Contracts for Difference: A Reinforcement Learning Approach
Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany
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J. Risk Financial Manag. 2020, 13(4), 78; https://doi.org/10.3390/jrfm13040078
Received: 21 March 2020
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Revised: 12 April 2020
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Accepted: 16 April 2020
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Published: 17 April 2020
(This article belongs to the Special Issue AI and Financial Markets)
We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory (LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low latency. In a real-world example, we show that an increased model size may compensate for a higher latency. As the noisy nature of economic trends complicates predictions, especially in speculative assets, our approach does not predict courses but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a virtual market environment, based on historical trading data. Our environment provides a partially observable Markov decision process (POMDP) to reinforcement learners and allows the training of various strategies.
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Keywords:
contract for difference; CfD; reinforcement learning; RL; neural networks; long short-term memory; LSTM; Q-learning; deep learning
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MDPI and ACS Style
Zengeler, N.; Handmann, U. Contracts for Difference: A Reinforcement Learning Approach. J. Risk Financial Manag. 2020, 13, 78. https://doi.org/10.3390/jrfm13040078
AMA Style
Zengeler N, Handmann U. Contracts for Difference: A Reinforcement Learning Approach. Journal of Risk and Financial Management. 2020; 13(4):78. https://doi.org/10.3390/jrfm13040078
Chicago/Turabian StyleZengeler, Nico, and Uwe Handmann. 2020. "Contracts for Difference: A Reinforcement Learning Approach" Journal of Risk and Financial Management 13, no. 4: 78. https://doi.org/10.3390/jrfm13040078
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