A Mean Reversion Strategy Explained
As shown in the table and figure below, using optimal exit rule improves profitability and leads to fewer trades and thus lower trading costs. Click here a full analysis pdf on all the pairs mentioned above. The associated python package is available. Lee and T. Zhang, A. Aravkin, and T. Leung T. Li , Optimal mean reversion trading: mathematical analysis and practical applications , World Scientific Publishing Co. See our Reader Terms for details. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss.
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The ABCs of creating a mean reversion strategy – Part 1 – Alvarez Quant Trading
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Sign in Get started. Get started Open in app. Creator: Bandy, Howard B. Format: Books.
Mean Reversion Trading Systems, Practical Methods for Swing Trading
Physical Description: pages :illustrations ;26 cm. The series deals with the importance of timing in stock trades, and in particular with the tendency of prices to fall back to their historical norm despite fluctuations in the market.
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The focus of this book is systems that enter when price is overextended -- far from the recent mean -- anticipating that price will revert to the mean, with holding periods of a few days. Bandy's] analysis of trading frequency and holding period concludes, after research and mathematical modeling using the Amibroker program, that the sweet spot for holding period is one to two days, and it is desirable to trade frequently. Mean reversion trading strategies consist of signals that bet on extended prices eventually snapping back from overbought or oversold conditions and reverting back to the mean of historical pricing.
It is a trade that takes a position on a quantifiable technical signal that price has moved too far and too fast in one direction and the probabilities that it will return to an average are high.
Real examples, performance summary, and python package
One simple way to look for a reversion to the mean trade signal is by setting visual deviations around prices using technical indicators like Bollinger Bands or Keltner Channels that usually measure the distance from the day moving average in standard deviations of price. When a market drops dramatically or rises parabolically three or four deviations from the day moving average the odds are that it will revert back to that average in days or weeks the majority of time.
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Of course this does not always happen as markets rotate from ranges to trends, and can go farther than anyone thought possible so you must always use a stop loss for the times when a trend continues and does not revert. One strategy is to buy dips to the lower 3rd standard deviation Keltner Channel or sell rallies short to the upper 3rd standard deviation Keltner Channel and set a stop loss if price closes back outside your entry channel signal.
Your profit target could be back to the day moving average.