In 2023, the returns on Nifty 50 reported a rise of 19.42 percent compared to the year before. Furthermore, since 2016, Nifty 50 has consistently demonstrated a positive trend in annual returns. Nifty 50 is a benchmark Indian stock market index representing the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange (NSE).

Prices for NSE Nifty 50 Index including live quotes, historical charts and news. NSE Nifty 50 Index was last updated by Trading Economics this July 3 of 2025.

This statistic depicts the average annual performance of the Nifty 50 Index in India from years 2011 to 2024. In 2024, the average annual Nifty 50 Index was reported as 23,644.8, an increase from the previous year where the value was 21,731.4.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: A Journey to 20,000 and Beyond?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In April 2024, among all the indices listed on the National Stock Exchange (NSE) of India, Nifty 50 had the highest dividend yield of 1.2 percent. This was closely followed by Nifty 100 and Nifty Next 50, both with a dividend yield of 1.15 percent, respectively.

What are broad market indices? 

Broad market indices, also called market indices, are utilized to monitor the performance of a collection of stocks that closely mirror the overall stock market. They generally consist of large, liquid stocks listed on the stock exchange. They serve as a benchmark for measuring the performance of the stock market or portfolios such as mutual fund investments. In many broad-based indexes, companies are weighted based on their market value. This means that larger companies carry more weight in determining the index price compared to smaller ones. For instance, in the Nifty-50 index, Cipla, a major pharmaceutical company, has a significant impact, while smaller companies like Natco Pharma have less influence due to their lower market capitalization.

What is Nifty 50? 

Nifty-50 is the flagship index of NSE. It tracks the movement of the portfolio of the 50 largest blue-chip companies and most liquid securities in the Indian market. It is extensively used by domestic and foreign investors as the barometer of the Indian capital market. Annual returns of Nifty-50 were around 20 percent in fiscal year 2023, indicating strong market performance.

This dataset contains a comprehensive collection of historical data for the Nifty 50 stocks, a diversified stock market index in India. The data covers the period from January 2018 to August 2023, providing valuable insights into the performance of the Indian stock market over the years.

Features: - Stock Symbol: The unique stock symbol of the company listed in the Nifty 50 index - Date: The date of the stock market data. - Open: The opening price of the stock on the given date. - High: The highest price reached by the stock during the trading session. - Low: The lowest price reached by the stock during the trading session. - Close: The closing price of the stock on the given date. - Volume: The trading volume of the stock on the given date.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: Climb or Crash?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Billion Dollar Question: Is Nifty 50 Ready for the Next Bull Run?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Analysis of ‘NIFTY-50 Stocks Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamsouravbanerjee/nifty50-stocks-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited. NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index has shaped up to be the largest single financial product in India, with an ecosystem consisting of exchange-traded funds (onshore and offshore), exchange-traded options at NSE, and futures and options abroad at the SGX. NIFTY 50 is the world's most actively traded contract. WFE, IOM, and FIA surveys endorse NSE's leadership position.

The NIFTY 50 index covers 13 sectors (as of 30 April 2021) of the Indian economy and offers investment managers exposure to the Indian market in one portfolio. Between 2008 & 2012, the NIFTY 50 index's share of NSE's market capitalization fell from 65% to 29% due to the rise of sectoral indices like NIFTY Bank, NIFTY IT, NIFTY Pharma, NIFTY SERV SECTOR, NIFTY Next 50, etc. The NIFTY 50 Index gives a weightage of 39.47% to financial services, 15.31% to Energy, 13.01% to IT, 12.38% to consumer goods, 6.11% to Automobiles a and 0% to the agricultural sector.

The NIFTY 50 index is a free-float market capitalization weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content

In this Dataset, we have records of all the NIFTY-50 stocks along with various parameters.

Important Note

• % change is calculated with respect to adjusted price on ex-date for Dividend, Bonus, Rights & Face Value Split.
• 52 weeks high & 52 week low prices are adjusted for Bonus, Split & Rights Corporate actions.
• 365 days % Change and 30 days % Change values are adjusted With respect to corporate actions.

Acknowledgements

For more, you can visit the website of the National Stock Exchange of India Limited (NSE): https://www1.nseindia.com/

--- Original source retains full ownership of the source dataset ---

Prices for NIFTY 50 - Harga Saham including live quotes, historical charts and news. NIFTY 50 - Harga Saham was last updated by Trading Economics this July 4 of 2025.

Context

You don't have to know about the whole universe of stocks to make money. You can earn if you can predict movement of one index only. It will suffice. Here is the data of Nifty 50 index. It has volume in millions. You have a chance to make an algorithm with this data and win in the markets.

Content

The data contains the open, high, low, close of NIFTY 50 index with time stamps. The data is available from 9 January 2015 to 25 March 2021.

Acknowledgements

I have downloaded this data using kite connect API.

Inspiration

Can you find a reliable pattern in a 5 minute or 30 minute candlestick NIFTY 50 chart? Even if we have to wait for a month for that pattern to reappear on the charts we can. But it should be trustworthy to go along. We just need a single trade to make our year and not a thousand.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: A Hold for Now, But Long-Term Gains Are Possible

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Welcome to Nifty50's collection of the latest and most up-to-date stock market data!

Stay ahead of the curve with real-time insights and comprehensive analyses of the ever-evolving financial landscape. Dive into our meticulously curated datasets to unlock the potential for groundbreaking research, innovative strategies, and informed decision-making.

Whether you're a seasoned investor, data scientist, or enthusiast, this dataset is your gateway to exploring the dynamic world of finance!

This collection comprises 50 individual files, each containing historical stock market data for a specific company listed on the Nifty stock index. The dataset features essential financial metrics including:

To format the feature description of your dataset in a Kaggle table format, you can present it as follows:

Close

NIFTY 50 data For the period 01-01-2020 to 11-08-2020

Nifty 50's upward trend is likely to continue, with volatility remaining low. However, global uncertainties like geopolitical tensions and the ongoing pandemic pose risks to this outlook. Additionally, profit-taking and technical corrections may lead to temporary setbacks. Investors should maintain caution and monitor market conditions closely.

Daily net purchase data for domestic institutional investors (DII’s) and foreign institutional investors (FII’s) collated and released by NSE. Also has daily index returns for NSE (S&P CNX Nifty50) and BSE (Sensex) separately as proxies for market returns.

The dataset shows monthly and annual averages of NSE Nifty 50

Note: 1. The averages are based on daily closing index. 2. S&P CNX Nifty has been re-branded as Nifty 50 w.e.f. November 09, 2015.

Context

Stock market data is widely analyzed for educational, business and personal interests.

Content

The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself. The data spans from 1st January, 2000 to 30th April, 2021.

Update Frequency

Since new stock market data is generated and made available every day, in order to have the latest and most useful information, the dataset will be updated once a month.

Acknowledgements

NSE India: https://www.nseindia.com/
Thanks to NSE for providing all the data publicly.

Inspiration

Various machine learning techniques can be applied and explored to stock market data, especially for trading algorithms and learning time series models.

As of financial year 2021, the environmental, social, and governance (ESG) composite score for the industrial sector in India was the highest among the NIFTY 50 companies and stood at 74 points. Communication services followed with the second-highest score of 63 points. NIFTY 50 is a National Stock Exchange index that tracks the performance of the largest fifty companies traded on the NSE.