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16:00:00,96.405,20260900,96.425,20261200,96.43,20270300 +2024-03-28 17:00:00,96.395,20260900,96.415,20261200,96.42,20270300 +2024-03-28 18:00:00,96.395,20260900,96.415,20261200,96.42,20270300 +2024-03-28 19:00:00,96.39,20260900,96.41,20261200,96.415,20270300 +2024-03-28 20:00:00,96.385,20260900,96.405,20261200,96.415,20270300 +2024-03-28 23:00:00,96.39,20260900,96.405,20261200,96.415,20270300 diff --git a/src/quantlib_st/data/futures/roll_calendars_csv/RUBBER.csv b/src/quantlib_st/data/futures/roll_calendars_csv/RUBBER.csv new file mode 100644 index 0000000..0db04f1 --- /dev/null +++ b/src/quantlib_st/data/futures/roll_calendars_csv/RUBBER.csv @@ -0,0 +1,15 @@ +DATE_TIME,current_contract,next_contract,carry_contract +2021-10-13 23:00:00,20211100,20211200,20211200 +2021-11-01 23:00:00,20211200,20220100,20220100 +2021-12-01 23:00:00,20220100,20220200,20220200 +2021-12-30 23:00:00,20220200,20220300,20220300 +2022-01-28 23:00:00,20220300,20220400,20220400 +2022-03-01 23:00:00,20220400,20220500,20220500 +2022-03-31 23:00:00,20220500,20220600,20220600 +2022-04-29 23:00:00,20220600,20220700,20220700 +2022-05-31 23:00:00,20220700,20220800,20220800 +2022-07-01 23:00:00,20220800,20220900,20220900 +2022-08-01 23:00:00,20220900,20221000,20221000 +2022-08-31 23:00:00,20221000,20221100,20221100 +2022-09-30 23:00:00,20221100,20221200,20221200 +2022-10-12 23:00:00,20221200,20230100,20230100 diff --git a/src/quantlib_st/estimators/vol.py b/src/quantlib_st/estimators/vol.py index 31c9b52..bf9a759 100644 --- a/src/quantlib_st/estimators/vol.py +++ b/src/quantlib_st/estimators/vol.py @@ -129,7 +129,9 @@ def mixed_vol_calc( **ignored_kwargs, ) -> pd.Series: """ - Robust exponential volatility calculation, assuming daily series of prices + Blending short-term (robust) vol with a long-term slow vol component, + assuming daily series of prices. + We apply an absolute minimum level of vol (absmin); and a volfloor based on lowest vol over recent history 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 index c9a0667..203c563 100644 --- 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 @@ -1,5 +1,7 @@ import pandas as pd +from typing import Literal, get_args + from quantlib_st.core.pandas.merge_data_with_label_column import ( merge_data_series_with_label_column, ) @@ -10,13 +12,16 @@ price_name = "PRICE" carry_name = "CARRY" forward_name = "FORWARD" + +price_column_names_literal = Literal["PRICE", "CARRY", "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 = list(get_args(price_column_names_literal)) list_of_price_column_names.sort() contract_suffix = "_CONTRACT" -def contract_name_from_column_name(column_name): +def contract_name_from_column_name(column_name: price_column_names_literal) -> str: return column_name + contract_suffix @@ -48,37 +53,33 @@ def _check_key_list_is_valid_against_names(some_dict): ) -class futuresNamedContractFinalPricesWithContractID(pd.DataFrame): +class futuresNamedContractFinalPricesWithContractID: """ - Just the final prices from a futures contract, plus - Columns are named 'NAME' and 'NAME_CONTRACT' + Just the final prices from a futures contract, plus contract IDs. + Encapsulates a pd.DataFrame with columns: [price_column_name, contract_column_name] """ def __init__( self, ts_of_prices: pd.Series, ts_of_contracts: pd.Series, - price_column_name: str = list_of_price_column_names[0], + price_column_name: price_column_names_literal = "PRICE", ): - """ - - :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) - + df = 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) + df.columns = [price_column_name, contract_column_name] - self._price_column_name = price_column_name + self._df = df + self._price_column_name: price_column_names_literal = price_column_name self._contract_column_name = contract_column_name + def __len__(self) -> int: + return len(self._df) + @property - def price_column_name(self) -> str: + def price_column_name(self) -> price_column_names_literal: return self._price_column_name @property @@ -87,21 +88,21 @@ def contract_column_name(self) -> str: @property def prices(self) -> pd.Series: - return self[self.price_column_name] + return self._df[self.price_column_name] @property def ts_of_contract_str(self) -> pd.Series: - return self[self.contract_column_name] + return self._df[self.contract_column_name] - def as_pd(self): - return pd.DataFrame(self) + def as_pd(self) -> pd.DataFrame: + return self._df @classmethod def create_with_single_contractid( - futuresNamedContractFinalPricesWithContractID, + cls, ts_of_prices: pd.Series, contractid: str, - price_column_name=price_name, + price_column_name: price_column_names_literal = price_name, ): """ @@ -114,9 +115,7 @@ def create_with_single_contractid( 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 - ) + return cls(ts_of_prices, ts_of_contracts, price_column_name=price_column_name) def prices_after_date(self, date_slice): prices = self.prices[date_slice:] @@ -179,8 +178,8 @@ def _merge_futures_contract_final_prices_with_contract_id( contract_column_name = original_data.contract_column_name merged_data_as_pd = merge_data_series_with_label_column( - original_data, - new_data, + original_data.as_pd(), + new_data.as_pd(), data_column=price_column_name, label_column=contract_column_name, ) @@ -267,7 +266,7 @@ def __repr__(self): @classmethod def create_from_two_dicts( - dictFuturesNamedContractFinalPricesWithContractID, + cls, dict_of_final_prices: dictNamedFuturesContractFinalPrices, dict_of_contract_ids: setOfNamedContracts, ): @@ -293,7 +292,7 @@ def create_from_two_dicts( ) new_dict[key] = prices_with_contractid - return dictFuturesNamedContractFinalPricesWithContractID(new_dict) + return cls(new_dict) def merge_data(self, new_price_dict): """ diff --git a/src/quantlib_st/objects/instruments.py b/src/quantlib_st/objects/instruments.py index e39ee64..67bad9a 100644 --- a/src/quantlib_st/objects/instruments.py +++ b/src/quantlib_st/objects/instruments.py @@ -1,4 +1,4 @@ -from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.constants import arg_not_supplied, named_object from quantlib_st.core.genutils import flatten_list from dataclasses import dataclass @@ -220,7 +220,7 @@ def as_pd(self) -> pd.Series: return pd.Series(asset_classes, index=instruments) def all_instruments_in_asset_class( - self, asset_class: str, must_be_in=arg_not_supplied + self, asset_class: str, must_be_in: list | named_object = arg_not_supplied ) -> list: asset_class_instrument_list = [ instrument diff --git a/src/quantlib_st/objects/multiple_prices.py b/src/quantlib_st/objects/multiple_prices.py index e9f3892..61bfce9 100644 --- a/src/quantlib_st/objects/multiple_prices.py +++ b/src/quantlib_st/objects/multiple_prices.py @@ -1,5 +1,6 @@ from copy import copy from dataclasses import dataclass +from typing import Optional import datetime as datetime import pandas as pd @@ -29,12 +30,12 @@ @dataclass class singleRowMultiplePrices: - price: float = None - carry: float = None - forward: float = None - price_contract: str = None - carry_contract: str = None - forward_contract: str = None + price: Optional[float] = None + carry: Optional[float] = None + forward: Optional[float] = None + price_contract: Optional[str] = None + carry_contract: Optional[str] = None + forward_contract: Optional[str] = None def concat_with_multiple_prices(self, multiple_prices, timedelta_seconds=1): new_time_index = multiple_prices.index[-1] + datetime.timedelta( @@ -70,7 +71,7 @@ def as_aligned_pd_row(self, time_index: datetime.timedelta) -> pd.DataFrame: class futuresMultiplePrices(pd.DataFrame): def __init__(self, data): _check_valid_multiple_price_data(data) - super().__init__(data) + super().