In this paper, high-frequency futures trading strategy has been built by LASSO variable selection method and the genetic network programming (GNP). The proposed strategy uses LASSO as a variable selection method, which is able to select the most effective variables from a large number of technical indicators. Then, the selected indicators will be treated as the judgment functions in GNP to determine the buying and selling points. The 5 minutes high-frequency futures trading data of gold, aluminum and rubber is also used as an example for backtesting. The results show that: First, when compared with the optimal subset method, LASSO method selects the least number of indicators, while it shows almost the same prediction accuracy and better robustness. These indicators are mainly applied to describe the trend and shock. Second, GNP gets higher search efficiency and builds a simple and effective trading strategy after combining with Q reinforcement learning method. The proposed method outperforms the "Buy & Hold" strategy in different futures contracts and finally obtains the excess returns. It shows its practical value in the field of quantitative trading.
CHEN Yan, WANG Xuan-cheng
. A Study on High-Frequency Futures Trading Strategy Based on Variable Selection and Genetic Network Programming[J]. Chinese Journal of Management Science, 2015
, 23(10)
: 47
-56
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.10.006
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