trade-study

Multi-objective design and evaluation: score competing configurations — model formulations, solver choices, measurement strategies, or any design decision — against known ground truth via scoring, Pareto optimization, and Bayesian stacking.

Overview

trade-study provides a structured framework for scoring competing configurations — model formulations, solver choices, measurement strategies, or any design decision — against known ground truth via observable-based scoring, multi-objective Pareto optimization, and Bayesian model stacking.

The core pattern (from MFAI §4–5):

1. Simulate — Generate synthetic ground truth with known latent state
2. Observe — Apply a realistic observation model (noise, masks, bias)
3. Score — Evaluate structured observables against known truth using proper scoring rules (not noisy observations)
4. Pareto — Extract the multi-objective Pareto front across competing objectives (e.g. quality vs. cost)
5. Stack — Combine models via Bayesian stacking weights or score-based optimization

Studies can be organized into hierarchical phases (discovery → refinement → benchmark), where each phase filters configs for the next.

Architecture

trade_study/
├── protocols.py    Simulator, Scorer, Observable, Annotation, ResultsTable
├── design.py       Factor screening (SALib) and grid construction (pyDOE3)
├── scoring.py      Proper scoring rules (scoringrules) and calibration
├── pareto.py       Non-dominated sorting and indicators (pymoo)
├── stacking.py     Bayesian stacking (arviz) and score-based weights (scipy)
├── runner.py       Grid execution (joblib) and adaptive search (optuna)
├── study.py        Multi-phase orchestration with filtering
└── io.py           Save/load results (npz + JSON)

Observable Hierarchy (MFAI §4)

Tier	Role	Examples
Embedded	Must hold by construction	ELBO monotonicity, conservation laws
Penalized	Soft constraints in objective	Stopping criteria, convergence rate
Diagnostic	Post-hoc evaluation only	Coverage, CRPS, PIT, rank error
Cost	Resource axis for Pareto	Wall time, dollar cost, field teams

Two Execution Modes

• Grid mode: Full factorial, LHS, or fractional factorial via pyDOE3 → run all → post-hoc Pareto extraction via pymoo.
• Adaptive mode: Multi-objective Bayesian optimization via optuna NSGA-II when the full grid is too expensive.

Stacking: Two Paths

• Bayesian (arviz): For models with log-likelihoods. Implements Yao et al 2018 stacking via arviz.compare(method='stacking').
• Score-based (scipy): For arbitrary score matrices. Optimizes simplex weights to minimize/maximize composite score.

Quick Example

from trade_study import Study, Phase, Observable, Tier, Direction
from trade_study.study import top_k_pareto_filter
from trade_study.design import build_grid, Factor, FactorType

# Define observables
coverage = Observable("coverage_95", Tier.DIAGNOSTIC, Direction.MAXIMIZE)
rank_err = Observable("rank_error", Tier.EMBEDDED, Direction.MINIMIZE)
wall_time = Observable("wall_seconds", Tier.COST, Direction.MINIMIZE)

# Build design grid
factors = [
    Factor("alpha", FactorType.CONTINUOUS, bounds=(0.01, 0.10)),
    Factor("method", FactorType.CATEGORICAL, levels=["A", "B", "C"]),
]
grid = build_grid(factors, method="lhs", n_samples=500)

# Run hierarchical study
study = Study(
    world=MySimulator(),
    scorer=MyScorer(),
    observables=[coverage, rank_err, wall_time],
    phases=[
        Phase("discovery", grid=grid, filter_fn=top_k_pareto_filter(k=20)),
        Phase(
            "benchmark",
            grid="carry",  # top-20 from discovery
            filter_fn=None,
        ),
    ],
)
study.run(n_jobs=-1)
print(study.summary())

Installation

pip install trade-study[all]

Or install only the extras you need:

pip install trade-study[scoring,pareto]  # just scoring + Pareto

Extra	Packages
scoring	scoringrules
pareto	pymoo
stacking	arviz, scipy
design	pyDOE3, SALib
adaptive	optuna
parallel	joblib
all	All of the above

Consumers

Package	Domain	Use Case
VBPCApy	Bayesian PCA	Convergence sweep: 15-factor grid, coverage/RMSE/CRPS vs. wall time
pp-eigentest	Rank selection	3-stage method selection: type-I/power/exact vs. robustness
TICCS	Surveillance design	relWIS/peak-timing vs. surveillance cost ($)

Related Packages

Package	Description
TradeStudy.jl	Julia implementation of this same framework

Development

uv sync --extra dev
just ci

License

MIT