用进化算法优化多指标选股，收益稳且风险低。
NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance
- 用NEAT算法自动演化交易策略，融合多个技术指标
- 收益与持有策略相当，但波动更小、风险更低
- 适合对量化交易稳定性有要求的研究者
本研究将神经演化算法NEAT应用于股票交易，结合多个技术指标，目标是最大化收益、控制风险并超越买入持有策略。通过渐进式训练数据和多目标适应度函数引导种群演化。实验结果表明，该模型收益与买入持有策略相近，但风险暴露更低、表现更稳定。研究也发现模型存在大量未使用节点和连接的问题。未来可探索改进NEAT算法，并在更短周期数据上验证其性能潜力。
原文摘要 · Abstract (English)
In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to guide the evolution of the population towards these objectives. The results of our study showed that the NEAT model achieved similar returns to the Buy & Hold strategy, but with lower risk exposure and greater stability. We also identified some challenges in the training process, including the presence of a large number of unused nodes and connections in the model architecture. In future work, it may be worthwhile to explore ways to improve the NEAT algorithm and apply it to shorter interval data in order to assess the potential impact on performance.
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