In 2024, the returns on Nifty 50 reported a ************ 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 October 7 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 ********, an increase from the previous year where the value was ********.

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

Overview

This dataset contains historical daily prices for Nifty 100 stocks and indices currently trading on the Indian Stock Market. - Data samples are of 15-minute intervals and the availability of data is from Jan 2015 to Feb 2022. - Along with OHLCV (Open, High, Low, Close, and Volume) data, we have created 55 technical indicators. - More details about these technical indicators are provided in the Data description file.

Content

The whole dataset is around 5 GB, and 100 stocks (Nifty 100 stocks) and 2 indices (Nifty 50 and Nifty Bank indices) are present in this dataset. Details about each file are - - OHLCV (Open, High, Low, Close, and Volume) data

Inspiration

• Data is uploaded for Research and Educational purposes.

Possible problem statements

• Univariate and Multi-variate time series forecasting of stock prices and index prices
• Multi-variate data can be used to predict the trend of the stock price (Buy or Sell or Hold)
• Different intraday or positional trading strategies can be built out of this multivariate data. [technical indicators are already added here]
• EDA on time series data

In September 2025, among all the indices listed on the National Stock Exchange (NSE) of India, Nifty 100 had the highest dividend yield. This was closely followed by Nifty 200. 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 ** 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 ** percent in fiscal year 2023, indicating strong market performance.

his dataset contains cleaned and time-synced OHLC (Open, High, Low, Close) data for the NIFTY 50 index, covering the period from 9th January 2015 to 25th April 2025.

It includes:

5-minute timeframe data (intraday)

25-minute aggregated interval (useful for trend and momentum strategies)

Daily candles for long-term technical setups

This dataset is ideal for:

Quantitative trading research

Algorithmic strategy backtesting (MACD, RSI, Price Action, etc.)

Time-series analysis & forecasting

No forward-filled or synthetic values were used — all data is from real market trading sessions.

Dataset contains entire 2024 data pertaining to Nifty options. This dataset has all expiry day and its trading data. The dataset is arranged in month wise. Each month, you can see multiple files. The file has specify format. The format of the file is Nifty-{expiry day}-{trade day}.csv. Also there is one folder 2024Nifty, which contains Nifty's daily data. Nifty's daily data is crunched into single file for every month. Also, expiry.csv is available, which is overall expiries for the entire year 2024

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.

SUMMARY & CONTEXT: This dataset aims to provide a comprehensive, rolling 20-year history of the constituent stocks and their corresponding weights in India's Nifty 50 index. The data begins on January 31, 2008, and is actively maintained with monthly updates. After hitting the 20-year mark, as new monthly data is added, the oldest month's data will be removed to maintain a consistent 20-year window. This dataset was developed as a foundational feature for a graph-based model analyzing the… See the full description on the dataset page: https://huggingface.co/datasets/AMP4010/Historical_Nifty_50_Constituent_Weights_20Y.

India's main stock market index, the SENSEX, rose to 82066 points on October 7, 2025, gaining 0.34% from the previous session. Over the past month, the index has climbed 1.58% and is up 0.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

