Algorithmic strategies: managing the overfitting bias

The business of algorithmic trading strategies creates incentives for model overfitting and backtest embellishment: researchers must pass Sharpe ratio thresholds for their strategies to be considered, while managers lack interest in realistic simulations of ideas. Overfitting leads to bad investment decisions and underestimated risk. Sound ethical principles are the best method for containing this risk (view post here). Where these principles have been compromised one should assume that all ‘tweaks’ to the original strategy idea that are motivated by backtest improvement contribute to overfitting. Their effects can be estimated by looking at the ratio of directional ‘flips’ in the trading signal. Overfitting typically increases with the Sharpe ratio targets of the business and the scope for applying ‘tweaks’ to the strategy. Realistic strategy performance expectations should be based on a range of plausible strategy versions, not on an optimized one.