Please enable JavaScript.
Coggle requires JavaScript to display documents.
MENTORING - Market Profile - Introduction - Coggle Diagram
MENTORING - Market Profile - Introduction
As we have understood by now, the market is an uncertain beast. That means it does not have a method or process to work.
The only uncertainty a market always grapples with is the stochastic-ness of the entry or arrival of a new entrant.
The market is a complex adaptive system. In this complex process, a trader enters or exits randomly without any prior information.
Because trading is a search problem, wherein buyers are searching for sellers and sellers are searching for buyers in a many-to-many function, the market is a complex process to deal with.
The market is a random process, which is almost always uncertain.
However, statisticians have found that financial prices, even though random, follow a distribution pattern, called a Normal Distribution. That means the price points (data) are distributed in a particular pattern.
Time controls the market, and as a result it creates an uncertainty.
To control the uncertainty in the market, we have to watch prices with respect to time.
If we consider price formation (or prices over a period of time) as a data set, the market data is randomly distributed, but is normally distributed.
Statisticians begin an inferential problem by understanding the nature of distribution of a data set. For this, they will first see if the distribution is already having an existing literature.
Statisticians have historically found that financial asset prices and their returns are random but almost always follow the Normal Distribution.
We have to accept this fact because there is no other way than giving a shape to the market to understand it.
Hence, we will start dealing with the market as if it follows the Normal Distribution.
The market is producing data daily with the price discovery process. This data set needs to be analysed statistically for using it to produce trades.
To use this data set, we will start considering it as a Normal Distribution.
The next challenge is analysing this Normal Distribution.
As per statistics, a Normally Distributed data has two features: (1) central tendency; (2) dispersion
Statistically the central tendency is shown by the averages (mean, median and
mode
)
Statistically, the dispersion is shown by various metrics, but more importantly
standard deviation
.