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从合成订单流生成日内价格路径

文章 arXiv papers · 作者: Ye-Sheen Lim et al.
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总结

本文将高频订单流建模为日内价格变化的来源，并使用序列生成对抗网络生成合成订单序列。研究利用这些序列模拟价格路径，再将生成的行为与真实市场数据进行比较。研究以量化金融中的参数模型作为基准，并在抽样随机订单流路径前分别拟合两个模型。

评估重点是模拟价格能否再现真实价格变动的实证特征，包括对数收益分布、波动率和厚尾。作者报告称，与基准模型相比，生成模型产生的订单序列所对应的价格变化更符合观察到的统计行为。摘录未指出市场、样本时期、模型细节或改进幅度。其证据涉及统计模拟的逼真度，因此不能证明合成路径支持盈利交易或涵盖所有相关市场特征。

核心观点

  • 该方法使用序列 GAN 生成合成高频订单流。
  • 生成的订单序列被转换为模拟的日内价格变化。
  • 以量化金融参数模型作为基准。
  • 评估将收益分布、波动率和厚尾特征与真实数据进行比较。
  • 所报告的改进涉及统计相似度，而非交易盈利能力。

标签

机器学习市场微观结构高频交易统计学
全文
# Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow


# Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow









Intra-day price variations in financial markets are driven by the sequence of orders, called the order flow, that is submitted at high frequency by traders. This paper introduces a novel application of the Sequence Generative Adversarial Networks framework to model the order flow, such that random sequences of the order flow can then be generated to simulate the intra-day variation of prices. As a benchmark, a well-known parametric model from the quantitative finance literature is selected. The models are fitted, and then multiple random paths of the order flow sequences are sampled from each model. Model performances are then evaluated by using the generated sequences to simulate price variations, and we compare the empirical regularities between the price variations produced by the generated and real sequences. The empirical regularities considered include the distribution of the price log-returns, the price volatility, and the heavy-tail of the log-returns distributions. The results show that the order sequences from the generative model are better able to reproduce the statistical behaviour of real price variations than the sequences from the benchmark.

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。