Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets

2022-01-15 · Mohsen Asgari, Seyed Hossein Khasteh

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.

📄 PDF Abstract BibTeX arXiv:2201.05906

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningImitation Learningreinforcement-learning Reinforcement Learning (RL)

Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques

2021-05-14 · Mohsen Asgari, Hossein Khasteh

AI and data driven solutions have been applied to different fields and achieved outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classi…

BIG-bench Machine Learning

Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting

2022-09-12 · Berend Jelmer Dirk Gort, Xiao-Yang Liu, Xinghang Sun, Jiechao Gao 외

Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits i…

Algorithmic Trading Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading

2023-10-14 · Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developin…

Decision Making Feature Engineering reinforcement-learning Reinforcement Learning (RL)

Optimizing Market Making using Multi-Agent Reinforcement Learning

2018-12-26 · Yagna Patel

In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The fram…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges

2020-02-27 · Paz Grimberg, Tobias Lauinger, Damon McCoy

Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$…

Algorithmic Trading