Machine Learning for Trading

Applying ML to market data — where it helps, where it overfits, and how to tell the difference.

Difficulty intermediate

Reading order

1. Feature Engineering — the most important step
2. Model Selection — linear, tree, neural; bias-variance
3. Backtesting Frameworks — purged k-fold, walk-forward
4. NLP and Sentiment — text-derived alpha
5. Reinforcement Learning — sequential decision-making

What you should walk away with

• A healthy skepticism of any ML-trading claim that doesn't show out-of-sample evidence.
• A reproducible pipeline from raw data to live signals.
• A view on which trading problems ML is actually well-suited to.