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

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Stonks by unknown memer.

Overview

This dataset provides daily stock prices for all companies listed on the National Stock Exchange (NSE) of India. The data spans several years and includes essential trading information that can be used for various financial analyses, stock market research, and machine learning applications.

Content

The dataset includes the following columns:

• Date: The date of the trading day in YYYY-MM-DD format.
• Open: The opening price of the stock on the given date.
• High: The highest price of the stock on the given date.
• Low: The lowest price of the stock on the given date.
• Close: The closing price of the stock on the given date.
• Adj Close: The adjusted closing price of the stock on the given date, which accounts for dividends, stock splits, and other corporate actions.
• Volume: The number of shares traded on the given date.
• Symbol: The unique ticker symbol of the stock.

Data Source

The data has been sourced using the Yahoo Finance API, providing a reliable and comprehensive view of stock performance over time.

Usage

This dataset is ideal for:

• Time series analysis and forecasting of stock prices.
• Developing and testing trading algorithms.
• Financial market research and trend analysis.
• Machine learning projects related to finance and economics.

File Format

The dataset is available in CSV format, making it easy to load into data analysis and machine learning libraries such as pandas, scikit-learn, and TensorFlow.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

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 ...

NSE Stock Historical Price Data (Market Cap > 500 Cr)

Dataset Description

This dataset contains the historical closing price data for all stocks listed on the National Stock Exchange (NSE) of India with a market capitalization exceeding 500 crore INR. The dataset is ideal for analysts, researchers, and enthusiasts who wish to perform detailed analysis, develop trading algorithms, or study market trends of substantial companies within the Indian stock market.

Features

1. Stock Ticker: Unique symbol representing each stock.
2. Date: The specific trading date.
3. Closing Price: The price at which the stock closed on a given day.

Source

The data is sourced from official NSE records and includes all companies meeting the market capitalization criteria as of the latest update.

Applications

• Trend Analysis: Understand how stock prices of major companies have fluctuated over time.
• Algorithmic Trading: Develop and backtest trading algorithms using real historical data.
• Market Research: Study the performance of large-cap stocks to gain insights into market dynamics.
• Educational Use: Serve as a practical dataset for educational purposes in finance, economics, and data science courses.

Usage

The dataset can be used for various purposes including but not limited to: - Financial modeling and forecasting - Risk management and portfolio optimization - Academic research and projects - Machine learning and AI-driven stock prediction models

Description:

Empowering Indian Investors: NSE's Historical Yearly Financial Ratios for 1800 Stocks with Predictive Modeling and Strategy Backtesting

Financial data is the lifeblood of investment analysis and decision-making, and for Indian investors navigating the dynamic National Stock Exchange (NSE), access to comprehensive and tailored datasets is crucial. The NSE 1800 Stocks Historical Yearly Financial Ratios dataset is a valuable resource designed to empower Indian investors, analysts, and financial professionals with the essential financial information needed for predictive modeling and strategy backtesting within the context of the Indian stock market.

Motivation:

The Indian stock market presents unique opportunities and challenges. The motivation behind this dataset is to provide Indian investors with a robust financial dataset that enables them to perform predictive modeling and strategy backtesting independently. It aims to streamline the analysis process, promote data-driven decision-making, and enhance the understanding of Indian stock market dynamics.

Context:

Understanding the financial performance of Indian companies is fundamental for Indian investors, and this dataset offers a wealth of historical financial metrics and ratios for 1800 stocks listed on the NSE. It is distinguished by the ability to:

1 Perform Predictive Modeling: Users can leverage this dataset to build their predictive models tailored to the Indian market's unique characteristics. These models can assist investors in forecasting financial metrics, stock prices, and market trends specific to the Indian context.

2 Conduct Strategy Backtesting: Indian investors can independently test their investment strategies using historical data from the NSE. This dataset serves as the foundation for users to assess the performance of their strategies while considering factors such as Indian economic cycles, regulatory changes, and market dynamics.

