + + # Turn all the arguments into things we can use to identify the cache + # element uniquely + cache_ref = self.cache_ref( + func, + this_stage, + *args, + use_arg_names=use_arg_names, + instrument_classify=instrument_classify, + **kwargs, + ) + + value = self._get_item_from_cache(cache_ref) + + if value is MISSING_FROM_CACHE: + # call the function. Note in the original function 'this_stage' was + # 'self' + value = func(this_stage, *args, **kwargs) + self.set_item_in_cache( + value, cache_ref, protected=protected, not_pickable=not_pickable + ) + + return value + + def cache_ref( + self, + func, + this_stage, + *args, + use_arg_names=True, + instrument_classify=True, + **kwargs, + ): + """ + Return cache key + + :param func: function we're calling + + :param this_stage: stage within system that is calling us + :type this_stage: system stage + + :param instrument_classify: if True then we find an argument that is an instrument code, and add as a cache key + + :returns: str + + + """ + + # Turn all the arguments into things we can use to identify the cache + # element uniquely + itemname = func.__name__ # use name of function as reference in cache + stage_name = ( + this_stage.name + ) # use stage_name in case same function used across multiple stages + + if instrument_classify: + list_of_codes = ( + self.get_instrument_list() + ) # needed to identify instrument_code amongst args + else: + # if we're calling from the base system we don't want infinite + # recursion + list_of_codes = [] + + (instrument_code, keyname) = resolve_args_to_code_and_key( + args, list_of_codes, use_arg_names=use_arg_names + ) # instrument involved, and/or other keys eg rule name + flags = resolve_kwargs_to_str( + kwargs, use_arg_names=use_arg_names + ) # used mostly in accounts, eg to identify delayed returns + + cache_ref = cacheRef( + stage_name, itemname, instrument_code, flags=flags, keyname=keyname + ) + + return cache_ref + + +def resolve_args_to_code_and_key(args, list_of_codes, use_arg_names=True): + """ + Resolves a list of placed args for a function + Pulls out the first arg that is an instrument_code (in list_of_codes) + + :param args: + :param list_of_codes: + :return: (instrument_code, keyname) + """ + keyname_list = [] + if use_arg_names: + args_to_process = list(args) + else: + args_to_process = [] + + instrument_code = None + + while len(args_to_process) > 0: + individual_arg = args_to_process.pop() + + # we only take the first arg that is an instrument code + if instrument_code is None: + if individual_arg in list_of_codes: + instrument_code = individual_arg + continue + # otherwise add to keynames + keyname_list.append(str(individual_arg)) + + if instrument_code is None: + # no instrument in arguments, so must be a cross market thing + instrument_code = ALL_KEYNAME + + # Make into a key that's nicer to look at + if len(keyname_list) == 0: + keyname = "" + elif len(keyname_list) == 1: + keyname = str(keyname_list[0]) + else: + keyname = str(keyname_list) + + return (instrument_code, keyname) + + +def resolve_kwargs_to_str(kwargs, use_arg_names: bool = True): + """ + Turn a list of named arguments into a flag string representing them, + eg resolve_flags_to_str(dict(a=1, b=2) + will return "a=1, b=2" + + :param kwargs: dict of arguments passed to some function + :return: str + """ + if not use_arg_names: + return "" + + def resolve_individual_flag(single_flag, kwargs): + argvalue = str(kwargs[single_flag]) + return "%s=%s" % (single_flag, str(argvalue)) + + long_flag_string = [ + resolve_individual_flag(single_flag, kwargs) for single_flag in kwargs.keys() + ] + + return ", ".join(long_flag_string) + + +# null decorator doesn't do anything +def null_decorator(func): + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + + return wrapper + + +# generic decorator for caching +def stage_access_cache_decorator(protected=False, not_pickable=False): + """ + + :param protected: is this protected from casual deletion? + :param not_pickable: can this not be saved using the pickle function (complex objects) + :return: decorator function + """ + + # this pattern from Beazleys book; function inside function to get + # arguments to wrapper + def decorate(func): + @wraps(func) + # note 'self' as always called from inside stage class + def wrapper(self, *args, **kwargs): + system = self.parent + this_stage = self + + ans = system.cache.calc_or_cache( + func, + this_stage, + *args, + protected=protected, + not_pickable=not_pickable, + instrument_classify=True, + **kwargs, + ) + + return ans + + return wrapper + + return decorate + + +# generic decorator for caching in base system +def base_system_cache(protected=False, not_pickable=False): + """ + + :param protected: is this protected from casual deletion? + :param not_pickable: can this not be saved using the pickle function (complex objects) + :return: decorator function + """ + + # this pattern from Beazleys book; function inside function to get + # arguments to wrapper + def decorate(func): + @wraps(func) + # note 'self' as always called from inside system class + def wrapper(self, *args, **kwargs): + system = self + + # instrument_classify has to be false, else infinite loop + ans = system.cache.calc_or_cache( + func, + system, + *args, + protected=protected, + not_pickable=not_pickable, + instrument_classify=False, + use_arg_names=False, + **kwargs, + ) + + return ans + + return wrapper + + return decorate + + +# actual decorators used, snappier names for 'stage wiring' +input = null_decorator +dont_cache = null_decorator +diagnostic = stage_access_cache_decorator +output = stage_access_cache_decorator diff --git a/src/quantlib_st/systems/tools/__init__.py b/src/quantlib_st/systems/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/quantlib_st/systems/tools/autogroup.py b/src/quantlib_st/systems/tools/autogroup.py new file mode 100644 index 0000000..5f2dee0 --- /dev/null +++ b/src/quantlib_st/systems/tools/autogroup.py @@ -0,0 +1,275 @@ +from quantlib_st.core.genutils import list_intersection +from quantlib_st.core.constants import arg_not_supplied, named_object + +from copy import copy + +WEIGHT_FLAG = "weight" +APPROX_IDM_PARAMETER = "use_approx_DM" + + +def calculate_autogroup_weights_given_parameters( + auto_group_weights: dict, + auto_group_parameters: dict | named_object = arg_not_supplied, + keys_to_exclude: list | named_object = arg_not_supplied, +) -> dict: + ## Example: + """ + auto_group_weights = dict(trendy = dict(weight=.7, + accel=dict(weight=.3, accel1=.5, accel2=.5), + mom=dict(weight=.7, mom1=1.0)), + carry = dict(weight=.3, carry1=1.0)) + """ + if keys_to_exclude is arg_not_supplied: + keys_to_exclude = [] + + tree_of_weights = autoGroupPortfolioWeight( + auto_group_weights=auto_group_weights, + auto_group_parameters=auto_group_parameters, + ) + tree_of_weights.remove_excluded_keys_and_reweight(keys_to_exclude) # type: ignore + + tree_of_weights.resolve_weights() + + collapsed_weights = _collapse_tree_of_weights(tree_of_weights) + + return collapsed_weights + + +class autoGroupPortfolioWeight(dict): + def __init__( + self, + auto_group_weights: dict, + auto_group_parameters: dict | named_object = arg_not_supplied, + ): + copy_auto_group_weights = copy(auto_group_weights) + group_weight = copy_auto_group_weights.pop(WEIGHT_FLAG, 1.0) + auto_group_weights_without_weight_entry = copy_auto_group_weights + + super().__init__(auto_group_weights_without_weight_entry) + + self.group_weight = group_weight + if auto_group_parameters is arg_not_supplied: + auto_group_parameters = {} + self._parameters = auto_group_parameters + + ## Must call on __init__ or does_not_contain_portfolio will fail + self._create_tree_below() + + def _create_tree_below(self): + ## Always call on __init__ + for key, value in self.items(): + if type(value) is dict: + self[key] = autoGroupPortfolioWeight(value, self.parameters) + + def remove_excluded_keys_and_reweight(self, keys_to_exclude: list): + if self.contains_portfolios: + self._remove_excluded_keys_and_reweight_over_subportfolios(keys_to_exclude) + else: + self._remove_excluded_keys_and_reweight_for_atomic_portfolio( + keys_to_exclude + ) + + ## Need to deal with completely empty dicts + + def resolve_weights(self, level: int = 1, cumulative_multiplier: float = 1.0): + if self.already_weighted: + raise Exception("Can't resolve weights more than once") + + approx_dm = self._approx_dm_if_required(level) + group_weight = self.group_weight + new_multiplier = cumulative_multiplier * approx_dm * group_weight + + if self.contains_portfolios: + self._resolve_weights_with_subportfolios( + level=level, cumulative_multiplier=new_multiplier + ) + else: + self._resolve_weights_for_atomic_portfolio( + cumulative_multiplier=new_multiplier + ) + + self.flag_as_weighted() + + def _remove_excluded_keys_and_reweight_over_subportfolios( + self, keys_to_exclude: list + ): + for sub_portfolio in self.sub_portfolios(): + sub_portfolio.remove_excluded_keys_and_reweight(keys_to_exclude) + ## remove empty portfolios and reweight + self._remove_empty_portfolios_one_level_down_and_reweight() + + def _remove_excluded_keys_and_reweight_for_atomic_portfolio( + self, keys_to_exclude: list + ): + ## eg if we had a=.3, b=.4, c=.3 and we lost c + keys_to_remove = list_intersection(keys_to_exclude, self.list_of_keys) + for key in keys_to_remove: + self.pop(key) + + self.reweight_atomic_portfolio() + + def _resolve_weights_with_subportfolios( + self, level: int, cumulative_multiplier: float + ): + new_level = level + 1 + for sub_portfolio in self.sub_portfolios(): + sub_portfolio.resolve_weights( + level=new_level, cumulative_multiplier=cumulative_multiplier + ) + + def _resolve_weights_for_atomic_portfolio(self, cumulative_multiplier: float): + self._apply_multiplier_to_atomic_portfolio(cumulative_multiplier) + + def _approx_dm_if_required(self, level: int) -> float: + if self.use_approx_dm: + ## At top level we assume zero correlation, at lower levels correlation will rise + ## and DM will get smaller + size = len(self) + approx_dm = size ** (0.5 / level) + else: + approx_dm = 1.0 + + return approx_dm + + def reweight_atomic_portfolio(self): + if self.is_empty: + pass + else: + total_weight = self.total_weight_in_atomic_portfolio + self._apply_multiplier_to_atomic_portfolio(1.0 / total_weight) + + def _apply_multiplier_to_atomic_portfolio(self, multiplier: float): + assert self.does_not_contain_portfolios + for key, value in self.items(): + self[key] = value * multiplier + + def _remove_empty_portfolios_one_level_down_and_reweight(self): + assert self.contains_portfolios + ## not recursive so has to be called within a recursive + initial_list_of_keys = self.list_of_keys + for key in initial_list_of_keys: + sub_portfolio = self[key] + if sub_portfolio.is_empty: + self.pop(key) + + self._reweight_subportfolios() + + def _reweight_subportfolios(self): + if self.is_empty: + pass + else: + total_weight = self.total_weight_of_subportfolios + self._apply_multiplier_to_subportfolios(1.0 / total_weight) + + def _apply_multiplier_to_subportfolios(self, multiplier: float): + for sub_portfolio in self.sub_portfolios(): + sub_portfolio.group_weight = sub_portfolio.group_weight * multiplier + + @property + def total_weight_of_subportfolios(self) -> float: + assert self.contains_portfolios + total_weight_in_portfolio = sum( + [sub_portfolio.group_weight for sub_portfolio in self.sub_portfolios()] + ) + return total_weight_in_portfolio + + def sub_portfolios(self) -> list: + assert self.contains_portfolios + return list(self.values()) + + @property + def total_weight_in_atomic_portfolio(self) -> float: + assert self.does_not_contain_portfolios + total_weight_in_portfolio = sum(self.values()) + return total_weight_in_portfolio + + @property + def is_empty(self) -> bool: + return len(self) == 0 + + @property + def list_of_keys(self) -> list: + return list(self.keys()) + + @property + def use_approx_dm(self) -> bool: + return self.parameters.get(APPROX_IDM_PARAMETER, False) + + @property + def parameters(self) -> dict: + assert isinstance(self._parameters, dict) + return self._parameters + + @property + def does_not_contain_portfolios(self) -> bool: + return not self.contains_portfolios + + @property + def contains_portfolios(self) -> bool: + ## low level + any_portfolios_in_self = any( + [type(value) is autoGroupPortfolioWeight for value in self.values()] + ) + return any_portfolios_in_self + + @property + def group_weight(self) -> float: + return self._group_weight + + @group_weight.setter + def group_weight(self, group_weight): + self._group_weight = group_weight + + @property + def already_weighted(self) -> bool: + return getattr(self, "_already_weighted", False) + + def flag_as_weighted(self): + self._already_weighted = True + + def __repr__(self): + return "Group weight %f: %s " % (self.group_weight, str(dict(self))) + + +def _collapse_tree_of_weights( + tree_of_weights: autoGroupPortfolioWeight, +) -> autoGroupPortfolioWeight: + weights_as_dict = {} + for key, sub_portfolio in tree_of_weights.items(): + if sub_portfolio.contains_portfolios: + weights_this_sub_portfolio = _collapse_tree_of_weights(sub_portfolio) + else: + weights_this_sub_portfolio = dict(sub_portfolio) + + weights_as_dict.update(weights_this_sub_portfolio) + + return weights_as_dict # type: ignore + + +AUTO_WEIGHTING_FLAG = "auto_weight_from_grouping" +AUTO_WEIGHTING_PARAMETERS = "parameters" +AUTO_WEIGHTING_GROUP_LABEL = "groups" + + +def config_is_auto_group(forecast_weights_config: dict) -> bool: + flag = forecast_weights_config.get(AUTO_WEIGHTING_FLAG, None) + auto_weighting_flag_in_config_dict = flag is not None + + return auto_weighting_flag_in_config_dict + + +def resolve_config_into_parameters_and_weights_for_autogrouping( + forecast_weights_config: dict, +) -> tuple: + try: + auto_group_config = copy(forecast_weights_config[AUTO_WEIGHTING_FLAG]) + auto_group_parameters = auto_group_config.pop(AUTO_WEIGHTING_PARAMETERS) + auto_group_weights = auto_group_config.pop(AUTO_WEIGHTING_GROUP_LABEL) + except: + error_msg = "Auto weighting should contain two elements: %s and %s" % ( + AUTO_WEIGHTING_PARAMETERS, + AUTO_WEIGHTING_GROUP_LABEL, + ) + raise Exception(error_msg) + + return auto_group_parameters, auto_group_weights diff --git a/src/quantlib_st/systems/trading_rules.py b/src/quantlib_st/systems/trading_rules.py new file mode 100644 index 0000000..f74a4f9 --- /dev/null +++ b/src/quantlib_st/systems/trading_rules.py @@ -0,0 +1,554 @@ +from copy import copy +from typing import ( + Optional, + Any, + TYPE_CHECKING, + List, + Dict, + Union, + Callable, + Tuple, + cast, +) + +import pandas as pd + +if TYPE_CHECKING: + from quantlib_st.systems.basesystem import System + +from quantlib_st.core.objects import ( + resolve_data_method, + resolve_function, + hasallattr, +) +from quantlib_st.core.pandas.strategy_functions import replace_all_zeros_with_nan +from quantlib_st.core.text import ( + sort_dict_by_underscore_length, + strip_underscores_from_dict_keys, + force_args_to_same_length, +) + + +DEFAULT_PRICE_SOURCE = "data.daily_prices" + +# Define a type for trading rule specifications +TradingRuleSpec = Union[ + "TradingRule", + Tuple[Union[Callable, str], List[str], Dict[str, Any]], + Dict[str, Any], + Callable, + str, +] + + +class TradingRule(object): + """ + Container for trading rules + + Can be called manually or will be called when configuring a system + """ + + def __init__( + self, + rule: TradingRuleSpec, + data: Optional[list] = None, + other_args: Optional[dict] = None, + ): + """ + Create a trading rule from a function + + Functions must be of the form function(*dataargs, **kwargs), where + *dataargs are unnamed data items, and **kwargs are named configuration + items data, an ordered list of strings identifying data to be used + (default, just price) other_args: a dictionary of named arguments to be + passed to the trading rule + + :param rule: Trading rule to be created + :type trading_rules: + The following describe a rule completely (ignore data and other_args arguments) + 3-tuple ; containing (function, data, other_args) + dict (containing key "function", and optionally keys "other_args" and "data") + TradingRule (object is created out of this rule) + + + The following will be combined with the data and other_args arguments + to produce a complete TradingRule: + + Other callable function + str (with path to function eg "quantlib_st.systems.provide.example.rules.ewmac_forecast_with_defaults") + + :param data: (list of) str pointing to location of inputs in a system method call (eg "data.get_instrument_price") + (Either passed in separately, or as part of a TradingRule, 3-tuple, or dict object) + :type data: single str, or list of str + + :param other_args: Other named arguments to be passed to trading rule function + Any arguments that are prefixed with "_" will be passed to the first data function call + Any arguments that are prefixed with "__" will be passed to the second data function call... and so on + (Either passed in separately , or as part of a TradingRule, 3-tuple, or dict object) + + :type other_args: dict + + :returns: single Tradingrule object + """ + + rule_components = _get_trading_rule_components_depending_on_rule_input( + rule, data=data, other_args=other_args + ) + + # fill the object with the components we need + self._function = rule_components.function + self._data = rule_components.data + self._other_args = rule_components.other_args + self._data_args = rule_components.data_args + + @property + def function(self): + return