This category covers concepts, code examples and new ideas about the application of fundamental supervised and unsupervised learning algorithms to trading. More specifically, relevant topics are related to Chapters 6-13 in part 2 of the book, namely:
- The Machine Learning Process
- Linear Models: From Risk Factors to Return Forecasts
- Time Series Models for Volatility Forecasts and Statistical Arbitrage
- Bayesian ML: Dynamic Sharpe Ratios and Pairs Trading
- Random Forests: A Long-Short Strategy for Japanese Stocks
- Boosting your Trading Strategy: From Daily to Intraday Data
- Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning