ml

name	ml
description	Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.

ML

ML contains reusable model training, prediction, ML factor, and sparse fitting helpers. It should produce predictions, ranks, labels, or weight matrices; return accounting belongs in skills.backtest.

Public API

from skills.ml.ml_engine import MLEngine, ModelPredictor
from skills.ml.ml_factor import MLFactorEngine, make_precomputed_factor
from skills.ml.lasso_tracker import lasso_track

Components

Module	Purpose
skills.ml.ml_engine	Lazy PyCaret classification/regression engine and inference wrapper
skills.ml.ml_factor	Compress factor configs into ML-ranked cross-sectional factor pivots
skills.ml.lasso_tracker	Rolling LASSO sparse index-tracking weight generation

Recipes

ML rank factor for ModularBacktester

from skills.compute import indicators as I
from skills.ml.ml_factor import MLFactorEngine, make_precomputed_factor

engine = MLFactorEngine(
    data=panel_df,
    factor_configs=[
        {"func": I.trend_score_v2, "kwargs": {"period": 24}, "name": "trend"},
        {"func": I.cci, "kwargs": {"period": 48}, "name": "cci"},
    ],
    model_type="xgboost",
    train_mode="rolling",
)
rank_pivot = engine.generate()
ml_factor_fn = make_precomputed_factor(rank_pivot, name="ml_rank")

Sparse index-tracking weights

from skills.ml.lasso_tracker import lasso_track

weights = lasso_track(
    etf_returns,
    index_returns,
    lookback=120,
    alpha=1e-5,
    rebalance_freq="M",
)

PyCaret classification

from skills.ml.ml_engine import MLEngine

engine = MLEngine(task="classification", model_name="xgboost")
model, metrics = engine.setup_and_train(train_df, target="label")
preds = engine.predict(test_df)