Mean Reversion Trading Strategies

When prices deviate significantly from the mean, these traders anticipate a reversion to the mean, potentially earning profits as prices adjust. Reliable indicators like Stochastics, RSI, and Bollinger bands use review the kelly capital growth investment criterion mean reversion to identify overbought and oversold conditions. Each day our team does live streaming where we focus on real-time group mentoring, coaching, and stock training. We teach day trading stocks, options or futures, as well as swing trading. Our live streams are a great way to learn in a real-world environment, without the pressure and noise of trying to do it all yourself or listening to “Talking Heads” on social media or tv.

This normalization can be due to various factors, including changes in market sentiment, economic factors, or simply the random fluctuations that occur in the financial markets. Market anomalies and unforeseen events, known as Black Swan events, can disrupt the expected reversion to the mean. The efficient market hypothesis has also been criticized for oversimplifying market dynamics, which may affect the reliability of mean reversion. Furthermore, market manipulation and insider trading can distort price movements and impede the predictability of mean reversion. It is essential to consider these limitations when applying mean reversion in financial analysis and decision-making.

Implementing a Mean Reversion Trading Strategy

Mean reversion is predicated on the idea that prices that reach an extreme will revert to their average value while trend following assumes that prices will persist in the direction they have been moving. From the Nikkei Chart below, using the Bollinger Bands indicator, we can see how mean reversion works in trading. We use the upper Bollinger bands to represent the overbought price level, the middle Bollinger Bands to represent the mean, and the lower Bollinger Bands to represent the oversold level. Conversely, if the stock drops to $20, the trader might buy it, anticipating a rise back to the $50 level. The rationale behind the mean reversion theory is that over time, prices that are unusually high or low will tend to normalize to their average values.

Fundamental Analysis vs Technical Analysis Differences

If you look at the price, the price tends to move above the average and then snap back in an uptrend and bounces back. And then in a downtrend, it moves down and intends to snap back, then moves down again, and tends to snap back. Mean reversion in price movements is attributed to market participants’ behavior, driven by sentiments like fear and greed. As prices rise due to increased demand (greed), eventually reaching a peak, profit-taking begins, leading to a decline.

Step 1 of 3

It shows you this huge equity curve with rapid growth and higher compound annual return, and it’s really appealing… It is like a Siren’s Call that lures sailors onto the rocks. The problem with this is that if it doesn’t bounce back, you will end up with a massive loss as well as having your capital tied up for a long time waiting for a recovery. The only exception to this is if you enter and exit your mean reversion trading strategy using limit orders. If you do that, you can use slightly lower liquidity levels in your trading, but you still have to be careful because you have to make sure you don’t let your slippage get out of hand. However, with mean reversion, the average profit per trade is small. If you make a small recurring mistake, or a single large mistake, it can really hurt the system.

• When the VIX is trading above its long-term average or mean, traders may consider selling options or shorting the underlying asset, with the expectation that volatility will eventually revert back to its mean.
• A common mistake is if you held onto a losing trade that kept moving against you, and you should have gotten out of it, that could be a much more significant loss than you expected.
• Price deviation refers to the extent to which an asset’s price has moved away from its historical mean.
• MACD is a commonly used tool in trading to identify potential overbought and oversold conditions.
• In mean reversion, you’ll have many small wins in the occasional significant loss.

What are the 6 Best Indicators for Mean Reversion Trading Strategies?

Here, prices spiked upward with multiple closes above the upper band. Traders take advantage of this—either buying or selling—to catch the move back towards its average value. While the concept itself is straightforward, applying it effectively in the market requires a keen eye, discipline, and a nuanced skills to manage the trade, especially if it doesn’t work out. As always, there are risks – a price rising away from the average does not mean it will fall again. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model python math libraries is used to estimate the volatility of returns. Standard deviation and variance are two critical statistical tools used in the theory of mean reversion.

Interest Rate Markets

Typically, the average trade duration of my mean reversion strategies on daily charts is one to three days, depending on the system. Mean reversion can work and be profitable trading against the primary trend, but the average profit portrayed tends to be much smaller. One of the challenges with mean reversion is that the average profit per trade in the mean reversion system is typically very low. Because it’s low, it makes it much more difficult to trade well and profitably because any amount of slippage and excess commissions or mistakes can ruin the strategy.

Also, by trading different strategies in different timeframes and markets, you can further improve your results. Then wrapped around all of that is the portfolio or risk management and position sizing rules. What I want to demonstrate now, is a simple set of indicators that give you a feel for how these trading strategies work on the chart.

The encoder compresses the sequential input and processes the input in the form of a context vector. We can introduce an attention mechanism to create a shortcut between the entire input and the context vector where the weights of the shortcut connection can be changeable for every output. To implement the attention mechanism we consider a weighted sum of the input from each time step of the encoder. The weights depend on the importance of that time step for the decoder to optimally generate the next value in the sequence. Table 1 shows the Forex atr weight of the portfolio constituents for a particular portfolio.