Yahoo Finance Nifty Historical Data May 2026

While platforms like Bloomberg Terminal and Reuters are gold standards, they come with a hefty price tag. Enter – a surprisingly powerful, free, and widely accessible source for Nifty historical data.

import yfinance as yf nifty = yf.download('^NSEI', start='2010-01-01', end='2023-12-31') View the first 5 rows print(nifty.head()) Save to CSV nifty.to_csv('nifty_historical.csv')

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Download ^NSEI data from 1995 to today. Plot the log-scale chart. You'll see India's growth story in a single graph – and you didn't pay a rupee for it. Disclaimer: This article is for educational purposes. Always verify critical data from official sources before making investment decisions.

For decades, the Nifty 50 has been the heartbeat of the Indian equity market. Whether you're backtesting a trading strategy, calculating beta against the broader market, or simply tracking long-term wealth creation, reliable historical data is non-negotiable. yahoo finance nifty historical data

print(nifty[['Adj Close', 'volatility']].tail())

import pandas as pd import numpy as np nifty = yf.download('^NSEI', period='1y') nifty['returns'] = nifty['Adj Close'].pct_change() nifty['volatility'] = nifty['returns'].rolling(30).std() * np.sqrt(252) While platforms like Bloomberg Terminal and Reuters are

This method gives you – though note: volume for an index is usually the total traded volume of its constituents. Decoding the Columns: What You're Actually Getting When you download the data, you'll see six columns. Here's what they mean for Nifty: