regime-detection

You can detect market regimes by applying volatility clustering and trend strength indicators to OHLCV price data. Using Python libraries, the process calculates metrics like the Hurst exponent and ADX to classify markets into distinct trend and volatility states for adaptive trading.

Market regime classification categorizes markets into four quadrants like quiet trend or volatile range. The Hurst exponent serves as a statistical method within this process to measure long-term memory, helping identify whether price series are trending, mean-reverting, or random walks.

You adapt trading strategies by classifying the current market regime and dynamically switching algorithms. For strong trends, you apply momentum strategies, while for volatile ranges, you switch to mean-reversion, simultaneously adjusting position sizing and stop-loss levels to match detected conditions.

Yes, you can use pandas and numpy for market regime detection. The process requires OHLCV data as input and leverages these Python libraries along with httpx to perform the technical analysis calculations needed for volatility and trend strength indicator computations.

You should adjust position sizing and stop-loss levels when the market transitions between different volatility and trend regimes. By detecting shifts using indicators like ATR and Bollinger Bands, you can optimize risk exposure and match your stop-loss levels to current market conditions.