Research into algorithmic trading using reinforcement learning has been garnering increasing popularity in recent years. While most research work focuses on solving a certain modelling problem or data problem with positive results, we believe that in an application as critical as financial trading, aligning the machine to human behaviours is imperative and should be regarded as the basis of all further improvements before machine algorithms are free to go their own innovative ways. In this paper, we are proposing a trading model whose design principles are based on bringing a machine trading agent close to a human trader. We study areas where human alignment is necessary and introduce as a solution a novel multi-loss function of the model combining supervised learning, single-step and multi-step Q learning, and also inject the paradigm of imitation learning in the training and trading processes. We also introduce a realistic backtesting setup and a holding position aware profit calculation scheme under which the machine algorithm conducts intra-day trading using minute tick data over a group of U. S. stocks chosen to represent different industrial sectors and liquidity levels. Our model's overall out-performance over a group of baseline models as well as our ablation study results justify the inclusion of individual model features all of which are introduced to bring aspects of the model behaviour more aligned with those of a human trader.
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Research into algorithmic trading using reinforcement learning has been garnering increasing popularity in recent years. While most research work focuses on solving a certain modelling problem or data problem with positive results, we believe that in an application as critical as financial trading, aligning the machine to human behaviours is imperative and should be regarded as the basis of all further improvements before machine algorithms are free to go their own innovative ways. In this paper...
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