Research on Quantitative Trading Investment Strategy Based on LSTM and Dynamic Programming

Abstract

As financial markets matured, more standardized and quantitative research emerged. In this paper, we model the historical price returns of U.S. gold and BTC over a five-year trading period from September 11, 2016, to September 10, 2021, and estimate the total investment returns as of September 10, 2021. First, we build a variety of price-prediction models to fit the image to the data to get the most accurate image of the actual price trend. It was found that the LSTM neural network-based multi-interval segmentation price prediction model, which has the best prediction effect by comparison with the actual value. Therefore, it was used as the core prediction model for this paper. Then, we use the planning model to find the optimal investment plan by using the difference between the price change of gold and bitcoin trading in the next three days as the decision variable, the highest Sharpe ratio on the third day as the objective function. At the same time, the variance of the forecast value in the next three days is used as the risk characterization, and different weights are assigned to the risk and return as the objective function to characterize the investment strategy under different investment personalities. In order to accelerate the convergence of the planning model, we added a particle swarm algorithm for optimization. In the end, we obtained the results for the specific daily investment scenarios for aggressive investors, intermediate investors, and steady investors.