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Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

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  • Sonam Srivastava
  • Ritabratta Bhattacharya

Abstract

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.

Suggested Citation

  • Sonam Srivastava & Ritabratta Bhattacharya, 2018. "Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy," Papers 1812.02527, arXiv.org.
    • Handle: RePEc:arx:papers:1812.02527
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    References listed on IDEAS

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    1. Don U. A. Galagedera & Robert Faff, 2005. "Modeling The Risk And Return Relation Conditional On Market Volatility And Market Conditions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 75-95.
    2. Wasim, Ahmad & Bandi, Kamaiah, 2011. "Identifying regime shifts in Indian stock market: A Markov switching approach," MPRA Paper 37174, University Library of Munich, Germany, revised 08 Mar 2012.
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    Cited by:

    1. Selim Amrouni & Aymeric Moulin & Tucker Balch, 2022. "CTMSTOU driven markets: simulated environment for regime-awareness in trading policies," Papers 2202.00941, arXiv.org, revised Feb 2022.

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