英文标题：
《Learning the dynamics of technical trading strategies》
---
作者：
Nicholas Murphy and Tim Gebbie
---
最新提交年份：
2019
---
英文摘要：
  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.
---
中文摘要：
我们使用基于对抗性专家的在线学习算法来学习最大化财富交易零成本投资组合策略所需的最佳参数。该学习算法用于确定技术交易策略的相对总体动态，这些策略可以通过历史回溯测试，并从每日和当日约翰内斯堡证券交易所数据上实施的一组基础交易策略中形成一个整体聚合投资组合交易策略。使用无监督学习对生成的人口时间序列进行降维和可视化研究。一个关键贡献是，使用Jarrow et al.（2012）提出的新假设检验，在每日抽样和日内时间尺度上，对总体聚合交易策略进行统计套利测试。（低频）日抽样策略未通过成本后的套利测试，而（高频）日内抽样策略未被篡改为成本后的统计套利。考虑交易策略成功、交易成本和下滑的估计，以及用于性能比较的在线基准投资组合算法。此外，通过使用Bailey et al.（2016）介绍的非参数程序恢复回测过度拟合估计的概率，分析了算法的广义误差。这项工作旨在从数据知情的角度探索和更好地理解不同技术交易策略之间的相互作用。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--
一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
--
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
--

---
PDF下载：
-->