__init__(data) # type: ignore data.index.name = "index" # arctic compatible @@ -152,7 +153,7 @@ def as_dict(self) -> dictFuturesNamedContractFinalPricesWithContractID: self[contract_column_name], price_column_name=price_column_name, ) - ) + ) # type: ignore self_as_dict = dictFuturesNamedContractFinalPricesWithContractID(self_as_dict) @@ -194,7 +195,7 @@ def from_merged_dict( return multiple_prices_object - def sort_index(self): + def sort_index(self): # type: ignore df = pd.DataFrame(self) sorted_df = df.sort_index() diff --git a/src/quantlib_st/sysdata/base_data.py b/src/quantlib_st/sysdata/base_data.py index 599a16a..b418249 100644 --- a/src/quantlib_st/sysdata/base_data.py +++ b/src/quantlib_st/sysdata/base_data.py @@ -54,7 +54,7 @@ def __getitem__(self, keyname): "__getitem__ not defined for baseData class: use a class where it has been overridden" ) - def keys(self): + def keys(self) -> list[str]: """ list of things in this data set (futures contracts, instruments...) diff --git a/src/quantlib_st/sysdata/csv/csv_multiple_prices.py b/src/quantlib_st/sysdata/csv/csv_multiple_prices.py index abcffe1..926e938 100644 --- a/src/quantlib_st/sysdata/csv/csv_multiple_prices.py +++ b/src/quantlib_st/sysdata/csv/csv_multiple_prices.py @@ -44,6 +44,10 @@ def datapath(self): return self._datapath def get_list_of_instruments(self): + self.log.info( + "Getting list of instruments with multiple prices from path %s" + % self.datapath + ) return files_with_extension_in_pathname(self.datapath, ".csv") def _get_multiple_prices_without_checking( diff --git a/src/quantlib_st/sysdata/data_blob.py b/src/quantlib_st/sysdata/data_blob.py index 3ea9366..56fc668 100644 --- a/src/quantlib_st/sysdata/data_blob.py +++ b/src/quantlib_st/sysdata/data_blob.py @@ -2,11 +2,11 @@ from typing import Any, TYPE_CHECKING from quantlib_st.core.objects import get_class_name -from quantlib_st.core.constants import arg_not_supplied +from quantlib_st.core.constants import arg_not_supplied, named_object 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 +from quantlib_st.logging.adaptor import COMPONENT_LOG_LABEL, DynamicAttributeLogger if TYPE_CHECKING: from quantlib_st.sysdata.production_config import Config # pragma: no cover @@ -23,13 +23,13 @@ class dataBlob(object): def __init__( self, - class_list: list = arg_not_supplied, + class_list: list | named_object = arg_not_supplied, log_name: str = "", - csv_data_paths: dict = arg_not_supplied, - parquet_store_path: str = arg_not_supplied, + csv_data_paths: dict[str, str] | named_object = arg_not_supplied, + parquet_store_path: str | named_object = arg_not_supplied, ib_conn: Any = arg_not_supplied, mongo_db: Any = arg_not_supplied, - log=arg_not_supplied, + log: DynamicAttributeLogger | named_object = arg_not_supplied, keep_original_prefix: bool = False, ): """ @@ -188,7 +188,7 @@ def _add_csv_class(self, class_object): return resolved_instance - def _get_csv_paths_for_class(self, class_object) -> str: + def _get_csv_paths_for_class(self, class_object) -> str | named_object: class_name = get_class_name(class_object) csv_data_paths = self.csv_data_paths if csv_data_paths is arg_not_supplied: @@ -205,7 +205,7 @@ def _get_csv_paths_for_class(self, class_object) -> str: return datapath @property - def csv_data_paths(self) -> dict: + def csv_data_paths(self) -> dict | named_object: csv_data_paths = getattr(self, "_csv_data_paths", arg_not_supplied) return csv_data_paths 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 e20826f..655d216 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 @@ -1,5 +1,6 @@ import pandas as pd from datetime import datetime +from typing import Optional from quantlib_st.sysdata.csv.csv_multiple_prices import csvFuturesMultiplePricesData from quantlib_st.sysdata.csv.csv_adjusted_prices import csvFuturesAdjustedPricesData @@ -33,13 +34,18 @@ class CsvFuturesSimTestData(genericBlobUsingFuturesSimData): 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") + self, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + log=get_logger("csvFuturesSimTestData"), ): data = dataBlob( log=log, csv_data_paths=dict( csvFuturesAdjustedPricesData="quantlib_st.data.test.adjusted_prices_csv", csvFuturesInstrumentData="quantlib_st.data.test.csvconfig", + csvFuturesMultiplePricesData="quantlib_st.data.futures.multiple_prices_csv", + csvRollParametersData="quantlib_st.data.futures.roll_calendars_csv", csvSpreadCostData="quantlib_st.data.test.csvconfig", ), class_list=[ @@ -86,7 +92,7 @@ def get_backadjusted_futures_price( instrument_code ) date_adjusted = data[self.start_date : self.end_date] - return date_adjusted + return date_adjusted # type: ignore def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: data = super().get_multiple_prices(instrument_code) @@ -98,7 +104,7 @@ def get_fx_for_instrument( ) -> fxPrices: data = super().get_fx_for_instrument(instrument_code, base_currency) date_adjusted = data[self.start_date : self.end_date] - return date_adjusted + return date_adjusted # type: ignore def daily_prices(self, instrument_code: str) -> pd.Series: data = super().daily_prices(instrument_code) diff --git a/src/quantlib_st/sysdata/sim/futures_sim_data.py b/src/quantlib_st/sysdata/sim/futures_sim_data.py index 85acca2..0fdcf5c 100644 --- a/src/quantlib_st/sysdata/sim/futures_sim_data.py +++ b/src/quantlib_st/sysdata/sim/futures_sim_data.py @@ -1,3 +1,6 @@ +from abc import ABC, abstractmethod + +import datetime import pandas as pd from quantlib_st.core.exceptions import missingInstrument @@ -17,13 +20,23 @@ contract_name_from_column_name, ) from quantlib_st.objects.rolls import rollParameters +from typing import Literal 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): +class futuresSimData(simData, ABC): + """ + Base class for futures simulation data sources + Provides methods to access futures data + + Minimally, we should implement: + 1. get_backadjusted_futures_price (for continuous price series) + 2. get_multiple_prices_from_start_date (unadjusted prices to calculate carry) + """ + def __repr__(self): return "futuresSimData object with %d instruments" % len( self.get_instrument_list() @@ -66,10 +79,10 @@ 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 + self, instrument_code: str, start_date: datetime.datetime ) -> pd.Series: """ - For futures the price is the backadjusted price + For futures, the price is the backadjusted price :param instrument_code: :return: price @@ -210,17 +223,70 @@ def get_instrument_asset_classes(self) -> assetClassesAndInstruments: def get_spread_cost(self, instrument_code: str) -> float: raise NotImplementedError + @abstractmethod def get_backadjusted_futures_price( self, instrument_code: str ) -> futuresAdjustedPrices: """ + Subclasses must implement this to return the continuous backadjusted price series. - :param instrument_code: - :return: + Implementers can use the helper method `_get_backadjusted_futures_price_from_multiple_prices` + to perform either 'diff_adjusted' or 'ratio_adjusted' calculations based on raw contract data. + + :param instrument_code: The instrument code + :return: futuresAdjustedPrices (backadjusted series) """ raise NotImplementedError() + def _get_backadjusted_futures_price_from_multiple_prices( + self, + instrument_code: str, + backadjust_methodology: Literal[ + "diff_adjusted", "ratio_adjusted" + ] = "diff_adjusted", + ) -> futuresAdjustedPrices: + """ + Helper to calculate backadjusted prices from raw multiple price data. + + Logic: + 1. Identify roll dates where PRICE_CONTRACT changes. + 2. Calculate the 'jump' between PRICE and FORWARD at that moment. + 3. Cumulatively apply these jumps/ratios backwards from the current price. + """ + multiple_prices = self.get_multiple_prices(instrument_code) + + # Identify rows where the contract is about to change (the day before the roll) + price_contract = multiple_prices[price_contract_name] + roll_mask = price_contract != price_contract.shift(-1) + + # We only care about the last row of each contract (the roll trigger) + # Shift handles the fact that on 'roll day' we calculate based on previous day's gap + if backadjust_methodology == "diff_adjusted": + # Gap = Forward Contract Price - Traded Contract Price + gaps = (multiple_prices[forward_name] - multiple_prices[price_name]).where( + roll_mask, 0.0 + ) + # Accumulate gaps backwards + cumulative_adj = gaps.iloc[::-1].cumsum().iloc[::-1].shift(-1).fillna(0.0) + adjusted_series = multiple_prices[price_name] + cumulative_adj + + elif backadjust_methodology == "ratio_adjusted": + # Ratio = Forward Contract Price / Traded Contract Price + ratios = ( + multiple_prices[forward_name] / multiple_prices[price_name] + ).where(roll_mask, 1.0) + # Accumulate ratios backwards + cumulative_adj = ( + ratios.iloc[::-1].cumprod().iloc[::-1].shift(-1).fillna(1.0) + ) + adjusted_series = multiple_prices[price_name] * cumulative_adj + + else: + raise ValueError(f"Unknown methodology: {backadjust_methodology}") + + return futuresAdjustedPrices(adjusted_series) + def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: start_date = self.start_date_for_data() @@ -228,13 +294,31 @@ def get_multiple_prices(self, instrument_code: str) -> futuresMultiplePrices: instrument_code, start_date=start_date ) + @abstractmethod def get_multiple_prices_from_start_date( - self, instrument_code: str, start_date + self, instrument_code: str, start_date: datetime.datetime ) -> futuresMultiplePrices: + """ + Get several different futures prices and contract IDs for an instrument. + + Subclasses must implement this to return a futuresMultiplePrices object (a pd.DataFrame) + with exactly following columns: + - PRICE: The price of the contract currently held (traded). + - CARRY: The price of the adjacent contract used for carry calculations. + - FORWARD: The price of the next contract used for roll/forward calculations. + - PRICE_CONTRACT: Expiry (YYYYMM) of the 'PRICE' contract (e.g., '202406'). + - CARRY_CONTRACT: Expiry (YYYYMM) of the 'CARRY_CONTRACT' contract. + - FORWARD_CONTRACT: Expiry (YYYYMM) of the 'FORWARD_CONTRACT' contract. + + :param instrument_code: Instrument code (e.g., 'CRUDE_OIL') + :param start_date: Start date for the data (inclusive) + :return: futuresMultiplePrices object containing the requested columns, indexed by datetime + """ + raise NotImplementedError() def