Here is a quick tutorial in python to compute Correlation Matrix between multiple stock instruments using python packages like NSEpy & Pandas. Generally Correlation Coefficient is a statistical measure that reflects the correlation between two stocks/financial instruments. Determining the relationship between two securities is useful for analyzing intermarket relationships, sector/stock relationships and sector/market relationships.

Here are some of the essential python libraries required for Correlation Matrix Data Visualization

**IPython** (Interactive Python)**Pandas** (Python Library to handle time-series data )**NSEpy** (Fetch Historical data from NSEindia – NSEpy 0.3 ver or higher)**Matplotlib** (Python library to handle 2D plotting)

**Import the required python modules**

i)from nsepy.archives we need to import the get_price_history:-for fetching the stock pricing details

ii) from datetime import we need to import date object :- for giving the date limit for the required stocks

iii) import pandas :- for the creation of dataframe

iv)import matplotlib for plotting the correlation heatmap

**Create a list of stocks**

https://gist.github.com/bc87b534fb63ec4faae83843fbdc1f39

**Fetch Historical Data**

Now fetch the price history of each stock based on a specific time limit and appending the last close value into an empty pandas data frame

https://gist.github.com/0256e8d6701a95f9c3d8e21d96ed8b66

**Compute and Plot Correlation Matrix**

Now compute the percentage change and Pearson correlation using the pandas dataframe functions **pct_change() , corr() ** and plot the correlation matrix using

matplotlib as shown below.

https://gist.github.com/1d2ba65cb5ba1da38cf542947d2885bc

Note : **%pylab inline** is used as a display variable on ipython notebook.

**Sample IPython Notebook to generate Stock Correlation Matrix Map as shown below :**