Contracts for Difference: A Reinforcement Learning Approach

Nico Zengeler and Uwe Handmann
Additional contact information
Nico Zengeler: Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany
Uwe Handmann: Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany

JRFM, 2020, vol. 13, issue 4, 1-12

Abstract: 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.

Keywords: contract for difference; CfD; reinforcement learning; RL; neural networks; long short-term memory; LSTM; Q-learning; deep learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/13/4/78/pdf (application/pdf)
https://www.mdpi.com/1911-8074/13/4/78/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:13:y:2020:i:4:p:78-:d:347179

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().