Aperçu

This dataset is a raw dump of the maximum recorded 1D price history of all 751 stocks listed under "Nifty Total Market", up until 2023-March-24. Sourced from Yahoo Finance. Original size of ~300 MB, compressed down to ~90 MB.

Motivation

The motivation originated out of the necessity of having as complete and reliable a database as possible of the NSE's widest official equity coverage. As of the time of this writing, many alternative datasets that are similar in nature have either been abandoned, not updated recently, or have unadjusted prices that do not align with publicly viewable charts. This data was sourced using adjusted OHLC values, such that it's as close to charted prices (within a personally satisfiable margin). The primary reason for publicising the dataset is to make future searches and personal access easier than if it were private.

Important Note

None of this data has been cleaned or has undergone deep verification. It has simply been pulled, randomly verified, archived, and uploaded (purely for personal convenience). This is directly related to the fact that the recommended contemporary Python equivalent for Yahoo Finance data retrieval removes rows that contain missing values. By extension, the Julia equivalent seeks to retain missing-values as NaN. As of the time of creation, Julia's package with the necessary change was undergoing pre-release testing and therefore unfortunately, this data had to be retrieved using Python's front. Whether this dataset will be updated in the near future or not remains to be decided.

Data Structure

• symbol: the NSE ticker of the scrip the CSV belongs to. Is also the filename of the CSV (for example, "AXISBANK.NS"). This column repeats itself throughout the entirety of the CSV and hence, contains only one unique value across all ~5000 rows.
• date: the recorded date of the economic observation, formatted as YYYY-MM-DD (for example, "1998-11-27"). Each row within this column contains unique values throughout the entirety of the CSV (i.e. all ~5000 rows are unique).
• open: the opening transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.
• high: the highest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.
• low: the lowest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 1.799554). Note that some scripts might have negative values due to adjustment artefacts.
• volume: the volume of shares that exchanged hands for that day. Formatted as an integer value (for example, 21000).
• close: the last recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.257743). Note that some scripts might have negative values due to adjustment artefacts.
• dividends: any dividend paid out on that day that affect prices. Formatted as a float value (for example, 2.5). This column is mostly filled with zeroes.
• splits: any stock splits that occurred on that day that affect prices. Formatted as an integer value (for example, 5). This column is mostly filled with zeroes.

Obligatory Acknowledgements

Appropriate recognition and appreciation goes to the National Stock Exchange of India and Yahoo Finance for their individual (and combined) efforts of economic and financial facilitation, proliferation, data collection, maintenance, management, and provision. Cover image credit.

All of this data is provided AS IS with no guarantee or warranty of any kind. The dataset is licensed under the GNU Affero General Public License.

Context

NIFTY 500 is India’s first broad-based stock market index of the Indian stock market. It contains the top 500 listed companies on the NSE. The NIFTY 500 index represents about 96.1% of free-float market capitalization and 96.5% of the total turnover on the National Stock Exchange (NSE).

NIFTY 500 companies are disaggregated into 72 industry indices. Industry weights in the index reflect industry weights in the market. For example, if the banking sector has a 5% weight in the universe of stocks traded on the NSE, banking stocks in the index would also have an approximate representation of 5% in the index. NIFTY 500 can be used for a variety of purposes such as benchmarking fund portfolios, launching index funds, ETFs, and other structured products.

• Other Notable Indices -
  • NIFTY 50: Top 50 listed companies on the NSE. A diversified 50-stock index accounting for 13 sectors of the Indian economy.
  • NIFTY Next 50: Also called NIFTY Juniors. Represents 50 companies from NIFTY 100 after excluding the NIFTY 50 companies.
  • NIFTY 100: Diversified 100 stock index representing major sectors of the economy. NIFTY 100 represents the top 100 companies based on full market capitalization from NIFTY 500.
  • NIFTY 200: Designed to reflect the behavior and performance of large and mid-market capitalization companies.

Content

The dataset comprises various parameters and features for each of the NIFTY 500 Stocks, including Company Name, Symbol, Industry, Series, Open, High, Low, Previous Close, Last Traded Price, Change, Percentage Change, Share Volume, Value in Indian Rupee, 52 Week High, 52 Week Low, 365 Day Percentage Change, and 30 Day Percentage Change.

Dataset Glossary (Column-Wise)

Company Name: Name of the Company.

Symbol: A stock symbol is a unique series of letters assigned to a security for trading purposes.

Industry: Name of the industry to which the stock belongs.

Series: EQ stands for Equity. In this series intraday trading is possible in addition to delivery and BE stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

Open: It is the price at which the financial security opens in the market when trading begins. It may or may not be different from the previous day's closing price. The security may open at a higher price than the closing price due to excess demand for the security.

High: It is the highest price at which a stock is traded during the course of the trading day and is typically higher than the closing or equal to the opening price.