self._function + + @property + def data(self) -> List[str]: + return self._data + + @property + def other_args(self) -> Dict[str, Any]: + return self._other_args + + @property + def data_args(self) -> List[Dict[str, Any]]: + return self._data_args + + def __repr__(self): + return _repr_trading_rule(self) + + def call(self, system: "System", instrument_code: str) -> pd.Series: + """ + Actually call a trading rule + + To do this we need some data from the system + """ + + list_of_data_for_call = self._get_data_from_system(system, instrument_code) + result = self._call_with_data(list_of_data_for_call) + + # Check for all zeros + result = replace_all_zeros_with_nan(result) + + return result + + def _get_data_from_system(self, system: "System", instrument_code: str): + """ + Prepare the data for a function call + + :param system: A system + :param instrument_code: str + :return: list of data + """ + + # Following is a list of additional kwargs to pass to the data functions. Can be empty dicts + # Use copy as can be overridden + + list_of_data_str_references = self.data + list_of_args_to_pass_to_data_calls = copy(self.data_args) + + list_of_data_methods = self._get_data_methods_from_list_of_data_string( + list_of_data_str_references=list_of_data_str_references, system=system + ) + + list_of_data_for_call = self._get_data_from_list_of_methods_and_arguments( + instrument_code=instrument_code, + list_of_args_to_pass_to_data_calls=list_of_args_to_pass_to_data_calls, + list_of_data_methods=list_of_data_methods, + ) + + return list_of_data_for_call + + def _get_data_methods_from_list_of_data_string( + self, list_of_data_str_references: list, system + ) -> list: + # Turn a list of strings into a list of function objects + list_of_data_methods = [ + resolve_data_method(system, data_string) + for data_string in list_of_data_str_references + ] + + return list_of_data_methods + + def _get_data_from_list_of_methods_and_arguments( + self, + instrument_code: str, + list_of_data_methods: list, + list_of_args_to_pass_to_data_calls: list, + ) -> list: + # Call the functions, providing additional data if neccesssary + list_of_data_for_call = [ + data_method(instrument_code, **data_arguments) + for data_method, data_arguments in zip( + list_of_data_methods, list_of_args_to_pass_to_data_calls + ) + ] + + return list_of_data_for_call + + def _call_with_data(self, list_of_data_for_call: list) -> pd.Series: + other_args = self.other_args + func = self._function + if not callable(func): + raise Exception( + "Trading rule function is not callable or could not be resolved" + ) + result = func(*list_of_data_for_call, **(other_args or {})) + assert isinstance(result, pd.Series) + + return result + + +def _repr_trading_rule(rule: TradingRule): + data = rule.data or [] + data_args = rule.data_args or [] + other_args = rule.other_args or {} + other_arg_names = list(other_args.keys()) + function = rule.function + + data_names = [ + "%s (args: %s)" % (data_name, str(data_arg)) + for data_name, data_arg in zip(data, data_args) + ] + data_names = ", ".join(data_names) + args_names = ", ".join(other_arg_names) + + return "TradingRule; function: %s, data: %s and other_args: %s" % ( + str(function), + data_names, + args_names, + ) + + +class tradingRuleComponents(object): + def __init__( + self, + rule_function: Union[Callable, str], + data: list, + other_args: dict, + data_args: Optional[list] = None, + ): + ( + processed_function, + processed_data, + processed_other_args, + processed_data_args, + ) = self._process_inputs( + rule_function=rule_function, + data=data, + other_args=other_args, + data_args=data_args, + ) + + self._rule_function = processed_function + self._data = processed_data + self._other_args = processed_other_args + self._data_args = processed_data_args + + self._check_values() + + def _process_inputs( + self, + rule_function: Union[Callable, str], + data: list, + other_args: dict, + data_args: Optional[list] = None, + ) -> Tuple[Callable, List[str], Dict[str, Any], List[Dict[str, Any]]]: + # turn string into a callable function if required + resolved_function = cast(Callable, resolve_function(rule_function)) + + if isinstance(data, str): + # turn into a 1 item list or wont' get parsed properly + data = [data] + + if len(data) == 0: + if data_args is not None: + print("WARNING! Ignoring data_args as data is list length zero") + # if no data provided defaults to using price + data = [DEFAULT_PRICE_SOURCE] + data_args = [{}] + + if data_args is None: + # This will be the case if the rule was built from arguments + # Resolve any _ prefixed other_args + other_args, data_args = _separate_other_args(other_args, data) + + return ( + resolved_function, + data, + other_args, + cast(List[Dict[str, Any]], data_args), + ) + + @property + def function(self) -> Callable: + return self._rule_function + + @property + def data(self) -> List[str]: + return self._data + + @property + def data_args(self) -> List[Dict[str, Any]]: + return self._data_args or [] + + @property + def other_args(self) -> Dict[str, Any]: + return self._other_args or {} + + def _check_values(self): + assert isinstance(self.data, list) + assert isinstance(self.data_args, list) + assert isinstance(self.other_args, dict) + assert len(self.data) == len(self.data_args) + + +def _get_trading_rule_components_depending_on_rule_input( + rule: TradingRuleSpec, data: Optional[list], other_args: Optional[dict] +) -> tradingRuleComponents: + if data is None: + data = [] + + if other_args is None: + other_args = {} + + if _already_a_trading_rule(rule): + # looks like it is already a trading rule + rule_components = _create_rule_from_existing_rule( + cast(TradingRule, rule), data=data, other_args=other_args + ) + + elif isinstance(rule, tuple): + rule_components = _create_rule_from_tuple( + rule, data=data, other_args=other_args + ) + + elif isinstance(rule, dict): + rule_components = _create_rule_from_dict(rule, data=data, other_args=other_args) + else: + rule_components = _create_rule_from_passed_elements( + cast(Union[Callable, str], rule), data=data, other_args=other_args + ) + + return rule_components + + +def _already_a_trading_rule(rule): + return hasallattr(rule, ["function", "data", "other_args"]) + + +def _create_rule_from_existing_rule( + rule: TradingRule, data: list, other_args: dict +) -> tradingRuleComponents: + _throw_warning_if_passed_rule_and_data( + "tradingRule", data=data, other_args=other_args + ) + return tradingRuleComponents( + rule_function=rule.function, + data=rule.data, + other_args=rule.other_args, + data_args=rule.data_args, + ) + + +def _throw_warning_if_passed_rule_and_data( + type_of_rule_passed: str, data: list, other_args: dict +): + if len(data) > 0 or len(other_args) > 0: + print( + "WARNING: Creating trade rule with 'rule' type %s argument, ignoring data and/or other args" + % type_of_rule_passed + ) + + +def _create_rule_from_tuple(rule: tuple, data: list, other_args: dict): + _throw_warning_if_passed_rule_and_data("tuple", data=data, other_args=other_args) + + if len(rule) != 3: + raise Exception( + "Creating trading rule with a tuple, must be length 3 exactly (function/name, data [...], args dict(...))" + ) + (rule_function, data, other_args) = rule + + rule_components = tradingRuleComponents( + rule_function=rule_function, data=data, other_args=other_args + ) + + return rule_components + + +def _create_rule_from_dict( + rule: dict, data: list, other_args: dict +) -> tradingRuleComponents: + _throw_warning_if_passed_rule_and_data("dict", data=data, other_args=other_args) + + try: + rule_function = rule["function"] + except KeyError: + raise Exception( + "If you specify a TradingRule as a dict it has to contain a 'function' keyname" + ) + + data = rule.get("data", []) + other_args = rule.get("other_args", {}) + + rule_components = tradingRuleComponents( + data=data, rule_function=rule_function, other_args=other_args + ) + + return rule_components + + +def _create_rule_from_passed_elements( + rule: Union[Callable, str], data: list, other_args: dict +) -> tradingRuleComponents: + rule_components = tradingRuleComponents( + rule_function=rule, data=data, other_args=other_args + ) + + return rule_components + + +def _separate_other_args(other_args: dict, data: list) -> tuple: + """ + Separate out other arguments into those passed to the trading rule function, and any + that will be passed to the data functions (data_args) + + :param other_args: dict containing args. Some may have "_" prefix of various lengths, these are data args + :param data: list of str pointing to where data lives. data_args has to be the same length as this + + :return: tuple. First element is other_args dict to pass to main function. + Second element is list, each element of which is a dict to data functions + List