Our discussion today rests on a research paper that explores using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm to create a stock-trading strategy. The researchers employed multiple technical indicators as inputs to a NEAT-evolved neural network, aiming to surpass a buy-and-hold strategy in terms of profit, risk management, and stability. Multiple fitness functions were tested, ultimately leading to a model that achieved comparable returns to buy-and-hold but with reduced risk. The study also noted challenges, such as unused nodes and connections within the evolved networks, suggesting areas for future improvements and exploration using higher-frequency data.

The research paper can be found here: https://arxiv.org/pdf/2501.14736

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