Mastering Python for Finance - Second Edition

Addressing common pitfalls in backtesting

The most common error made in backtesting is look-ahead bias, and it comes in many forms. For example, parameter estimates may be derived from the entire period of the sample data, which constitute using information from the future. Statistical estimates such as these and model selection should be estimated sequentially, which could actually be difficult to do.

Errors in data come in all forms, from hardware, software, and human errors that could occur while routed by data distribution vendors. Listed companies may split, merge, or de-list, resulting in substantial changes to their stock prices. These actions could lead to survivorship bias in our models. Failure to clean data properly will give ...