Deep Reinforcement Learning for Active High Frequency Trading

We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading in the stock market. We train DRL agents to trade one unit of Intel Corporation stock by employing the Proximal Policy Optimization algorithm. The training is performed on three cont...

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Bibliographic Details
Published inarXiv.org
Main Authors Briola, Antonio, Turiel, Jeremy, Marcaccioli, Riccardo, Cauderan, Alvaro, Aste, Tomaso
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.08.2023
Subjects
Algorithms
Deep learning
High frequencies
High frequency trading
Nonstationary environments
Optimization
Optimization techniques
Signal to noise ratio
Training
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Summary:We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading in the stock market. We train DRL agents to trade one unit of Intel Corporation stock by employing the Proximal Policy Optimization algorithm. The training is performed on three contiguous months of high frequency Limit Order Book data, of which the last month constitutes the validation data. In order to maximise the signal to noise ratio in the training data, we compose the latter by only selecting training samples with largest price changes. The test is then carried out on the following month of data. Hyperparameters are tuned using the Sequential Model Based Optimization technique. We consider three different state characterizations, which differ in their LOB-based meta-features. Analysing the agents' performances on test data, we argue that the agents are able to create a dynamic representation of the underlying environment. They identify occasional regularities present in the data and exploit them to create long-term profitable trading strategies. Indeed, agents learn trading strategies able to produce stable positive returns in spite of the highly stochastic and non-stationary environment.
ISSN:2331-8422