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A comparison among Reinforcement Learning algorithms in financial trading systems

Author

Listed:
  • Marco Corazza

    (Department of Economics, Ca' Foscari University of Venice)

  • Giovanni Fasano

    (Department of Management, Ca' Foscari University of Venice)

  • Riccardo Gusso

    (Department of Economics, Ca' Foscari University of Venice)

  • Raffaele Pesenti

    (Department of Management, Ca' Foscari University of Venice)

Abstract

In this work we analyze and implement different Reinforcement Learning (RL) algorithms in financial trading system applications. RL-based algorithms applied to financial systems aim to find an optimal policy, that is an optimal mapping between the variables describing the state of the system and the actions available to an agent, by interacting with the system itself in order to maximize a cumulative return. In this contribution we compare the results obtained considering different on-policy (SARSA) and off-policy (Q-Learning, Greedy-GQ) RL algorithms applied to daily trading in the Italian stock market. We consider both computational issues related to the implementation of the algorithms, and issues originating from practical application to real stock markets, in an effort to improve previous results while keeping a simple and understandable structure of the used models.

Suggested Citation

  • Marco Corazza & Giovanni Fasano & Riccardo Gusso & Raffaele Pesenti, 2019. "A comparison among Reinforcement Learning algorithms in financial trading systems," Working Papers 2019:33, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2019:33
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    References listed on IDEAS

    as
    1. Marco Corazza & Andrea Sangalli, 2015. "Q-Learning and SARSA: a comparison between two intelligent stochastic control approaches for financial trading," Working Papers 2015:15, Department of Economics, University of Venice "Ca' Foscari", revised 2015.
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    Cited by:

    1. Yuling Huang & Kai Cui & Yunlin Song & Zongren Chen, 2023. "A Multi-Scaling Reinforcement Learning Trading System Based on Multi-Scaling Convolutional Neural Networks," Mathematics, MDPI, vol. 11(11), pages 1-19, May.

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    1. Yuling Huang & Kai Cui & Yunlin Song & Zongren Chen, 2023. "A Multi-Scaling Reinforcement Learning Trading System Based on Multi-Scaling Convolutional Neural Networks," Mathematics, MDPI, vol. 11(11), pages 1-19, May.
    2. Terry Lingze Meng & Matloob Khushi, 2019. "Reinforcement Learning in Financial Markets," Data, MDPI, vol. 4(3), pages 1-17, July.

    More about this item

    Keywords

    Reinforcement Learning; SARSA; Q-Learning; Greedy-GQ; financial trading system; Italian FTSE Mib stock market.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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