Optimization of Daily Trading Strategy

Abstract

This study proposes a novel method that integrates variational pattern decomposition (VMD), long short-term memory (LSTM) models, and particle swarm optimization (PSO) to optimize daily trading strategies for a portfolio. By analyzing historical price data for gold and bitcoin, we have successfully identified the optimal investment actions (buy, hold, or sell) for a portfolio comprising different assets. The VMD-LSTM model proposed by us mitigates the long-term dependence problem of machine learning algorithm RNN in financial time series prediction through batch normalization optimization, and realizes the accurate prediction of the future price of gold and bitcoin, whose R-squared values reach 0.99 and 0.98 respectively. We also applied the particle swarm optimization algorithm to a sequence of trading decisions generated by predicting prices, ultimately obtaining a maximum portfolio value of $20,560,265.5, which is more than 14.2 times the 10-year growth of Bitcoin and 12,310.5 times the 10-year growth of gold. By verifying the universality and local optimal properties of our model, we use the Sharpe ratio as an objective function to evaluate the effect of the portfolio. After several perturbation tests, the results show that the original scheme is superior to other perturbation schemes considering the Sharpe ratio and the final return, which verifies the validity of the investment scheme selected by us as the local optimal value. These findings make our research more coherent and provide investors with a reliable basis for making decisions to achieve long-term capital appreciation and risk control.