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Reinforcement Learning for automatic financial trading: Introduction and some applications


  • Francesco Bertoluzzo

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

  • Marco Corazza

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


The construction of automatic Financial Trading Systems (FTSs) is a subject of research of high interest for both academic environment and financial one due to the potential promises by self-learning methodologies and by the increasing power of actual computers. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that optimize their behavior in relation to the responses they get from the environment in which they operate, without the need for a supervisor. In particular, first we introduce the essential aspects of RL which are of interest for our purposes, then we present some original automatic FTSs based on differently configured RL algorithms and apply such FTSs to artificial and real time series of daily financial asset prices.

Suggested Citation

  • 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.
  • Handle: RePEc:ven:wpaper:2012:33

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    Cited by:

    1. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665,, revised Aug 2019.
    2. 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.
    3. Haoqian Li & Thomas Lau, 2019. "Reinforcement Learning: Prediction, Control and Value Function Approximation," Papers 1908.10771,
    4. Petrus Strydom, 2017. "Funding optimization for a bank integrating credit and liquidity risk," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(2), pages 1-1.
    5. Zhuoran Xiong & Xiao-Yang Liu & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522,, revised Dec 2018.
    6. Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

    More about this item


    Financial Trading System; Reinforcement Learning; Stochastic control; Q-learning algorithm; Kernel-based Reinforcement Learning.;

    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
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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