docs(optimizr): add logo and fix rtd deps

docs/source/algorithms/risk_metrics.md

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+# Risk Metrics
+
+Time-series utilities for risk analysis and mean-reversion signals.
+
+## Quick Start
+
+```python
+import numpy as np
+from optimizr import (
+    hurst_exponent_py,
+    estimate_half_life_py,
+    bootstrap_returns_py,
+)
+
+returns = np.random.randn(2000) * 0.01
+print("Hurst:", hurst_exponent_py(returns))
+print("Half-life:", estimate_half_life_py(returns))
+
+bootstrapped = bootstrap_returns_py(returns, n_samples=1000)
+print("Bootstrap sample shape:", 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.