is same length as data + Lists may consist of empty dicts to pad in case earlier data functions have no entries + """ + + # Split arguments up into groups depending on number of leading _ + # 0 (passed as other_args to data function), 1, 2, 3 ... + if len(other_args) == 0: + return ({}, [{}] * len(data)) + + sorted_other_args = sort_dict_by_underscore_length(other_args) + + # The first item in the list has no underscores, and is for the main + # trading rule function + other_args_for_trading_rule = sorted_other_args.pop(0) + + # The rest are data_args. At this point the key values still have "_" so + # let's drop them + data_args = [ + strip_underscores_from_dict_keys(arg_dict) for arg_dict in sorted_other_args + ] + + # Force them to be the same length so things don't break later + # Pad if required + data_args_forced_to_length = force_args_to_same_length(data_args, data) + assert len(data) == len(data_args_forced_to_length) + + return other_args_for_trading_rule, data_args_forced_to_length + + +def create_variations_oneparameter(baseRule, list_of_args, argname, nameformat="%s_%s"): + """ + Returns a dict of trading rule variations, varying only one named parameter + + :param baseRule: Trading rule to copy + :type baseRule: TradingRule object + + :param list_of_args: set of parameters to use + :type list_of_args: list + + :param argname: Argument passed to trading rule which will be changed + :type argname: str + + :param nameformat: Format to use when naming trading rules; nameformat % (argname, argvalue) will be used + :type nameformat: str containing two '%s' elements + + :returns: dict of Tradingrule objects + + >>> + >>> rule=TradingRule(("quantlib_st.systems.provided.rules.ewmac.ewmac_forecast_with_defaults", [], {})) + >>> variations=create_variations_oneparameter(rule, [4,10,100], "Lfast") + >>> ans=list(variations.keys()) + >>> ans.sort() + >>> ans + ['Lfast_10', 'Lfast_100', 'Lfast_4'] + """ + list_of_args_dict = [] + for arg_value in list_of_args: + thisdict = dict() + thisdict[argname] = arg_value + list_of_args_dict.append(thisdict) + + ans = create_variations( + baseRule, list_of_args_dict, key_argname=argname, nameformat=nameformat + ) + + return ans + + +def create_variations( + baseRule, list_of_args_dict, key_argname=None, nameformat="%s_%s" +): + """ + Returns a dict of trading rule variations + + eg create_variations(ewmacrule, [dict(fast=2, slow=8), dict(fast=4, ...) ], argname="fast", basename="ewmac") + + + :param baseRule: Trading rule to copy + :type baseRule: TradingRule object + + :param list_of_args_dict: sets of parameters to use. + :type list_of_args: list of dicts; each dict contains a set of parameters to vary for each instance + + :param key_argname: Non + :type key_argname: str or None (None is allowed if only one parameter is changed) + + :param nameformat: Format to use when naming trading rules; nameformat % (argname, argvalue) will be used + :type nameformat: str containing two '%s' elements + + :returns: dict of Tradingrule objects + + >>> rule=TradingRule(("quantlib_st.systems.provided.rules.ewmac.ewmac_forecast_with_defaults", [], {})) + >>> variations=create_variations(rule, [dict(Lfast=2, Lslow=8), dict(Lfast=4, Lslow=16)], "Lfast", nameformat="ewmac_%s_%s") + >>> ans=list(variations.keys()) + >>> ans.sort() + >>> ans + ['ewmac_Lfast_2', 'ewmac_Lfast_4'] + """ + + if key_argname is None: + if all([len(args_dict) == 1 for args_dict in list_of_args_dict]): + # okay to use argname as only seems to be one of them + key_argname = list_of_args_dict[0].keys()[0] + else: + raise Exception("need to specify argname if more than one possibility") + + baseRulefunction = baseRule.function + baseRuledata = baseRule.data + + variations = dict() + + for args_dict in list_of_args_dict: + if key_argname not in args_dict.keys(): + raise Exception( + "Argname %s missing from at least one set of argument values" + % key_argname + ) + + # these will be overwritten or added to as we run through + baseRuleargs = copy(baseRule.other_args) + + for arg_name in args_dict.keys(): + baseRuleargs[arg_name] = args_dict[arg_name] + + rule_variation = TradingRule(baseRulefunction, baseRuledata, baseRuleargs) + var_name = nameformat % (key_argname, str(args_dict[key_argname])) + + variations[var_name] = rule_variation + + return variations diff --git a/src/tests/core/test_fileutils.py b/src/tests/core/test_fileutils.py new file mode 100644 index 0000000..68a98da --- /dev/null +++ b/src/tests/core/test_fileutils.py @@ -0,0 +1,90 @@ +import os + +from quantlib_st.core.fileutils import ( + resolve_path_and_filename_for_package, + transform_path_into_list, + extract_filename_from_combined_path_and_filename_list, + get_pathname_from_list, + is_window_path_list, +) + + +def test_transform_path_into_list(): + assert transform_path_into_list("/home/user/file.csv") == [ + "", + "home", + "user", + "file", + "csv", + ] + assert transform_path_into_list("C:\\Data\\test.txt") == [ + "C:", + "Data", + "test", + "txt", + ] + assert transform_path_into_list("package.sub.module") == [ + "package", + "sub", + "module", + ] + # Trailing slash should be popped if there are other elements + assert transform_path_into_list("/home/user/") == ["", "home", "user"] + # Only a slash should remain as ["", ""] or similar? + # Actually split("/") on "/" gives ["", ""] + # The logic says: if len > 1 and last is "" pop. + # So "/" -> ["", ""] -> [""] + assert transform_path_into_list("/") == [""] + + +def test_extract_filename(): + path_list = ["home", "user", "data", "csv"] + p, f = extract_filename_from_combined_path_and_filename_list(path_list) + assert p == ["home", "user"] + assert f == "data.csv" + + +def test_is_window_path(): + assert is_window_path_list(["C:", "Users"]) is True + assert is_window_path_list(["", "home"]) is False + assert is_window_path_list(["package"]) is False + + +def test_get_pathname_from_list_linux(monkeypatch): + # Test absolute Linux path + # get_pathname_from_list(["", "home", "user"]) -> "/home/user" + path = get_pathname_from_list(["", "home", "user"]) + # On non-windows it should use / + if os.name != "nt": + assert path == "/home/user" + + +def test_get_pathname_from_list_windows(): + # Test Windows path + path = get_pathname_from_list(["C:", "Data"]) + # it should handle C:\Data + assert "C:" in path + assert "Data" in path + + +def test_resolve_path_and_filename_smoke(): + # Smoke test for resolve_path_and_filename_for_package + # Absolute path + res = resolve_path_and_filename_for_package("/tmp/test", "file.txt") + assert "test" in res + assert "file.txt" in res + + # Combined path + res = resolve_path_and_filename_for_package("/tmp/test/file.txt") + assert "test" in res + assert "file.txt" in res + + +def test_package_resolution(): + # Test resolution of an actual package in the workspace + # Need to make sure PYTHONPATH includes quantlib/src + res = resolve_path_and_filename_for_package("quantlib_st.core", "fileutils.py") + assert "quantlib_st" in res + assert "core" in res + assert "fileutils.py" in res + assert os.path.exists(res) diff --git a/uv.lock b/uv.lock index 59acad7..be2ce16 100644 --- a/uv.lock +++ b/uv.lock @@ -671,13 +671,69 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/de/3d/8161f7711c017e01ac9f008dfddd9410dff3674334c233bde66e7ba65bbf/pywin32_ctypes-0.2.3-py3-none-any.whl", hash = "sha256:8a1513379d709975552d202d942d9837758905c8d01eb82b8bcc30918929e7b8", size = 30756, upload-time = 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+version = "1.5.0" source = { editable = "." } dependencies = [ { name = "numpy" }, { name = "pandas" }, + { name = "pyyaml" }, { name = "scipy" }, ] @@ -696,6 +752,7 @@ requires-dist = [ { name = "pip", marker = "extra == 'dev'", specifier = ">=25.3" }, { name = "pyinstaller", marker = "extra == 'dev'", specifier = ">=6.17.0" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.2" }, + { name = "pyyaml", specifier = ">=6.0.2" }, { name = "scipy", specifier = ">=1.17.0" }, { name = "twine", marker = "extra == 'dev'", specifier = ">=6.2.0" }, ] From 9df6232fd4c3d5bb5e6429319562a497a9feb02e Mon Sep 17 00:00:00 2001 From: Rodion Lim Date: Mon, 19 Jan 2026 00:11:57 +0800 Subject: [PATCH 2/2] feat: fully port account and rawdata stage --- README.md | 6 +- pyproject.toml | 2 +- .../core/pandas/strategy_functions.py | 22 + src/quantlib_st/costs/README.md | 6 +- .../data/test/adjusted_prices_csv/EDOLLAR.csv | 22344 ++++++++++ .../data/test/adjusted_prices_csv/US10.csv | 34133 ++++++++++++++++ src/quantlib_st/objects/carry_data.py | 85 + src/quantlib_st/objects/instruments.py | 7 +- src/quantlib_st/sysdata/csv/__init__.py | 2 + .../sysdata/csv/csv_instrument_data.py | 2 +- .../sysdata/csv/csv_spread_costs.py | 71 + src/quantlib_st/sysdata/data_blob.py | 2 +- .../sysdata/sim/csv_futures_sim_test_data.py | 7 +- .../provided/config/test_account_config.yaml | 2 +- .../provided/futures_chapter15/basesystem.py | 5 +- src/quantlib_st/systems/rawdata.py | 745 + 16 files changed, 57426 insertions(+), 15 deletions(-) create mode 100644 src/quantlib_st/data/test/adjusted_prices_csv/EDOLLAR.csv create mode 100644 src/quantlib_st/data/test/adjusted_prices_csv/US10.csv create mode 100644 src/quantlib_st/objects/carry_data.py create mode 100644 src/quantlib_st/sysdata/csv/csv_spread_costs.py create mode 100644 src/quantlib_st/systems/rawdata.py diff --git a/README.md b/README.md index fafa979..37223df 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Minimal, self-contained CLI tools and library for quantitative finance. ## Modules -- **[estimators](src/quantlib_st/estimators/README.md)**: Volatility estimators — contains `robust_vol_calc` and `mixed_vol_calc` for daily volatility estimation. +- **[estimators](src/quantlib_st/estimators/README.md)**: Volatility and Forecast Scalar estimators — contains `robust_vol_calc` and `mixed_vol_calc` for daily volatility estimation, and `forecast_scalar` for scaling forecasts. - **[accounts](src/quantlib_st/accounts/README.md)**: P&L calculation framework — contains `account_forecast` for generating P&L curves from forecasts and prices. ## Install (editable - for developers) @@ -25,6 +25,8 @@ From the repo root: This installs the `quantlib` command. +- `uv sync --extra dev` + ## Docker Pull a published image from GitHub Container Registry: @@ -39,6 +41,8 @@ When publishing the image the Makefile also tags and pushes `:latest` in additio ## Package Sample Usage +### Correlation + ```python import pandas as pd import numpy as np diff --git a/pyproject.toml b/pyproject.toml index ef4c46a..68cd384 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ include = ["*"] [tool.pytest.ini_options] testpaths = ["src", "tests"] -pythonpath = ["src"] +pythonpath = ["src", "src/quantlib_st"] [build-system] requires = ["setuptools>=80.9", "wheel"] diff --git a/src/quantlib_st/core/pandas/strategy_functions.py b/src/quantlib_st/core/pandas/strategy_functions.py index 289ee18..1622769 100644 --- a/src/quantlib_st/core/pandas/strategy_functions.py +++ b/src/quantlib_st/core/pandas/strategy_functions.py @@ -55,6 +55,28 @@ def drawdown(x: Union[pd.DataFrame, pd.Series]) -> Union[pd.DataFrame, pd.Series return x - maxx +def apply_abs_min(x: pd.Series, min_value: float = 0.1) -> pd.Series: + """ + >>> import datetime + >>> from syscore.pandas.pdutils import create_arbitrary_pdseries + >>> s1=create_arbitrary_pdseries([1,2,3,-1,-2,-3], date_start = datetime.datetime(2000,1,1)) + >>> apply_abs_min(s1, 2) + 2000-01-03 2 + 2000-01-04 2 + 2000-01-05 3 + 2000-01-06 -2 + 2000-01-07 -2 + 2000-01-10 -3 + Freq: B, dtype: int64 + """ + + ## Could also use clip but no quicker and this is more intuitive + x[(x < min_value) & (x > 0)] = min_value + x[(x > -min_value) & (x < 0)] = -min_value + + return x + + def spread_out_annualised_return_over_periods(data_as_annual: pd.Series) -> pd.Series: """ Spread an annualised return series into per-period returns. diff --git a/src/quantlib_st/costs/README.md b/src/quantlib_st/costs/README.md index 0370020..f0c115d 100644 --- a/src/quantlib_st/costs/README.md +++ b/src/quantlib_st/costs/README.md @@ -28,7 +28,11 @@ Volatility is calculated using a robust method: ### 3. Average Price -Costs are calculated using the average price over the last year (256 business days) to ensure the estimate is not skewed by recent price spikes. +Costs are calculated using the average price over the last year (256 business days). This ensures the cost estimate is representative of the entire period and serves three purposes: + +- **Percentage Commissions**: For instruments with percentage-based fees, the cost depends on the contract value ($\text{Price} \times \text{Point Size}$). +- **Stability**: It prevents temporary price spikes or crashes from distorting long-term cost expectations. +- **Consistency**: It matches the 256-day window used for the volatility calculation in the SR Cost denominator. ## Usage diff --git a/src/quantlib_st/data/test/adjusted_prices_csv/EDOLLAR.csv b/src/quantlib_st/data/test/adjusted_prices_csv/EDOLLAR.csv new file mode 100644 index 0000000..1fef992 --- /dev/null +++ b/src/quantlib_st/data/test/adjusted_prices_csv/EDOLLAR.csv @@ -0,0 +1,22344 @@ +DATETIME,price +2015-01-02 12:35:22, +2015-01-02 13:43:27, +2015-01-02 15:20:14, +2015-01-02 16:44:35, +2015-01-02 17:48:43, +2015-01-02 19:02:03, +2015-01-02 23:00:00,96.49998999999997 +2015-01-05 12:00:07, +2015-01-05 13:02:15, 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15:00:00,133.046875 +2021-03-04 16:00:00,133.03125 +2021-03-04 17:00:00,132.578125 +2021-03-04 18:00:00,132.421875 +2021-03-04 19:00:00,132.375 +2021-03-04 20:00:00,132.5 +2021-03-04 21:00:00,132.46875 +2021-03-05 14:30:00,132.09375 +2021-03-05 15:00:00,132.09375 +2021-03-05 16:00:00,132.203125 +2021-03-05 17:00:00,132.328125 +2021-03-05 18:00:00,132.328125 +2021-03-05 19:00:00,132.328125 +2021-03-05 20:00:00,132.34375 +2021-03-05 21:00:00,132.234375 +2021-03-08 14:30:00,131.90625 +2021-03-08 15:00:00,131.9375 +2021-03-08 16:00:00,131.84375 +2021-03-08 17:00:00,131.859375 +2021-03-08 18:00:00,131.875 +2021-03-08 19:00:00,131.875 +2021-03-08 20:00:00,131.828125 +2021-03-08 21:00:00,131.78125 diff --git a/src/quantlib_st/objects/carry_data.py b/src/quantlib_st/objects/carry_data.py new file mode 100644 index 0000000..859d6e2 --- /dev/null +++ b/src/quantlib_st/objects/carry_data.py @@ -0,0 +1,85 @@ +import numpy as np +import pandas as pd + +from quantlib_st.core.dateutils import CALENDAR_DAYS_IN_YEAR +from quantlib_st.core.pandas.pdutils import uniquets +from quantlib_st.core.pandas.strategy_functions import apply_abs_min + + +class rawCarryData(pd.DataFrame): + def roll_differentials( + self, floor_date_diff: float = 1 / CALENDAR_DAYS_IN_YEAR + ) -> pd.Series: + raw_differential = self.raw_differential() + + ## This prevents the roll differential from being zero in a corner + ## case when the two contract months match - it has to be at least one day + + floored_differential = apply_abs_min(raw_differential, floor_date_diff) + unique_differential = uniquets(floored_differential) + + return unique_differential + + def raw_differential(self) -> pd.Series: + price_contract_as_frac = self.price_contract_as_year_frac() + carry_contract_as_frac = self.carry_contract_as_year_frac() + + return carry_contract_as_frac - price_contract_as_frac + + def price_contract_as_year_frac(self) -> pd.Series: + return _total_year_frac_from_contract_series(self.price_contract_as_float()) + + def carry_contract_as_year_frac(self) -> pd.Series: + return _total_year_frac_from_contract_series(self.carry_contract_as_float()) + + def raw_futures_roll(self) -> pd.Series: + raw_roll = self.price - self.carry + + raw_roll[raw_roll == 0] = np.nan + + raw_roll = uniquets(raw_roll) + + return raw_roll + + def carry_contract_as_float(self) -> pd.Series: + carry_contract_as_float = self.carry_contract.astype(float) + return carry_contract_as_float + + def price_contract_as_float(self) -> pd.Series: + price_contract_as_float = self.price_contract.astype(float) + return price_contract_as_float + + @property + def carry_contract(self) -> pd.Series: + return self.CARRY_CONTRACT + + @property + def price_contract(self) -> pd.Series: + return self.PRICE_CONTRACT + + @property + def price(self) -> pd.Series: + return self.PRICE + + @property + def carry(self) -> pd.Series: + return self.CARRY + + +def _total_year_frac_from_contract_series(x): + years = _year_from_contract_series(x) + month_frac = _month_as_year_frac_from_contract_series(x) + + return years + month_frac + + +def _year_from_contract_series(x): + return x.floordiv(10000) + + +def _month_as_year_frac_from_contract_series(x): + return _month_from_contract_series(x) / 12.0 + + +def _month_from_contract_series(x): + return x.mod(10000) / 100.0 diff --git a/src/quantlib_st/objects/instruments.py b/src/quantlib_st/objects/instruments.py index eefff12..e39ee64 100644 --- a/src/quantlib_st/objects/instruments.py +++ b/src/quantlib_st/objects/instruments.py @@ -98,10 +98,9 @@ def as_dict(self) -> dict: @classmethod def from_dict(instrumentMetaData, input_dict): - keys = list(input_dict.keys()) - args_list = [input_dict[key] for key in keys] - - return instrumentMetaData(*args_list) + valid_keys = [key for key in input_dict.keys() if key in META_FIELD_LIST] + filtered_dict = {key: input_dict[key] for key in valid_keys} + return instrumentMetaData(**filtered_dict) def __eq__(self, other): return self.as_dict() == other.as_dict() diff --git a/src/quantlib_st/sysdata/csv/__init__.py b/src/quantlib_st/sysdata/csv/__init__.py