Observation Time Effects in Reinforcement Learning on Contracts for Difference

Maximilian Wehrmann, Nico Zengeler and Uwe Handmann
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Maximilian Wehrmann: Hochschule Ruhr West, University of Applied Sciences, Duisburger Str. 100, 45479 Mülheim an der Ruhr, Germany
Nico Zengeler: Hochschule Ruhr West, University of Applied Sciences, Duisburger Str. 100, 45479 Mülheim an der Ruhr, Germany
Uwe Handmann: Hochschule Ruhr West, University of Applied Sciences, Duisburger Str. 100, 45479 Mülheim an der Ruhr, Germany

JRFM, 2021, vol. 14, issue 2, 1-15

Abstract: In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) and act on the basis of different observation time spans. Each agent tries to maximize trading profit by buying or selling one of a number of contracts in a simulated market environment for Contracts for Difference (CfD), considering correlations between individual assets by architecture. To decide which action to take on a specific contract, an agent develops a policy which relies on an observation of the whole market for a certain period of time. We investigate whether or not there exists an optimal observation sequence length, and conclude that such a value depends on market dynamics.

Keywords: machine learning; contracts for difference; deep neural networks (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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