The share market is always an exciting place for professional managers and in this exercise we demonstrate how to get started with technical analysis. India has two stock exchanges, namely NSE and the BSE, and historical data for share prices for all companies are available at their respective websites.
NSE data is available at https://www.nseindia.com/products/content/equities/equities/eq_security.htm
BSE data is available at https://www.bseindia.com/markets/equity/EQReports/StockPrcHistori.html
In both cases, the data for specific companies can be downloaded as CSV for any reasonable range of dates. The structure of the data, in terms of the columns, is roughly the same in both cases. They carry identical information but the column names are different and so the two cases need to be treated separately.
In this exercise, we will use NSE data to demonstrate how to get started with technical analysis. In the process, we will learn two new pandas functions, namely df.merge() to merge or join dataframes to create larger dataframes and df.iloc[..] that allows us to slice a dataframe on rows or columns.
We also introduce the mplfinance module that facilitates the creation of professional quality technical charts for stock market analysis
The code is available in the github repository as a Colab Notebook and sample data for four companies are also available. Students need to click on the open-with-colab button to activate the Colab VM and exeucute the cells one after the other. Students are encouraged to download fresh data for other companies and do a similar analysis.
