Add make docs commands for html build

docs/source/algorithms/risk_metrics.md

@@ -1,8 +1,8 @@
 # Risk Metrics
 
-Time-series utilities for risk analysis and mean-reversion signals.
+Time-series utilities for risk analysis, mean-reversion detection, and bootstrapped P&L distributions.
 
-## Quick Start
+## Quick start
 
 ```python
 import numpy as np
@@ -10,17 +10,27 @@ from optimizr import (
     hurst_exponent_py,
     estimate_half_life_py,
     bootstrap_returns_py,
+    compute_risk_metrics_py,
 )
 
 returns = np.random.randn(2000) * 0.01
 print("Hurst:", hurst_exponent_py(returns))
 print("Half-life:", estimate_half_life_py(returns))
 
+metrics = compute_risk_metrics_py(returns)
+print(metrics)  # mean, std, skew, kurtosis, sharpe
+
 bootstrapped = bootstrap_returns_py(returns, n_samples=1000)
-print("Bootstrap sample shape:", len(bootstrapped))
+print("Bootstrap samples:", len(bootstrapped))
 ```
 
-## Notes
-- Input arrays should be 1D NumPy arrays of returns.
-- Half-life is useful for calibrating mean-reversion strategies.
-- Bootstrap utilities help estimate drawdown and VaR distributions.
+## Rolling and integration helpers
+
+- Use `rolling_hurst_exponent_py` and `rolling_half_life_py` (from `timeseries_utils`) for sliding-window diagnostics on trading pairs.
+- Combine with HMM: feed rolling statistics as features for regime detection.
+- Pair with DE/Grid search: optimize strategy thresholds while computing half-life inside the objective.
+
+## Practical guidance
+- Input should be 1D NumPy arrays of returns; winsorize extreme tails before estimating Hurst/half-life for stability.
+- Half-life helps size holding periods for mean-reversion trades; revisit whenever volatility regime changes.
+- Bootstrap outputs can feed VaR/ES estimates; increase `n_samples` for tighter confidence bands.