Transferring trading strategy knowledge to deep learning models

摘要

Trading strategies are constantly being employed in the financial markets in order to increase consistency, reduce human errors of judgment and boost the probability of taking profitable market positions. In this work, we attempt to transfer the knowledge of several different types of trading strategies to deep learning models. The trading strategies are applied on price data of foreign exchange trading pairs and are actual strategies used in production trading environments. Along with our approach to transfer the strategy knowledge, we introduce a preprocessing method of the original price candles making it suitable for use with Neural Networks. Our results suggest that the deep models that are tested perform better than simpler models and they can accurately learn a variety of trading strategies.