Regime Features

Momentum works in trending markets, mean-reversion in range-bound ones. Instead of manually switching strategies, feed regime indicators as features and let the model learn when each signal works.

The Problem

Models trained on pooled data learn average relationships. If momentum has IC of +0.08 in trends and -0.04 in mean-reverting regimes, the pooled IC is near zero. Regime features let the model condition on the current environment rather than averaging across all of them.

The Pattern

WRONG

import polars as pl

# Raw VIX level - non-stationary, scale-dependent, model cannot generalize
features = df.with_columns(regime_vix=pl.col("vix"))

CORRECT

import polars as pl

# Rolling-ranked regime indicator - stationary, bounded [0, 1]
features = df.sort("timestamp").with_columns(
    regime_vix_pctl=(
        pl.col("vix") - pl.col("vix").rolling_min(window_size=1260).shift(1)  # 1260 ≈ 5 trading years
    ) / (
        pl.col("vix").rolling_max(window_size=1260).shift(1)
        - pl.col("vix").rolling_min(window_size=1260).shift(1)
    ),
    regime_vol_zscore=(
        pl.col("realized_vol") - pl.col("realized_vol").rolling_mean(252).shift(1)
    )
    / pl.col("realized_vol").rolling_std(252).shift(1),
)

Regime Indicator Catalog

Indicator	Captures	Computation
VIX percentile	Fear vs complacency	Rolling min-max rank of VIX
Realized vol z-score	Current turbulence vs history	Rolling z-score of 21d vol
ADX level	Trend strength	14-period ADX (0-100 scale)
Yield curve slope	Growth expectations	10Y - 2Y treasury rate
Average correlation	Diversification regime	Rolling pairwise correlation
Credit spread	Risk appetite	HY - IG spread

Building Regime Features

import polars as pl
import numpy as np

df = df.sort("timestamp").with_columns(
    # Trend strength (ADX-inspired: ratio of directional move to range)
    trend_strength=(
        pl.col("close").pct_change(21).abs()
        / (pl.col("close").rolling_std(21) * np.sqrt(21))
    ),
    # Correlation regime (requires panel data)
    avg_corr=pl.col("returns").rolling_corr(pl.col("market_returns"), window=63),
)

Guardrails

• Always use lagged values - .shift(1) on all expanding/rolling regime stats
• Rank or z-score raw indicators - VIX at 20 means different things in 2017 vs 2020
• Multiple indicators - no single regime variable captures the full environment
• HMM regimes have lookahead risk - fit HMM walk-forward only, never on the full sample

Production Implementation

ml4t-engineer includes regime features in its catalog:

from ml4t.engineer import compute_features

features = compute_features(data, [
    "adx",
    "choppiness_index",
    "volatility_percentile_rank",
    "volatility_regime_probability",
])

Macro regime inputs like VIX term structure or yield-curve slope still need to be sourced separately and joined in as external features.

Checklist

• Regime features are stationary (percentile-ranked or z-scored)
• All use .shift(1) - no current-bar value in its own feature
• At least 2-3 independent regime indicators included
• Features are inputs to the model, not if/else trading rules
• HMM or changepoint models (if used) fitted walk-forward only