factrix 0.18.0

Polars-native factor signal validation toolkit for quantitative finance

Tests one factor. Screens a thousand.

📖 Full documentation

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))

More scenarios, runnable end to end:

• Quickstart — same example, plus .to_dict() output and warnings.
• Multi-factor screening — BHY false-discovery-rate control across candidate factors.
• Multi-horizon evaluation — sweeping forward_periods with evaluate_horizons.
• PANEL vs TIMESERIES — single-asset evaluation with predictive_beta.

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.