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Market Profile - Introduction 2 (In trading, instead of looking at one…
Market Profile - Introduction 2
In trading, instead of looking at one price, we will look at set of prices and their behavior over time. So we need to brush up some basics from statistics.
Before analyzing any data, statisticians see if that data fits into any of the data types that have been named earlier already.
The nature or type of data is called distribution, because each data or data type has certain features and based on them the data types are differentiated using something called distributions.
A distribution means how a type of data is behaving or placed to give you strategic information for analysis.
If a statistician sees that a particular type of data follows a certain distribution, or a data type, he will simply plugin that distribution and apply the attributes determined already by the earlier statisticians.
When Steidelmayer tried to find out what kind of distribution does stock market prices follow, he easily found out that stock price returns follow a statistical distribution, called a NORMAL DISTRIBUTION.
A lot of research has already been done by then under the ambit of financial physics or financial statistics or statistical finance.
What statistical finance found out is that stock price returns follow something called normal distribution. This is the most used distribution in statistics.
What is a normal distribution?
If returns can follow normal distribution, why cannot be the random behavior of prices be explained by the normal distribution.
A set of data that has a central tendency and dispersion feature is called a normal distribution. The distribution should have a center point that can define the feature of that data. Similarly, each data should be comparable to that central point. Using these two features, we can understand the nature of a normally distributed data set.
The normal distribution is represented by something called a bell curve. Because the curve looks like a bell.
A normal distribution can be explained well by the centre of the data and how far each data point is distributed or dispersed from the centre. If you can understand this, you have understood the nature or behavior of that data.
In a normal distribution, half of the data is on the right (for Market Profile, above) side of the centre. The other half data is on the left (for Market Profile, below) side of the centre.
In a normal distribution, 68% of the data will like on both the sides of the centre at -34% to the left and +34% to the right. These are called -1sigma nd +1sigma.
In a normal distribution, 68% of the data will like bewteen -1Sigma and +1Sigma. Similarly, 95% of the data will lie between -2Sigma and +2Sigma. This 95% includes the 68% also. Almost 99% or 100% of the data will lie between -3Sigma and +3Sigma. That is why we call extreme events (in financial markets also) as sigma events.
Because financial market data or stock price data are vertically distributed, that is as they move up and down (not left and right), we will observe data vertically. That is thought a bell curve that is rotated vertically (90 degrees).
What is sigma here? Sigma is simply the standard deviation of a data point from the central point.
Similalry since the prices in a stock market are moving up and down, we are observing the movement of data around the centre point, and how far they are going up or down from the centre point.
The central point is called the average. This can be a mean, a median or a mode.
The dispersion or the deviation from the central point is called standard deviation. This can be explained or quantified in terms of sigma points.