Low: Today's low is a security's intraday low trading price. Today's low is the lowest price at which a stock trades over the course of a trading day.

Previous Close: The previous close almost always refers to the prior day's final price of a security when the market officially closes for the day. It can apply to a stock, bond, commodity, futures or option co-contract, market index, or any other security.

Last Traded Price: The last traded price (LTP) usually differs from the closing price of the day. This is because the closing price of the day on NSE is the weighted average price of the last 30 mins of trading. The last traded price of the day is the actual last traded price.

Change: For a stock or bond quote, change is the difference between the current price and the last trade of the previous day. For interest rates, change is benchmarked against a major market rate (e.g., LIBOR) and may only be updated as infrequently as once a quarter.

Percentage Change: Take the selling price and subtract the initial purchase price. The result is the gain or loss. Take the gain or loss from the investment and divide it by the original amount or purchase price of the investment. Finally, multiply the result by 100 to arrive at the percentage change in the investment.

Share Volume: Volume is an indicator that means the total number of shares that have been bought or sold in a specific period of time or during the trading day. It will also involve the buying and selling of every share during a specific time period.

Value (Indian Rupee): Market value—also known as market cap—is calculated by multiplying a company's outstanding shares by its current market price.

52-Week High: A 52-week high is the highest share price that a stock has traded at during a passing year. Many market aficionados view the 52-week high as an important factor in determining a stock's current value and predicting future price movement. 52-week High prices are adjusted for Bonus, Split & Rights Corporate actions.

52-Week Low: A 52-week low is the lowest ...

Context : NIFTY 50 is the flagship stock market index of the National Stock Exchange (NSE) in India which is one of the leading stock exchanges in India. NIFTY 50 represents the performance of 50 large-cap companies across various sectors of the Indian economy.
Similarly NIFTY 100 represents the performance of the top 100 companies listed on the NSE based on market capitalization. NIFTY 100 is also part of several other indices, such as NIFTY 200, NIFTY 500, and NIFTY 100 Equal Weight Index.

In the National Stock Exchange (NSE) of India, there are three market segments based on the market capitalization of the listed companies. They are: - Large-cap: This segment includes the top 100 companies listed on the NSE based on market capitalization. - Mid-cap: This segment includes companies that rank between 101 and 250 based on market capitalization. - Small-cap: This segment includes companies that rank below the top 250 companies based on market capitalization. Market capitalization is calculated by multiplying a company's total outstanding shares by its current market price per share. The NSE's NIFTY Mid-cap 100 and NIFTY Small-cap 250 indices track the performance of companies in the mid-cap and small-cap segments of the market, respectively.

The NIFTY500 Multicap 50:25:25 index is a variant of the NIFTY 500 index, which represents the top 500 companies listed on India's National Stock Exchange (NSE). The Multicap 50:25:25 variant is a modified version of the NIFTY500 index that divides stocks into three categories based on market capitalization. The top 50 companies by market capitalization are classified as large-cap companies under this variant, while the next 150 companies are classified as mid-cap companies. The remaining 300 businesses are classified as small-cap.

Content : This Dataset contains records for all NIFTY-50 , NIFTY 200, NIFTY Midcap 100, NIFTY Smallcap 250, NIFTY500 Multicap 50:25:25 stocks, as on 1st March, 2023 - 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 - PREV. CLOSE - Previous Close Value - LTP - Last Traded Price - CHNG - Change in the price - %CHNG - Percentage change - Volume - volume of transaction - Value - Turn over in lakhs - 52W H - 52 Week High price - 52W L - 52 Week Lowest price - 365 D % CHNG - Past 365 Days Change Percentage - 30 D % CHNG - Past 30 Days Change Percentage

Note : - %CHNG: % change is calculated with respect to adjusted price on ex-date for Corporate Actions like: Dividend, Bonus, Rights & Face Value Split and also adjusted for Past 365 days & 30 days. - 52 W H/L: 52 week High & Low prices are adjusted for Bonus, Split & Rights Corporate actions.

Acknowledgements : The data is obtained from NSE website This is just daily level data provided here, you will get vast and detailed real-time & historical data from the official website.

Image Credit : https://gettyimages.com

In 2024, the returns on Nifty 50 reported a rise of 8.75 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).

Last Update - 9th FEB 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. 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 money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

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 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 This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% 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 December of 2025.

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.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

Key information about India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 85,706.7 points in Nov 2025, compared with 83,938.7 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 Nov 2025, with an average number of 2,987.7 points
• The data reached an all-time high of 85,706.7 points in Nov 2025 and a record low of 115.6 points in Nov 1979

[COVID-19-IMPACT]


Further information about India Sensitive 30 (Sensex)

• In the latest reports, SENSEX recorded a daily P/E ratio of 23.2 in Dec 2025

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

Splash banner

Stonks by unknown memer.

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 ********.

