Backtesting is a critical step to select successful algorithmic trading strategies. Cross-validation using synthetic data is a key ML technique to generate reliable out-of-sample results when combined with appropriate methods to correct for multiple testing. The time series nature of financial data requires modifications to the standard approach to avoid look-ahead bias or otherwise contaminate the data used for training, validation, and testing. In addition, the limited availability of historical data has given rise to alternative approaches that use synthetic data:

• We will demonstrate various methods to test ML models using market, fundamental, and alternative that obtain sound estimates of out-of-sample errors.
• In Chapter 20, Autoencoders and Generative Adversarial Nets, we present GAN that are capable of producing high-quality synthetic data.