quantskills 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.
Install model dependencies with uv sync --extra ml.
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
from skills.ml.pca_fold import fit_fold_transform, make_regressor
from skills.ml.walk_forward import date_level_mask, expanding_purged_folds
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 |
skills.ml.pca_fold |
Train-only PCA fold transforms (no StandardScaler) and frozen regressors |
skills.ml.walk_forward |
Expanding walk-forward folds with label purge, plus date row masks for (symbol, eob) panels |
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")
Expanding purged walk-forward folds
from skills.ml.walk_forward import expanding_purged_folds
dates = panel.index.get_level_values("eob").unique()
folds = expanding_purged_folds(
dates,
min_train=250,
retrain_step=370,
purge=20,
)
for fold in folds:
train_mask = date_level_mask(feature_panel.index, fold.train_dates)
pred_mask = date_level_mask(feature_panel.index, fold.pred_dates)
Train-only PCA fold transform
from skills.ml.pca_fold import fit_fold_transform, make_regressor
transform = fit_fold_transform(train_X, pred_X, n_pca=50, random_state=42)
model = make_regressor("lasso") # or "ols" / "rf" / "xgboost"
model.fit(transform.train_X, train_y)
pred = model.predict(transform.pred_X)
Cross-sectional expanding PCA model scores
from skills.compute.features import make_logdiff_panel_features
from strategies.cross_sectional.ml_rank import (
expanding_pca_model_scores,
expanding_pca_multi_model_scores,
)
features = make_logdiff_panel_features(panel) # reuse across horizons
# Prefer multi-model so each fold's PCA is fit once:
results = expanding_pca_multi_model_scores(
panel, models=("ols", "lasso", "rf", "xgboost"), horizon=20, features=features
)
# Or a single model:
result = expanding_pca_model_scores(panel, model="rf", horizon=20, features=features)
scores = result.scores
fold_metrics = result.fold_metrics
overall_metrics = result.overall_metrics
Lesson 07 workflow (18 ETF, equal-weight Top 3)
uv run python -m strategies.cross_sectional.workflows.run_lesson07_etf18_logdiff_pca_ml
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)