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A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Abstract : Nowadays, Artificial Intelligence (AI) is changing our daily life in many application fields. Automatic trading has inspired a large number of field experts and scientists in developing innovative techniques and deploying cutting-edge technologies to trade different markets. In this context, cryptocurrency has given new interest in the application of AI techniques for predicting the future price of a financial asset. In this work Deep Reinforcement Learning is applied to trade bitcoin. More precisely, Double and Dueling Double Deep Q-learning Networks are compared over a period of almost four years. Two reward functions are also tested: Sharpe ratio and profit reward functions. The Double Deep Q-learning trading system based on Sharpe ratio reward function demonstrated to be the most profitable approach for trading bitcoin.
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Submitted on : Thursday, October 24, 2019 - 12:51:18 PM
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Giorgio Lucarelli, Matteo Borrotti. A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.247-258, ⟨10.1007/978-3-030-19823-7_20⟩. ⟨hal-02331326⟩



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