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Trading Cryptocurrency with Deep Deterministic Policy Gradients

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12489)


The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the best strategy is to buy a cryptocurrency and hold it until the price rises over a long period. This project aims to automate short term trading using Reinforcement Learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. The algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. The most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.


  • Reinforcement Learning
  • Cryptocurrency
  • Trading

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Correspondence to Evan Tummon .

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Tummon, E., Raja, M.A., Ryan, C. (2020). Trading Cryptocurrency with Deep Deterministic Policy Gradients. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham.

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  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

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