index 246c54f..153f589 100644 --- a/src/quantlib_st/sysdata/csv/__init__.py +++ b/src/quantlib_st/sysdata/csv/__init__.py @@ -4,6 +4,7 @@ "csvFxPricesData", "csvFuturesInstrumentData", "csvRollParametersData", + "csvSpreadCostData", ] from quantlib_st.sysdata.csv.csv_multiple_prices import csvFuturesMultiplePricesData @@ -11,3 +12,4 @@ from quantlib_st.sysdata.csv.csv_spot_fx import csvFxPricesData from quantlib_st.sysdata.csv.csv_instrument_data import csvFuturesInstrumentData from quantlib_st.sysdata.csv.csv_roll_parameters import csvRollParametersData +from quantlib_st.sysdata.csv.csv_spread_costs import csvSpreadCostData diff --git a/src/quantlib_st/sysdata/csv/csv_instrument_data.py b/src/quantlib_st/sysdata/csv/csv_instrument_data.py index c1e347f..779182b 100644 --- a/src/quantlib_st/sysdata/csv/csv_instrument_data.py +++ b/src/quantlib_st/sysdata/csv/csv_instrument_data.py @@ -10,7 +10,7 @@ from quantlib_st.logging.logger import get_logger import pandas as pd -INSTRUMENT_CONFIG_PATH = "data.futures.csvconfig" +INSTRUMENT_CONFIG_PATH = "quantlib_st.data.futures.csvconfig" CONFIG_FILE_NAME = "instrumentconfig.csv" diff --git a/src/quantlib_st/sysdata/csv/csv_spread_costs.py b/src/quantlib_st/sysdata/csv/csv_spread_costs.py new file mode 100644 index 0000000..153ac48 --- /dev/null +++ b/src/quantlib_st/sysdata/csv/csv_spread_costs.py @@ -0,0 +1,71 @@ +from quantlib_st.core.fileutils import resolve_path_and_filename_for_package +from quantlib_st.sysdata.futures.spread_costs import spreadCostData +from quantlib_st.sysdata.csv.csv_instrument_data import INSTRUMENT_CONFIG_PATH +from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.logging.logger import get_logger + +import pandas as pd + +CONFIG_FILE_NAME = "spreadcosts.csv" +INSTRUMENT_COLUMN_NAME = "Instrument" +SPREAD_COST_COLUMN_NAME = "SpreadCost" + + +class csvSpreadCostData(spreadCostData): + """ + Get data about instruments from a special configuration used for initialising the system + """ + + def __init__( + self, + datapath=arg_not_supplied, + log=get_logger("csvSpreadCostData"), + ): + super().__init__(log=log) + + if datapath is arg_not_supplied: + datapath = INSTRUMENT_CONFIG_PATH + config_file = resolve_path_and_filename_for_package(datapath, CONFIG_FILE_NAME) + self._config_file = config_file + + @property + def config_file(self): + return self._config_file + + def delete_spread_cost(self, instrument_code: str): + raise Exception( + "Cannot do a partial update of spread costs .csv, have to do whole thing using write_all_instrument_spreads" + ) + + def update_spread_cost(self, instrument_code: str, spread_cost: float): + raise Exception( + "Cannot do a partial update of spread costs .csv, have to do whole thing using write_all_instrument_spreads" + ) + + def get_spread_cost(self, instrument_code: str) -> float: + return self._get_spread_cost_if_series_provided(instrument_code) + + def get_spread_costs_as_series(self) -> pd.Series: + try: + spread_cost_data = pd.read_csv(self.config_file) + except BaseException: + self.log.warning("Can't read file %s, returning empty" % self.config_file) + return pd.Series(dtype=float) + + try: + spread_cost_data.index = spread_cost_data[INSTRUMENT_COLUMN_NAME] + spread_cost_series = spread_cost_data[SPREAD_COST_COLUMN_NAME] + + except BaseException: + raise Exception("Badly configured file %s" % (self._config_file)) + + return spread_cost_series + + def write_all_instrument_spreads(self, spread_cost_as_series: pd.Series): + spread_cost_as_df = pd.DataFrame(spread_cost_as_series) + spread_cost_as_df.columns = [SPREAD_COST_COLUMN_NAME] + spread_cost_as_df.to_csv(self._config_file, index_label="Instrument") + + def get_list_of_instruments(self) -> list: + all_data_as_series = self.get_spread_costs_as_series() + return list(all_data_as_series.keys()) diff --git a/src/quantlib_st/sysdata/data_blob.py b/src/quantlib_st/sysdata/data_blob.py index 6ba5fb0..3ea9366 100644 --- a/src/quantlib_st/sysdata/data_blob.py +++ b/src/quantlib_st/sysdata/data_blob.py @@ -1,5 +1,5 @@ from copy import copy -from typing import Any, Optional, TYPE_CHECKING +from typing import Any, TYPE_CHECKING from quantlib_st.core.objects import get_class_name from quantlib_st.core.constants import arg_not_supplied diff --git a/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py b/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py index c28e737..e20826f 100644 --- a/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py +++ b/src/quantlib_st/sysdata/sim/csv_futures_sim_test_data.py @@ -6,6 +6,7 @@ from quantlib_st.sysdata.csv.csv_spot_fx import csvFxPricesData from quantlib_st.sysdata.csv.csv_instrument_data import csvFuturesInstrumentData from quantlib_st.sysdata.csv.csv_roll_parameters import csvRollParametersData +from quantlib_st.sysdata.csv.csv_spread_costs import csvSpreadCostData from quantlib_st.sysdata.data_blob import dataBlob from quantlib_st.objects.spot_fx_prices import fxPrices @@ -37,8 +38,9 @@ def __init__( data = dataBlob( log=log, csv_data_paths=dict( - csvFuturesAdjustedPricesData="data.test.adjusted_prices_csv", - csvFuturesInstrumentData="data.test.csvconfig", + csvFuturesAdjustedPricesData="quantlib_st.data.test.adjusted_prices_csv", + csvFuturesInstrumentData="quantlib_st.data.test.csvconfig", + csvSpreadCostData="quantlib_st.data.test.csvconfig", ), class_list=[ csvFuturesAdjustedPricesData, @@ -46,6 +48,7 @@ def __init__( csvFuturesInstrumentData, csvFxPricesData, csvRollParametersData, + csvSpreadCostData, ], ) diff --git a/src/quantlib_st/systems/provided/config/test_account_config.yaml b/src/quantlib_st/systems/provided/config/test_account_config.yaml index dd8fae9..7027450 100644 --- a/src/quantlib_st/systems/provided/config/test_account_config.yaml +++ b/src/quantlib_st/systems/provided/config/test_account_config.yaml @@ -6,7 +6,7 @@ # Trading rules # # -percentage_vol_target: 20.0 +percentage_vol_target: 16.0 notional_trading_capital: 250000 base_currency: "USD" diff --git a/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py b/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py index a364465..7bbc842 100644 --- a/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py +++ b/src/quantlib_st/systems/provided/futures_chapter15/basesystem.py @@ -8,6 +8,7 @@ from quantlib_st.sysdata.sim.csv_futures_sim_test_data import CsvFuturesSimTestData from quantlib_st.systems.basesystem import System from quantlib_st.systems.accounts.accounts_stage import Account +from quantlib_st.systems.rawdata import RawData def futures_system( @@ -31,9 +32,7 @@ def futures_system( config = Config("systems.provided.config.test_account_config.yaml") system = System( - [ - Account(), - ], + [Account(), RawData()], data, config, ) diff --git a/src/quantlib_st/systems/rawdata.py b/src/quantlib_st/systems/rawdata.py new file mode 100644 index 0000000..c0d1abc --- /dev/null +++ b/src/quantlib_st/systems/rawdata.py @@ -0,0 +1,745 @@ +from copy import copy + +import pandas as pd + +from systems.stage import SystemStage +from quantlib_st.core.objects import resolve_function +from quantlib_st.core.dateutils import ROOT_BDAYS_INYEAR +from quantlib_st.core.genutils import list_intersection +from quantlib_st.core.exceptions import missingData +from quantlib_st.systems.system_cache import input, diagnostic, output + +from quantlib_st.sysdata.sim.futures_sim_data import futuresSimData +from quantlib_st.config.configdata import Config + +from quantlib_st.objects.carry_data import rawCarryData + + +class RawData(SystemStage): + """ + A SystemStage that does some fairly common calculations before we do + forecasting and which gives access to some widely used methods. + + This is optional; forecasts can go straight to system.data + The advantages of using RawData are: + - preliminary calculations that are reused can be cached, to + save time (eg volatility) + - preliminary calculations are available for inspection when + diagnosing what is going on + + Name: rawdata + """ + + @property + def name(self): + return "rawdata" + + @property + def data_stage(self) -> futuresSimData: + return self.parent.data + + @property + def config(self) -> Config: + return self.parent.config + + def get_raw_cost_data(self, instrument_code: str): + return self.data_stage.get_raw_cost_data(instrument_code) + + def get_value_of_block_price_move(self, instrument_code: str) -> float: + return self.data_stage.get_value_of_block_price_move(instrument_code) + + def get_fx_for_instrument(self, instrument_code: str, base_currency: str): + return self.data_stage.get_fx_for_instrument( + instrument_code=instrument_code, base_currency=base_currency + ) + + @input + def get_daily_prices(self, instrument_code) -> pd.Series: + """ + Gets daily prices + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + