Backtesting pitfalls and how to avoid them
Backtesting simulates an algorithmic strategy based on historical data, with the goal of producing performance results that generalize to new market conditions. In addition to the generic uncertainty around predictions in the context of ever-changing markets, several implementation aspects can bias the results and increase the risk of mistaking in-sample performance for patterns that will hold out-of-sample.
These aspects are under our control and include the selection and preparation of data, unrealistic assumptions about the trading environment, and the flawed application and interpretation of statistical tests. The risks of false backtest discoveries multiply with increasing computing power, bigger datasets, and more complex algorithms that facilitate the misidentification of apparent signals in a noisy sample.
In this section, we will outline the most serious and common methodological mistakes. Please refer to the literature on...
