英文标题：
《Evaluating the Building Blocks of a Dynamically Adaptive Systematic
  Trading Strategy》
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作者：
Sonam Srivastava, Ritabratta Bhattacharya
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最新提交年份：
2018
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英文摘要：
  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.
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中文摘要：
金融市场突然改变了他们的行为。由于宏观经济变量、政策或法规的根本变化，股票的均值、方差和相关性模式可能会发生巨大变化。交易员需要调整自己的交易风格，以充分利用股市的不同阶段。同样，投资者可能希望在不同的市场制度下投资不同的资产类别，以获得稳定的风险调整后回报。在这里，我们探讨了使用状态切换马尔可夫自回归模型来识别和预测不同的市场机制，这些市场机制松散地模拟了积累、分配、上涨和下跌的Wyckoff价格机制。我们探讨了已确定制度中各种资产类别和市场部门的行为。我们研究给定市场机制中的交易策略，如趋势跟踪、区间交易、回溯交易和突破交易，并根据具体机制进行调整。我们将确定的市场制度的最佳交易策略和资产配置结合在一起，形成一个强大的动态自适应交易系统，以超越简单的传统Alpha。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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