Research on Trend Following Strategy and CTA Funds Based on Chinese Market

Introduction

A Commodity trading advisor (CTA) is US financial regulatory term for an individual or organization who is retained by a fund or individual client to provide advice and services related to trading in futures contracts, commodity options or swaps.CTA managed futures have always used as hedging asset because of their low correlation with stocks and bond markets and played an important role in the allocation of large-scale assets. However, the domestic market CTA strategy net value disclosure data is different, the development time of China sunshine private equity is short, and there is no suitable and public CTA strategy market benchmark. Hence, our team has done the following work:

• We study of the relationship between the simple trend following strategy —double average strategy and the CTA funds based on the Chinese market, found that they are highly relevant.
• We study the CTA funds through the double-average strategy and then we empirically study the relationships between the strategy revenue and the close price, between strategy returns and market volatility.
• In order to figure out the reason why the trend-following strategy can make profit, we should fully understand the mechanism behind the trend. A lifecycle of a trend can be divided into three parts: initial under-reaction, delayed over-reaction and final reversal. In the project, we discuss the causes of this three stages based on the behavior of investors and analysts.
• Finally, we make some useful conclusions for CTA fund managers from the above research.

Process

• Backtesting date and double moving average period

Result

• Double moving average strategy return & CTA return curve
• Close position & hold position return curve
• Double moving average strategy return v.s stock index return
• Double moving average strategy return v.s stock index volatility
• Lifecycle of trend

Contribution

Contributors

• Yun Zhang
• Xuerong Yuan
• Ziyi Chen

Institutions

• AI&FintechLab of Likelihood Technology
• Sun Yat-sen University

Acknowledegment

First of all, we would like to express our sincere gratitude to Mingwen Liu from Shining Midas Investment Management, for the excellent communication platform and instructive advice he provided. And we are deeply grateful the Likelihood Lab for providing abundant study resources throughout the research. Special thanks should go to Xingyu Fu from Sun Yat-sen University, whose patient guidance and valuable suggestions help in the completion of this thesis. With their help, our research has been completed successfully.

Set up

Python Version

• 3.6

Modules Needed

• numpy
• pandas
• matplotlib
• seaborn
• mpl_finance
• statsmodels
• ...

Contact

1. zy_serafina@hotmail.com
2. yuanxr3@mail2.sysu.edu.cn
3. chenzy58@mail2.sysu.edu.cn