3 Evaluate Financial Health: Users can assess the financial stability, profitability, and operational efficiency of Indian companies by utilizing a comprehensive collection of historical financial ratios and metrics.

4 Support Informed Decision-Making: By providing access to the historical financial data of 1800 stocks listed on the NSE, this dataset equips Indian investors with the information needed to make well-informed investment decisions, navigate the Indian stock market, and manage their portfolios effectively.

In summary, the NSE 1800 Stocks Historical Yearly Financial Ratios for Predictive Modeling and Strategy Backtesting (Tailored for Indian Investors) dataset is a robust resource that empowers Indian investors to independently perform predictive modeling and strategy backtesting. It serves as a foundational dataset to support data-driven investment decisions within the unique context of the Indian stock market. Whether you are an Indian investor, analyst, or financial professional, this dataset equips you with the financial data needed to enhance your investment strategies and decision-making.

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

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 | | | |

Explore detailed historical data from January 2002 to June 2024 for companies listed on the National Stock Exchange (NSE). This dataset includes daily records of open, high, low, close prices, adjusted close prices, trading volumes, and company symbols, enabling comprehensive analysis and modelling of stock market trends over two decades. Ideal for financial analysis, predictive modelling, and algorithmic trading strategies

This dataset includes essential information for companies listed in the NSE 500, including their names, industries, symbols, series, and ISIN codes. It is useful for financial analysis, stock market research, and data science projects involving Indian stock market data.

Context

In-order to validate various trading strategies and to come up with new trading strategies, historical data is a must. Once you have the data you can use it to analyze and visualize the market. This data can be use to learn about different types of trading strategies. You can use various libraries like TA-Lib, pandas_ta, etc.

Content

I have collected this data from TradingView and in this dataset I've only gather NSE listed companies data since they listed on NSE. In this dataset you will get Historical data of over 2500 companies and this data is based on daily candles. In this dataset there are over 2500 csv files and each csv file is named on company's NSE symbol (e.g. SBIN.csv, TATAMOTORS.csv, etc.).

For stocks, it has EOD OHLC,change and Last day change data

Columns in each csv file - datetime - symbol - open - low - high - close - volume - change(%) - last day change(%)

Acknowledgements This data is sourced from TradingView using tvDatafeed

The data is unprocessed and retained as obtained from the source.

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.

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.

Dataset

This dataset was created by Siddharth Shrivastava

Released under Apache 2.0

Contents

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It 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.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

"Alone we can do so little; together we can do so much." – Helen Keller

🌟Welcome to the 𝗡𝗦𝗘-𝗗𝗮𝘁𝗮-𝟮𝟬𝟮𝟰 𝗱𝗮𝘁𝗮𝘀𝗲𝘁, a comprehensive and meticulously curated dataset covering the 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗮𝘁𝗮 𝗼𝗳 𝘁𝗼𝗽 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝗼𝗰𝗸 𝗘𝘅𝗰𝗵𝗮𝗻𝗴𝗲 (𝗡𝗦𝗘) 𝘀𝗰𝗿𝗶𝗽𝘁𝘀. This dataset spans from January 1, 2024, to the present, offering valuable insights for financial analysis and data-driven decision-making.

📝𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 1. P͟e͟r͟i͟o͟d͟ ͟C͟o͟v͟e͟r͟e͟d͟:͟ January 1, 2024, to the present 2. U͟p͟d͟a͟t͟e͟ ͟F͟r͟e͟q͟u͟e͟n͟c͟y͟: Weekly 3. F͟i͟l͟e͟ ͟F͟o͟r͟m͟a͟t͟:͟ Parquet (efficient and optimized for large-scale data) 4. S͟o͟u͟r͟c͟e͟:͟ Bhavcopies of top NSE companies 5. D͟a͟t͟a͟ ͟V͟a͟l͟i͟d͟a͟t͟i͟o͟n͟:͟ Each CSV sample is validated and formatted into the parquet extension to ensure data integrity and accuracy.

