Kochliaridis, V., Kouloumpris, E., Vlahavas, I. (2022). TraderNet-CR: Cryptocurrency Trading with Deep Reinforcement Learning. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 https://doi.org/10.1007/978-3-031-08333-4_25
Author(s): Kochliaridis, V., Kouloumpris, E., Vlahavas, I.
Appeared In: IFIP Advances in Information and Communication Technology
Keywords: Cryptocurreny trading, Deep reinforcement learnin,g Public activity analysis, Technical analysis, Risk management
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Abstract: 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.
See Also: https://link.springer.com/chapter/10.1007/978-3-031-08333-4_25