MENTORING - Market Profile - Introduction - Coggle Diagram
MENTORING - Market Profile - Introduction
Peter Steidelmayer, an accountant by qualification and a commodities options trader by profession, was always intrigued by the technical analysis. He could not get the method the market work in a madness.
He joined a program in Columbia University to learn about how large players invest. He wanted to know what large players who can move the market look for while investing or trading.
His gurus, Graham and Dodd (who are also the gurus of Warren Buffett), said that value is everything. They said price is an entry ticket to get value.
Steildelmayer understood the concept of value vs price from Graham, but was not sure how traders can benefit from it.
Steidelmayer's problem was to design a system to capture the price-value equation in trading.
Steidelmayer proposed that value for traders can be the area where maximum members are trading. That is the place where maximum liquidity is. Why? Because that is where large players come to trade.
Now the struggle is how to identify the liquid zones where we can buy or sell.
For this, he had to go back to statistics to find answers.
A stock can be certain historically but can be uncertain in future sense. There will be multiple probabilities of where the stock can go. So when there is uncertainty, decision science takes help of statistics to solve the problems.
In a historical sense, the market is taking one data point at a time.
So the future price of a stock is always a set of probable data, unlike a historical price, which is a certain point with respect to time.
What is the way to analyse a set of data? Statistics provides a wonderful way to analyse this problem.
To analyse a data set, always look for statistical literature to see what kind of statistical distribution it follows. If you do not have the data following any statistical distribution, get a statistician to work on it.
Statisticians have found that financial assets, especially stocks, always follow a normal distribution, in terms of returns.
From here, Steidelmayer took over observing stock, future and option prices as normal distribution.
He started working with CBOT (Chicago Board of Options Trade) to observe data as Normal Distribution with a new software. They started arranging the market data as Normal Distribution and started analysing them.
But the challenge is that the distribution had to be aligned vertically, because prices are moving up and down.
A data set that follows a Normal Distribution has two characteristics: (a) central tendency; (b) measure of dispersion.
Because the market is dynamic, we have to choose the right measure for central tendency. So a mean and median are not right for a market data set. So for the market, a mode is chosen as the right average or central tendency.
For measuring the dispersion of all the data points in the market data, we use standard deviation, which is by far the best measure of dispersion.