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Factor Research

Doc map: alphaswarm_docs/index.md · See alphaswarm_docs/strategy-lifecycle.md for the broader strategy lifecycle.

AlphaSwarm ships an Alphalens-inspired factor evaluation pipeline plus the purged / walk-forward cross-validators described in Lopez de Prado's Advances in Financial ML and ML4T's utility module.

One-liner evaluation​

from alphaswarm.data.factors import evaluate_factor

report = evaluate_factor(
    factor=factor_df,        # long: timestamp, vt_symbol, factor
    prices=prices_df,        # long: timestamp, vt_symbol, close
    factor_name="my_factor",
    periods=(1, 5, 10, 21),
    n_quantiles=5,
)
report.ic_stats       # {"fwd_1": {"mean": ..., "ir": ..., ...}, ...}
report.cumulative_returns  # wide DataFrame: Q1..Q5
report.turnover       # Series: top-quantile daily rotation fraction

UI​

The Factor Evaluation page posts to POST /factors/evaluate which enqueues a Celery task. The task logs the tear sheet to MLflow with tag alphaswarm.component=factor_eval so every report is historically comparable.

Cross-validators​

  • :class:alphaswarm.data.cv.MultipleTimeSeriesCV — rolling train/test on panel data, matches ML4T utils.MultipleTimeSeriesCV.
  • :class:alphaswarm.data.cv.PurgedKFold — k-fold with embargo days between the training window and the test fold boundary.
  • :class:alphaswarm.data.cv.TimeSeriesWalkForward — rolling or expanding train windows with a fixed test-step cadence.

ML alphas​

Two gradient-boosted alpha models drop directly into the framework:

  • :class:alphaswarm.strategies.ml_alphas.XGBoostAlpha
  • :class:alphaswarm.strategies.ml_alphas.LightGBMAlpha

Both accept a feature_specs list (passed through :class:alphaswarm.data.indicators_zoo.IndicatorZoo) and a model_path that gets pickled after train(). Training auto-logs to MLflow via the :mod:alphaswarm.mlops.model_registry helper and can then be loaded in production by calling :func:alphaswarm.mlops.model_registry.load_alpha_path.

Factor evaluation flow​