A Multi-Objective Genetic Algorithm for Financial Time Series Reversal Mode Mining

This paper studies the mining of financial time series flipping mode based on multi-objective genetic algorithm. First of all, on the basis of data preprocessing and feature extraction, various financial data, such as stock prices and trading volumes, are prepared. Secondly, a multi-objective genetic algorithm was designed and implemented, and each parameter was adjusted to optimize the performance of the algorithm. In the experimental phase, different historical financial data is selected for testing. In addition, the convergence of the algorithm and its performance are analyzed, and the results are compared with other methods. The results show that the optimized multi-objective genetic algorithm can well identify the reversal pattern, improve the rate of return and reduce the risk, so as to provide strong support for the determination of financial decision-making. The multi-objective genetic algorithm has shown good optimization effect on different financial datasets, such as stock price data, trading volume data, and comprehensive financial data.