Udacity - Machine Learning for Trading
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Updated
Dec 23, 2020 - Jupyter Notebook
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 engine with realistic execution, portfolio, and risk modeling.
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.
Finance-specific latent-factor, asset prediction, and portfolio-learning models.
This repository documents the evolution of a trading experiment, inspired by Stefan Jansen's "Machine Learning for Algorithmic Trading" workflow.
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