  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.
