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基于签名特征的路径依赖统计套利执行

文章 arXiv papers · 作者: Gianmarco Morbelli et al.
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总结

本文为预测信号依赖市场数据路径的统计套利提出最优执行框架。研究将阿尔法信号和交易速度都表示为时间扩展市场路径的截断签名特征的线性函数，使执行能够响应已实现的信号历史。该框架考虑临时市场冲击、持仓、最终平仓和近似美元中性。

二次约化结果将受约束的签名特征策略转化为针对策略系数的有限维凹二次优化。在使用均值回归对数价差模型的合成测试中，拟合策略的换手收益率高于传统的Z分数阈值基准。历史股票配对交易回测也报告了更好的会计绩效。所提供的描述未给出实施细节，也未证明结果能推广到这些设定以外的情况。

核心观点

  • 签名特征为路径依赖阿尔法和执行速度提供了统一表示。
  • 执行目标包含市场冲击、持仓敞口、平仓和近似美元中性。
  • 受限策略优化可约化为有限维凹二次规划。
  • 合成测试和历史配对交易实验报告称，结果优于Z分数阈值基准。

标签

股票配对交易交易执行
全文
# Signature-Based Optimal Execution for Statistical Arbitrage with Path-Dependent Trading Signals


# Signature-Based Optimal Execution for Statistical Arbitrage with Path-Dependent Trading Signals









We develop a signature-based framework for optimal execution in statistical arbitrage strategies with path-dependent predictive signals. Both the alpha process and the trading speed are modelled as linear functionals of the truncated signature of a time-augmented market path, placing signal generation and execution on the same truncated signature basis. This allows the trading rule to react to the realised history of the signal while accounting for temporary impact, inventory exposure, terminal liquidation, and approximate dollar neutrality. The main contribution is a quadratic reduction theorem: within the class of signature-linear trading speeds, the restricted path-dependent execution problem becomes a finite-dimensional concave quadratic programme in the policy coefficients. After running synthetic experiments under a mean-reverting log-spread model, we find that the fitted policy achieves a higher return on turnover than a classical $z$-score threshold benchmark. We show how the same workflow can be deployed on a historical equity pairs-trading backtest, where the fitted signature policy again outperforms the benchmark in accounting terms.

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。