Inspecting Daily Machine Learning Stock Rankings in a Backtest
Summary
The document answers a practical question about viewing the daily ranking of stocks produced by a machine learning selection process during a backtest. It says the prediction output can be understood as the model’s ordering of candidates, and recommends printing the daily ranking and selected stocks from within the backtest function to inspect what happened on a given date.
This is a debugging and analysis tip rather than an explanation of how the prediction score is calculated. The text does not define whether the score represents direction, expected return, probability, or another target, and it provides no example output or validation method. Printing rankings can help reveal which securities were considered and chosen, but interpreting the score requires checking the model’s target and platform documentation.
Key ideas
- A backtest can print the model’s daily stock ordering and selected securities for inspection.
- The prediction field is treated as an input to ranking, but its precise meaning is not explained.
- The document gives a debugging suggestion rather than a method for evaluating predictive quality.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.