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Possible implementation of regime predictor with LSTM

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2025-01-29 22:15:50 -05:00
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>HMMs for Market Regime Detection</title>
<style>
body { font-family: Arial, sans-serif; line-height: 1.6; padding: 20px; }
.container { max-width: 800px; margin: auto; }
h2 { color: #2c3e50; border-bottom: 2px solid #3498db; }
h3 { color: #34495e; }
h4 { color: #7f8c8d; }
ul { margin: 10px 0; }
li { margin: 5px 0; }
.example { background: #f8f9fa; padding: 15px; border-left: 4px solid #3498db; margin: 15px 0; }
blockquote { color: #666; margin: 10px 0; padding: 10px 20px; }
</style>
</head>
<body>
<div class="container">
<h2>Using Hidden Markov Models (HMMs) to Enhance Algorithmic Trading</h2>
<!-- Feature Selection Section -->
<h3>1. Feature Selection for Regime Detection</h3>
<ul>
<li><strong>Key Features:</strong> Returns, volatility, trading volume, asset correlations</li>
<li><strong>Preprocessing:</strong> Normalize/standardize features</li>
</ul>
<!-- Model Training Section -->
<h3>2. Model Training & State Identification</h3>
<ul>
<li><strong>States:</strong> 3-4 regimes (bull/bear/volatile)</li>
<li><strong>Validation:</strong> Use BIC/AIC for state count selection</li>
</ul>
<div class="example">
<strong>Code Example:</strong><br>
<code>from hmmlearn import hmm<br>
model = hmm.GaussianHMM(n_components=3)</code>
</div>
<!-- Real-Time Inference Section -->
<h3>3. Real-Time Regime Probabilities</h3>
<ul>
<li>Use Viterbi algorithm for state sequence decoding</li>
<li>Posterior probabilities for regime confidence</li>
</ul>
<blockquote>
If HMM assigns >80% probability to "high volatility", trigger risk reduction protocols.
</blockquote>
<!-- LSTM Integration Section -->
<h3>4. Integration with LSTM Predictions</h3>
<h4>Contextual Filtering</h4>
<ul>
<li>Feed HMM states as features to LSTM</li>
</ul>
<h4>Model Ensembling</h4>
<ul>
<li>Train separate LSTMs for different regimes</li>
</ul>
<!-- Dynamic Strategy Section -->
<h3>5. Dynamic Strategy Adaptation</h3>
<ul>
<li>Adjust position sizing based on regime</li>
<li>Align trades with regime-LSTM consensus</li>
</ul>
<!-- Backtesting Section -->
<h3>6. Regime-Aware Backtesting</h3>
<ul>
<li>Test strategy performance per regime</li>
<li>Optimize parameters for each market state</li>
</ul>
<!-- Advanced Applications -->
<h3>7. Advanced Architectures</h3>
<ul>
<li>AR-HMMs for temporal dependencies</li>
<li>Hierarchical HMMs for nested regimes</li>
</ul>
<!-- Implementation Tips -->
<div class="example">
<h4>Implementation Checklist</h4>
<ul>
<li>Validate with walk-forward analysis</li>
<li>Ensure regime persistence > transaction cost window</li>
<li>Use libraries like hmmlearn or pomegranate</li>
</ul>
</div>
</div>
</body>
</html>