A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis

Abstract

This study investigates the potential of using reinforcement learning (RL) to establish a financial trading system (FTS), taking into account the main constraint imposed by the stock market, e.g., transaction costs. More specifically, this paper shows the inferior performance of the pure reinforcement learning model when it is applied in a multi-dimensional and noisy stock market environment. To solve this problem and to get a practical and reasonable trading strategies process, a modified RL model is proposed based on the actor-critic method where we have amended the actor by incorporating three metrics from technical analysis. The results show significant improvement compared with traditional trading strategies. The reliability of the model is verified by experimental results on financial data (S&P500 index) and a fair evaluation of the proposed method and pure RL and three benchmarks is demonstrated. Statistical analysis proves that a combination of a) technical analysis (role-based strategies) and b) RL (machine learning strategies) and c) restricting the action of the RL policy network with a few realistic conditions results in trading decisions with higher investment return rates.