Research on Quantitative Trading Strategy Based on Neural Network Algorithm and Fisher Linear Discriminant

Zi-Yu Li, Yuan-Biao Zhang, Jia-Yu Zhong, Xiao-Xu Yan and Xin-Guang Lv

International Journal of Economics and Finance, 2017, vol. 9, issue 2, 133-141

Abstract: Based on the trend background of financial development in China in recent years, and statistical analysis of trend line, this paper establishes the quantitative trading strategy through the BP Neural Network Algorithm and the Fisher Linear Discriminant. Firstly, the data is linearly regressed into equal-length trend lines and the slope is fuzzified to build the matrix of upward trend and downward trend. And then use BP Neural Network Algorithm and Fisher Linear Discriminant to carry on the price forecast respectively and take transaction behavior, and correspondingly we take Shanghai and Shenzhen 300 stock index futures as an example to carry on the back test. The result shows that, firstly, the initial price trend is well retained by fitting; secondly, the profitability and risk control ability of the trading system are improved through the training optimization of Neural Network and Fisher Linear Discriminant.

Keywords: neural network algorithm; fisher linear discriminant; quantitative trading (search for similar items in EconPapers)
Date: 2017
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