Stock Data Collection Using Python

Here is a step-by-step guide to collecting Indian stock data in Python:

Install Required Libraries: 

The first step is to install the required libraries for data collection. You will need to install Pandas, which is a data manipulation library, and nsepy, which is a library for downloading financial data from National Stock Exchange (NSE) of India. You can install these libraries using the following commands:

!pip install pandas

!pip install nsepy

Import Libraries: 

After installing the required libraries, import them into your Python script using the following code:

import pandas as pd

from nsepy import get_history

from datetime import date

Define Ticker and Time Period: 

Define the stock symbol (ticker) for which you want to collect data and the time period. For example, if you want to collect data for the past year for the stock of Reliance Industries Limited, you can define the ticker and time period as follows:

ticker = 'RELIANCE'

start_date = date(2022, 5, 1)

end_date = date(2023, 5, 1)

Download Data: 

Download the stock data using the get_history() function. This function takes the following parameters: ticker, start date, end date, and index (e.g., NIFTY 50). For example, to download daily data for Reliance Industries Limited for the defined time period, use the following code:

data = get_history(symbol=ticker, start=start_date, end=end_date, index=False)

Save Data: 

Save the downloaded data as a CSV file using the to_csv() function of Pandas. For example, to save the data as a CSV file named reliance_data.csv, use the following code:

data.to_csv('reliance_data.csv')

View Data: 

View the downloaded data using the head() function of Pandas. For example, to view the first five rows of the downloaded data, use the following code:

print(data.head())

This is a basic step-by-step guide to collecting Indian stock data in Python using nsepy. You can further customize the data collection process by adding additional parameters to the get_history() function, such as adjusting the frequency of the data or specifying additional data points to download. Additionally, you can collect data for multiple stocks by defining a list of tickers and using a for loop to download data for each ticker.

Additional Data Points: 

In addition to the standard data points that are downloaded by default, such as Open, High, Low, Close, Volume, and Turnover, you can also download additional data points such as VWAP (Volume-Weighted Average Price) and Delivery Percentage. To download these additional data points, add the parameters vwap=True and/or delivery=True to the get_history() function.

Adjusting Frequency: 

By default, the get_history() function downloads daily data for the specified time period. However, you can also adjust the frequency of the data to weekly, monthly, or yearly by adding the parameter index='weekly', index='monthly', or index='yearly' to the get_history() function.

Multiple Stocks: 

To download data for multiple stocks, define a list of tickers and use a for loop to download data for each ticker. For example, to download data for Reliance Industries Limited and Tata Consultancy Services Limited, define the tickers as follows:

tickers = ['RELIANCE', 'TCS']

Then, use a for loop to download data for each ticker:

for ticker in tickers:

    data = get_history(symbol=ticker, start=start_date, end=end_date, index=False)

    data.to_csv(ticker+'_data.csv')

This will download data for each stock and save it as a separate CSV file.

Conclusion:

Python provides a simple and efficient way to collect Indian stock data using the nsepy library. By following the steps outlined in this guide, you can download and save stock data for individual stocks or multiple stocks for a specified time period. Additionally, you can customize the data collection process by adjusting the frequency of the data, downloading additional data points, and performing further data analysis using Python's data manipulation libraries.