get_instrument_meta_data( - self, instrument_code + self, instrument_code: str ) -> futuresInstrumentWithMetaData: """ Get a futures instrument where the meta data is cost data diff --git a/src/quantlib_st/sysdata/sim/sim_data.py b/src/quantlib_st/sysdata/sim/sim_data.py index 2ce0d20..2070d7b 100644 --- a/src/quantlib_st/sysdata/sim/sim_data.py +++ b/src/quantlib_st/sysdata/sim/sim_data.py @@ -1,5 +1,7 @@ -import pandas as pd import datetime +import pandas as pd + +from typing import TYPE_CHECKING from quantlib_st.core.exceptions import missingData from quantlib_st.core.objects import get_methods @@ -8,10 +10,13 @@ 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 +from quantlib_st.objects.spot_fx_prices import fxPrices +from quantlib_st.sysdata.sim.base_data import baseData + +if TYPE_CHECKING: + from quantlib_st.systems.basesystem import System class simData(baseData): @@ -37,7 +42,7 @@ class simData(baseData): def __repr__(self): # type: ignore return "simData object with %d instruments" % len(self.get_instrument_list()) - def __getitem__(self, keyname: str): + def __getitem__(self, keyname: str): # type: ignore """ convenience method to get the price, make it look like a dict @@ -50,7 +55,7 @@ def __getitem__(self, keyname: str): return price - def keys(self) -> list: + def keys(self) -> list[str]: """ list of instruments in this data set @@ -85,7 +90,7 @@ def start_date_for_data(self): 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: + def _get_and_set_start_date_for_data_from_config(self) -> datetime.datetime: start_date = _resolve_start_date(self) self._start_date_for_data_from_config = start_date @@ -126,12 +131,12 @@ def _get_daily_prices_for_directional_instrument( return dailyprice - def hourly_prices(self, instrument_code: str) -> pd.Series: + def hourly_prices(self, instrument_code: str) -> pd.Series | pd.DataFrame: return self._get_hourly_prices_for_directional_instrument(instrument_code) def _get_hourly_prices_for_directional_instrument( self, instrument_code: str - ) -> pd.Series: + ) -> pd.Series | pd.DataFrame: instrprice = self.get_raw_price(instrument_code) if len(instrprice) == 0: raise Exception("No adjusted hourly prices for %s" % instrument_code) @@ -204,7 +209,7 @@ def get_raw_price_from_start_date( """ raise NotImplementedError("Need to inherit from simData") - def get_instrument_list(self) -> list: + def get_instrument_list(self) -> list[str]: """ list of instruments in this data set @@ -265,6 +270,49 @@ def get_instrument_currency(self, instrument_code: str) -> str: "Need to inherit from base class for specific data source" ) + def asset_class_for_instrument(self, instrument_code: str) -> str: + """ + Get the asset class 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_rolls_per_year(self, instrument_code: str) -> int: + """ + Gets number of rolls per year for an instrument + + :param instrument_code: instrument to get carry data for + :type instrument_code: str + + :returns: int + + """ + + raise NotImplementedError("Need to inherit for a specific data source") + + def get_instrument_raw_carry_data(self, instrument_code: str) -> pd.DataFrame: + """ + Gets carry data for an instrument + """ + raise NotImplementedError("Need to inherit for a specific data source") + + 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 + """ + raise NotImplementedError("Need to inherit for a specific data source") + def _get_fx_data(self, currency1: str, currency2: str) -> fxPrices: """ Get the FX rate currency1/currency2 between two currencies @@ -294,7 +342,7 @@ def _get_fx_data_from_start_date( raise NotImplementedError("Need to inherit for a specific data source") -def _resolve_start_date(sim_data: simData): +def _resolve_start_date(sim_data: simData) -> datetime.datetime: try: config = _resolve_config(sim_data) except missingData: diff --git a/src/quantlib_st/systems/forecast_combine.py b/src/quantlib_st/systems/forecast_combine.py new file mode 100755 index 0000000..9a1c761 --- /dev/null +++ b/src/quantlib_st/systems/forecast_combine.py @@ -0,0 +1,1543 @@ +from copy import copy + +import pandas as pd + +from quantlib_st.core.exceptions import missingData +from systems.forecast_mapping import map_forecast_value +from quantlib_st.core.genutils import str2Bool, list_difference +from quantlib_st.core.objects import resolve_function +from quantlib_st.core.pandas.pdutils import ( + dataframe_pad, + from_dict_of_values_to_df, + from_scalar_values_to_ts, +) +from core.pandas.frequency import reindex_last_monthly_include_first_date +from syscore.pandas.strategy_functions import ( + weights_sum_to_one, + fix_weights_vs_position_or_forecast, +) +from syscore.pandas.list_of_df import listOfDataFrames + +from quantlib_st.config.configdata import Config + +from sysquant.estimators.correlations import CorrelationList +from sysquant.optimisation.pre_processing import returnsPreProcessor +from sysquant.returns import ( + dictOfReturnsForOptimisationWithCosts, + returnsForOptimisationWithCosts, +) +from sysquant.estimators.turnover import ( + turnoverDataAcrossTradingRules, + turnoverDataForTradingRule, +) + +from quantlib_st.systems.stage import SystemStage +from quantlib_st.systems.system_cache import diagnostic, dont_cache, input, output +from quantlib_st.systems.forecasting import Rules +from systems.forecast_scale_cap import ForecastScaleCap +from quantlib_st.systems.tools.autogroup import ( + calculate_autogroup_weights_given_parameters, + config_is_auto_group, + resolve_config_into_parameters_and_weights_for_autogrouping, +) + + +class ForecastCombine(SystemStage): + """ + Stage for combining forecasts (already capped and scaled) + """ + + @property + def name(self): + return "combForecast" + + @output() + def get_combined_forecast(self, instrument_code: str) -> pd.Series: + """ + Get a combined forecast, linear combination of individual forecasts + with FDM applied + + We forward fill all forecasts. We then adjust forecast weights so that + they are 1.0 in every period; after setting to zero when no forecast + is available. Finally we multiply up, and apply the FDM. Then we cap. + + :param instrument_code: + :type str: + + :returns: Tx1 pd.DataFrame + + KEY OUTPUT + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> + >>> system.combForecast.get_combined_forecast("EDOLLAR").tail(2) + comb_forecast + 2015-12-10 1.619134 + 2015-12-11 2.462610 + >>> + >>> config.forecast_div_multiplier=1000.0 + >>> system2=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system2.combForecast.get_combined_forecast("EDOLLAR").tail(2) + comb_forecast + 2015-12-10 21 + 2015-12-11 21 + """ + self.log.debug( + "Calculating combined forecast for %s" % (instrument_code), + instrument_code=instrument_code, + ) + raw_multiplied_combined_forecast = ( + self.get_raw_combined_forecast_before_mapping(instrument_code) + ) + + # apply cap and /or any non linear mapping + ( + mapping_and_capping_function, + mapping_and_capping_kwargs, + ) = self._get_forecast_mapping_function(instrument_code) + + combined_forecast = mapping_and_capping_function( + raw_multiplied_combined_forecast, **mapping_and_capping_kwargs + ) + + return combined_forecast + + # FORECAST = AGGREGATE SUM OF FORECASTS * IDM + @diagnostic() + def get_raw_combined_forecast_before_mapping( + self, instrument_code: str + ) -> pd.Series: + # sum + raw_combined_forecast = self.get_combined_forecast_without_multiplier( + instrument_code + ) + + # probably daily frequency + forecast_div_multiplier = self.get_forecast_diversification_multiplier( + instrument_code + ) + + forecast_div_multiplier = forecast_div_multiplier.reindex( + raw_combined_forecast.index + ).ffill() + + # apply fdm + raw_multiplied_combined_forecast = ( + raw_combined_forecast * forecast_div_multiplier.ffill() + ) + + return raw_multiplied_combined_forecast + + @diagnostic() + def get_combined_forecast_without_multiplier( + self, instrument_code: str + ) -> pd.Series: + # We take our list of rule variations from the forecasts, since it + # might be that some rules were omitted in the weight calculation + + weighted_forecasts = self.get_weighted_forecasts_without_multiplier( + instrument_code + ) + + # sum + raw_combined_forecast = weighted_forecasts.sum(axis=1) + + return raw_combined_forecast + + @diagnostic() + def get_weighted_forecasts_without_multiplier( + self, instrument_code: str + ) -> pd.DataFrame: + # We take our list of rule variations from the forecasts, since it + # might be that some rules were omitted in the weight calculation + rule_variation_list = self.get_trading_rule_list(instrument_code) + forecasts = self.get_all_forecasts(instrument_code, rule_variation_list) + + smoothed_daily_forecast_weights = self.get_forecast_weights(instrument_code) + smoothed_forecast_weights = smoothed_daily_forecast_weights.reindex( + forecasts.index, method="ffill" + ) + + weighted_forecasts = smoothed_forecast_weights * forecasts + + return weighted_forecasts + + # GET FORECAST WEIGHTS ALIGNED TO FORECASTS AND SMOOTHED + + @diagnostic() + def