Optimizing MACD Trading Strategies A Dance of Finance, Wavelets, and Genetics

TL;DR

This paper enhances MACD trading strategies by integrating wavelet noise reduction, divergence principles, and genetic algorithms, resulting in improved performance metrics and robustness in volatile markets.

Contribution

It introduces a novel combination of wavelet transforms, divergence principles, and genetic algorithms to optimize MACD-based trading strategies, with efficient implementation using MindSpore.

Findings

Annualized return increased by 5%

Improved win rates and Sharpe ratios

Reduced maximum drawdown in backtests

Abstract

In today's financial markets, quantitative trading has become an essential trading method, with the MACD indicator widely employed in quantitative trading strategies. This paper begins by screening and cleaning the dataset, establishing a model that adheres to the basic buy and sell rules of the MACD, and calculating key metrics such as the win rate, return, Sharpe ratio, and maximum drawdown for each stock. However, the MACD often generates erroneous signals in highly volatile markets. To address this, wavelet transform is applied to reduce noise, smoothing the DIF image, and a model is developed based on this to optimize the identification of buy and sell points. The results show that the annualized return has increased by 5%, verifying the feasibility of the method. Subsequently, the divergence principle is used to further optimize the trading strategy, enhancing the model's performance. Additionally, a genetic algorithm is employed to optimize the MACD parameters, tailoring the strategy to the characteristics of different stocks. To improve computational efficiency, the MindSpore framework is used for resource management and parallel computing. The optimized strategy demonstrates improved win rates, returns, Sharpe ratios, and a reduction in maximum drawdown in backtesting.