18th AIAI 2022, 17 - 20 June 2022, Greece

TraderNet-CR: Cryptocurrency Trading with Deep Reinforcement Learning

Vasileios Kochliaridis, Eleftherios Kouloumpris, Ioannis Vlahavas


  The predominant method of developing trading strategies is technical analysis on historical market data. Other financial analysts monitor the public activity towards cryptocurrencies, in order to forecast upcoming trends in the market. Until now, the best cryptocurrency trading models rely solely on one of the two methodologies and attempt to maximize their profits, while disregarding the trading risk. In this paper, we present a new machine learning approach, named TraderNet-CR, which is based on deep reinforcement learning. TraderNet-CR combines both methodologies in order to detect profitable round trips in the cryptocurrency market and maximize a trader's profits. Additionally, we have added an extension method, named N-Consecutive Actions, which examines the model's previous actions, before suggesting a new action. This method is complementary to the model's training and can be fruitfully combined, in order to further decrease the trading risk. Our experiments show that our model can properly forecast profitable round trips, despite high market commission fees.  

*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.