get_forecast_weights(self, instrument_code: str) -> pd.DataFrame: + # These will be in daily frequency + daily_forecast_weights_fixed_to_forecasts_unsmoothed = ( + self.get_unsmoothed_forecast_weights(instrument_code) + ) + + # smooth out weights + forecast_smoothing_ewma_span = self.config.forecast_weight_ewma_span + smoothed_daily_forecast_weights = ( + daily_forecast_weights_fixed_to_forecasts_unsmoothed.ewm( + span=forecast_smoothing_ewma_span + ).mean() + ) + + # change rows so weights add to one (except for special case where all zeros) + smoothed_normalised_daily_weights = weights_sum_to_one( + smoothed_daily_forecast_weights + ) + + # still daily + + return smoothed_normalised_daily_weights + + @diagnostic() + def get_unsmoothed_forecast_weights(self, instrument_code: str): + """ + Get the forecast weights + + We forward fill all forecasts. We then adjust forecast weights so that + they are 1.0 in every period; after setting to zero when no forecast + is available. + + :param instrument_code: + :type str: + + :returns: TxK pd.DataFrame containing weights, columns are trading rule variation names, T covers all + + KEY OUTPUT + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> + >>> ## from config + >>> system.combForecast.get_forecast_weights("EDOLLAR").tail(2) + ewmac16 ewmac8 + 2015-12-10 0.5 0.5 + 2015-12-11 0.5 0.5 + >>> + >>> config.forecast_weights=dict(EDOLLAR=dict(ewmac8=0.9, ewmac16=0.1)) + >>> system2=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system2.combForecast.get_forecast_weights("EDOLLAR").tail(2) + ewmac16 ewmac8 + 2015-12-10 0.1 0.9 + 2015-12-11 0.1 0.9 + >>> + >>> del(config.forecast_weights) + >>> system3=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system3.combForecast.get_forecast_weights("EDOLLAR").tail(2) + WARNING: No forecast weights - using equal weights of 0.5000 over all 2 trading rules in system + ewmac16 ewmac8 + 2015-12-10 0.5 0.5 + 2015-12-11 0.5 0.5 + """ + self.log.debug( + "Calculating forecast weights for %s" % (instrument_code), + instrument_code=instrument_code, + ) + + # note these might include missing weights, eg too expensive, or absent + # from fixed weights + # These are monthly to save space, or possibly even only 2 rows long + monthly_forecast_weights = self.get_raw_monthly_forecast_weights( + instrument_code + ) + + # fix to forecast time series + forecast_weights_fixed_to_forecasts = self._fix_weights_to_forecasts( + instrument_code=instrument_code, + monthly_forecast_weights=monthly_forecast_weights, + ) + + if forecast_weights_fixed_to_forecasts.empty: + return forecast_weights_fixed_to_forecasts + + # Remap to business day frequency so the smoothing makes sense also space saver + daily_forecast_weights_fixed_to_forecasts_unsmoothed = ( + forecast_weights_fixed_to_forecasts.resample("1B").mean() + ) + + return daily_forecast_weights_fixed_to_forecasts_unsmoothed + + @dont_cache + def _fix_weights_to_forecasts( + self, instrument_code: str, monthly_forecast_weights: pd.DataFrame + ) -> pd.DataFrame: + # we get the rule variations from forecast_weight columns, as if we've dropped + # expensive rules (when estimating) then get_trading_rules will give + # the wrong answer + rule_variation_list = list(monthly_forecast_weights.columns) + + # time series may vary + forecasts = self.get_all_forecasts(instrument_code, rule_variation_list) + + # adjust weights for missing data + # also aligns them together with forecasts + forecast_weights_fixed_to_forecasts = fix_weights_vs_position_or_forecast( + monthly_forecast_weights, forecasts + ) + + return forecast_weights_fixed_to_forecasts + + # GET FORECASTS + @input + def get_all_forecasts( + self, instrument_code: str, rule_variation_list: list = None + ) -> pd.DataFrame: + """ + Returns a data frame of forecasts for a particular instrument + + KEY INPUT + + :param instrument_code: + :type str: + + :param rule_variation_list: + :type list: list of str to get forecasts for, if None uses get_trading_rule_list + + :returns: TxN pd.DataFrames; columns rule_variation_name + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system1=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system1.combForecast.get_all_forecasts("EDOLLAR",["ewmac8"]).tail(2) + ewmac8 + 2015-12-10 -0.190583 + 2015-12-11 0.871231 + >>> + >>> system2=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system2.combForecast.get_all_forecasts("EDOLLAR").tail(2) + ewmac16 ewmac8 + 2015-12-10 3.134462 -0.190583 + 2015-12-11 3.606243 0.871231 + """ + + if rule_variation_list is None: + rule_variation_list = self.get_trading_rule_list(instrument_code) + + forecasts = self.get_forecasts_given_rule_list( + instrument_code, rule_variation_list + ) + + return forecasts + + @dont_cache + def get_trading_rule_list(self, instrument_code: str) -> list: + """ + Get list of trading rules + + We remove any rules with a constant zero or nan forecast + + :param instrument_code: + :return: list of str + """ + + if self._use_estimated_weights(): + # Note for estimated weights we apply the 'is this cheap enough' + # rule, but not here + trial_rule_list = self.get_trading_rule_list_for_estimated_weights( + instrument_code + ) + else: + trial_rule_list = self._get_trading_rule_list_with_fixed_weights( + instrument_code + ) + + return trial_rule_list + + @diagnostic() + def _get_trading_rule_list_with_fixed_weights(self, instrument_code: str) -> list: + """ + Get list of all trading rule names when weights are fixed + + If we have fixed weights use those; otherwise get from trading rules + + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> + >>> system.combForecast.get_trading_rule_list("EDOLLAR") + ['ewmac16', 'ewmac8'] + """ + + # Let's try the config + config = self.config + rules = _get_list_of_rules_from_config_for_instrument( + config=config, instrument_code=instrument_code, forecast_combine_stage=self + ) + + rules = sorted(rules) + + return rules + + @property + def config(self) -> Config: + return self.parent.config + + @input + def _get_list_of_all_trading_rules_from_forecasting_stage(self) -> list: + return list(self.rules_stage.trading_rules().keys()) + + @property + def rules_stage(self) -> Rules: + return self.parent.rules + + @diagnostic() + def get_trading_rule_list_for_estimated_weights(self, instrument_code: str) -> list: + """ + Get list of all trading rule names when weights are estimated + + If rule_variations is specified in config use that, otherwise use all available rules + + :param instrument_code: + :type str: + + :returns: list of str + + KEY INPUT + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([accounts, rawdata, rules, fcs, ForecastCombineEstimated()], data, config) + >>> system.combForecast.get_trading_rule_list("EDOLLAR") + ['carry', 'ewmac16', 'ewmac8'] + >>> system.config.rule_variations=dict(EDOLLAR=["ewmac8"]) + >>> system.combForecast.get_trading_rule_list("EDOLLAR") + ['ewmac8'] + """ + # Let's try the config + config = self.config + + if hasattr(config, "rule_variations"): + ### + if instrument_code in config.rule_variations: + # nested dict of lists + rules = config.rule_variations[instrument_code] + else: + # assume it's a non nested list + # this will break if you have put an incomplete list of + # instruments into a nested dict + rules = config.rule_variations + else: + ## not supplied in config + rules = self._get_list_of_all_trading_rules_from_forecasting_stage() + + rules = sorted(rules) + + return rules + + @diagnostic() + def get_forecasts_given_rule_list( + self, instrument_code: str, rule_variation_list: list + ) -> pd.DataFrame: + """ + Convenience function to get a list of forecasts + + :param instrument_code: str + :param rule_variation_list: list of str + :return: pd.DataFrame + """ + forecasts = [ + self._get_capped_individual_forecast(instrument_code, rule_variation_name) + for rule_variation_name in rule_variation_list + ] + + if not forecasts: + return pd.DataFrame(index=pd.DatetimeIndex([])) + + forecasts = pd.concat(forecasts, axis=1) + + forecasts.columns = rule_variation_list + + forecasts = forecasts.ffill() + + return forecasts + + @property + def raw_data_stage(self): + return self.parent.rawdata + + @input + def _get_capped_individual_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> pd.Series: + """ + Get the capped forecast from the previous module + + KEY INPUT + + :param instrument_code: + :type str: + + :param rule_variation_name: + :type str: name of the trading rule variation + + :returns: dict of Tx1 pd.DataFrames; keynames rule_variation_name + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system.combForecast._get_capped_individual_forecast("EDOLLAR","ewmac8").tail(2) + ewmac8 + 2015-12-10 -0.190583 + 2015-12-11 0.871231 + """ + + return self.forecast_scale_cap_stage.get_capped_forecast( + instrument_code, rule_variation_name + ) + + @property + def forecast_scale_cap_stage(self) -> ForecastScaleCap: + return self.parent.forecastScaleCap + + # FORECAST WEIGHT CALCULATIONS + @dont_cache + def get_raw_monthly_forecast_weights(self, instrument_code: