ml4t Meta-Labels
A momentum signal fires 1,000 times per year but only 40% are profitable. Instead of discarding the signal, train a second model to predict which of those 1,000 trades will work.
The Problem
Raw trading signals typically have acceptable recall (they catch most real moves) but poor precision (many false positives). Tuning the primary model to improve precision degrades recall. Meta-labeling decouples the two: the primary model generates candidates, the meta-model filters them.
The Pattern
WRONG
from sklearn.ensemble import GradientBoostingClassifier
# Use raw signal directly - many false positives passed through
signal = primary_model.predict(X) # 1=buy, -1=sell, 0=hold
positions = signal # Every signal becomes a trade
CORRECT
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import TimeSeriesSplit
def purged(split, horizon):
"""Drop training rows whose label window reaches into the test fold."""
for train, test in split:
yield train[train < test[0] - horizon], test
def oof_signal(X, y, splits):
"""Primary predictions, each from a model that never saw that row."""
out = np.zeros(len(X))
for train, test in splits:
out[test] = fit_primary(X[train], y[train]).predict(X[test])
return out
signal = np.zeros(len(X))
prob_win = np.full(len(X), np.nan)
for train, test in purged(TimeSeriesSplit(n_splits=5).split(X), horizon):
# Inner folds first. Fitting the meta-model on primary predictions the
# primary made in sample teaches it the primary's memorisation, not its edge.
inner = purged(TimeSeriesSplit(n_splits=3).split(X[train]), horizon)
fired = oof_signal(X[train], y[train], inner) != 0
meta = GradientBoostingClassifier(n_estimators=100, max_depth=3)
meta.fit(X[train][fired], outcomes[train][fired]) # 1=profitable, 0=not
signal[test] = fit_primary(X[train], y[train]).predict(X[test])
acted = test[signal[test] != 0]
prob_win[acted] = meta.predict_proba(X[acted])[:, 1]
positions = np.where(prob_win > 0.55, signal, 0.0) # NaN compares False
Two-Stage Architecture
Primary Model ──→ Signal (direction + timing)
│
▼ (only where signal fired)
Meta-Model ──→ P(profitable) ──→ Filter / Size position
The meta-model receives the same features plus signal-specific ones: the primary's own confidence, the current volatility regime, and its rolling hit rate. Build them from the outer training fold only, like everything else here.
Position Sizing
# Kelly-inspired sizing: size proportional to edge. Every prob_win above is an
# out-of-fold score; refit on all of it for rows after the last fold.
edge = 2 * np.nan_to_num(prob_win, nan=0.5) - 1 # unscored rows get zero size
position_size = base_size * np.clip(edge, 0, 1)
Guardrails
- Meta-model never overrides direction - it only decides whether to act, and how much
- Separate CV for primary and meta - meta-model must not see primary's test data
- Requires sufficient primary signals - if primary fires <100 times, meta-model will overfit
Production Implementation
ml4t-engineer provides integrated meta-labeling with triple-barrier outcomes:
from ml4t.engineer.config import LabelingConfig
from ml4t.engineer.labeling import atr_triple_barrier_labels, meta_labels
config = LabelingConfig.atr_barrier(
atr_tp_multiple=2.0,
atr_sl_multiple=1.5,
atr_period=14,
max_holding_period=10,
)
labeled = atr_triple_barrier_labels(df, config=config, price_col="close")
labeled = labeled.with_columns(primary_signal.alias("signal"))
meta = meta_labels(labeled, signal_col="signal", return_col="label_return")
# Returns: original signal plus binary meta_label for trade filtering/sizing
Checklist
- Primary model has persistent, fold-stable IC (check worst-fold, not just mean)
- Meta-model trained only on samples where primary signal fired
- CV is nested: primary and meta models use separate folds
- Meta-model probability used for position sizing or filtering
- Sufficient signal count (>200) to train meta-model reliably