I've been doing a lot with ML recently and ironically have had better success with simpler ML strategies (ie. less features and complex data). This is not an awfully complex algorithm and I thought some of you might be interested in trying something similar yourselves.
This strategy uses the previous 6 month prices of various SPDR Funds as training data to predict the following months price of the fund. Each month, the algorithm evenly invests in all of the SPDR Funds it predicts will report higher prices. Other than that, the only restriction I placed on the algorithm was to allow it to trade only once a month.
This is a black box algorithm that uses a random forest so if you backtest it yourself, you are almost definitely going to get different results. But, I've run over 25 backtests on this algorithm from Jan 03, 2002 to Jan 01, 2017 and the average return over that period seems to be around 270% (thus, the backtest I'm sharing is an average case). The SPY over the same period returns only 155%.
Like all ML algorithms, the more data you give this program the smarter (better predictive power) it gets. Thus, this algorithm performs better in the long run than the short run. For optimized results you'd want to run a 10+ year backtest.
