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技术交易策略的在线学习与统计检验

文章 arXiv papers · 作者: Nicholas Murphy et al.
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总结

该研究使用基于专家的对抗式在线学习，为零成本投资组合策略选择参数，并将技术交易策略组合成综合投资组合。研究考察约翰内斯堡证券交易所的日线和日内策略集合，再使用无监督学习缩减并可视化其构成变化。分析还纳入交易成本和滑点，将表现与在线基准投资组合算法进行比较，并估计回测过拟合风险。

研究使用假设检验评估综合策略的统计套利属性，不同采样频率的结果有所不同：日线策略在扣除成本后未通过检验，而日内策略在扣除成本后未被证伪为统计套利。这一区别是基于研究所用历史数据集和程序得到的证据，并非对未来盈利能力的证明。报告结论取决于所选策略、市场数据、成本和滑点估计以及统计检验；摘录未提供详细的数值表现结果。

核心观点

  • 在线学习算法会选择参数，并将技术交易策略组合成投资组合。
  • 研究考察约翰内斯堡证券交易所的日线和日内数据。
  • 扣除成本后，日线综合策略未通过统计套利检验，而日内策略未被证伪。
  • 分析纳入滑点、交易成本、基准算法和回测过拟合。
  • 历史统计结果不能证明这些策略未来仍会盈利。

标签

技术指标机器学习回测统计学交易执行
全文
# Learning the dynamics of technical trading strategies


# Learning the dynamics of technical trading strategies









We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. (2012) on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an online benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. (2016). The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.

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。