Mean reversion refers to the fact that, statistically, the price of an asset should tend back toward the historical average price. Extreme deviations from this price imply overbought or oversold conditions and the likelihood of a reversal.
Even for something like Bitcoin, which has really only ever been in a bear market, there can be notable highs or lows that stray from the trajectory the price has historically followed. More often than not, markets will trend back toward this mean price before long. By watching the long-term averages, algorithms can safely bet that massive deviations from these prices are likely not to last for long and set trade orders accordingly.
For example, one specific form of this is called standard deviation reversion, and it is measured by an indicator called Bollinger Bands. Basically, these bands act as upward and downward limits on deviations from a central moving average. When the price action moves toward one of these extremes, odds are high that a reversal toward the center is coming soon.
Of course, one of the biggest risks here is that the algorithm can’t account for changes in fundamentals. If a market is crashing due to some flaw in the underlying asset, then it is possible the price will actually never recover — or at least not swiftly. This is, again, where traders need to monitor and account for certain conditions that their algorithms cannot see.
Another form of mean reversion can occur across multiple assets, and utilizing this technique is called pairs trading. Let’s say, two assets are traditionally correlated. That is, when one goes up or down, then statistically, so does the other. An algorithm can be crafted to watch for one of these assets to make a move, then place a trade based on the likelihood that the other commodity will soon follow. The timeframes for these discrepancies can sometimes be rather short, making the automated nature of this strategy far more valuable.