I am developing framework to create strategies with machine learning. The problem I am facing now is to refine how algorithm should decide which of two strategies is “better”.
- Usually we have a strategy, automate it and then backtest. Having few strategies with few parameter sets we can freely compare them using multiple backtest data points. eg. Profit factor, average profit, Calmar ratio, expectancy, max drowdown etc.
- With machine learning the engine expects to have one number which will mean “the higher the better” (in short). Then it will pick strategies with highest “scores” and work with them further. Until now I am using profit factor as single “score”, yet it does not incorporate max drowdowns or very small number of trades.
The question is - can you help me come up with single formula to calculate score of given strategy?