Validating Machine Learning Models with Out-of-Sample Trading Backtests

Summary

This personal account describes an algorithmic trader’s move from strategies written in Rust without machine learning to Python-based work that combines machine learning with trading-system development. The central lesson is that predictive-model metrics such as mean squared error and mean absolute error do not necessarily indicate whether a model can support a successful trading strategy. The trader wanted to connect model validation with strategy backtesting and reports updating his code to run an out-of-sample backtest after taking a course.

The account illustrates a useful distinction between evaluating a model as a statistical predictor and evaluating its use in a trading process. An out-of-sample backtest can provide a more relevant assessment of a strategy than fitting or model metrics alone, though the document gives no performance figures, detailed validation protocol, or controls for overfitting and trading costs. It is a single learner’s experience, not evidence that a particular course or machine-learning approach will produce profitable results.

Key ideas

• Predictive accuracy metrics do not by themselves show whether a model will make a useful trading system.
• Model validation should be connected to strategy-level backtesting.
• The trader reports adding an out-of-sample backtest to assess his system.
• The account gives no measured returns or detailed safeguards against overfitting.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.