✍🏻𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 - Comprehensive Coverage: Includes performance data of the top scripts traded on the NSE. - Weekly Updates: Stay up-to-date with the latest financial trends and market movements. - Efficient Format: Parquet files ensure faster data processing and reduced storage costs.

🤝🏻𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: This platform is designed to foster collaboration and innovation within our data science community. I sincerely request my fellow data enthusiasts to build and share notebooks and solutions using this dataset. Together, we can explore the depths of financial data analysis and drive insightful, data-driven decisions.

⚠️𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿: If anyone believes that there are any copyright violations associated with this dataset, please let me know, and I will make the necessary changes.

⏳𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝗲𝗱 - 𝘿𝙤𝙬𝙣𝙡𝙤𝙖𝙙 𝙩𝙝𝙚 𝘿𝙖𝙩𝙖𝙨𝙚𝙩: NSE-Data-2024.parquet - 𝙀𝙭𝙥𝙡𝙤𝙧𝙚 𝙎𝙖𝙢𝙥𝙡𝙚 𝙉𝙤𝙩𝙚𝙗𝙤𝙤𝙠𝙨: Gain inspiration and insights from sample notebooks shared by the community. - 𝙎𝙝𝙖𝙧𝙚 𝙔𝙤𝙪𝙧 𝙒𝙤𝙧𝙠: Contribute your notebooks and findings to help others in their analysis.

👣Join the conversation, contribute your expertise, and let's make this dataset a cornerstone for financial data analysis on Kaggle!👥

Context

The data is of National Stock Exchange of India. The data is compiled to felicitate Machine Learning, without bothering much about Stock APIs.

Content

The data is of National Stock Exchange of India's stock listings for each trading day of 2016 and 2017. A brief description of columns. SYMBOL: Symbol of the listed company. SERIES: Series of the equity. Values are [EQ, BE, BL, BT, GC and IL] OPEN: The opening market price of the equity symbol on the date. HIGH: The highest market price of the equity symbol on the date. LOW: The lowest recorded market price of the equity symbol on the date. CLOSE: The closing recorded price of the equity symbol on the date. LAST: The last traded price of the equity symbol on the date. PREVCLOSE: The previous day closing price of the equity symbol on the date. TOTTRDQTY: Total traded quantity of the equity symbol on the date. TOTTRDVAL: Total traded volume of the equity symbol on the date. TIMESTAMP: Date of record. TOTALTRADES: Total trades executed on the day. ISIN: International Securities Identification Number.

Acknowledgements

All data is fetched from NSE official site. https://www.nseindia.com/

Inspiration

This dataset is compiled to felicitate Machine learning on Stocks.

The NIFTY500 Stocks Data dataset contains comprehensive historical stock data for the NIFTY 500 index. This dataset includes detailed information on stock prices at various time intervals for each company listed in the NIFTY 500. The data is organized into individual Excel files, each representing a different stock, with multiple sheets within each file representing different timeframes.

File Naming Convention The Excel files are named according to the NSE code of each stock. For example, the file for the stock with the NSE code RELIANCE will be named RELIANCE.xlsx.

Data Description Each sheet within an Excel file contains the following columns:

• Date: The date and time of the data point.
• Open: The opening price of the stock at the given date and time.
• High: The highest price of the stock at the given date and time.
• Low: The lowest price of the stock at the given date and time.
• Close: The closing price of the stock at the given date and time.
• Volume: The number of shares traded at the given date and time

Usage This dataset is ideal for financial analysts, data scientists, and researchers who are interested in analyzing stock market trends, developing trading strategies, or conducting market research. The variety of timeframes allows for both long-term and short-term analysis.

Licensing This dataset is available under the CC0-1.0 License. This means it is free to use for any purpose, including commercial use, without needing to request permission.

How to Access the Data To access and use this dataset, follow these steps:

• Download the dataset from Kaggle.
• Unzip the downloaded file to access the individual Excel files.
• Open each Excel file to explore the historical stock data for each stock in the NIFTY 500 index.

