One thing that any consistent market speculator knows about trading is that it is essentially a game of probabilities. For Forex traders, our goal is to consistently predict what direction the currency price is headed. When I say consistent, let’s be clear in stating that I am talking about making gains consistently and not losses. One of the very first snippets of market knowledge which was drilled into my early learnings was that none of us ever really know what is going to come next. We may have an idea and be able to occasionally be right more than wrong for a period of time, yet in the long run it is impossible to be right one hundred percent of the time. When we can accept this simple fact and learn to develop a trading system and methodology that puts probability on our side to some degree, we can then look to improve our overall rate of success and potential gains in our trading.
The largest banks and financial institutions know just how vital understanding the game of probability is to their profits; and here at Online Trading Academy we teach this approach to our students through the application of our rules-based core strategy which recognizes imbalances between Supply and Demand on price charts, which in turn lead us to low risk, high probability trading opportunities where we can buy near levels of demand and sell near levels of supply. However, before I explain more, let’s understand probabilities in the markets in a little more detail.
To keep things related to currency price (which is the most important indicator of all) it helps us to understand the concepts of probability theory. You may have heard of Normal Distribution, a major aspect of statistical analysis. Now please let me be clear, I am not in any way a statistician or mathematician but there are some things we must still pay attention to. I will keep this concept very simple as there are many moving parts to it. Normal Distribution models are typically used to represent random variables and predictions of values. The model takes a slew of data and then gives us an idea of what outcomes we can generally expect as new data is recorded. When you have enough data, you can understand its behavior more clearly. Take a look at the below example and notice the shape of the diagram:
The above diagram shows us basic normal distribution, which is also sometimes referred to as a bell curve. The picture highlights how a variable set of data moves around and away from its average value or mean. Essentially, after we have calculated the average, the bell curve shows us that the following fluctuations in price or value will stay pretty close to the mean itself. In fact, these values will move within 1 and up to 2 standard deviations of the mean 95% of the time. In a nutshell, the values spend a lot of time in the middle. So what do you think happens when these values get beyond the 1 and 2 deviations? Are they likely to stay there? According to probability, these rare times when the data gets too far from the mean are highly likely to revert back to normal or, in other words, its average.
So, if we now know that data forms averages and we in turn know that it rarely likes to stray too far away from its mean, how can we apply this to our trading? Well, let’s look at a chart of EURGBP over the last few months:
As we can see, for well over six months of this year the EURGBP has been trading in the long term across an extended range. I have drawn a line just around the currency price of 0.7200 which gives us an idea of the mean or general average price. Now, do you notice how prices tended to move a certain distance away from the mean and then snapped back, almost to the complete opposite side? It is almost like a rubber band is tethered to the candlesticks and the mean line. The further the price moves from the mean, the harder it retraces this move. This is very common in ranging markets and I try to avoid trading in the middle when these conditions are present. This is where we can make the most of our supply and demand levels for entries into low risk but higher reward trades:
Of course, this means that I will get far fewer trades when playing the ends of the price extremes, but the quality of the potential rewards more than makes it a better and more practical option. The further from the mean, the more interested I get.
For some of you, it has probably entered your head that this applies nicely to a ranging market but what about when the market is trending more clearly over longer periods of time? Well, you will be glad to know that we can still apply these principals with our levels and an understanding of the normal distribution model. Join me in 2 weeks for the second part of this article as we explore the average in the context of trends.
Be well and take care,