Trade filtering method for trend following strategy based on LSTM-extracted feature and machine learning

Abstract

Trend following strategies have a wide-ranging role in quantitative trading fields, which can capture important unilateral market trends for large gains, while this is vulnerable to losses in the period of consolidation. In this paper, we explored the trend trading system in the Chinese futures market based on machine learning techniques and statistical methods. This research utilized the Long-Short-Term Memory network to extract features of time series then predicted the price movements by Machine Learning classifiers. Moreover, based on rebar futures data, the results reveal that the annualized return improved from 6.39% to 15.68% after the trading signals generated in the trading strategy were filtered using the XGBoost model. Also, futures on gold and soybean were used to further test the integrated strategy and the results of the experiment show the effectiveness of the model in filtering false trading signals.