ml-strategy

You can generate trading signals from OHLCV data by training machine-learning models with walk-forward validation. This process applies feature engineering to derive momentum and volatility factors, mapping model probabilities to continuous signals in [-1.0, 1.0].

Walk-forward validation in machine learning trading models is a technique that trains models sequentially on past data to predict future periods. This approach prevents data leakage and ensures robust signal generation across time.

Yes, you can apply machine learning feature engineering to multiple symbols simultaneously. The workflow scales across OHLCV time-series data, performing data validation and generating per-symbol signals for each asset.

An ML model outputs continuous trading signals by mapping its predicted market direction probabilities to a continuous scale of [-1.0, 1.0]. This produces safe signal outputs suitable for automated trading systems.

Features derived from OHLCV data for predictive modeling include momentum, volatility, RSI, moving-average ratios, and volume-based factors. These engineered features train models to predict market direction accurately.

You prevent data leakage when training ML models on time-series data by using walk-forward validation. This method strictly trains on past OHLCV data before predicting subsequent periods, ensuring robust forecasts.