Reinforcement learning in optimisation of financial market trading strategy parameters

Vetrin R.L., Koberg K.
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High frequency algorithmic trading became is a subclass of trading which is focused on gaining basis-point like profitability on sub-second time frames. Such trading strategies do not depend on most of the factors eligible for the longer-term trading and require specific approach. There were many attempts to utilize machine learning techniques to both high and low frequency trading. However, it is still having limited application in the real world trading due to high exposure to overfitting, requirements for rapid adaptation to new market regimes and overall instability of the results. We conducted a comprehensive research on combination of known quantitative theory and reinforcement learning methods in order derive more effective and robust approach at construction of automated trading system in an attempt to create a support for a known algorithmic trading techniques. Using classical price behavior theories as well as modern application cases in sub-millisecond trading, we utilized the Reinforcement Learning models in order to improve quality of the algorithms. As a result, we derived a robust model which utilize Deep Reinforcement learning in order to optimise static market making trading algorithms’ parameters capable of online learning on live data. More specifically, we explored the system in the derivatives cryptocurrency market which mostly not dependent on external factors in short terms. Our research was implemented in high-frequency environment and the final models showed capability to operate within accepted high-frequency trading time-frames. We compared various combinations of Deep Reinforcement Learning approaches and the classic algorithms and evaluated robustness and effectiveness of improvements for each combination.

Keywords: deep reinforcement learning, algorithmic trading, high-frequency trading, market making
Citation in English: Vetrin R.L., Koberg K. Reinforcement learning in optimisation of financial market trading strategy parameters // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1793-1812
Citation in English: Vetrin R.L., Koberg K. Reinforcement learning in optimisation of financial market trading strategy parameters // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1793-1812
DOI: 10.20537/2076-7633-2024-16-7-1793-1812
URL: http://crm-en.ics.org.ru/journal/article/3569/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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