KEY OUTPUT + """ + self.log.debug( + "Calculating daily prices for %s" % instrument_code, + instrument_code=instrument_code, + ) + dailyprice = self.data_stage.daily_prices(instrument_code) + + if len(dailyprice) == 0: + raise Exception( + "Data for %s not found! Remove from instrument list, or add to config.ignore_instruments" + % instrument_code + ) + + return dailyprice + + @input + def get_natural_frequency_prices(self, instrument_code: str) -> pd.Series: + self.log.debug( + "Retrieving natural prices for %s" % instrument_code, + instrument_code=instrument_code, + ) + + natural_prices = self.data_stage.get_raw_price(instrument_code) + + if len(natural_prices) == 0: + raise Exception( + "Data for %s not found! Remove from instrument list, or add to config.ignore_instruments" + ) + + return natural_prices + + @input + def get_hourly_prices(self, instrument_code: str) -> pd.Series: + hourly_prices = self.data_stage.hourly_prices(instrument_code) + + return hourly_prices + + @output() + def daily_returns(self, instrument_code: str) -> pd.Series: + """ + Gets daily returns (not % returns) + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + + >>> from systems.tests.testdata import get_test_object + >>> from systems.basesystem import System + >>> + >>> (rawdata, data, config)=get_test_object() + >>> system=System([rawdata], data) + >>> system.rawdata.daily_returns("SOFR").tail(2) + price + 2015-12-10 -0.0650 + 2015-12-11 0.1075 + """ + instrdailyprice = self.get_daily_prices(instrument_code) + dailyreturns = instrdailyprice.diff() + + return dailyreturns + + @output() + def hourly_returns(self, instrument_code: str) -> pd.Series: + """ + Gets hourly returns (not % returns) + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + + """ + hourly_prices = self.get_hourly_prices(instrument_code) + hourly_returns = hourly_prices.diff() + + return hourly_returns + + @output() + def annualised_returns_volatility(self, instrument_code: str) -> pd.Series: + daily_returns_volatility = self.daily_returns_volatility(instrument_code) + + return daily_returns_volatility * ROOT_BDAYS_INYEAR + + @output() + def daily_returns_volatility(self, instrument_code: str) -> pd.Series: + """ + Gets volatility of daily returns (not % returns) + + This is done using a user defined function + + We get this from: + the configuration object + or if not found, system.defaults.py + + The dict must contain func key; anything else is optional + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object + >>> from systems.basesystem import System + >>> + >>> (rawdata, data, config)=get_test_object() + >>> system=System([rawdata], data) + >>> ## uses defaults + >>> system.rawdata.daily_returns_volatility("SOFR").tail(2) + vol + 2015-12-10 0.054145 + 2015-12-11 0.058522 + >>> + >>> from sysdata.config.configdata import Config + >>> config=Config("systems.provided.example.exampleconfig.yaml") + >>> system=System([rawdata], data, config) + >>> system.rawdata.daily_returns_volatility("SOFR").tail(2) + vol + 2015-12-10 0.054145 + 2015-12-11 0.058522 + >>> + >>> config=Config(dict(volatility_calculation=dict(func="sysquant.estimators.vol.robust_vol_calc", days=200))) + >>> system2=System([rawdata], data, config) + >>> system2.rawdata.daily_returns_volatility("SOFR").tail(2) + vol + 2015-12-10 0.057946 + 2015-12-11 0.058626 + + """ + self.log.debug( + "Calculating daily volatility for %s" % instrument_code, + instrument_code=instrument_code, + ) + + volconfig = copy(self.config.volatility_calculation) + + which_returns = volconfig.pop("name_returns_attr_in_rawdata") + returns_func = getattr(self, which_returns) + price_returns = returns_func(instrument_code) + + # volconfig contains 'func' and some other arguments + # we turn func which could be a string into a function, and then + # call it with the other args + vol_multiplier = volconfig.pop("multiplier_to_get_daily_vol") + + volfunction = resolve_function(volconfig.pop("func")) + raw_vol = volfunction(price_returns, **volconfig) + + vol = vol_multiplier * raw_vol + + return vol + + @output() + def get_daily_percentage_returns(self, instrument_code: str) -> pd.Series: + """ + Get percentage returns + + Useful statistic, also used for some trading rules + + This is an optional subsystem; forecasts can go straight to system.data + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + """ + + # UGLY + denom_price = self.daily_denominator_price(instrument_code) + num_returns = self.daily_returns(instrument_code) + perc_returns = num_returns / denom_price.ffill() + + return perc_returns + + @output() + def get_daily_percentage_volatility(self, instrument_code: str) -> pd.Series: + """ + Get percentage returns normalised by recent vol + + Useful statistic, also used for some trading rules + + This is an optional subsystem; forecasts can go straight to system.data + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object + >>> from systems.basesystem import System + >>> + >>> (rawdata, data, config)=get_test_object() + >>> system=System([rawdata], data) + >>> system.rawdata.get_daily_percentage_volatility("SOFR").tail(2) + vol + 2015-12-10 0.055281 + 2015-12-11 0.059789 + """ + denom_price = self.daily_denominator_price(instrument_code) + return_vol = self.daily_returns_volatility(instrument_code) + (denom_price, return_vol) = denom_price.align(return_vol, join="right") + perc_vol = 100.0 * (return_vol / denom_price.ffill().abs()) + + return perc_vol + + @diagnostic() + def get_daily_vol_normalised_returns(self, instrument_code: str) -> pd.Series: + """ + Get returns normalised by recent vol + + Useful statistic, also used for some trading rules + + This is an optional subsystem; forecasts can go straight to system.data + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object + >>> from systems.basesystem import System + >>> + >>> (rawdata, data, config)=get_test_object() + >>> system=System([rawdata], data) + >>> system.rawdata.get_daily_vol_normalised_returns("SOFR").tail(2) + norm_return + 2015-12-10 -1.219510 + 2015-12-11 1.985413 + """ + self.log.debug( + "Calculating normalised return for %s" % instrument_code, + instrument_code=instrument_code, + ) + + returnvol = self.daily_returns_volatility(instrument_code).shift(1) + dailyreturns = self.daily_returns(instrument_code) + norm_return = dailyreturns / returnvol + + return norm_return + + @diagnostic() + def get_cumulative_daily_vol_normalised_returns( + self, instrument_code: str + ) -> pd.Series: + """ + Returns a cumulative normalised return. This is like a price, but with equal expected vol + Used for a few different trading rules + + :param instrument_code: str + :return: pd.Series + """ + + self.log.debug( + "Calculating cumulative normalised return for %s" % instrument_code, + instrument_code=instrument_code, + ) + + norm_returns = self.get_daily_vol_normalised_returns(instrument_code) + + cum_norm_returns = norm_returns.cumsum() + + return cum_norm_returns + + @diagnostic() + def _aggregate_daily_vol_normalised_returns_for_list_of_instruments( + self, list_of_instruments: list + ) -> pd.Series: + """ + Average normalised returns across an asset class + + :param asset_class: str + :return: pd.Series + """ + + aggregate_returns_across_instruments_list = [ + self.get_daily_vol_normalised_returns(instrument_code) + for instrument_code in list_of_instruments + ] + + aggregate_returns_across_instruments = pd.concat( + aggregate_returns_across_instruments_list, axis=1 + ) + + # we don't ffill before working out the median as this could lead to + # bad data + median_returns = aggregate_returns_across_instruments.median(axis=1) + + return median_returns + + @diagnostic() + def _daily_vol_normalised_price_for_list_of_instruments( + self, list_of_instruments: list + ) -> pd.Series: + norm_returns = ( + self._aggregate_daily_vol_normalised_returns_for_list_of_instruments( + list_of_instruments + ) + ) + norm_price = norm_returns.cumsum() + + return norm_price + + @diagnostic() + def _by_asset_class_daily_vol_normalised_price_for_asset_class( + self, asset_class: str + ) -> pd.Series: + """ + Price for an asset class, built up from cumulative returns + + :param asset_class: str + :return: pd.Series + """ + + instruments_in_asset_class = self.all_instruments_in_asset_class(asset_class) + + norm_price = self._daily_vol_normalised_price_for_list_of_instruments( + instruments_in_asset_class + ) + + return norm_price + + @diagnostic() + def daily_vol_normalised_price_for_asset_class_with_redundant_instrument_code( + self, instrument_code: str, asset_class: str + ) -> pd.Series: + """ + Price for an asset class, built up from cumulative returns + + :param asset_class: str + :return: pd.Series + """ + + return self._by_asset_class_daily_vol_normalised_price_for_asset_class( + asset_class + ) + + @diagnostic() + def system_with_redundant_instrument_code_passed( + self, instrument_code: str, asset_class: str + ): + ## allows ultimate flexibility when creating trading rules but be careful! + + return self.parent + + @diagnostic() + def instrument_code(self, instrument_code: str) -> pd.Series: + ## allows ultimate flexibility when creating trading rules + + return instrument_code + + @output() + def normalised_price_for_asset_class(self, instrument_code: str) -> pd.Series: + """ + + :param instrument_code: + :return: + """ + + asset_class = self.data_stage.asset_class_for_instrument(instrument_code) + normalised_price_for_asset_class = ( + self._by_asset_class_daily_vol_normalised_price_for_asset_class(asset_class) + ) + normalised_price_this_instrument = ( + self.get_cumulative_daily_vol_normalised_returns(instrument_code) + ) + + # Align for an easy life + # As usual forward fill at last moment + normalised_price_for_asset_class_aligned = ( + normalised_price_for_asset_class.reindex( + normalised_price_this_instrument.index + ).ffill() + ) + + return normalised_price_for_asset_class_aligned + + @diagnostic() + def rolls_per_year(self, instrument_code: str) -> int: + ## an input but we cache to avoid spamming with errors + try: + rolls_per_year = self.data_stage.get_rolls_per_year(instrument_code) + except: + self.log.warning( + "No roll data for %s, this is fine for spot instruments but not for futures" + % instrument_code + ) + rolls_per_year = 0 + + return rolls_per_year + + @input + def get_instrument_raw_carry_data(self, instrument_code: str) -> rawCarryData: + """ + Returns the 4 columns PRICE, CARRY, PRICE_CONTRACT, CARRY_CONTRACT + + :param instrument_code: instrument to get data for + :type instrument_code: str + + :returns: Tx4 pd.DataFrame + + KEY INPUT + + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> system.rawdata.get_instrument_raw_carry_data("SOFR").tail(2) + PRICE CARRY CARRY_CONTRACT PRICE_CONTRACT + 2015-12-11 17:08:14 97.9675 NaN 201812 201903 + 2015-12-11 19:33:39 97.9875 NaN 201812 201903 + """ + + instrcarrydata = self.data_stage.get_instrument_raw_carry_data(instrument_code) + if len(instrcarrydata) == 0: + raise missingData( + "Data for %s not found! Remove from instrument list, or add to config.ignore_instruments" + % instrument_code + ) + + instrcarrydata = rawCarryData(instrcarrydata) + + return instrcarrydata + + @diagnostic() + def raw_futures_roll(self, instrument_code: str) -> pd.Series: + """ + Returns the raw difference between price and carry + + :param instrument_code: instrument to get data for + :type instrument_code: str + + :returns: Tx4 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> system.rawdata.raw_futures_roll("SOFR").ffill().tail(2) + 2015-12-11 17:08:14 -0.07 + 2015-12-11 19:33:39 -0.07 + dtype: float64 + """ + + carrydata = self.get_instrument_raw_carry_data(instrument_code) + raw_roll = carrydata.raw_futures_roll() + + return raw_roll + + @diagnostic() + def roll_differentials(self, instrument_code: str) -> pd.Series: + """ + Work out the annualisation factor + + :param instrument_code: instrument to get data for + :type instrument_code: str + + :returns: Tx4 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> system.rawdata.roll_differentials("SOFR").ffill().tail(2) + 2015-12-11 17:08:14 -0.246407 + 2015-12-11 19:33:39 -0.246407 + dtype: float64 + """ + carrydata = self.get_instrument_raw_carry_data(instrument_code) + roll_diff = carrydata.roll_differentials() + + return roll_diff + + @diagnostic() + def annualised_roll(self, instrument_code: str) -> pd.Series: + """ + Work out annualised futures roll + + :param instrument_code: instrument to get data for + :type instrument_code: str + + :returns: Tx4 pd.DataFrame + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> system.rawdata.annualised_roll("SOFR").ffill().tail(2) + 2015-12-11 17:08:14 0.284083 + 2015-12-11 19:33:39 0.284083 + dtype: float64 + >>> system.rawdata.annualised_roll("US10").ffill().tail(2) + 2015-12-11 16:06:35 2.320441 + 2015-12-11 17:24:06 2.320441 + dtype: float64 + + """ + + rolldiffs = self.roll_differentials(instrument_code) + rawrollvalues = self.raw_futures_roll(instrument_code) + + annroll = rawrollvalues / rolldiffs + + return annroll + + @diagnostic() + def daily_annualised_roll(self, instrument_code: str) -> pd.Series: + """ + Resample annualised roll to daily frequency + + We don't resample earlier, or we'll get bad data + + :param instrument_code: instrument to get data for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> system.rawdata.daily_annualised_roll("SOFR").ffill().tail(2) + 2015-12-10 0.284083 + 2015-12-11 0.284083 + Freq: B, dtype: float64 + """ + + annroll = self.annualised_roll(instrument_code) + annroll = annroll.resample("1B").mean() + + return annroll + + @output() + def raw_carry(self, instrument_code: str) -> pd.Series: + """ + Returns the raw carry (annualised roll, divided by annualised vol) + Only thing needed now is smoothing, that is done in the actual trading rule + + Added to rawdata to support relative carry trading rule + + :param instrument_code: + + :return: Tx1 pd.DataFrame + """ + + daily_ann_roll = self.daily_annualised_roll(instrument_code) + vol = self.daily_returns_volatility(instrument_code) + + ann_stdev = vol * ROOT_BDAYS_INYEAR + raw_carry = daily_ann_roll / ann_stdev + + return raw_carry + + @output() + def smoothed_carry(self, instrument_code: str, smooth_days: int = 90) -> pd.Series: + """ + Returns the smoothed raw carry + Added to rawdata to support relative carry trading rule + + :param instrument_code: + + :return: Tx1 pd.DataFrame + """ + + raw_carry = self.raw_carry(instrument_code) + smooth_carry = raw_carry.ewm(smooth_days).mean() + + return smooth_carry + + @diagnostic() + def _by_asset_class_median_carry_for_asset_class( + self, asset_class: str, smooth_days: int = 90 + ) -> pd.Series: + """ + + :param asset_class: + :return: + """ + + instruments_in_asset_class = self.all_instruments_in_asset_class(asset_class) + + raw_carry_across_asset_class = [ + self.raw_carry(instrument_code) + for instrument_code in instruments_in_asset_class + ] + + raw_carry_across_asset_class_pd = pd.concat( + raw_carry_across_asset_class, axis=1 + ) + + smoothed_carrys_across_asset_class = raw_carry_across_asset_class_pd.ewm( + smooth_days + ).mean() + + # we don't ffill before working out the median as this could lead to + # bad data + median_carry = smoothed_carrys_across_asset_class.median(axis=1) + + return median_carry + + @output() + def median_carry_for_asset_class(self, instrument_code: str) -> pd.Series: + """ + Median carry for the asset class relating to a given instrument + + + :param instrument_code: str + :return: pd.Series + """ + + asset_class = self.data_stage.asset_class_for_instrument(instrument_code) + median_carry = self._by_asset_class_median_carry_for_asset_class(asset_class) + instrument_carry = self.raw_carry(instrument_code) + + # Align for an easy life + # As usual forward fill at last moment + median_carry = median_carry.reindex(instrument_carry.index).ffill() + + return median_carry + + # sys.data.get_instrument_asset_classes() + + @output() + def daily_denominator_price(self, instrument_code: str) -> pd.Series: + """ + Gets daily prices for use with % volatility + This won't always be the same as the normal 'price' + + :param instrument_code: Instrument to get prices for + :type trading_rules: str + + :returns: Tx1 pd.DataFrame + + KEY OUTPUT + + >>> from systems.tests.testdata import get_test_object_futures + >>> from systems.basesystem import System + >>> (data, config)=get_test_object_futures() + >>> system=System([RawData()], data) + >>> + >>> system.rawdata.daily_denominator_price("SOFR").ffill().tail(2) + 2015-12-10 97.8800 + 2015-12-11 97.9875 + Freq: B, Name: PRICE, dtype: float64 + """ + try: + prices = self.get_instrument_raw_carry_data(instrument_code).PRICE + except missingData: + self.log.warning( + "No carry data found for %s, using adjusted prices to calculate percentage returns" + % instrument_code + ) + return self.get_daily_prices(instrument_code) + + daily_prices = prices.resample("1B").last() + + return daily_prices + + def all_instruments_in_asset_class(self, asset_class: str) -> list: + instruments_in_asset_class = self.data_stage.all_instruments_in_asset_class( + asset_class + ) + instrument_list = self.instrument_list() + instruments_in_asset_class_and_master_list = list_intersection( + instruments_in_asset_class, instrument_list + ) + + return instruments_in_asset_class_and_master_list + + def instrument_list(self) -> list: + instrument_list = self.parent.get_instrument_list() + return instrument_list + + +if __name__ == "__main__": + import doctest + + doctest.testmod()