英文标题：
《Generative Adversarial Networks for Financial Trading Strategies
  Fine-Tuning and Combination》
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作者：
Adriano Koshiyama and Nick Firoozye and Philip Treleaven
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最新提交年份：
2019
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英文摘要：
  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.
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中文摘要：
系统交易策略是分配资产的算法程序，旨在优化特定的性能标准。为了在高度竞争的环境中获得优势，分析师需要适当微调其策略，或者发现如何以新颖的阿尔法创造方式组合微弱信号。这两个方面，即微调和组合，已使用多种方法进行了广泛研究，但新兴技术，如生成性对抗网络，可能会对这两个方面产生影响。因此，我们的工作建议使用条件生成对抗网络（CGAN）进行交易策略校准和聚合。为此，我们提供了一个完整的方法论：（i）时间序列数据cGAN的培训和选择；（ii）每个样本如何用于策略校准；以及（iii）如何将所有生成的样本用于集合建模。为了证明我们的方法有很好的基础，我们设计了一个包含579项资产的多种交易策略的实验。我们将cGAN与集成方案和模型验证方法进行了比较，两者都适用于时间序列。我们的研究结果表明，CGAN是一种合适的策略校准和组合的替代方案，在传统技术无法生成任何阿尔法的情况下，CGAN具有优异的性能。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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