A long-short trading strategy with boosting
In this section, we'll design, implement, and evaluate a trading strategy for US equities driven by daily return forecasts produced by gradient boosting models. We'll use the Quandl Wiki data to engineer a few simple features (see the notebook preparing_the_model_data for details), select a model while using 2015/16 as validation period, and run an out-of-sample test for 2017.
As in the previous examples, we'll lay out a framework and build a specific example that you can adapt to run your own experiments. There are numerous aspects that you can vary, from the asset class and investment universe to more granular aspects like the features, holding period, or trading rules. See, for example, the Alpha Factor Library in the Appendix for numerous additional features.
We'll keep the trading strategy simple and only use a single ML signal; a real-life application will likely use multiple signals from different sources...
