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Rather than directly predicting future prices or returns, we follow a morerecent trend in asset management and classify the state of a market based onlabels. We use numerous standard labels and even construct our own ones. Thelabels rely on future data to be calculated, and can be used a target fortraining a market state classifier using an appropriate set of market features,e.g. moving averages. The construction of those features relies on their labelseparation power. Only a set of reasonable distinct features can approximatethe labels. For each label we use a specific neural network to classify thestate using the market features from our feature space. Each classifier gives aprobability to buy or to sell and combining all their recommendations (hereonly done in a linear way) results in what we call a trading strategy. Thereare many such strategies and some of them are somewhat dubious and misleading.We construct our own metric based on past returns but penalising for a lownumber of transactions or small capital involvement. Only topscore-performance-wise trading strategies end up in final ensembles. Using theBitcoin market we show that the strategy ensembles outperform both in returnsand risk-adjusted returns in the out-of-sample period. Even more so wedemonstrate that there is a clear correlation between the success achieved inthe past (if measured in our custom metric) and the future.

Constructing trading strategy ensembles by classifying market states