4 datasets found
  1. National Stock Exchange : Time Series

    • kaggle.com
    Updated Dec 4, 2019
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    Atul Anand {Jha} (2019). National Stock Exchange : Time Series [Dataset]. https://www.kaggle.com/atulanandjha/national-stock-exchange-time-series/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 4, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Atul Anand {Jha}
    License

    http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html

    Description

    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!

  2. k

    ADNOC Logistics & Services' IPO a Success (Forecast)

    • kappasignal.com
    Updated May 31, 2023
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    KappaSignal (2023). ADNOC Logistics & Services' IPO a Success (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/adnoc-logistics-services-ipo-success.html
    Explore at:
    Dataset updated
    May 31, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    ADNOC Logistics & Services' IPO a Success

    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

  3. Olaplex Holdings Inc. (OLPX) Stock: Can It Replicate Its Post-IPO Success?...

    • kappasignal.com
    Updated Apr 11, 2024
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    KappaSignal (2024). Olaplex Holdings Inc. (OLPX) Stock: Can It Replicate Its Post-IPO Success? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/olaplex-holdings-inc-olpx-stock-can-it.html
    Explore at:
    Dataset updated
    Apr 11, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Olaplex Holdings Inc. (OLPX) Stock: Can It Replicate Its Post-IPO Success?

    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

  4. d

    Digital Payments and Transactions: Year-, Month- and Bank-wise Volume and...

    • dataful.in
    Updated Jun 30, 2025
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    Dataful (Factly) (2025). Digital Payments and Transactions: Year-, Month- and Bank-wise Volume and Performance of UPI Initial Public Offering (IPO) Mandate Creation and Execution [Dataset]. https://dataful.in/datasets/18247
    Explore at:
    csv, xlsx, application/x-parquetAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    All India
    Variables measured
    Volume of UPI Mandate Transactions
    Description

    High Frequency Indicator: The dataset contains year-, month- and bank-wise compiled data from the year 2021 to till date on number of Initial Public Offering (IPO) Mandates which have been created and executed through Unified Payment Interface (UPI), along with percentage of transactions which have been approved, business declined (BD) and technical declined (TD)

    Notes:

    1. Mandate Creation means transactions where the customers have created a successful block of amount in the bank account for an IPO application
    2. Mandate Execution means transactions where the customers have applied for an IPO and have been allotted shares for which the funds are debited from their applicant accounts
    3. Business Declines (BD) are declined transactions due to a customer entering an invalid pin, incorrect beneficiary account etc. or due to other business reasons such as exceeding per transaction limit, exceeding permitted count of transactions per day, exceeding amount limit for the day etc.
    4. Technical Declines (TD) transactions are those transactions are declined due to any technical reasons such as bank ID is empty or not in correct format or exception code not in Database or not in correct format, etc
  5. Not seeing a result you expected?
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Share
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Click to copy link
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Cite
Atul Anand {Jha} (2019). National Stock Exchange : Time Series [Dataset]. https://www.kaggle.com/atulanandjha/national-stock-exchange-time-series/code
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National Stock Exchange : Time Series

national stock exchange dataset of indian IT companies for time series analysis

Explore at:
4 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Dec 4, 2019
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Atul Anand {Jha}
License

http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html

Description

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!

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