Research on the Optimization of Investment Strategy Based on Genetic Algorithm Using Python Programming

To address the issue of optimizing investment strategies for systematic investment plans (SIPs), a novel approach is proposed where the investment satisfaction of a variable SIP (the probability that the return rate exceeds that of a fixed SIP) is used as the fitness function in a genetic algorithm....

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Bibliographic Details
Published in:2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT) pp. 1 - 8
Main Author: Wang, Jiawei
Format: Conference Proceeding
Language:English
Published: IEEE 20.09.2024
Subjects:
Codes
genetic algorithm
Genetic algorithms
Indexes
Information technology
Investment
investment strategy
Optimization
Programming
Python
systematic investment plan
Systematics
Timing
variable SIP
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Summary:To address the issue of optimizing investment strategies for systematic investment plans (SIPs), a novel approach is proposed where the investment satisfaction of a variable SIP (the probability that the return rate exceeds that of a fixed SIP) is used as the fitness function in a genetic algorithm. This study analyzes the algorithmic results using a Python program with the Shanghai Composite Index (SH000001) as the research subject. The analysis of results for both fixed and variable SIPs indicates that the variable SIP investment strategy, derived from the genetic algorithm based on Python, can significantly improve investment satisfaction, providing substantial reference value for formulating investment strategies.
DOI:10.1109/AICIT62434.2024.10730019