Quantitative Trading Strategy Based on Simplified DPG
DOI:
https://doi.org/10.54097/hbem.v13i.8616Keywords:
Price prediction, Goal programming, Trading strategy, ARIMA.Abstract
In recent years, investment has become more and more popular, and asset management has also received more and more attention. At the same time, with the development of computer science, more and more machine learning or deep learning algorithms can be used for investment management, such as price forecasting, portfolio and quantitative trading strategies. First of all, in the trading market, most of the ups and downs are cyclical, but they are easily affected by factors such as policies and investment fever, which brings great challenges to the establishment of a reasonable price forecasting model. This paper builds a sliding price forecasting model that aims to reduce various noises such as frequent fluctuations. In order to get the best forecast results, we searched the parameters of each sliding forecast, trying to get the forecast closest to the actual price. Second, developing trading strategies based on forecast results is clearly related to goal planning. Therefore, this paper first establishes the objective programming problem, designs the objective function and constraints, and then optimizes the problem. At the same time, the article also considers investor styles, such as the investment boom in gold and bitcoin, and a more cautious approach to buying and selling. Ultimately, this paper designs a flexible feedback model based on style parameters to adjust the trading strategy, by searching for the best parameters of the final model, our trading strategy model converts assets from $1000 from September 11, 2016 to September 10, 2021 It increased to $238,869, and the cumulative return reached 23,886.9%.
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