str) -> pd.DataFrame: + """ + Get forecast weights depending on whether we are estimating these or + not + + :param instrument_code: str + :return: forecast weights + """ + + # get estimated weights, will probably come back as annual data frame + if self._use_estimated_weights(): + forecast_weights = self.get_monthly_raw_forecast_weights_estimated( + instrument_code + ) + else: + ## will come back as 2*N data frame + forecast_weights = self.get_raw_fixed_forecast_weights(instrument_code) + + ## FIXME NEED THIS TO APPLY TO GROUPINGS + forecast_weights_cheap_rules_only = self._remove_expensive_rules_from_weights( + instrument_code, forecast_weights + ) + + return forecast_weights_cheap_rules_only + + @dont_cache + def _remove_expensive_rules_from_weights( + self, instrument_code, monthly_forecast_weights: pd.DataFrame + ) -> pd.DataFrame: + cheap_rules = self.cheap_trading_rules_post_processing(instrument_code) + if len(cheap_rules) == 0: + ## special case all zeros + monthly_forecast_weights_cheap_rules_only = copy(monthly_forecast_weights) + monthly_forecast_weights_cheap_rules_only[:] = 0.0 + + return monthly_forecast_weights_cheap_rules_only + + original_rules = list(monthly_forecast_weights.columns) + + cheap_rules_in_weights = list(set(original_rules).intersection(cheap_rules)) + + monthly_forecast_weights_cheap_rules_only = monthly_forecast_weights[ + cheap_rules_in_weights + ] + + return monthly_forecast_weights_cheap_rules_only + + @dont_cache + def _use_estimated_weights(self): + return str2Bool(self.config.use_forecast_weight_estimates) + + def get_monthly_raw_forecast_weights_estimated( + self, instrument_code: str + ) -> pd.DataFrame: + """ + Estimate the forecast weights for this instrument + + :param instrument_code: + :type str: + + :returns: TxK pd.DataFrame containing weights, columns are trading rule variation names, T covers all + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([accounts, rawdata, rules, fcs, ForecastCombineEstimated()], data, config) + >>> system.config.forecast_weight_estimate['method']="shrinkage" + >>> system.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(3) + carry ewmac16 ewmac8 + 2015-05-30 0.437915 0.258300 0.303785 + 2015-06-01 0.442438 0.256319 0.301243 + 2015-12-12 0.442438 0.256319 0.301243 + >>> system.delete_all_items(True) + >>> system.config.forecast_weight_estimate['method']="one_period" + >>> system.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(3) + 2015-05-30 0.484279 8.867313e-17 0.515721 + 2015-06-01 0.515626 7.408912e-17 0.484374 + 2015-12-12 0.515626 7.408912e-17 0.484374 + >>> system.delete_all_items(True) + >>> system.config.forecast_weight_estimate['method']="bootstrap" + >>> system.config.forecast_weight_estimate['monte_runs']=50 + >>> system.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(3) + carry ewmac16 ewmac8 + 2015-05-30 0.446446 0.222678 0.330876 + 2015-06-01 0.464240 0.192962 0.342798 + 2015-12-12 0.464240 0.192962 0.342798 + """ + optimiser = self.calculation_of_raw_estimated_monthly_forecast_weights( + instrument_code + ) + forecast_weights = optimiser.weights() + + return forecast_weights + + @diagnostic(not_pickable=True, protected=True) + def calculation_of_raw_estimated_monthly_forecast_weights(self, instrument_code): + """ + Does an optimisation for a single instrument + + We do this if we can't do the special case of a fully pooled + optimisation (both costs and returns pooled) + + Estimate the forecast weights for this instrument + + We store this intermediate step to expose the calculation object + + :param instrument_code: + :type str: + + :returns: TxK pd.DataFrame containing weights, columns are trading rule variation names, T covers all + """ + + self.log.info("Calculating raw forecast weights for %s" % instrument_code) + + config = self.config + # Get some useful stuff from the config + weighting_params = copy(config.forecast_weight_estimate) + + # which function to use for calculation + weighting_func = resolve_function(weighting_params.pop("func")) + + returns_pre_processor = self.returns_pre_processor_for_code(instrument_code) + + weight_func = weighting_func( + returns_pre_processor, + asset_name=instrument_code, + log=self.log, + **weighting_params, + ) + + return weight_func + + @diagnostic(not_pickable=True) + def returns_pre_processor_for_code(self, instrument_code) -> returnsPreProcessor: + # Because we might be pooling, we get a stack of p&l data + codes_to_use = self.has_same_cheap_rules_as_code(instrument_code) + trading_rule_list = self.cheap_trading_rules(instrument_code) + + returns_pre_processor = self.returns_pre_processing( + codes_to_use, trading_rule_list=trading_rule_list + ) + + return returns_pre_processor + + @diagnostic(not_pickable=True) + def returns_pre_processing( + self, codes_to_use: list, trading_rule_list: list + ) -> returnsPreProcessor: + pandl_forecasts = self.get_pandl_forecasts(codes_to_use, trading_rule_list) + turnovers = self.get_turnover_for_list_of_rules(codes_to_use, trading_rule_list) + config = self.config + + weighting_params = copy(config.forecast_weight_estimate) + cost_params = copy(config.forecast_cost_estimates) + weighting_params = {**weighting_params, **cost_params} + + returns_pre_processor = returnsPreProcessor( + pandl_forecasts, turnovers=turnovers, log=self.log, **weighting_params + ) + + return returns_pre_processor + + @dont_cache + def get_turnover_for_list_of_rules( + self, codes_to_use: list, list_of_rules: list + ) -> turnoverDataAcrossTradingRules: + turnover_dict = dict( + [ + (rule_name, self.get_turnover_for_forecast(codes_to_use, rule_name)) + for rule_name in list_of_rules + ] + ) + + turnover_data = turnoverDataAcrossTradingRules(turnover_dict) + + ## dict of lists + return turnover_data + + @input + def get_turnover_for_forecast( + self, codes_to_use: list, rule_variation_name: str + ) -> turnoverDataForTradingRule: + return self.accounts_stage.get_turnover_for_forecast_combination( + codes_to_use=codes_to_use, rule_variation_name=rule_variation_name + ) + + @dont_cache + def get_pandl_forecasts( + self, codes_to_use: list, trading_rule_list: list + ) -> dictOfReturnsForOptimisationWithCosts: + # returns a dict of accountCurveGroups + pandl_forecasts = dict( + [ + (code, self.get_returns_for_optimisation(code, trading_rule_list)) + for code in codes_to_use + ] + ) + + pandl_forecasts = dictOfReturnsForOptimisationWithCosts(pandl_forecasts) + + return pandl_forecasts + + @dont_cache + def has_same_cheap_rules_as_code(self, instrument_code: str) -> list: + """ + Returns all instruments with same set of trading rules as this one, after max cost applied + + :param instrument_code: + :type str: + + :returns: list of str + + """ + + my_rules = self.cheap_trading_rules(instrument_code) + instrument_list = self.parent.get_instrument_list() + + def _rule_list_matches(list_of_rules, other_list_of_rules): + list_of_rules.sort() + other_list_of_rules.sort() + return list_of_rules == other_list_of_rules + + matching_instruments = [ + other_code + for other_code in instrument_list + if _rule_list_matches(my_rules, self.cheap_trading_rules(other_code)) + ] + + matching_instruments.sort() + + return matching_instruments + + def expensive_trading_rules_post_processing(self, instrument_code: str) -> list: + all_rule_names = self.get_trading_rule_list(instrument_code) + cheap_rule_names = self.cheap_trading_rules_post_processing(instrument_code) + + expensive_rules = list_difference(all_rule_names, cheap_rule_names) + + return expensive_rules + + @diagnostic() + def cheap_trading_rules_post_processing(self, instrument_code: str) -> list: + """ + Returns a list of trading rules which are cheap enough to trade, given a max tolerable + annualised SR cost + + :param instrument_code: + :type str: + + :returns: list of str + + + """ + + ceiling_cost_SR = self.config.forecast_post_ceiling_cost_SR + + cheap_rule_list = self._cheap_trading_rules_generic( + instrument_code, ceiling_cost_SR=ceiling_cost_SR + ) + + return cheap_rule_list + + @diagnostic() + def cheap_trading_rules(self, instrument_code: str) -> list: + """ + Returns a list of trading rules which are cheap enough to trade, given a max tolerable + annualised SR cost + + :param instrument_code: + :type str: + + :returns: list of str + + + """ + + ceiling_cost_SR = self.config.forecast_weight_estimate["ceiling_cost_SR"] + + cheap_rule_list = self._cheap_trading_rules_generic( + instrument_code, ceiling_cost_SR=ceiling_cost_SR + ) + + return cheap_rule_list + + @dont_cache + def _cheap_trading_rules_generic( + self, instrument_code: str, ceiling_cost_SR: float + ) -> list: + """ + Returns a list of trading rules which are cheap enough to trade, given a max tolerable + annualised SR cost + + :param instrument_code: + :type str: + + :returns: list of str + + + """ + + rule_list = self.get_trading_rule_list(instrument_code) + SR_cost_list = [ + self.get_SR_cost_for_instrument_forecast( + instrument_code, rule_variation_name + ) + for