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- W2946661738 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." @default.
- W2946661738 created "2019-05-29" @default.
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- W2946661738 date "2019-01-01" @default.
- W2946661738 modified "2023-10-03" @default.
- W2946661738 title "A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading" @default.
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- W2946661738 doi "https://doi.org/10.1007/978-3-030-19823-7_20" @default.
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