Regime-Specific Bitcoin Trading with Hidden Markov Models and Random Forests

Summary

The document presents an adaptive Bitcoin strategy that first infers market regimes from daily returns with a Hidden Markov Model, then uses a regime-specific Random Forest classifier to predict the next day’s direction. Within a rolling historical window, the HMM labels past observations and estimates the next regime; the strategy selects the specialist model associated with that forecast. It uses engineered technical indicators as features and takes a long position only when the selected model’s upward probability exceeds a threshold, otherwise remaining neutral.

The article describes walk-forward retraining and compares a sample backtest with buy-and-hold. Its displayed results report higher returns and risk-adjusted ratios alongside lower volatility and drawdown for the strategy over the stated evaluation, but the text itself cautions that this is only an example. The strategy depends on its features, regime labels, threshold and testing setup; it adds model risk and does not establish live performance. The document recommends further validation and risk controls before deployment.

Key ideas

• An HMM infers latent market regimes from observed returns and estimates the likely next regime.
• Separate Random Forest classifiers are trained on observations assigned to each detected regime.
• The strategy selects a specialist model using the forecast regime and filters signals below a probability threshold.
• Walk-forward backtesting retrains models on recent historical windows and compares the strategy with buy-and-hold.
• The reported Bitcoin backtest is a sample result and does not establish live performance or remove model risk.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.