27 lines
681 B
Markdown
27 lines
681 B
Markdown
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# Risk Metrics
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Time-series utilities for risk analysis and mean-reversion signals.
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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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)
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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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bootstrapped = bootstrap_returns_py(returns, n_samples=1000)
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print("Bootstrap sample shape:", len(bootstrapped))
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```
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## Notes
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- Input arrays should be 1D NumPy arrays of returns.
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- Half-life is useful for calibrating mean-reversion strategies.
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- Bootstrap utilities help estimate drawdown and VaR distributions.
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