ml-trading

What problem does it solve?

This skill enables teams to build, validate, and deploy machine learning models for trading, including classification, regression, regime detection, and RL-based strategies, to improve decision-making and automation.

Core Features & Use Cases

• End-to-end ML pipeline for trading: data ingestion, feature engineering, model training, evaluation, and deployment readiness.
• Supports supervised learning (classification/regression), unsupervised regime detection, and reinforcement learning workflows for strategy development.
• Use cases include predicting regime shifts, forecasting returns, and generating actionable trading plans with risk controls.

Quick Start

Train an ML trading model on historical data and evaluate its predictive performance on a held-out test period.