Optimal Trading Strategy Based on BP Neural Network and Dynamic Programming Model

Abstract

In order to avoid the risks brought by blind investment, it is necessary to forecast the trading price of gold and bitcoin on that trading day before investment. To aim at this prediction problem, this paper puts forward the price prediction model of the BP neural network. The experimental results show that the model can reasonably predict the trading price of gold and bitcoin on the next trading day, and the relative error between them is less than 10%.However, predicting the trading price of gold and bitcoin can roughly determine the future direction, and it cannot provide scientific and reasonable trading strategies for traders, so it is necessary to design a trading strategy planning model. Considering that every trading activity except holding financial products will charge additional trade costs, the trade costs of gold and bitcoin are 1% and 2% of the trade amount, respectively. By comparing various classic trading models, we propose to use a dynamic programming model to provide traders with effective trading strategies, which takes the trading changes of gold and bitcoin in the next three trading days as decision variables. Meanwhile, it takes the highest value of the Sharp ratio in the next trading day as the objective function. It takes the proportion of each part as equal to or greater than 0 as the constraint condition to establish a trading strategy planning model to obtain the maximum profit.