How to backtest an ML-driven strategy

In a nutshell, the ML4T workflow, illustrated in Figure 8.1, is about backtesting a trading strategy that leverages machine learning to generate trading signals, select and size positions, or optimize the execution of trades. It involves the following steps, with a specific investment universe and horizon in mind:

1. Source and prepare market, fundamental, and alternative data
2. Engineer predictive alpha factors and features
3. Design, tune, and evaluate ML models to generate trading signals
4. Decide on trades based on these signals, for example, by applying rules
5. Size individual positions in the portfolio context
6. Simulate the resulting trades triggered using historical market data
7. Evaluate how the resulting positions would have performed

Figure 8.1: The ML4T workflow

When we discussed the ML process in Chapter 6, The Machine Learning Process, we emphasized that the model's learning should...