From d32023c8826029d1cd0050e91f56bcb900524f51 Mon Sep 17 00:00:00 2001 From: grmwtt Date: Wed, 29 Jan 2025 22:15:50 -0500 Subject: [PATCH] Possible implementation of regime predictor with LSTM --- HMM.html | 94 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 HMM.html diff --git a/HMM.html b/HMM.html new file mode 100644 index 0000000..827d9e6 --- /dev/null +++ b/HMM.html @@ -0,0 +1,94 @@ + + + + + HMMs for Market Regime Detection + + + +
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Using Hidden Markov Models (HMMs) to Enhance Algorithmic Trading

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1. Feature Selection for Regime Detection

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2. Model Training & State Identification

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+ Code Example:
+ from hmmlearn import hmm
+ model = hmm.GaussianHMM(n_components=3) +
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3. Real-Time Regime Probabilities

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+ If HMM assigns >80% probability to "high volatility", trigger risk reduction protocols. +
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4. Integration with LSTM Predictions

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Contextual Filtering

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Model Ensembling

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5. Dynamic Strategy Adaptation

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6. Regime-Aware Backtesting

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7. Advanced Architectures

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Implementation Checklist

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