Modeling and Analysis of Metrics for Quantitative Investment

Abstract

In recent years, the issue of quantitative investment has been hot in the investment field amidst the rapid development of quantitative finance. It is necessary for different investment objects to build corresponding portfolios and set investment strategies based on historical data pairs to get maximum returns. To help investors better solve the investment problem of gold and bitcoin portfolios. This paper constructs a BP neural network price prediction model, predicts the closing price on the last day of the transaction, and then uses the greedy algorithm to solve the optimal daily trading strategy to pursue the overall optimal solution and obtain the maximum profit. Combined with grey forecasting and time series forecasting methods, the forecasting models for gold and bitcoin prices are constructed again.