docs(optimizr): add logo and fix rtd deps

docs/source/algorithms/hmm.md

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+# Hidden Markov Models
+
+Gaussian HMM for regime detection and sequence modelling.
+
+## Usage
+
+```python
+import numpy as np
+from optimizr import HMM
+
+returns = np.concatenate([
+    np.random.normal(0.01, 0.02, 500),
+    np.random.normal(-0.015, 0.03, 500),
+])
+
+model = HMM(n_states=2)
+model.fit(returns, n_iterations=100)
+
+states = model.predict(returns)
+print(np.unique(states, return_counts=True))
+```
+
+## Notes
+- Uses Rust backend when available; falls back to Python.
+- `fit` runs Baum-Welch; `predict` runs Viterbi.
+- Call `score(X)` to compute log-likelihood.