There are numerous ways to trade the currency markets, and one of the most intricate is statistical trading. Statistical trading is using mathematical formulas to define the way a currency pair will move in the future. One of the most common forms of statistical trading of the currency market is a mean reversion strategy.
Mean reverting means that the currency pair will fall back to its mean (average) after moving away from it over a period of time. A good analogy is to think about how far wills a rubber band stretch before it snaps back. Currencies whose economies are very similar or who are large trading partners of each other tend to have mean reverting currency values.
For example, if you look at the monthly chart of AUD/CAD which are both commodity oriented exporting countries, their currencies seem to revert to a mean over the period of the last 10 years. When describing statistical trading, a technical indicator that is used by analyst to measure mean reversion are Bollinger Bands. Bollinger Bands are a mathematical formulas used in statistical trading that calculate a specific standard deviation around a specific average.
In the chart below, when the currency pair AUD/CAD moved above the high Bollinger Band in late 2008, early 2009, the currency pair had stretch beyond the 2 standard deviation level and subsequently reverted back to the mean. Conversely, in mid 2006, when the currency pair moved below the lower Bollinger Band, it again subsequently moved back to the mean over a period of time. These movements are a great depiction of statistical trading.
A second group of technical indicators that are used to measure mean reversion are Relative Strength and Stochastic indicators. Both of these technical indicators measure how fast a market has moved in the short term, relative to movements over a longer period. These indicators create an index that is used by traders to determine if a market is overbought or oversold. Levels on the RSI above 70 are considered overbought, while RSI index levels below 30 are considered oversold.
The RSI on its own is a solid measure of market sentiment, but this can perpetuate for a while, and should probably be used in conjunction with other statistical measurements. When traders or analyst create statistical trading strategies, they normally back test the strategies to see how they performed in the past.
Back testing can be performed on numerous trading platforms, and traders will create statistical trading strategies that have performed well in multiple time periods throughout history. Most robust statistical trading strategies win more times that they lose. Since these strategies do not define a trend, but rather a point will the market will revert to the mean, the risk that is taken is usually equal to the loss that a trader is willing to assume.
For example, in a trend following strategy, the signal to enter a trade might create more losing trades than winners. Since the winning trades profits are much larger than the losing trades losses, the strategy will probably work. With statistical trading strategies, the amount you win on a trade will probably be equal to the amount you lose on a trade. Given this risk profile, it is important to have a strategy that wins more than it loses when employing statistical trading.
Statistical trading strategies also work very well with binary option trading platforms. Traders can back test specific situations and take binary risk when the specific situation occurs.