ml-trading

To build a machine learning pipeline for time-series trading signals, you need end-to-end workflows covering data ingestion, feature engineering, model training, evaluation, and deployment readiness. This supports supervised learning, regime detection, and reinforcement learning workflows.

Regime detection in trading identifies underlying market states to improve forecasting accuracy. By applying unsupervised learning to time-series asset data, models can predict regime shifts and generate actionable trading plans with appropriate risk controls.

Yes, you can use reinforcement learning for trading strategy development on historical data. The workflow supports RL-based strategies alongside classification and regression, enabling you to train models and evaluate predictive performance on held-out test periods.

Yes, this approach supports cross-validation and reproducible training for ML models. It satisfies requirements for modular model development, feature tooling, and scalable evaluation, ensuring reliable validation across different time-series asset data regimes.

The best way to forecast returns using feature-engineered financial datasets is to apply supervised learning models within a modular pipeline. This allows you to optimize classification or regression models, validate them through cross-validation, and evaluate on held-out test periods.

You should not use machine learning models for trading decisions when your time-series asset data lacks sufficient historical depth for regime detection, or when feature-engineered datasets cannot support the cross-validation and reproducible training pipelines required for scalable evaluation.