SUMMARY & CONTEXTThis dataset aims to provide a comprehensive, rolling 20-year history of the constituent stocks and their corresponding weights in India's Nifty 50 index. The data begins on January 31, 2008, and is actively maintained with monthly updates. After hitting the 20-year mark, as new monthly data is added, the oldest month's data will be removed to maintain a consistent 20-year window. This dataset was developed as a foundational feature for a graph-based model analyzing the market structure of the Indian stock market. Unlike typical snapshots that only show the current 50 stocks, this dataset is a survivorship bias-free compilation that includes all stocks that have been part of the Nifty 50 index during this period. The data has been meticulously cleaned and adjusted for corporate actions, making it a robust feature set for financial analysis and quantitative modeling.DATA SOURCE & FREQUENCYPrimary Source: All raw data is sourced from the official historical data reports published by Nifty Indices (niftyindices.com), ensuring the highest level of accuracy.Data Frequency: The data is recorded on a monthly and event-driven basis. It includes end-of-month (EOM) weights but also captures intra-month data points for any date on which the Nifty 50 index was reshuffled or rebalanced. For periods between these data points, the weights can be considered static.METHODOLOGY & DATA INTEGRITYThe dataset was constructed based on official Nifty 50 rebalancing announcements. It relies on the observed assumption that on most reshuffles, the weights of stocks that aren’t being reshuffled stay almost the same before and after the change. Significant effort was made to handle exceptions and complex corporate actions:Corporate Actions: Adjustments were systematically made for major events like mergers (HDFC/HDFCBANK), demergers (Reliance/JIOFIN, ITC/ITCHOTELS), and dual listings (TATAMOTORS/TATAMTRDVR).Rebalancing Extrapolation: In cases where EOM weights did not align with beginning-of-month (BOM) realities post-reshuffle, a logarithmic-linear extrapolation method was used to estimate the weights of incoming/outgoing stocks.2013 Rebalancing Exception: For the second half rebalancing of 2013, due to significant discrepancies, all 50 stocks' weights were recalculated using the extrapolation method instead of carrying over previous values.Weight Normalization: On any given date, the sum of all 50 constituent weights is normalized to equal 100%. The weights are provided with a precision of up to 5 decimal places, and the sum for all observations is validated to a strict tolerance of 1e-6.TICKER & NAMING CONVENTIONSFor consistency across the time series, several historical stock tickers have been mapped to their modern or unified equivalents:INFOSYSTCH -> INFYHEROHONDA -> HEROMOTOCOBAJAJ-AUTO -> BAJAUTOSSTL -> VEDLREL -> RELINFRAZOMATO -> ETERNALCONTENTS & FILE STRUCTUREThis dataset is distributed as a collection of files. The primary data is contained in weights.csv, with several supplementary files provided for context, validation, and analysis.weights.csv: The main data file.Layout: This file is in a standard CSV format. The first row contains the headers, with DATE in the first column and stock tickers in the subsequent columns. Each row corresponds to a specific date.Values: The cells contain the stock's weight (as a percentage) in the Nifty 50 index on a given date. A value of 0 indicates the stock was not an index constituent at that time.sectors.csv: A helper file that maps each stock ticker to its corresponding industry sector.summary.csv: A simple summary file containing the first and last observed dates for each stock, along with a count of its non-zero weight observations.validate.py: A Python script to check weights.csv for data integrity issues (e.g., ensuring daily weights sum to 100).validation_report.txt: The output report generated by validate.py, showing the results of the latest data validation checks.analysis.ipynb: A Jupyter Notebook providing sample analyses that can be performed using this dataset, such as visualizing sector rotation and calculating HHI score over time.README.md: This file, containing the complete documentation for the dataset.CHANGELOG.md: A file for tracking all updates and changes made to the dataset over time.LICENSE.txt: The full legal text of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license, which is applicable to this dataset.POTENTIAL USE CASESAnalyzing historical sector rotation and weight concentration in the Indian market.Building features for quantitative models that aim to predict market movements.Backtesting investment strategies benchmarked against the Nifty 50.ACKNOWLEDGEMENTS & CITATIONThis dataset was created by Sukrit Bera. A permanent, versioned archive of this dataset is available on Figshare. If you use this dataset in your research, please use the following official citation, which includes the permanent DOI:Bera, S. (2025). Historical Nifty 50 Constituent Weights (Rolling 20-Year Window) [Data set]. figshare. https://doi.org/10.6084/m9.figshare.30217915LICENSINGThis dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. The license selected in the metadata dropdown (CC BY 4.0) is the closest available option on this platform. The full terms of the applicable CC BY-NC-SA 4.0 license is available HERE, as well as in the uploaded LICENSE.txt file in the dataset. The CC BY-NC-SA 4.0 license DOES NOT permit commercial use. This dataset is FREE for academic and non-commercial research with attribution. If you wish to use this dataset for commercial purposes, please contact Sukrit Bera at sukritb2005@gmail.com to negotiate a separate, commercial license.DATA DICTIONARYColumn Name: DATEData Type: DateDescription: The date of the weight recording. This is the first column.Column Name: [Stock Ticker]Data Type: floatDescription: The percentage weight of the stock (e.g., 'RELIANCE', 'TCS') in the Nifty 50 index. A value of 0 indicates it was not an index constituent on that 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: Retracement or Rebound?

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

The News-Informed Financial Trend Yield (NIFTY) Dataset.

The News-Informed Financial Trend Yield (NIFTY) Dataset. Details of the dataset, including data procurement and filtering can be found in the paper here: https://arxiv.org/abs/2405.09747. For the NIFTY-RL LLM alignment dataset please use nifty-rl.

  📋 Table of Contents

🧩 NIFTY Dataset 📋 Table of Contents 📖 Usage Downloading the dataset Dataset structure

Large Language Models ✍️ Contributing 📝 Citing 🙏… See the full description on the dataset page: https://huggingface.co/datasets/raeidsaqur/NIFTY.

Context

The National Stock Exchange of India Limited (NSE) is the leading stock exchange of India, located in Mumbai. The NIFTY 50 index is National Stock Exchange of India's benchmark broad based stock market index for the Indian equity market.

Apart from NIFTY 50 index, there are also other indices like NIFTY Next 50, Nifty Midcap 150 etc. Exploring these indices may help in taking investment decisions.

Content

This dataset has day level information on major NIFTY indices starting from 01 January 2000.

Each file represents an index and has the following columns

• Date - date of observation
• Open - open value of the index on that day
• High - highest value of the index on that day
• Low - lowest value of the index on that day
• Close - closing value of the index on that day
• Volume - volume of transaction
• Turnover - turn over
• P/E - price to earnings ratio
• P/B - price to book value
• Div Yield - dividend yield

Acknowledgements

The data is obtained from NSE website with the help of nsepy python package.

Photo credits: Photo by M. B. M. on Unsplash

Inspiration

Who wants to predict the future of stock prices? ;)

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

The Data contains, NIFTY 50 & Dow Jones Industrial Average historical data from 01/01/2020 to 31/12/2022.

Index Time Series for First Trust India NIFTY 50 Equal Weight ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund will normally invest at least 90% of its net assets (including investment borrowings) in the securities that comprise the index. The index is designed to track the performance of the 50 largest and most liquid Indian securities listed on the National Stock Exchange of India (NSE) by investing in all of the components of the NIFTY 50.

Dataset

This dataset was created by Dhruv Mathur

Contents

After some rigorous SQL queries and coding on python. I made this dataset. In this dataset, all stocks of the Indian Stock Market are present a total of 2435 stocks. The data is of 1-year rows represent stock name and column represent date and I have filled the table with closing price. Enjoy and do some stock price predictions.

Key information about India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 73,198.1 points in Feb 2025, compared with 77,500.6 points at the previous month end
• India Equity Market Index: Month End: BSE: Sensitive 30 (Sensex) data is updated monthly, available from Apr 1979 to Feb 2025, with an average number of 4,285.0 points
• The data reached an all-time high of 84,299.8 points in Sep 2024 and a record low of 115.6 points in Nov 1979

• In the latest reports, SENSEX recorded a daily P/E ratio of 20.6 in Mar 2025