factrix 0.17.0

Polars-native factor signal validation toolkit for quantitative finance

Tests one factor. Screens a thousand.

Where factrix fits

Does this factor possess predictive edge?

factrix is the first Polars-native Python toolkit that picks the right statistical test for each factor type. Cross-sectional, event, common factor — each gets the tests that fit its data-generating process.

factor construction  →  factrix (inference)  →  strategy construction  →  backtest  →  live trading
                            ▲ you are here

For each candidate factor factrix answers — is the predictive power real? — and corrects for multiple testing when you screen at scale. Kill fakes before they cost you a backtest.

Why factrix?

• Type-routed evaluation — Information Coefficient + Fama-MacBeth for cross-sectional factors; Cumulative Average Abnormal Return for events.
• Financial statistics built in — autocorrelation-robust standard errors (Newey-West), overlapping-forward-return correction, persistent-predictor flagging (Stambaugh bias), and false-discovery-rate control across batches (Benjamini-Hochberg-Yekutieli).
• Polars-native — modern Polars alternative to the pandas-based alphalens.

factrix stops at the inference — primary test plus diagnostic battery. It does not size positions, model slippage, optimise weights, or compose alphas; those belong to the later stages of the pipeline.

Is factrix the right tool?

You want to…	Use this
Inference on a factor (cross-sectional / event / common factor)	factrix
Screen many factors with multiple-testing correction	factrix
Backtest with positions / slippage / margin	zipline-reloaded, backtrader, bt, vectorbt, nautilus_trader
Optimise portfolio weights	skfolio, riskfolio-lib
Returns-level tear-sheet (P&L diagnostics)	pyfolio-reloaded, QuantStats
Familiar cross-sectional tear-sheet	alphalens-reloaded
End-to-end machine-learning pipeline	qlib
Deflated / probabilistic Sharpe today (commercial)	mlfinlab

Installation

pip install factrix
# or
uv add factrix

See the installation guide for version pinning and development setup.

Typical usage

Single factor — IC evaluation

import factrix as fx
from factrix.metrics import ic

raw   = fx.datasets.make_cs_panel(n_assets=100, n_dates=500, ic_target=0.08, seed=2024)
data  = fx.preprocess.compute_forward_return(raw, forward_periods=5)

results = fx.evaluate(
    data,
    metrics={"ic": ic(inference=fx.inference.NEWEY_WEST)},
    factor_cols=["factor"],
    forward_periods=5,
)
res = results["factor"]
ic_res = res.metrics["ic"]

print('ic_mean =', round(ic_res.value, 4))
print('p_value =', round(ic_res.p_value, 4))

Multi-factor BHY screening

# A panel can carry many candidate factors — pass every column to factor_cols.
raw_mf  = fx.datasets.make_multi_factor_panel(n_factors=3, n_assets=100, n_dates=500, seed=2024)
data_mf = fx.preprocess.compute_forward_return(raw_mf, forward_periods=5)

results = fx.evaluate(
    data_mf,
    metrics={"ic": ic(inference=fx.inference.NEWEY_WEST)},
    factor_cols=["factor_0000", "factor_0001", "factor_0002"],
    forward_periods=5,
)

# evaluate returns dict[str, EvaluationResult] keyed by factor column;
# bhy screens the list of results as one hypothesis family.
fdr_results = fx.multi_factor.bhy(list(results.values()), metrics=["ic"], q=0.05)
bhy_ic = fdr_results["ic"]
print("survivors =", [r.factor for r in bhy_ic.survivors])

Multi-horizon sweep and BHY screening

# evaluate_horizons sweeps the factors across forward periods and returns a
# flat list[EvaluationResult] — one per (factor, forward_periods) — so it feeds
# bhy directly (no list(...values()) needed). expand_over=("forward_periods",)
# runs an independent BHY step-up per horizon, the correct cross-horizon screen
# (each horizon bucket is its own family of the swept factors).
results_sweep = fx.evaluate_horizons(
    raw_mf,  # raw multi-factor panel — no forward_return attached
    metrics={"ic": ic(inference=fx.inference.NEWEY_WEST)},
    factor_cols=["factor_0000", "factor_0001", "factor_0002"],
    forward_periods=[1, 5, 10],
)

fdr_results = fx.multi_factor.bhy(
    results_sweep,
    metrics=["ic"],
    expand_over=("forward_periods",),
    q=0.05,
)
bhy_ic = fdr_results["ic"]
print("survivors =", [(r.factor, r.forward_periods) for r in bhy_ic.survivors])

Single-asset (timeseries) evaluation

import numpy as np
import polars as pl
from datetime import datetime, timedelta
from factrix.metrics import predictive_beta

# Build a one-asset panel by hand (the cross-section generators need N >= 2).
rng    = np.random.default_rng(7)
dates  = [datetime(2020, 1, 1) + timedelta(days=i) for i in range(250)]
factor = rng.standard_normal(250)
ret    = 0.05 * factor + rng.standard_normal(250)        # factor leads next return
single_asset_data = pl.DataFrame({
    "date": dates,
    "asset_id": "SPX",
    "price": 100 * np.exp(np.cumsum(ret) / 100),
    "macro_factor": factor,
})
data = fx.preprocess.compute_forward_return(single_asset_data, forward_periods=5)

# A single-asset panel auto-resolves the structure axis to
# DataStructure.TIMESERIES (N == 1); predictive_beta is the explicit
# dense single-asset predictive-regression metric.
results = fx.evaluate(
    data,
    metrics={"beta": predictive_beta()},
    factor_cols=["macro_factor"],
    forward_periods=5,
)
print(results["macro_factor"].metrics["beta"].value)

Documentation

• Get Started — install, quickstart, where factrix fits
• User Guide — concepts (three-axis design, architecture), how-to (PANEL vs TIMESERIES, BHY screening, slice analysis), examples
• API Reference — entry points, results, lookup tables, per-metric pages
• Development — contributing, design notes
• Release Notes — changelog

License

Released under the Apache License 2.0.