Mean Reversion Strategies: Backtested
Today we are presenting 3 mean reversion strategies. They were partially published on this website a few years back, and today we make some minor adjustments and present their performance on different ETFs. Furthermore, we discuss why mean reversion works and which markets are best for mean reversion trading strategies, and also some historical backtesting.
Mean reversion is originally a statistical term, first mentioned by Francis Galton, the hero of the famous trader Victor Niederhoffer, but today mean reversion is mainly a financial term. In financial theory, mean reversion is a fundamental concept, with financial theory suggesting that asset prices fluctuate around their historical mean and that historical returns eventually revert to a mean price over time.
We start with defining what mean reversion is: The first step in mean reversion trading is identifying the historical average or mean price of an asset. Mean reversion in trading and investment strategies relies on detecting price deviations from this mean, as these deviations can signal potential trading opportunities such as overbought or oversold conditions.
What is mean reversion trading?
Mean reversion means the same in trading as in statistics: over time, the price of the asset gravitates over and under a moving average. Abnormal rises tend to revert down and vice versa. Trading mean reversion involves identifying price deviations from the historical average and recognizing overbought or oversold conditions, which can signal potential reversals.
A good example of mean reversion can be found in this chart:
Mean reversion trading strategy
The ticker code is SPY, and the blue line is simply a trendline drawn randomly from the bottom to the upper right corner. But it illustrates mean reversion:
The price of SPY moves in waves as it creeps upwards. It never goes too long in either direction before it “reverts”.
Traders use various indicators such as Bollinger Bands and moving averages to identify potential mean reversion opportunities. Moving averages help traders identify price deviations from typical levels and act as benchmarks for price trends. Bollinger Bands provide a statistical framework for identifying price extremes, consisting of a middle band (SMA) and two outer bands (standard deviations).
Even during the bear market of 2008/09, the market had sharp reversals, even though the trend was down:
Mean reversion strategies
Put simply, the market never goes in one direction without breaks. It’s like ebb and flow.
Perhaps quite remarkably, most mean reversion strategies perform the best during a bear market; see more about that below.
5 swing trading strategies video
In the video below, we present 5 swing strategies that are not included in the article. Complete with backtesting as well. The footage includes trading rules, equity curves, statistics, and metrics (we have plenty of videos on our YouTube Channel):
Many trading strategies can be labeled mean reversion. The most popular are these:
- Overbought and oversold indicators (Relative Strength Index (RSI), moving average convergence divergence (MACD), average convergence divergence, average convergence divergence MACD, Stochastics, Bollinger Bands, etc.). The Relative Strength Index (RSI) measures whether an asset is overbought or oversold, providing buy and sell signals. MACD and average convergence divergence indicators help identify mean reversion points and trend reversals.
- Reversals
- Pullbacks
- Statistical arbitrage: Used in quantitative trading to exploit temporary mispricings between correlated or cointegrated assets.
Identifying trading signals is a key step in implementing reversion strategies effectively.
Mean reversion strategies can be implemented using various indicators such as moving averages, Bollinger Bands, and the Relative Strength Index (RSI). RSI readings above 70 indicate overbought conditions, while readings below 30 indicate oversold conditions.
You can find examples of all these strategies on our landing page which contains all our free trading strategies (Backtested)
The opposite of mean reversion is trend-following:
Mean reversion vs. trend following
Mean reversion is the opposite of trend following: Mean reversion normally has a high win ratio with many small winners and occasionally a big loser. Trend following shows the opposite statistics: many losers and more reliant on the rare big winners and outliers. Momentum trading is a trend-following approach that involves trading in the direction of strong market trends, often using indicators like moving averages to identify and capitalize on assets moving with substantial force.
This means the trade distribution is different. Mean reversion has more left-tail losers, while trend following has more right-tail winners. Mean reversion strategies achieve win rates of 60–80% but often yield smaller average profits compared to trend-following systems:
Why Does mean reversion work? Which markets are most mean revertive?
Mean reversion strategies work best in stocks and are less helpful in other markets, such as commodities, for example. These strategies are most effective in stable, range-bound market conditions, while in trending markets, mean reversion strategies often struggle and can lead to potential losses.
Why is that?
