ml-strategy

To generate trading signals without future leakage, apply walk-forward training with expanding-window retraining to ensure models only use past data. This method isolates scalers and maps predict_proba outputs to continuous or discrete signals, keeping OHLCV-driven strategy predictions strictly historical.

For a scikit-learn trading strategy, you should engineer momentum, RSI, Bollinger position, volume, and volatility factors from raw OHLCV candles. These features enrich signal quality and provide the necessary inputs for RandomForest, GradientBoosting, or Ridge classifiers.

Walk-forward training prevents look-ahead bias by using expanding-window retraining with controlled frequency, ensuring the model updates only with newly available data. This approach strictly separates training periods from testing periods to eliminate future data exposure.

Yes, you can map predict_proba outputs to continuous [-1, 1] signals or discrete up/neutral/down decisions for live trading overlays. This probability-to-signal mapping translates model confidence into actionable trading alerts for equity, crypto, or futures data.

Yes, this walk-forward machine-learning approach works for crypto and futures OHLCV data. It applies to any OHLCV-driven strategy development, allowing you to train models like RandomForest across asset classes for backtesting and live market alerts.

Scaler isolation is required during expanding-window retraining to prevent future data leakage. Fitting scalers on the entire dataset would expose the model to future distribution statistics, so isolating them within each walk-forward window ensures strict temporal boundaries.