Using Machine Learning to Predict Out-Of-Sample Performance of Trading Algorithms - DataRobot

Earlier this year, we used DataRobot to test a large number of preprocessing, imputation and classifier combinations to predict out-of-sample performance. In this blog post, I'll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian. From these results, it became clear that while the Sharpe ratio of a backtest was a very weak predictor of the future performance of a trading strategy, we could instead use DataRobot to train a classifier on a variety of features to predict out-of-sample performance with much higher accuracy. Backtesting is ubiquitous in algorithmic trading. Quants run backtests to assess the merit of a strategy, academics publish papers showing phenomenal backtest results, and asset allocators at hedge funds take backtests into account when deciding where to deploy capital and who to hire.