Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

Hascol

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

Stock Market Tweets Data

  Overview

This dataset is the same as the Stock Market Tweets Data on IEEE by Bruno Taborda.

  Data Description

This dataset contains 943,672 tweets collected between April 9 and July 16, 2020, using the S&P 500 tag (#SPX500), the references to the top 25 companies in the S&P 500 index, and the Bloomberg tag (#stocks).

  Dataset Structure

created_at: The exact time this tweet was posted. text: The text of the tweet, providing… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/stock-market-tweets-data.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Korean Companies’ Stock Market Data provides important information to analyze and predict trends in stock trading. This data includes trading volume, price fluctuations, and stock indices. Collected from stock exchanges and financial data sources, it helps investors predict stock market movements and develop investment strategies, which are crucial for assessing the value of publicly traded Korean companies.

This dataset is about stocks. It has 4 rows and is filtered where the company is Singtel. It features 8 columns including stock name, company, exchange, and exchange symbol.

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!

NASDAQ (National Association of Securities Dealers Automated Quotation) is the world's second largest automated and electronic stock exchange and securities market in the United States, the first being the New York Stock Exchange, with more than 8,000 companies and corporations. It has more trading volume per hour than any other stock exchange in the world. More than 7,000 small and mid-cap stocks are traded on the NASDAQ. It is characterized by comprising high-tech companies in electronics, computers, telecommunications, biotechnology, and many others.

This dataset was created as a result of an automatic extraction of open & public data available in nasdaq.com, using web scraping techniques. The only purpose of creating it was for academic reasons

List of Licensed Companies to Deal at Amman Stock Market and any other related data such as Address, company capital, Legal Capacity and Granted License

Japan's main stock market index, the JP225, fell to 39432 points on July 14, 2025, losing 0.35% from the previous session. Over the past month, the index has climbed 2.93%, though it remains 4.47% lower 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 July of 2025.

Jordan's main stock market index, the ASE, rose to 2860 points on July 14, 2025, gaining 0.09% from the previous session. Over the past month, the index has climbed 8.51% and is up 18.14% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Jordan. Jordan Stock Market (ASE General) - values, historical data, forecasts and news - updated on July of 2025.

Hong Kong's main stock market index, the HK50, rose to 24219 points on July 14, 2025, gaining 0.33% from the previous session. Over the past month, the index has climbed 0.66% and is up 34.43% compared to the same time last year, 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 July of 2025.

this group contains a list of listed companies in Amman stock exchange and their sector , .symbol, code , market and number of shares .

This dataset is about stocks. It has 451 rows and is filtered where the exchange is Stock Exchange of Thailand. It features 8 columns including stock name, company, exchange, and exchange symbol.

📈 S&P 500 Comprehensive Stock Market Dataset

  🎯 Dataset Overview

This comprehensive dataset contains 620,095 daily observations of S&P 500 companies with 73 meticulously engineered features spanning the last 5 years. Designed specifically for time series forecasting, stock price prediction, and advanced financial modeling tasks.

  📊 Key Statistics

Metric Value

Total Records 620,095 daily observations

Features 73 comprehensive features… See the full description on the dataset page: https://huggingface.co/datasets/Adilbai/stock-dataset.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.

Indonesia's main stock market index, the JCI, rose to 7047 points on July 11, 2025, gaining 0.60% from the previous session. Over the past month, the index has declined 2.18% and is down 3.82% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on July of 2025.