Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets
This provides an incremental expert system to help investors exploit cryptocurrency markets for profit.
The paper tackled the problem of designing profitable trading strategies for cryptocurrency markets using deep reinforcement learning, achieving a gain of $4,850 on a $10,000 investment over 66 days on unseen data.
Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.