Research on quantitative trading strategy based on prediction and dynamic programming model

Abstract

Today, the digital currency market represented by bitcoin has become an important part of the capital market. As the COVID-19 pandemic has increased volatility in capital markets in the past two years, effective trend forecasting and risk-averse investment strategies can bring substantial profits to companies and individuals and reduce the possibility of financial risks. To achieve the above objectives, we established the following models: (1)price prediction models of gold and bitcoin; (2)trading market risk judgment model; (3)revenue valuation model based on dynamic programming. This paper uses ARIMA and LSTM neural network to establish the prediction model. After checking the historical data, the best parameter fitting values of ARIMA model were obtained by using the exhaustive method according to BIC criterion, which were ARIMA (4,1,3) (gold) and ARIMA (2,1,2) (bitcoin) respectively. Then LSTM recurrent neural network is used to modify the forecast results, and the price forecast graph with high fitting degree with the real data is obtained. Then, this paper combines the technical indicators commonly used in the financial market to form a market risk judgment model. On the basis of the eight laws of buying and selling, the weight index is introduced to put forward the bull and bear market judgment rules of gold and bitcoin trading, and the tendency distribution map of the market environment in the past five years is obtained. At the same time, the buying risk coefficient is quantified by combining the maximum retraction rate. On the basis of the first two sub-models, this paper establishes a revenue valuation model based on the idea of dynamic programming. By setting the policy trigger threshold to control the risk, the problem that local optimal solution may appear in the model is improved.