rule_variation_name in rule_list + ] + + cheap_rule_list = [ + rule_name + for (rule_name, rule_cost) in zip(rule_list, SR_cost_list) + if rule_cost <= ceiling_cost_SR + ] + + if len(cheap_rule_list) == 0: + error_msg = ( + "No rules are cheap enough for %s with threshold %.3f SR units! Raise threshold (system.config.forecast_weight_estimate['ceiling_cost_SR']), add rules, or drop instrument." + % (instrument_code, ceiling_cost_SR) + ) + self.log.warning(error_msg) + + else: + self.log.debug( + "Only this set of rules %s is cheap enough to trade for %s" + % (str(cheap_rule_list), instrument_code), + instrument_code=instrument_code, + ) + + return cheap_rule_list + + @input + def get_SR_cost_for_instrument_forecast( + self, instrument_code: str, rule_variation_name: str + ) -> float: + """ + + Get the cost in SR units per year of trading this instrument / rule + + :param instrument_code: + :type str: + + :param rule_variation_name: + :type str: + + :returns: float + + KEY INPUT + """ + try: + accounts = self.accounts_stage + except missingData: + warn_msg = ( + "You need an accounts stage in the system to estimate forecast costs for %s %s. Using costs of zero" + % (instrument_code, rule_variation_name) + ) + self.log.warning(warn_msg) + return 0.0 + + return accounts.get_SR_cost_for_instrument_forecast( + instrument_code, rule_variation_name + ) + + @property + def accounts_stage(self): + if not hasattr(self.parent, "accounts"): + raise missingData + + # no -> to avoid circular imports + return self.parent.accounts + + @input + def get_returns_for_optimisation( + self, instrument_code: str, trading_rule_list: list + ) -> returnsForOptimisationWithCosts: + """ + Get pandl for forecasts for a given rule + THese will include both gross and net returns, in case we do any pooling + + KEY INPUT + + :param instrument_code: + :type str: + + :returns: accountCurveGroup object + + """ + try: + accounts = self.accounts_stage + except missingData: + error_msg = ( + "You need an accounts stage in the system to estimate forecast weights" + ) + self.log.critical(error_msg) + raise Exception(error_msg) + + pandl = accounts.pandl_for_instrument_rules_unweighted( + instrument_code, trading_rule_list=trading_rule_list + ) + + pandl = returnsForOptimisationWithCosts(pandl) + + return pandl + + # FIXED FORECAST WEIGHTS + + def get_raw_fixed_forecast_weights(self, instrument_code: str) -> pd.DataFrame: + """ + Get the forecast weights for this instrument + + From: (a) passed into subsystem when created + (b) ... if not found then: in system.config.instrument_weights + + :param instrument_code: + :type str: + + :returns: TxK pd.DataFrame containing weights, columns are trading rule variation names, T covers all + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> + >>> ## from config + >>> system.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(2) + ewmac16 ewmac8 + 2015-12-10 0.5 0.5 + 2015-12-11 0.5 0.5 + >>> + >>> config.forecast_weights=dict(EDOLLAR=dict(ewmac8=0.9, ewmac16=0.1)) + >>> system2=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system2.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(2) + ewmac16 ewmac8 + 2015-12-10 0.1 0.9 + 2015-12-11 0.1 0.9 + >>> + >>> del(config.forecast_weights) + >>> system3=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system3.combForecast.get_raw_monthly_forecast_weights("EDOLLAR").tail(2) + WARNING: No forecast weights - using equal weights of 0.5000 over all 2 trading rules in system + ewmac16 ewmac8 + 2015-12-10 0.5 0.5 + 2015-12-11 0.5 0.5 + """ + + # Now we have a dict, fixed_weights. + # Need to turn into a timeseries covering the range of forecast + # dates + + fixed_weights = self._get_fixed_forecast_weights_as_dict(instrument_code) + + rule_variation_list = sorted(fixed_weights.keys()) + + forecasts = self.get_all_forecasts(instrument_code, rule_variation_list) + forecasts_time_index = forecasts.index + forecast_columns_to_align = forecasts.columns + + # Turn into a 2 row data frame aligned to forecast names + forecast_weights = from_dict_of_values_to_df( + fixed_weights, forecasts_time_index, columns=forecast_columns_to_align + ) + + ## Should be monthly for consistency, but span all data + monthly_forecast_weights = reindex_last_monthly_include_first_date( + forecast_weights + ) + + return monthly_forecast_weights + + @diagnostic() + def _get_fixed_forecast_weights_as_dict(self, instrument_code: str) -> dict: + config = self.parent.config + # Let's try the config + try: + forecast_weights_config = config.get_element("forecast_weights") + except missingData: + fixed_weights = self._get_one_over_n_weights(instrument_code) + else: + expensive_trading_rules_post_processing = ( + self.expensive_trading_rules_post_processing(instrument_code) + ) + fixed_weights = _get_fixed_weights_from_config( + forecast_weights_config=forecast_weights_config, + instrument_code=instrument_code, + log=self.log, + expensive_trading_rules_post_processing=expensive_trading_rules_post_processing, + ) + return fixed_weights + + def _get_one_over_n_weights(self, instrument_code: str) -> dict: + rules = self.get_trading_rule_list(instrument_code) + equal_weight = 1.0 / len(rules) + + warn_msg = ( + "WARNING: No forecast weights - using equal weights of %.3f over all %d trading rules in system" + % (equal_weight, len(rules)) + ) + + self.log.warning(warn_msg, instrument_code=instrument_code) + + fixed_weights = dict([(rule_name, equal_weight) for rule_name in rules]) + + return fixed_weights + + # DIVERSIFICATION MULTIPLIER + @dont_cache + def get_forecast_diversification_multiplier( + self, instrument_code: str + ) -> pd.Series: + if self.use_estimated_div_mult(): + fdm = self.get_forecast_diversification_multiplier_estimated( + instrument_code + ) + else: + fdm = self.get_forecast_diversification_multiplier_fixed(instrument_code) + + return fdm + + @dont_cache + def use_estimated_div_mult(self): + return str2Bool(self.config.use_forecast_div_mult_estimates) + + # FIXED FDM + @diagnostic() + def get_forecast_diversification_multiplier_fixed( + self, instrument_code: str + ) -> pd.Series: + """ + + Get the diversification multiplier for this instrument + + From: system.config.instrument_weights + + :param instrument_code: instrument to get multiplier for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping + >>> from systems.basesystem import System + >>> (fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping() + >>> system=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> + >>> ## from config + >>> system.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(2) + fdm + 2015-12-10 1.1 + 2015-12-11 1.1 + >>> + >>> config.forecast_div_multiplier=dict(EDOLLAR=2.0) + >>> system2=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system2.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(2) + fdm + 2015-12-10 2 + 2015-12-11 2 + >>> + >>> ## defaults + >>> del(config.forecast_div_multiplier) + >>> system3=System([rawdata, rules, fcs, ForecastCombineFixed()], data, config) + >>> system3.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(2) + fdm + 2015-12-10 1 + 2015-12-11 1 + """ + + # Let's try the config + system = self.parent + try: + forecast_div_multiplier_config = system.config.get_element( + "forecast_div_multiplier" + ) + except missingData as e: + error_msg = ( + "Need to specify 'forecast_div_multiplier' in config or system_defaults" + ) + self.log.critical(error_msg, instrument_code=instrument_code) + raise missingData(error_msg) from e + + fixed_div_mult = _get_fixed_fdm_scalar_value_from_config( + forecast_div_multiplier_config=forecast_div_multiplier_config, + instrument_code=instrument_code, + log=self.log, + ) + # Now we have a dict, fixed_weights. + # Need to turn into a timeseries covering the range of forecast dates + # get forecast weights first + forecast_weights = self.get_all_forecasts(instrument_code) + + fixed_div_mult_as_ts = from_scalar_values_to_ts( + fixed_div_mult, forecast_weights.index + ) + + return fixed_div_mult_as_ts + + # ESTIMATED FDM + + @diagnostic(protected=True) + def get_forecast_diversification_multiplier_estimated( + self, instrument_code: str + ) -> pd.Series: + """ + + Get the diversification multiplier for this instrument + + Estimated from correlations and weights + + :param instrument_code: instrument to get multiplier for + :type instrument_code: str + + :returns: Tx1 pd.DataFrame + + + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([accounts, rawdata, rules, fcs, ForecastCombineEstimated()], data, config) + >>> system.config.forecast_weight_estimate['method']="shrinkage" + >>> system.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(3) + FDM + 2015-12-09 1.367351 + 2015-12-10 1.367349 + 2015-12-11 1.367347 + >>> system.config.forecast_div_mult_estimate['dm_max']=1.1 + >>> system=System([accounts, rawdata, rules, fcs, ForecastCombineEstimated()], data, system.config) + >>> system.