Dataset Overview:

The dataset contains information about 6,717 financial instruments listed on the National Stock Exchange (NSE) of India. The data includes a variety of instruments such as equity indices, stocks, and other financial products. Here’s a breakdown of the columns:

• instrument_token: A unique identifier for each instrument.
• exchange_token: Another identifier specific to the exchange.
• tradingsymbol: The trading symbol of the instrument.
• name: The full name of the instrument (some entries might be missing this information).
• last_price: The last traded price of the instrument.
• expiry: Expiry date for derivative instruments (mostly null for this dataset).
• strike: Strike price for options (mostly zero or not applicable here).
• tick_size: The minimum price movement for the instrument.
• lot_size: The lot size for trading the instrument.
• instrument_type: The type of instrument (e.g., equity, index).
• segment: The market segment to which the instrument belongs (e.g., INDICES, EQUITIES).
• exchange: The exchange where the instrument is listed (in this case, all are from NSE).

Sample Data:

Here’s a quick look at the first few entries:

Dataset Description for Kaggle:

Instruments NSE Dataset

Description:

This dataset contains detailed information on 6,717 financial instruments listed on the National Stock Exchange (NSE) of India. It includes a range of instruments such as equity indices, stocks, and derivatives. This dataset can be used for financial analysis, trading strategy development, and backtesting.

Columns:

• instrument_token: Unique identifier for each instrument.
• exchange_token: NSE-specific identifier.
• tradingsymbol: The trading symbol used to identify the instrument on the NSE.
• name: Full name of the instrument (where available).
• last_price: The most recent traded price of the instrument.
• expiry: Expiry date for derivatives (where applicable).
• strike: Strike price for options (mostly irrelevant for this dataset).
• tick_size: The smallest price movement allowed in trading this instrument.
• lot_size: The lot size, indicating the number of units per trade.
• instrument_type: Indicates the type of instrument, such as equity or index.
• segment: The market segment, such as indices or equities.
• exchange: The exchange where the instrument is listed, which in this dataset is the NSE.

Usage:

This dataset is ideal for: - Market Analysis: Understanding the structure and constituents of the NSE. - Trading Strategies: Developing and backtesting trading strategies using historical data. - Educational Purposes: Learning about financial markets and instruments.

Acknowledgments:

The data has been sourced from the National Stock Exchange of India (NSE).

You can use this description as the text when you upload the dataset to Kaggle. It covers all the essential details, making it easy for users to understand the contents and potential applications of the dataset.

Context

The Dataset here is the CSV (Comma Separated Value) formatted data of 1000+ Indian companies' historical stock data which are listed on NSE web scrapped using python. This data helps the community to dive into algorithmic trading using the ML techniques and can be used for any task. Hope this will be of great use for everyone.

Content

This dataset(.zip) is a collection of numerous CSV formatted files that are in format of ['Date','Open','high','low','close','adj close','volume']. I've acquired this data using the yahoo finance v7 server using the python requests and a bit of pre-processing.

• Maruti_data.csv is the sample data of Maruti stock data from 2003-07 to till data (updated on 18-Feb-2018) .
• Companies_dict.d is the python pickle dictionary variable to get company name from the SYMBOL or name if the csv file. You can load this using the pickle library and get the actual company SYMBOL to Legal Name. ###### e.g.Python Code ###### Symbol2Name = pickle.load(open('company_symbol_name_dict.d','rb')) ###### print(Symbol2Name['MARUTI']) #Will give you Maruti_Suzuki_India_Ltd

Acknowledgements

I would like to thank this githubrepo for making the python file this script of mine is based on.

Inspiration

I would love to see many people like me to get their hands dirty with this data and use it effectively to correlate the inter relationships among the companies.

Context

This dataset provides comprehensive historical data for the Nifty 50 Index, including daily open, high, low, close prices, and trade volumes. Spanning the period for Year 2024-2025, it captures market trends across India's leading stock index during a time of significant economic shifts, including the global pandemic and post-recovery phases.

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.

Data can be useful for trend analysis, volatility studies, and investment strategy development for both long-term and short-term market assessments.

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.