Regime Awareness

Markets alternate between regimes (low/high volatility, trending/mean-reverting, risk-on/risk-off). Regime detection for diagnostics and risk scaling is reliable. Regime detection for market timing is not.

The Problem

Regime-switching models promise to predict when to be in or out of the market. In practice, regime transitions are identified with high confidence only after they have already occurred. A model that correctly labels the March 2020 crash as "crisis" does so 2-4 weeks late, after the drawdown has already happened. Trading on regime predictions produces whipsaw losses and underperforms a regime-conditioned but always-invested approach.

The correct use of regimes is as a conditioning feature: scale risk, adjust position sizes, and evaluate strategy performance per regime - but stay invested.

The Pattern

WRONG

# Regime-timing: go to cash when model predicts "bear"
def generate_signal(data, regime_model):
    regime = regime_model.predict(data)
    if regime == "bear":
        return 0.0       # exit market entirely
    else:
        return model.predict(data)  # normal signal

CORRECT

import numpy as np

# Regime-as-feature: condition risk scaling on observable regime indicator
realized_vol = returns.rolling(21).std() * np.sqrt(252)
vol_rank = realized_vol.rolling(252).rank(pct=True)

# Tercile-based regime label (observable, no prediction needed)
regime = np.where(vol_rank < 0.33, "low_vol",
         np.where(vol_rank < 0.66, "mid_vol", "high_vol"))

# Scale position sizes by regime (always invested, risk-adjusted)
vol_scale = {"low_vol": 1.3, "mid_vol": 1.0, "high_vol": 0.5}
position = base_signal * np.vectorize(vol_scale.get)(regime)

Regime Indicators

Type	Indicators	Use case
Volatility	Realized vol, VIX, ATR percentile	Risk scaling
Trend	ADX, SMA slope, momentum sign	Feature conditioning
Liquidity	Bid-ask spread, volume ratio, Amihud	Position sizing
Macro	Yield curve slope, credit spread	Regime label

Regime-Sliced Evaluation

Always evaluate strategy performance per regime, not just in aggregate:

import numpy as np

for label in ["low_vol", "mid_vol", "high_vol"]:
    mask = regime == label
    regime_ret = strategy_returns[mask]
    sharpe = regime_ret.mean() / regime_ret.std() * np.sqrt(252)
    max_dd = (np.maximum.accumulate(regime_ret.cumsum()) - regime_ret.cumsum()).max()
    print(f"{label}: Sharpe={sharpe:.2f}, MaxDD={max_dd:.1%}, N={mask.sum()}")

A strategy with Sharpe 1.5 that comes entirely from one regime is fragile. Robust strategies have positive (if unequal) performance across all regimes.

Guardrails

• Define regime labels BEFORE backtesting - choosing regimes after seeing results is snooping.
• Use observable indicators (realized vol, yield curve slope), not latent model outputs, for regime classification.
• Report strategy metrics per regime in every backtest report.
• Never use regime prediction for binary in/out decisions - use it for continuous risk scaling.
• Regime labels must use expanding or rolling windows to avoid lookahead bias.

Production Implementation

ml4t-engineer exposes regime indicators as model inputs:

from ml4t.engineer import compute_features

regime_inputs = compute_features(data, [
    "adx",
    "choppiness_index",
    "volatility_percentile_rank",
])
data = data.join(regime_inputs, on=["timestamp", "symbol"], how="left")

Use these as conditioning features or sizing inputs, not binary in/out switches.

Checklist

• Regime definitions specified ex-ante (in strategy term sheet, before backtesting)
• Regime labels use only backward-looking data (no lookahead)
• Strategy metrics reported per regime (not just aggregate Sharpe)
• Position sizing or risk parameters vary with regime (continuous scaling)
• No binary market-timing signals based on regime prediction