Mean reversion in the stock market didn’t work well before the 1990s. We believe one of the reasons is the rise in futures trading, which leads to arbitrage between stocks and the futures contract.
Another reason for mean reversion is the constant battle between buyers and sellers. When a stock goes up in value, many are tempted to sell to realize some gains, while others might want to short. This creates selling pressure.
Opposite, when a stock falls in value, at some point, there are more buyers than sellers, and the price decline stops and reverses. Additionally, those who are short might want to lock in profits and thus buy back their shares. The latter might be the reason for many of the very powerful bear market rallies.
Volatility assessment is crucial for optimizing performance in mean reversion trading, and effective risk management techniques—such as diversification, stop-loss orders, and position sizing—are essential risk management practices to control potential losses. The Kalman filter provides a real-time method for tracking mean reversion, dynamically updating estimates as market conditions change. Additionally, the Augmented Dickey-Fuller (ADF) test is a statistical test used to confirm that a market demonstrates mean-reverting behavior. Incorporating these risk management strategies and tools helps traders adapt to changing market conditions and improve trading outcomes.
We provided some interesting data on the bear market of 2000/02 in a previous article:
Mean reversion work best in a bear market
Contrary to what many believe, our experience is that mean reversion in the stock market works best during a bear market. It might sound counterintuitive, but the reason is increased volatility. And perhaps even more counterintuitive is that long works better than short!
Back in 2008/09, we were day trading, and we made the most money on the long side even though the market fell over 50%.
The velocity of a bear market creates opportunities. The bleak future gets discounted fast, while the opposite, a bull market, rises slowly over time. The stock market spends significantly more time above the 200-day moving average than below.
Perhaps more importantly, bear-market rallies are very explosive. Traders capitalize on deviations by buying oversold assets or undervalued assets and selling overvalued assets, especially after sharp price rises, expecting a return to normal levels. Setting a predefined exit point is crucial to manage risk if the market moves against your expectations. We have published some data from the GFC in other articles, but we repeat them:
From May 2008 until early March 2009, the S&P 500 lost about 50% of its value. Still, look at these numbers:
There were 99 up days and 104 down days during that period.
The average up day was 1.79%. The average down day was minus 2.32%.
From May 2008 to early March 2009, it was 51 days with a rise >1%, 30 days with a rise >2%, 76 days with losses >1%, and 45 days with losses >2%.
The market fell, but still we had many explosive days on the upside. This makes shorting difficult, but it also makes buying weakness and selling strength very profitable. Most of our monthly Trading Edges make the most money in bear markets!
Short-term trading needs prey and this comes in the form of volatility. The graph below shows the 25-day moving average of the absolute values in the daily changes from close to close:
The GFC in 2008/09 and the euro sovereign crisis in 2011 stick out.
3 free mean reversion strategies
Let’s present our free mean reversion strategies. A mean reversion trading system uses various tools and indicators—such as moving averages, MACD, PPO, and regression lines—to help traders implement reversion strategies effectively. As mentioned at the beginning of the article, all these strategies were published earlier, but today we made some minor changes to them.
Pairs Trading: A Classic Mean Reversion Approach
Pairs trading stands out as one of the most established mean reversion strategies in quantitative trading. This approach involves selecting two correlated assets—such as stocks from the same sector or ETFs tracking similar indices—whose prices tend to move in the same direction over time. The core idea is that, while these asset prices generally follow a similar path, short-term divergences from their historical average relationship can and do occur.
When the price spread between the two correlated assets widens beyond its typical range, pairs traders anticipate that this deviation is temporary. They will typically buy the undervalued asset and simultaneously sell the overvalued one, betting that the prices will revert to their historical mean. This process allows traders to profit from the convergence of asset prices, regardless of the overall market direction.
Pairs trading is particularly effective in range bound markets, where prices tend to oscillate around a mean rather than trend strongly in one direction. By focusing on the relative performance of two assets rather than their absolute price movements, this strategy can help manage risk and reduce exposure to broader market swings. The success of pairs trading relies on careful selection of highly correlated assets and robust statistical analysis to identify significant deviations from the historical average, making it a favorite among reversion traders and those seeking to exploit market inefficiencies through quantitative trading strategies.
