Tests one factor. Screens a thousand.
What factrix does
factrix is a Polars-native toolkit for testing whether a candidate factor has predictive evidence. It provides factor-type-specific tests for cross-sectional, event, and common factors, and rejects metric/data combinations that do not apply.
- Inference built for factor data — overlap-aware standard errors, persistence diagnostics, and event-study methods.
- Batch screening — false-discovery-rate control for testing many factors.
- Structured results — estimates, p-values, metadata, and warnings remain separate instead of being collapsed into one score.
factrix covers factor inference and screening. Portfolio construction, backtesting, and execution remain downstream; see Where factrix fits for the scope boundary and neighbouring tools.
Installation
pip install factrix
# or
uv add factrix
See the installation guide for version pinning and development setup.
Quickstart
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, rng=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))
The minimum input is a long Polars frame keyed by date and asset_id, with
a factor column and forward_return. Read the returned p_value together with
the result's sample metadata and warnings; the
quickstart
shows the complete result shape.
Next steps
- Preparing data — validate and align your own panel.
- Choosing a metric — map a research question to a metric.
- Multi-factor screening — BHY false-discovery-rate control across candidate factors.
- Multi-horizon evaluation — sweeping
forward_periodswithevaluate_horizons. - Panel vs timeseries — understand data-shape dispatch.
Documentation
- Get started — installation and quickstart
- User guide — concepts, how-to guides, examples, and reference tables
- API reference — entry points, result types, and per-metric pages
- Development — contribution and design guidance
- Release notes — changelog
License
Released under the Apache License 2.0.
1 maintainer
Metadata
1 maintainer
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