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MENTORING - Market Profile - Introduction 2 ((1) The more data we have the…
MENTORING - Market Profile - Introduction 2
(1) The more data we have the more decision points we need to make use of data.
(2) To analyse a price in the future, we need various data points to come to a conclusion of where it will arrive.
(3) Statistics deals with problems with data. Unlike in the case of numbers, where mathematical equations come to help.
(4) If we have innumberable data points, statistics helps in arriving at a decent decision.
(5) To understand the market better, which is uncertain in the future, we take a set of data instead of one data point.
(6) If we analyse the set of data as per statistical theory, we can decently estimate where it can and when it can go.
(7) So now we have established the theory that we will analyse a
set of data points i
nstead of one data point per time that happens in future.
(8) To analyse a set of data, whether it is time series data or panel data, we need three important things: (a) data capture/saving; (b) data analysis; (c) data visualisation.
(9) We will use the future market data as a set of dat and analyse it in live.
(10) We will analyse live market data in a way that takes us to exactness almost.
(11) What we are going to do in Market Profile is take the historical data and arrange it visually in such a way that you can analyse a set of historical data points in live - which has closest approximation for future.
(12) If we want to analyse any set of data statistically, we need to observe the statistical properties of that data first.
(13) Any data has come basic features. Any set of data has also some basic features. For example, if there is a set of data of milk bought everyday, then there are certain features that this data gives out.
(14) A data set has three important features: (a) central tendency; (b) dispersion; (c) distribution.
(15) Since we are not statisticians, and are traders, we will just observe the above variables in trading terms.
(16) The first step of beginning a statistical analysis over a set of data is nailing the distribution.
(17) As a result, in finance, that too in stock market returns data, we presume that the data follows a normal distribution. Because it is an established fact.
(18) Why we have to do that? Because each distribution is established with some basic features. Those features will be the starting point of solving your problem.
(19) What is a normal distribution? A data that is distributed as a bell curve is called a normal distribution, simply.
(20) A normal distribution has a central tendency, is dispersed well and is distributed around the center of gravity.
(21) Half of the data lies on one side and the rest on the other in a normal distribution. That center is called measure of central tendency.
(22) This central tendency is measured in three ways: (a) mean; (b) median; (c) mode.
(23) Dynamic or repetitive data are different from static data and hence the measure of central tendency should be used appropriately.
(24) IN trading, we use mode as the preferred measure of central tendency as the data is dynamic.
(25) A mode is the most occurring or repetitive data point in a data set.
(26) Now we are left with understanding dispersion. For this we will simply use standard deviation.