Quickstart

Run the complete factor-analysis example after building from source:

python examples/factor_quickstart.py --output-dir quickstart-output

The example prepares a deterministic factor dataset, computes information coefficient, quantile returns, factor returns, alpha/beta, turnover, and rank autocorrelation, then writes:

• quickstart-output/results.json
• quickstart-output/factor-report.html

The Python package uses Polars as its default runtime dependency.

Core Flow

import ferric_alpha as fa

factor_data = fa.get_clean_factor_and_forward_returns(
    factor=factor,
    prices=prices,
    quantiles=5,
    periods=[1, 5, 10],
)

mean_ic = fa.performance.mean_information_coefficient(factor_data)
quantile_returns = fa.performance.mean_return_by_quantile(factor_data)
report = fa.tears.create_full_tear_sheet_data(factor_data)
fa.plotting.render(report).save("factor-report.html")

Outputs

results.json is intended for automated checks and scripted research workflows. factor-report.html is a self-contained native HTML tear sheet that can be opened without Matplotlib.

Visual Output

The quickstart also renders a native tear sheet. The preview below is generated from the same deterministic example data used by examples/factor_quickstart.py.

The full HTML report keeps the same charts and tables in a self-contained file:

open quickstart-output/factor-report.html