Vectorbt Regime Filtering: Combining HMM with
What if your trading strategy could automatically pause during chaotic market crashes and resume only when conditions stabilize? By combining Hidden Mar...
What if your trading strategy could automatically pause during chaotic market crashes and resume only when conditions stabilize? By combining Hidden Markov Models with XGBoost in vectorbt, you can build a regime filter that adapts to the volatile nature of crypto trading without manual intervention. Regime filtering acts as a gatekeeper for your strategy, identifying whether the market is in a trending, ranging, or high-volatility state before executing trades. Hidden Markov Models (HMMs) are probabilistic models that capture patterns in sequential data by assuming an underlying sequence of hidden states that transition over time. These models are particularly effective for inferring hidden structures in dynamic systems where the unobservable causes must be inferred from observable effects. In the context of crypto markets, prices often shift abruptly between distinct behaviors. A simple moving average crossover might work well in a trending regime but fail catastrophically in a choppy, mean-reverting environment. HMMs allow you to mathematically define these states.