Prices for NSE Nifty 50 Index including live quotes, historical charts and news. NSE Nifty 50 Index was last updated by Trading Economics this December 2 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.

Key information about India Market Capitalization

• India Market Capitalization accounted for 5,298.749 USD bn in Oct 2025, compared with a percentage of 5,084.976 USD bn in the previous month
• India Market Capitalization is updated monthly, available from Jan 1993 to Oct 2025
• The data reached an all-time high of 5,663.221 USD bn in Sep 2024 and a record low of 55.322 USD bn in Apr 1993

CEIC converts monthly Market Capitalization into USD. BSE Limited provides Market Capitalization in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions.

Comprehensive 27+ years of daily stock market data for Indian indices (SENSEX & NIFTY 50) and all their constituent companies. This dataset includes OHLCV data along with pre-calculated technical indicators, making it perfect for time series analysis, algorithmic trading strategies, and machine learning applications.

Total Records: 400,000+
Companies: 80 stocks (30 SENSEX + 50 NIFTY 50)
Features: 21 columns per record

Use Cases:

Machine Learning & Prediction:

• Stock price forecasting using LSTM, GRU, or Transformers
• Next-day close price prediction
• Multi-stock portfolio prediction
• Market regime detection (bull/bear markets)

Technical Analysis:

• Backtest trading strategies (RSI, MACD, Moving Average crossovers)
• Identify support/resistance levels
• Bollinger Band squeeze patterns
• Golden Cross / Death Cross detection

Statistical Analysis:

-Correlation analysis between stocks - Volatility clustering analysis - Market crash impact studies (2008 financial crisis, 2020 COVID) - Sectoral performance comparison

Portfolio Optimization:

• Modern Portfolio Theory implementation
• Risk-return optimization
• Diversification analysis
• Sharpe ratio calculations

Education:

• Financial markets course projects
• Time series analysis tutorials
• Data science portfolio projects
• Algorithmic trading education

Company List:

SENSEX 30 Companies:

Adani Enterprises, Asian Paints, Axis Bank, Bajaj Finance, Bajaj Finserv, Bharti Airtel, HDFC Bank, HCL Technologies, Hindustan Unilever, ICICI Bank, IndusInd Bank, Infosys, ITC, Kotak Mahindra Bank, Larsen & Toubro, Mahindra & Mahindra, Maruti Suzuki, Nestle India, NTPC, ONGC, Power Grid Corporation, Reliance Industries, State Bank of India, Sun Pharmaceutical, Tata Consultancy Services, Tata Motors, Tata Steel, Tech Mahindra, Titan Company, UltraTech Cement, Wipro

NIFTY 50 Companies:

All SENSEX 30 companies plus: Adani Ports, Apollo Hospitals, Bajaj Auto, Bharat Petroleum, Britannia Industries, Cipla, Coal India, Divi's Laboratories, Dr. Reddy's Laboratories, Eicher Motors, Grasim Industries, Hero MotoCorp, Hindalco Industries, Hindustan Zinc, JSW Steel, LTIMindtree, Shriram Finance, Tata Consumer Products, Trent

Ticker Conventions: - .BO suffix = Bombay Stock Exchange (BSE) - .NS suffix = National Stock Exchange (NSE)

Citation Policy:

If you use this dataset in your research, please cite:

Indian Stock Market Historical Data - SENSEX & NIFTY 50 (1997-2024)
Kaggle Dataset, November 2024
URL: https://www.kaggle.com/datasets/rockyt07/stock-market-sensex-nifty-all-time-dataset

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

The S&P BSE Sensex index, one of India's two main stock indices, lost almost *********** of its value between the end of February and the end of March 2020, owing to the economic impact of the global coronavirus (COVID-19) pandemic. It has since recovered, surpassing its pre-corona level in *************.The S&P BSE Sensex index includes 30 companies listed on the Bombay Stock Exchange which are representative of various industrial sectors of the Indian economy. It is considered one of the main Indicators of the Indian stock market, along with the CNX Nifty Index (which includes shares from India's other main stock exchange, the National Stock Exchange).

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

Gain access to LSEG's National Stock Exchange of India data, India's largest stock exchange with more than 180,000 terminals across 600 districts.

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.

For the first time, Nifty 50 stocks data and two indices data, along with 55 technical indicators used by Market experts are calculated and made available. Kindly download the data and make sure to share your code in public and if you like this data, do upvote. Thank you.

About Nifty 50

The NIFTY 50 index is a well-diversified 50 companies index reflecting overall market conditions. NIFTY 50 Index is computed using the free float market capitalization method.

NIFTY 50 can be used for a variety of purposes such as benchmarking fund portfolios, launching of index funds, ETFs and structured products.

Overview

This dataset contains historical daily prices for Nifty 100 stocks and indices currently trading on the Indian Stock Market. - Data samples are of 5-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 33 GB (compressed here to 13 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 - 55 Technical indicator values are also present

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

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |