Autonomous Trading System using Reinforcement Learning by Melissa Tan
The idea of predicting financial instruments has been the goal of many due in part to the expectation that predicting these instruments can prove lucrative. Whilst the accurate rediction of price seemed reasonable, they do not necessarily guarantee positive returns due to commissions, large profit draw-downs and excessive switching behaviours. Reinforcement Learning (RL) is an autonomous approach to decision making process through repetitive self-learning and evaluation. The idea is to train an agent to learn to execute an order by acting on a suitable strategy that maximizes profit. In this capstone project, we first conduct a systematic review of 50 literature that applies RL in trading, in particular, to uncover the common theme to maximizing the chance of a successful model. We then prototyped a trading system that applies Proximal Policy Optimization (PPO) which is the brainchild of Schulman et al. (Schulman, Wolski, Dhariwal, Radford, & Klimov, 2017). Thismodel achieved an annualised return of 34.06%and outperformed thestudies by Xiong et al. (Xiong, Liu, Zhong, Yang, & Walid, 2018)whose DDPG model produced an annualised return of 25.87%. We also found that adding technical indicators altered the agent’s trading activities significantly.With the added information, the model achieved a lower annualised return of 27.47% but the result was more consistent to the training performance. In summary, we conclude that RL can be successfully applied to trading, however the models are highly dependent on the characteristics of the underlyingdata, training regimeand the RL model itself, thus a rigorous hyperparameters tuning is required to achieve good result.
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