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Q-Learning and SARSA: a comparison between two intelligent stochastic control approaches for financial trading

Author

Listed:
  • Marco Corazza

    (Department of Economics, C� Foscari University Of Venice)

  • Andrea Sangalli

    (�)

Abstract

The purpose of this paper is to solve a stochastic control problem consisting of optimizing the management of a trading system. Two model free machine learning algorithms based on Reinforcement Learning method are compared: the Q-Learning and the SARSA ones. Both these models optimize their behaviours in real time on the basis of the reactions they get from the environment in which operate. This idea is based on a new emerging theory about the market efficiency, the Adaptive Market Hypothesis. We apply the algorithms on single stock price time series using simple state variables. These algorithms operate selecting an action among three possible ones: buy, sell and stay out from the market. We perform several applications based on different parameter settings that are tested on an artificial daily stock prices time series and on different real ones from Italian stock market. Furthermore, performances are both gross and net of transaction costs.

Suggested Citation

  • 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.
    • Handle: RePEc:ven:wpaper:2015:15
    as

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    References listed on IDEAS

    as
    1. Francesco Bertoluzzo & Marco Corazza, 2012. "Reinforcement Learning for automatic financial trading: Introduction and some applications," Working Papers 2012:33, Department of Economics, University of Venice "Ca' Foscari", revised 2012.
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    Cited by:

    1. Terry Lingze Meng & Matloob Khushi, 2019. "Reinforcement Learning in Financial Markets," Data, MDPI, vol. 4(3), pages 1-17, July.
    2. 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.
    3. 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".

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    More about this item

    Keywords

    Financial trading system; Adaptive Market Hypothesis; model free machine learning; Reinforcement Learning; Q-Learning; SARSA; Italian stock market.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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