## Using Hidden Markov Models (HMMs) to Enhance Algorithmic Trading

### 1. Feature Selection for Regime Detection

- Key Features: Returns, volatility, trading volume, asset correlations
- Preprocessing: Normalize/standardize features

### 2. Model Training & State Identification

- States: 3-4 regimes (bull/bear/volatile)
- Validation: Use BIC/AIC for state count selection

Code Example:
`from hmmlearn import hmm
model = hmm.GaussianHMM(n_components=3)`

### 3. Real-Time Regime Probabilities

- Use Viterbi algorithm for state sequence decoding
- Posterior probabilities for regime confidence

> If HMM assigns >80% probability to "high volatility", trigger risk reduction protocols.

### 4. Integration with LSTM Predictions

#### Contextual Filtering

- Feed HMM states as features to LSTM

#### Model Ensembling

- Train separate LSTMs for different regimes

### 5. Dynamic Strategy Adaptation

- Adjust position sizing based on regime
- Align trades with regime-LSTM consensus

### 6. Regime-Aware Backtesting

- Test strategy performance per regime
- Optimize parameters for each market state

### 7. Advanced Architectures

- AR-HMMs for temporal dependencies
- Hierarchical HMMs for nested regimes

#### Implementation Checklist

- Validate with walk-forward analysis
- Ensure regime persistence > transaction cost window
- Use libraries like hmmlearn or pomegranate