دانلود مقاله انگلیسی رایگان:استراتژی های تطبیقی معاملات سهام با روش های یادگیری تقویت عمیق - 2020
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  • Adaptive stock trading strategies with deep reinforcement learning methods Adaptive stock trading strategies with deep reinforcement learning methods
    Adaptive stock trading strategies with deep reinforcement learning methods

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Adaptive stock trading strategies with deep reinforcement learning methods


    ترجمه فارسی عنوان مقاله:

    استراتژی های تطبیقی معاملات سهام با روش های یادگیری تقویت عمیق


    منبع:

    Sciencedirect - Elsevier - Information Sciences, 538 (2020) 142-158. doi:10.1016/j.ins.2020.05.066


    نویسنده:

    Xing Wua,b, Haolei Chen a, Jianjia Wang a,b, Luigi Troiano c, Vincenzo Loia d, Hamido Fujita e,f,g,⇑,1


    چکیده انگلیسی:

    The increasing complexity and dynamical property in stock markets are key challenges of the financial industry, in which inflexible trading strategies designed by experienced financial practitioners fail to achieve satisfactory performance in all market conditions. To meet this challenge, adaptive stock trading strategies with deep reinforcement learning methods are proposed. For the time-series nature of stock market data, the Gated Recurrent Unit (GRU) is applied to extract informative financial features, which can represent the intrinsic characteristics of the stock market for adaptive trading decisions. Furthermore, with the tailored design of state and action spaces, two trading strategies with reinforcement learning methods are proposed as GDQN (Gated Deep Q-learning trading strategy) and GDPG (Gated Deterministic Policy Gradient trading strategy). To verify the robustness and effectiveness of GDQN and GDPG, they are tested both in the trending and in the volatile stock market from different countries. Experimental results show that the proposed GDQN and GDPG not only outperform the Turtle trading strategy but also achieve more stable returns than a state-of-the-art direct reinforcement learning method, DRL trading strategy, in the volatile stock market. As far as the GDQN and the GDPG are compared, experimental results demonstrate that the GDPG with an actor-critic framework is more stable than the GDQN with a critic-only framework in the ever-evolving stock market.
    Keywords: Stock trading strategy | Gated recurrent unit | Deep Q-learning | Deep deterministic policy gradient


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 17
    حجم فایل: 1632 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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