采用机器学习与二维伽马函数的股票指数量化交易策略

柴昱白; 陈伟; 赵舒欣; 毛新越

doi:10.7652/xjtuxb202305020

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采用机器学习与二维伽马函数的股票指数量化交易策略

—————————————————————— | 更新时间：2025-07-09

- 采用机器学习与二维伽马函数的股票指数量化交易策略
- Quantitative Trading Strategy for Stock Index Based on Machining Learning and 2D Gamma Function
- 西安交通大学学报 2023年57卷第5期页码：204-212
- 作者机构：
  
  西安交通大学电子与信息学部,西安,710049
- 作者简介：
  
  : 2022-10-01。作者简介: 柴昱白(1997—),男,硕士生
  陈伟(通信作者),男,高级工程师。基金项目: 陕西省重点研发计划资助项目(2018ZDCXL-GY-04-07)。
- 基金信息：
  
  : 2022-10-01。作者简介: 柴昱白(1997—),男,硕士生;陈伟(通信作者),男,高级工程师。基金项目: 陕西省重点研发计划资助项目(2018ZDCXL-GY-04-07)。
- DOI：10.7652/xjtuxb202305020
  中图分类号： F832.51;TP181
- 网络首发：2023-05-10，
  
  纸质出版：2023
- 稿件说明：
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柴昱白, 陈伟, 赵舒欣, 等. 采用机器学习与二维伽马函数的股票指数量化交易策略[J]. 西安交通大学学报, 2023,57(5):204-212.

CHAI Yubai, CHEN Wei, ZHAO Shuxin, et al. Quantitative Trading Strategy for Stock Index Based on Machining Learning and 2D Gamma Function[J]. 2023, 57(5): 204-212.
柴昱白, 陈伟, 赵舒欣, 等. 采用机器学习与二维伽马函数的股票指数量化交易策略[J]. 西安交通大学学报, 2023,57(5):204-212. DOI： 10.7652/xjtuxb202305020.

CHAI Yubai, CHEN Wei, ZHAO Shuxin, et al. Quantitative Trading Strategy for Stock Index Based on Machining Learning and 2D Gamma Function[J]. 2023, 57(5): 204-212. DOI： 10.7652/xjtuxb202305020.

摘要

为了通过短线交易获取对标股票指数的超额收益

提出一种基于机器学习的股票指数增强型量化交易策略

并实现程序化自动交易。首先生成股票指数的初始特征集

通过最大互信息系数法筛选得21维特征; 然后设计双阈值涨跌不平衡标签与3种预测分类方式(T、B-B、B-T)

组合构建不同机器学习模型

并对比择优得到多空交易方向的最优机器学习模型分别为LSTM-B-T模型与RF-B-T模型; 接着设计基于日内涨跌幅的二维伽马函数; 最后使用经二维伽马函数计算得到的类概率判别阈值与最优机器学习模型预测的类概率进行涨跌类别判定

得到交易信号。将该策略应用于中证500指数的股指ETF进行回测与模拟盘交易验证

实验结果表明:相较于随机建仓策略

采用该策略使交易评价指标得到整体性提升; 在回测与为期3个月的模拟盘交易验证中使用该策略均能获得对标指数的理想超额收益

分别为11.24%、11.08%。

Abstract

This paper proposes a stock index enhanced quantitative trading strategy based on machine learning and realizes programmed automatic trading to obtain the excess return of the benchmark stock index through short-term trading. First

the initial feature set of the stock index is generated

and the 21-dimensional features are screened by the maximum information coefficient method. Then a double-threshold unbalanced label and three prediction classification methods(T

B-B

B-T)are designed. In addition

different machine learning models are built and compared to obtain the optimal machine learning models for the long-short trading direction: LSTM-B-T model and RF-B-T model respectively. After that

a two-dimensional gamma function based on intraday rise and fall is designed. Finally

ups and downs are determined and trading signals obtained using the class probability discrimination threshold calculated by the two-dimensional gamma function and the class probability predicted by the optimal machine learning models. This strategy is applied to the backtesting and simulation trading verification of the CSI 500 ETF. The experimental results show that

compared with the random position opening strategy

the trading strategy proposed in this paper makes the transaction evaluation index improve on the whole. This strategy can obtain the ideal excess return of the benchmark index in both backtesting and three-month simulation trading verification

which are 11.24% and 11.08% respectively.

关键词

Keywords

references

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