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(3) + FDM + 2015-12-09 1.1 + 2015-12-10 1.1 + 2015-12-11 1.1 + """ + self.log.info( + "Calculating forecast div multiplier for %s" % instrument_code, + instrument_code=instrument_code, + ) + + # Get some useful stuff from the config + div_mult_params = copy(self.parent.config.forecast_div_mult_estimate) + + # an example of an idm calculation function is + # sysquant.estimators.diversification_multipliers.diversification_multiplier_from_list + idm_func = resolve_function(div_mult_params.pop("func")) + + correlation_list = self.get_forecast_correlation_matrices(instrument_code) + + ## weights will be on same frequency as forecaster + weight_df = self.get_forecast_weights(instrument_code) + + # note there is a possibility that the forecast_weights contain a subset of the rules in the correlation + # matrices, because the forecast weights could have rules removed for being too expensive + # To deal with this we pad the weights data frame so it is exactly + # aligned with the correlations + + weight_df = dataframe_pad( + weight_df, correlation_list.column_names, pad_with_value=0.0 + ) + + ts_fdm = idm_func(correlation_list, weight_df, **div_mult_params) + + ## Returns smoothed IDM in business days + + return ts_fdm + + @diagnostic(protected=True, not_pickable=True) + def get_forecast_correlation_matrices( + self, instrument_code: str + ) -> CorrelationList: + """ + Returns a correlationList object which contains a history of correlation matricies + + :param instrument_code: + :type str: + + :returns: correlation_list object + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([rawdata, rules, fcs, accounts, ForecastCombineEstimated()], data, config) + >>> ans=system.combForecast.get_forecast_correlation_matrices("EDOLLAR") + >>> ans.corr_list[-1] + array([[ 1. , 0.1168699 , 0.08038547], + [ 0.1168699 , 1. , 0.86907623], + [ 0.08038547, 0.86907623, 1. ]]) + >>> print(ans.columns) + ['carry', 'ewmac16', 'ewmac8'] + """ + + # Get some useful stuff from the config + corr_params = copy(self.config.forecast_correlation_estimate) + + # do we pool our estimation? + pooling = str2Bool(corr_params.pop("pool_instruments")) + + if pooling: + # find set of instruments with same trading rules as I have + if self._use_estimated_weights(): + codes_to_use = self.has_same_cheap_rules_as_code(instrument_code) + else: + codes_to_use = self.has_same_rules_as_code(instrument_code) + else: + codes_to_use = [instrument_code] + + correlation_list = ( + self.get_forecast_correlation_matrices_from_instrument_code_list( + codes_to_use + ) + ) + + return correlation_list + + @diagnostic() + def has_same_rules_as_code(self, instrument_code: str) -> list: + """ + Returns all instruments with same set of trading rules as this one + + :param instrument_code: + :type str: + + :returns: list of str + + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([accounts, rawdata, rules, fcs, ForecastCombineEstimated()], data, config) + >>> system.combForecast.has_same_rules_as_code("EDOLLAR") + ['EDOLLAR', 'US10'] + >>> system.combForecast.has_same_rules_as_code("BUND") + ['BUND'] + """ + + my_rules = self.get_trading_rule_list(instrument_code) + instrument_list = self.parent.get_instrument_list() + + def _matches(xlist, ylist): + xlist.sort() + ylist.sort() + return xlist == ylist + + matching_instruments = sorted( + [ + other_code + for other_code in instrument_list + if _matches(my_rules, self.get_trading_rule_list(other_code)) + ] + ) + + return matching_instruments + + @diagnostic(protected=True, not_pickable=True) + def get_forecast_correlation_matrices_from_instrument_code_list( + self, codes_to_use: list + ) -> CorrelationList: + """ + Returns a correlationList object which contains a history of correlation matricies + + :param codes_to_use: + :type str: + + :returns: correlation_list object + + >>> from systems.tests.testdata import get_test_object_futures_with_rules_and_capping_estimate + >>> from systems.basesystem import System + >>> (accounts, fcs, rules, rawdata, data, config)=get_test_object_futures_with_rules_and_capping_estimate() + >>> system=System([rawdata, rules, fcs, accounts, ForecastCombineEstimated()], data, config) + >>> ans=system.combForecast.get_forecast_correlation_matrices("EDOLLAR") + >>> ans.corr_list[-1] + array([[ 1. , 0.1168699 , 0.08038547], + [ 0.1168699 , 1. , 0.86907623], + [ 0.08038547, 0.86907623, 1. ]]) + >>> print(ans.columns) + ['carry', 'ewmac16', 'ewmac8'] + """ + + # Get some useful stuff from the config + corr_params = copy(self.config.forecast_correlation_estimate) + + # do we pool our estimation? + # not used here since we've looked at this already + _pooling_already_decided = str2Bool(corr_params.pop("pool_instruments")) + + # which function to use for calculation + corr_func = resolve_function(corr_params.pop("func")) + + self.log.info( + "Calculating forecast correlations over %s" % ", ".join(codes_to_use) + ) + + forecast_data = self.get_all_forecasts_for_a_list_of_instruments(codes_to_use) + correlation_list = corr_func(forecast_data, **corr_params) + + return correlation_list + + @dont_cache + def get_all_forecasts_for_a_list_of_instruments( + self, codes_to_use: list + ) -> listOfDataFrames: + forecast_data = listOfDataFrames( + [ + self.get_all_forecasts( + instr_code, self.get_trading_rule_list(instr_code) + ) + for instr_code in codes_to_use + ] + ) + + return forecast_data + + # FORECAST MAPPING + @diagnostic(not_pickable=True) + def _get_forecast_mapping_function(self, instrument_code): + """ + Get the function to apply non linear forecast mapping, and any parameters + + :param instrument_code: instrument code + :return: (function, kwargs) to do the mapping, with arguments + """ + + forecast_cap = self.get_forecast_cap() + forecast_floor = self.get_forecast_floor() + if hasattr(self.parent.config, "forecast_mapping"): + if instrument_code in self.parent.config.forecast_mapping: + configuration = self.parent.config.forecast_mapping[instrument_code] + post_process_func = map_forecast_value + kwargs = dict( + threshold=configuration["threshold"], + a_param=configuration["a_param"], + b_param=configuration["b_param"], + capped_value=forecast_cap, + ) + self.log.debug( + "Applying threshold mapping for %s threshold %.2f" + % (instrument_code, configuration["threshold"]), + instrument_code=instrument_code, + ) + return post_process_func, kwargs + + # just use the default, applying capping + post_process_func = _cap_combined_forecast + kwargs = dict(forecast_cap=forecast_cap, forecast_floor=forecast_floor) + self.log.debug( + "No mapping applied for %s" % instrument_code, + instrument_code=instrument_code, + ) + + return post_process_func, kwargs + + @input + def get_forecast_cap(self) -> float: + """ + Get the forecast cap from the previous module + + :returns: float + + KEY INPUT + """ + + return self.forecast_scale_cap_stage.get_forecast_cap() + + @input + def get_forecast_floor(self) -> float: + """ + Get the forecast cap from the previous module + + :returns: float + + KEY INPUT + """ + + return self.forecast_scale_cap_stage.get_forecast_floor() + + +def _cap_combined_forecast( + raw_multiplied_combined_forecast: pd.Series, + forecast_cap: float = 20.0, + forecast_floor: float = -20, +) -> pd.Series: + capped_combined_forecast = raw_multiplied_combined_forecast.clip( + lower=forecast_floor, upper=forecast_cap + ) + return capped_combined_forecast + + +def _get_fixed_weights_from_config( + forecast_weights_config: dict, + instrument_code: str, + expensive_trading_rules_post_processing: list, + log, +) -> dict: + if instrument_code in forecast_weights_config: + # nested dict + fixed_weights = forecast_weights_config[instrument_code] + log.debug( + "Nested dict of forecast weights for %s %s: weights different by instrument" + % (instrument_code, str(fixed_weights)) + ) + elif config_is_auto_group(forecast_weights_config): + ## autogrouping + log.debug("Auto grouping of weights for %s" % instrument_code) + fixed_weights = _get_forecast_weights_for_instrument_with_autogrouping( + forecast_weights_config=forecast_weights_config, + expensive_trading_rules_post_processing=expensive_trading_rules_post_processing, + ) + else: + fixed_weights = forecast_weights_config + log.debug( + "Non-nested dict of forecast weights for %s %s: weights the same for all instruments" + % (instrument_code, str(fixed_weights)) + ) + + return fixed_weights + + +## AUTO GROUPING +## EG +## forecast_weights: +## auto_weight_from_grouping: +## parameters: +## use_approx_DM: True +## apply_forecast_post_ceiling_cost_SR_before_weighting: True +## groups: +## trendy: +## weight: 0.7 +## accel: +## weight: 0.3 +## + + +AUTO_WEIGHTING_APPLY_POSTCOST_SR = ( + "apply_forecast_post_ceiling_cost_SR_before_weighting" +) + + +def _get_forecast_weights_for_instrument_with_autogrouping( + forecast_weights_config: dict, expensive_trading_rules_post_processing: list +) -> dict: + ( + auto_group_parameters, + auto_group_weights, + ) = resolve_config_into_parameters_and_weights_for_autogrouping( + forecast_weights_config + ) + auto_group_parameters_copy = copy(auto_group_parameters) + apply_costs = auto_group_parameters_copy.pop( + AUTO_WEIGHTING_APPLY_POSTCOST_SR, False + ) + if apply_costs: + ## only include rules cheap enough to trade + keys_to_exclude = expensive_trading_rules_post_processing + else: + # include everything + keys_to_exclude = [] + + group_weights = calculate_autogroup_weights_given_parameters( + auto_group_weights=auto_group_weights, + auto_group_parameters=auto_group_parameters_copy, + keys_to_exclude=keys_to_exclude, + ) + + return group_weights + + +### FDM + + +def _get_fixed_fdm_scalar_value_from_config( + forecast_div_multiplier_config: dict, instrument_code: str, log +) -> float: + error_msg = "" + fixed_div_mult = None + + if isinstance(forecast_div_multiplier_config, float) or isinstance( + forecast_div_multiplier_config, int + ): + fixed_div_mult = forecast_div_multiplier_config + + elif isinstance(forecast_div_multiplier_config, dict): + fixed_div_mult = forecast_div_multiplier_config.get(instrument_code, None) + if fixed_div_mult is None: + error_msg = ( + "Instrument %s missing from 'config.forecast_div_multiplier' dict" + % instrument_code + ) + + else: + error_msg = ( + "FDM in config needs to be either float, or dict with instrument_code keys" + ) + + if error_msg == "": + log.debug( + "Using fixed FDM multiplier of %.3f for %s" + % (fixed_div_mult, instrument_code), + instrument_code=instrument_code, + ) + else: + log.critical(error_msg, instrument_code=instrument_code) + raise Exception(error_msg) + + return fixed_div_mult + + +def _get_list_of_rules_from_config_for_instrument( + config: Config, instrument_code: str, forecast_combine_stage: ForecastCombine +) -> list: + if hasattr(config, "forecast_weights"): + forecast_weights_config = config.forecast_weights + # a dict of weights, nested or un nested + if instrument_code in forecast_weights_config: + # nested dict + rules = forecast_weights_config[instrument_code].keys() + elif config_is_auto_group(forecast_weights_config): + ## Will still include expensive rules but they get weeded out on second pass + forecast_weights = _get_forecast_weights_for_instrument_with_autogrouping( + forecast_weights_config, expensive_trading_rules_post_processing=[] + ) + return list(forecast_weights.keys()) + else: + # seems it's a non nested dict (weights same across instruments), but let's check + # that just in case it IS nested dict but instrument weight is missing + for val in forecast_weights_config: + if isinstance(val, dict): + # so it is a nested dict.. + raise Exception( + "Missing forecast weight for instrument ", instrument_code + ) + rules = config.forecast_weights.keys() + else: + ## forecast weights not supplied as a config item, use the name of the rules + rules = ( + forecast_combine_stage._get_list_of_all_trading_rules_from_forecasting_stage() + ) + + return rules + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/src/quantlib_st/systems/provided/config/test_rawdata_config.yaml b/src/quantlib_st/systems/provided/config/test_rawdata_config.yaml new file mode 100644 index 0000000..bea904c --- /dev/null +++ b/src/quantlib_st/systems/provided/config/test_rawdata_config.yaml @@ -0,0 +1,20 @@ +trading_rules: + ewmac8: + function: systems.provided.rules.ewmac.ewmac_forecast_with_defaults_no_vol + data: + - "rawdata.get_daily_prices" + - "rawdata.daily_returns_volatility" + other_args: + Lfast: 8 + Lslow: 32 + forecast_scalar: 5.3 + ewmac16: + function: systems.provided.rules.ewmac.ewmac_forecast_with_defaults_no_vol + data: + - "rawdata.get_daily_prices" + - "rawdata.daily_returns_volatility" + other_args: + Lfast: 16 + Lslow: 64 + forecast_scalar: 7.5 +forecast_cap: 21.0 diff --git a/src/quantlib_st/systems/rawdata.py b/src/quantlib_st/systems/rawdata.py index 2d50f36..4531be9 100644 --- a/src/quantlib_st/systems/rawdata.py +++ b/src/quantlib_st/systems/rawdata.py @@ -9,7 +9,7 @@ from quantlib_st.systems.stage import SystemStage from quantlib_st.systems.system_cache import input, diagnostic, output -from quantlib_st.sysdata.sim.futures_sim_data import futuresSimData +from quantlib_st.sysdata.sim.futures_sim_data import simData from quantlib_st.config.configdata import Config from quantlib_st.objects.carry_data import rawCarryData @@ -35,7 +35,7 @@ def name(self): return "rawdata" @property - def data_stage(self) -> futuresSimData: + def data_stage(self) -> simData: return self.parent.data @property @@ -96,7 +96,7 @@ def get_natural_frequency_prices(self, instrument_code: str) -> pd.Series: return natural_prices @input - def get_hourly_prices(self, instrument_code: str) -> pd.Series: + def get_hourly_prices(self, instrument_code: str) -> pd.Series | pd.DataFrame: hourly_prices = self.data_stage.hourly_prices(instrument_code) return hourly_prices @@ -128,7 +128,7 @@ def daily_returns(self, instrument_code: str) -> pd.Series: return dailyreturns @output() - def hourly_returns(self, instrument_code: str) -> pd.Series: + def hourly_returns(self, instrument_code: str) -> pd.Series | pd.DataFrame: """ Gets hourly returns (not % returns) @@ -212,6 +212,7 @@ def daily_returns_volatility(self, instrument_code: str) -> pd.Series: vol_multiplier = volconfig.pop("multiplier_to_get_daily_vol") volfunction = resolve_function(volconfig.pop("func")) + assert callable(volfunction) raw_vol = volfunction(price_returns, **volconfig) vol = vol_multiplier * raw_vol @@ -407,12 +408,6 @@ def system_with_redundant_instrument_code_passed( 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: """ diff --git a/src/quantlib_st/systems/tests/test_rawdata_futures.py b/src/quantlib_st/systems/tests/test_rawdata_futures.py new file mode 100644 index 0000000..eefcd28 --- /dev/null +++ b/src/quantlib_st/systems/tests/test_rawdata_futures.py @@ -0,0 +1,89 @@ +import datetime +import numpy as np +import pytest + +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.forecasting import Rules +from quantlib_st.systems.provided.futures_chapter15.basesystem import futures_system + + +@pytest.fixture +def system() -> System: + """Minimal system fixture that can be parametrized with trading rules.""" + system = futures_system( + trading_rules=Rules(), + data=CsvFuturesSimTestData(end_date=datetime.datetime(2024, 3, 28)), + config=Config("systems.provided.config.test_forecast_config.yaml"), + ) + return system + + +""" +PRICE_CONTRACT: The contract currently held in the portfolio for trading. +CARRY_CONTRACT: The adjacent contract used as a benchmark to determine the curve slope (roll). + +### Calculation Logic +`Raw Roll = PRICE - CARRY` + +### Interpretation +- **Negative Roll**: Occurs in **Contango** (e.g., RUBBER where Front Price < Back Carry). This represents a "cost of carry" for long positions. +- **Positive Roll**: Occurs in **Backwardation** (e.g., Price > Carry). This represents a "gain" from the roll. + +*Note for STIRs (e.g., SOFR):* Since prices are inversed ($100 - Rate$), if the held forward contract (`PRICE`) is more expensive than the front contract (`CARRY`), the value is expected to drift down toward the spot price as it approaches expiry, resulting in a negative carry. +""" + + +def test_get_instrument_raw_carry_data(system): + carry_values = system.rawdata.get_instrument_raw_carry_data("RUBBER").tail(1) + assert carry_values.CARRY.values[0] == 163.4 + assert carry_values.CARRY_CONTRACT.values[0] == "20240700" + assert carry_values.PRICE.values[0] == 162.9 + assert carry_values.PRICE_CONTRACT.values[0] == "20240600" + + +def test_raw_futures_roll(system): + roll = system.rawdata.raw_futures_roll("RUBBER").ffill().tail(1) + roll_value = roll.values[0] + assert roll_value == (162.9 - 163.4) + + +def test_roll_differentials(system): + roll = system.rawdata.roll_differentials("RUBBER").ffill().tail(1) + roll_diff = roll.values[0] + assert np.isclose(roll_diff, 0.083333, atol=1e-6) # +ve + + +def test_annualised_roll(system): + roll = system.rawdata.annualised_roll("RUBBER").ffill().tail(1) + roll_value = roll.values[0] + assert np.isclose(roll_value, -6, atol=1e-6) # -ve + + +def test_get_instrument_raw_carry_data_not_nearest_contract(system): + carry_values = system.rawdata.get_instrument_raw_carry_data("SOFR").tail(1) + assert carry_values.CARRY.values[0] == 96.415 + assert carry_values.CARRY_CONTRACT.values[0] == "20260900" + assert carry_values.PRICE.values[0] == 96.42 + assert carry_values.PRICE_CONTRACT.values[0] == "20261200" + + +def test_raw_futures_roll_not_nearest_contract(system): + roll = system.rawdata.raw_futures_roll("SOFR").ffill().tail(1) + roll_value = roll.values[0] + assert roll_value == (96.42 - 96.415) + + +def test_roll_differentials_not_nearest_contract(system): + roll = system.rawdata.roll_differentials("SOFR").ffill().tail(1) + roll_diff = roll.values[0] + assert np.isclose(roll_diff, -0.25, atol=1e-6) # +ve + + +def test_annualised_roll_not_nearest_contract(system): + roll = system.rawdata.annualised_roll("SOFR").ffill().tail(1) + roll_value = roll.values[0] + assert np.isclose(roll_value, -0.0199999, atol=1e-6) # -ve