AdvancedQuantitative MethodsPython
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Market Regime Detection¶
Identifies distinct market states (bull/bear, low/high volatility) using statistical methods. Regime-aware strategies adapt parameters to the current market environment.
Functions¶
| Function | Description |
|---|---|
moving_average_regime(prices, short, long) |
MA crossover bull/bear detection |
volatility_regime(returns, window, n_regimes) |
Quantile-based volatility buckets |
gaussian_mixture_regime(returns, n_regimes) |
GMM-based unsupervised regime detection |
regime_stats(returns, labels) |
Per-regime return statistics |
Methods¶
Moving Average Crossover¶
Classic technical approach: Bull when 50-day MA > 200-day MA (golden cross), Bear otherwise. Simple, interpretable, but lagging.
Volatility Regime¶
Rolling realized volatility classified into low/medium/high buckets using quantile thresholds. Useful for dynamic position sizing.
Gaussian Mixture Model (GMM)¶
Unsupervised learning: fit a mixture of Gaussians to the return distribution. Regime 0 = lowest mean (bear), Regime 1 = highest mean (bull). Requires scikit-learn.
Example¶
from regime_detection import gaussian_mixture_regime, regime_stats
import numpy as np
returns = np.random.normal(0.001, 0.015, 500)
result = gaussian_mixture_regime(returns, n_regimes=2)
stats = regime_stats(returns, result["labels"])
Applications¶
- Strategy switching: Use momentum in bull regimes, mean-reversion in bear
- Risk scaling: Reduce position sizes in high-volatility regimes
- Macro overlay: Override signals when macro regime shifts
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