Algorithmic Trading: Winning Strategies and Their Rationale

CHAPTER 3

Implementing Mean Reversion Strategies

In the previous chapter, we described the statistical tests for determining whether a price series is stationary and therefore suitable for mean reversion trading. This price series may be the market value of a single asset, though it is rare that such stationary assets exist, or it may be the market value of a portfolio of cointegrating assets, such as the familiar long-short stock pair.

In practice, though, we should remember that we don't necessarily need true stationarity or cointegration in order to implement a successful mean reversion strategy: If we are clever, we can capture short-term or seasonal mean reversion, and liquidate our positions before the prices go to their next equilibrium level. (Seasonal mean reversion means that a price series will mean-revert only during specific periods of the day or under specific conditions.) Conversely, not all stationary series will lead to great profits—not if their half-life for mean reversion is 10 years long.

We also described a simple linear mean reversion strategy that simply “scales” into an asset in proportion to its price's deviation from the mean. It is not a very practical strategy due to the constant infinitesimal rebalancing and the demand of unlimited buying power. In this chapter, we discuss a more practical, but still simple, mean reversion strategy—the Bollinger bands. We describe variations of this technique, including the pros and cons of using multiple entry ...