|Table of Contents|

Research of Stock Quantitative Trading Algorithm Based on Deep Reinforcement Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2025年01期
Page:
85-92
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research of Stock Quantitative Trading Algorithm Based on Deep Reinforcement Learning
Author(s):
Song Fei1; 2; Tian Chenlei2; Feng Chuanwei3
(1.School of Information Management,Nanjing University,Nanjing 210023,China)
(2.College of Science,Nanjing Forestry University,Nanjing 210037,China)
(3.School of Mathematics,Nanjing University,Nanjing 210093,China)
Keywords:
quantitative investment; DQN; reinforcement learning; algorithmic trading
PACS:
TP18
DOI:
10.3969/j.issn.1001-4616.2025.01.011
Abstract:
This paper focuses on quantitative stock investment and applies the Deep Q-learning(DQN)model from deep reinforcement learning to algorithmic trading,constructing an end-to-end algorithmic trading system. Firstly,the system utilizes stock technology analysis index to design a stock trading environment,expanding the feature set from a time scale; secondly,it defines the reward function and action space for intelligent agent transactions; then,it designs Q-network structure and incorporate Support Vector Machine and eXtreme Gradient Boosting method to learn the rise and fall signals of stock historical data into reinforcement learning; finally,the algorithmic trading system is applied to the Chinese stock market and China Merchants Bank and Taihe Technology,as well as the remaining four stocks are selected for validation. The investment performance is evaluated from three aspects:return rate,sharpe ratio,and maximum drawdown rate. The results are shown that the algorithmic system significantly improved the return rate while reducing the maximum drawdown rate,indicating that the model has a high risk resistance ability.

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Last Update: 2025-02-15