标题： 基于NEAT算法的多技术指标共振股票交易策略 显示英文标题

标题： NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance

摘要： 在本研究中，我们将NEAT（增强拓扑的神经进化）算法应用于使用多个技术指标的股票交易。 我们的方法专注于最大化收益，避免风险，并超越买入并持有策略。 我们使用了渐进式训练数据和多目标适应度函数，以引导种群向这些目标进化。 我们研究的结果显示，NEAT模型实现了与买入并持有策略相似的回报，但风险暴露更低且更稳定。 我们还发现了一些训练过程中的挑战，包括模型架构中存在大量未使用的节点和连接。 在未来的工作中，可能值得探索改进NEAT算法的方法，并将其应用于更短时间间隔的数据，以评估对性能的潜在影响。 显示英文摘要

摘要： 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.