Mean reversion strategy number 1: Deviation from a recent high in XLP
This strategy is in the ETF with the ticker code XLP (consumer staples). The strategy was published in 2013, and in plain English, it is like this:
Mean reversion trading strategies can involve selling multiple assets within a session or focusing on one asset at a time, depending on the approach. When evaluating the profitability of such strategies, it is important to consider transaction costs, as the potential profit from trade reversion should outweigh these costs.
Trading Rules
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100 000 invested at inception and compounded until today has produced decent returns (from the year 2000 until today):
3 best mean reversion strategies
- No. of trades: 514
- Average gain per trade: 0.32%
- CAGR: 6.7% (buy and hold 4.8%)
- Time spent in the market: 33%
- Max drawdown: 20%
- Profit factor: 1.5
Mean reversion strategy number 2: An IBS short strategy in FXI
FXI is the ticker code for the Chinese ETF. It has shown strong mean reversion tendencies over the last decade, and the below strategy has worked pretty well. Mean reversion trading strategies are also widely used in forex trading, where traders often apply similar techniques to currency pairs such as EUR/USD. In these cases, strategies may involve related assets, and pairs trading relies on the statistical relationship between correlated assets to identify mean reversion opportunities.
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100 000 shorted in 2010 and reinvested until today has produced this equity curve from its inception until today:
Best mean reversion strategy
- No. of trades: 450
- Average gain per trade: 0.67%
- CAGR: 14.8% (buy and hold 1.8%)
- Time spent in the market: 36%
- Max drawdown: 47%
- Profit factor: 1.75
Mean reversion strategy number 3: 5-day low in the S&P 500
Strategy number three is a strategy that is made for the S&P 500.
This strategy was published in 2012 and in plain English, can be described like this:
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In mean reversion trading strategies, the mean price—often calculated using a simple or exponential moving average—serves as a key reference point for identifying the average price of the S&P 500 over a specific period. Traders look for price deviations, where the current price moves significantly away from the mean price, as these deviations can signal potential trading opportunities for a reversion back to the mean. Effective mean reversion strategies often use statistical envelopes, such as Bollinger Bands, to visualize how far the price has stretched from the moving average. Additionally, the Z-score is a statistically rigorous approach used to identify significant deviations from the mean, calculated as Z-score = (Current price – Mean) / Standard deviation.
It can hardly get any simpler than this!
How has this strategy performed? 100 000 compounded from 1993 until today has produced this equity curve (from the year 2000 until today):
Best mean reversion strategies
- No. of trades: 393
- Average gain per trade: 0.45%
- CAGR: 7.1% (buy and hold 5.9%) (Reinvested dividends not included)
- Time spent in the market: 21%
- Max drawdown: 23%
- Profit factor: 1.6
The strategy might also work on future contracts (of course), but beware of the drawdown and leverage.
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If you want the Amibroker or Tradestation (Easy Language) code for the 3 mean reversion strategies, plus all the other testable strategies among our free trading strategies, you can order it at the link below:
3 free mean reversion trading strategies
As you have learned in this article, trading doesn’t need to be complicated to get good results. These three mean reverting trading strategies (Backtested) we presented in this article will give you a head start.
FAQ:
Why is mean reversion considered a valuable trading strategy in the financial markets?
Mean reversion is valuable as it capitalizes on the natural ebb and flow of market prices. The strategy assumes that abnormal rises or falls will eventually revert to the average. In essence, it leverages the principle that markets rarely move in one direction without corrections. Mean reversion is a widely used approach in investment strategies and is valuable for both beginners and the experienced trader, making it relevant across different levels of trading expertise.
Which markets are best suited for mean reversion trading strategies, and why?
Mean reversion strategies tend to work best in stocks and may be less effective in other markets like commodities. This is attributed to factors such as increased volatility in stocks, the influence of futures trading, and the constant battle between buyers and sellers in the stock market. Mean reversion strategies work best in stable, range-bound markets where prices oscillate around their historical averages.
How do mean reversion strategies perform during a bull market compared to a bear market?
While mean reversion strategies can be effective in both bull and bear markets, historical data suggests that they perform exceptionally well during bear markets. The velocity of a bear market often leads to more explosive trading days, making it conducive for short-term mean reversion strategies.
