Melvin Alvarez
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
# Risk Metrics
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Time-series utilities for risk analysis, mean-reversion detection, and bootstrapped P&L distributions.
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## Quick start
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```python
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import numpy as np
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from optimizr import (
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hurst_exponent_py,
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estimate_half_life_py,
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bootstrap_returns_py,
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compute_risk_metrics_py,
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)
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returns = np.random.randn(2000) * 0.01
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print("Hurst:", hurst_exponent_py(returns))
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print("Half-life:", estimate_half_life_py(returns))
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metrics = compute_risk_metrics_py(returns)
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print(metrics) # mean, std, skew, kurtosis, sharpe
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bootstrapped = bootstrap_returns_py(returns, n_samples=1000)
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print("Bootstrap samples:", len(bootstrapped))
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```
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## Rolling and integration helpers
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- Use `rolling_hurst_exponent_py` and `rolling_half_life_py` (from `timeseries_utils`) for sliding-window diagnostics on trading pairs.
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- Combine with HMM: feed rolling statistics as features for regime detection.
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- Pair with DE/Grid search: optimize strategy thresholds while computing half-life inside the objective.
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## Practical guidance
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- Input should be 1D NumPy arrays of returns; winsorize extreme tails before estimating Hurst/half-life for stability.
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- Half-life helps size holding periods for mean-reversion trades; revisit whenever volatility regime changes.
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- Bootstrap outputs can feed VaR/ES estimates; increase `n_samples` for tighter confidence bands.
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