Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

21 Pages Posted: 20 Mar 2018 Last revised: 23 Feb 2020

Date Written: March 20, 2018


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.

Keywords: Markov Switching Auto Regression, Regime Switching Model, Systematic Strategy, Technical Analysis, Alpha Generation, Markov Model, Asset Allocation

JEL Classification: G11, G12

Suggested Citation

Srivastava, Sonam and Bhattacharyya, Ritabrata, Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy (March 20, 2018). Available at SSRN: https://ssrn.com/abstract=3144169 or http://dx.doi.org/10.2139/ssrn.3144169

Sonam Srivastava (Contact Author)

Wright Research ( email )

Mumbai, 400098

Ritabrata Bhattacharyya

WorldQuant University ( email )

Place St Charles
201 St Charles Ave #2500
New Orleans, LA 70170
United States

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