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利用条件生成对抗网络调整和组合交易策略

文章 arXiv papers · 作者: Adriano Koshiyama et al.
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总结

本文提出条件生成对抗网络，用于校准单个交易策略并组合其信号。文章介绍了针对时间序列数据训练和选择条件生成对抗网络的工作流程，利用生成样本调整策略，并在集成建模中使用样本集合。目标是改进策略设置，或将较弱的信号汇总为投资组合层面的方法。

作者在579项资产上使用多种交易策略评估该方法，并将其与集成方法和时间序列验证方法进行比较。他们报告称，条件生成对抗网络可以作为合适的替代方案，并且在传统技术无法产生阿尔法时表现更好。摘要未说明资产、评估时期、交易成本或稳健性检验，因此可用于判断泛化能力或实现方式的细节有限。其证据来自实验，应结合所测试的设定来解读。

核心观点

  • 研究提出使用条件生成对抗网络校准策略并汇总信号。
  • 工作流程涵盖针对时间序列数据训练和选择条件生成对抗网络。
  • 生成样本既用于调整策略，也用于构建集成模型。
  • 实验涵盖多种策略和579项资产，并进行时间序列比较。
  • 所报告结果表明，传统方法未能找到阿尔法时，该方法可能具有优势。

标签

机器学习回测投资组合构建股票
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# Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination


# Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination









Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。