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. The full scope comparison against neighbouring libraries is in Where factrix fits.
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_periodswithevaluate_horizons. - PANEL vs TIMESERIES — single-asset evaluation with
predictive_beta.
Documentation
- Get Started — install, quickstart, where factrix fits
- User Guide — concepts and conventions, how-to (preparing data, choosing a metric, panel vs timeseries, reading results, 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.
1 maintainer
Metadata
1 maintainer
Release files for factrix 0.27.0
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