用于自适应交易与资产配置的状态切换模型

文章 arXiv papers · 作者: Sonam Srivastava et al.

总结

本文概述了一种系统方法，以便在市场环境变化时调整交易策略和资产配置。文章提出使用状态切换马尔可夫自回归模型，识别并预测与威科夫吸筹、派发、上涨和下跌阶段大致对应的市场状态。随后，研究考察不同资产类别和市场板块在所识别状态下的表现。

策略框架将趋势跟踪、区间交易、回撤交易和突破交易分别匹配到不同状态，再将选定的交易方式与状态特定的资产配置结合起来。研究旨在构建动态自适应系统，并将其与传统阿尔法策略比较。摘录介绍了该框架，但没有提供数据、表现数字或详细实现选择，因此无法证明所提系统优于更简单的方案。文中将状态标签描述为宽泛的类比，也未说明如何验证状态，或如何处理交易成本和不断变化的市场关系。

核心观点

市场均值、方差和相关性可能随经济及政策环境变化。
研究提出使用状态切换马尔可夫自回归模型识别并预测市场状态。
该框架将市场状态与吸筹、派发、上涨和下跌阶段相联系。
趋势、区间、回撤和突破策略可根据识别出的状态进行调整。
框架将状态特定的资产配置与交易策略结合，但摘录未提供表现证据。

# Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy


# Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy









Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock markets. Similarly, an investor might want to invest in different asset classes in different market regimes for a stable risk adjusted return profile. Here, we explore the use of State Switching Markov Autoregressive models for identifying and predicting different market regimes loosely modeled on the Wyckoff Price Regimes of accumulation, distribution, advance and decline. We explore the behaviour of various asset classes and market sectors in the identified regimes. We look at the trading strategies like trend following, range trading, retracement trading and breakout trading in the given market regimes and tailor them for the specific regimes. We tie together the best trading strategy and asset allocation for the identified market regimes to come up with a robust dynamically adaptive trading system to outperform simple traditional alphas.

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。