ml4t

Udacity - Machine Learning for Trading

Market data acquisition, storage, and update workflows for machine learning for trading.

Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.

Event-driven backtesting for quantitative strategies with configurable execution, accounting, risk, and framework-parity validation.

Feature engineering, labeling, alternative bars, and leakage-safe datasets for financial ML.

Live trading runtime for ML4T strategies with broker integrations, risk checks, and shadow mode.

Agent skills for Machine Learning for Algorithmic Trading: leakage-safe quant ML, backtesting, validation, and autonomous research workflows.

Finance-specific models for asset pricing, prediction, and portfolio learning.

Serializable, runtime-neutral contracts for market data, artifacts, strategy lifecycles, and execution across ML4T libraries.

트레이딩을 위한 머신러닝 — 금융공학 대학원 16주 강의자료. Stefan Jansen의 Machine Learning for Trading 3판을 읽는 데 도움이 되도록 공개합니다.

This repository documents the evolution of a trading experiment, inspired by Stefan Jansen's "Machine Learning for Algorithmic Trading" workflow.