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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
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
قیمت: رایگان
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