2020/12/05 by Michał Balcerak, Michal Balcerak, Thomas Schmelzer +3
Computer Science · Economics, Econometrics and Finance · #Evolutionary Algorithms and Applications #FOS: Economics and business #Machine Learning and Data Classification #Metaheuristic Optimization Algorithms Research #Trading and Market Microstructure (q-fin.TR) #q-fin.TR
paper · pdf · doi:10.48550/arxiv.2012.03078
20 pages, 18 figures
arxiv created 2020/12/05 · openalex publication_date 2020/12/05 · arxiv updated 2020/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The construction of those features relies on their label separation power. Only a set of reasonable distinct features can approximate the labels. For each label we use a specific neural network to classify the state using the market features from our feature space. Each classifier gives a probability to buy or to sell and combining all their recommendations (here only done in a linear way) results in what we call a trading strategy. There are 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 low number of transactions or small capital involvement. Only top score-performance-wise trading strategies end up in final ensembles. Using the Bitcoin market we show that the strategy ensembles outperform both in returns and risk-adjusted returns in the out-of-sample period. Even more so we demonstrate that there is a clear correlation between the success achieved in the past (if measured in our custom metric) and the future.