100+ datasets found
  1. Yahoo Finance Dataset

    • brightdata.com
    .json, .csv, .xlsx
    Updated Feb 21, 2023
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    Bright Data (2023). Yahoo Finance Dataset [Dataset]. https://brightdata.com/products/datasets/yahoo-finance
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Feb 21, 2023
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    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.

  2. c

    Yahoo Stocks Dataset

    • crawlfeeds.com
    csv, zip
    Updated Apr 27, 2025
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    Crawl Feeds (2025). Yahoo Stocks Dataset [Dataset]. https://crawlfeeds.com/datasets/yahoo-stocks-dataset
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Apr 27, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    The Yahoo Stocks Dataset is an invaluable resource for analysts, traders, and developers looking to enhance their financial data models or trading strategies. Sourced from Yahoo Finance, this dataset includes historical stock prices, market trends, and financial indicators. With its accurate and comprehensive data, it empowers users to analyze patterns, forecast trends, and build robust machine learning models.

    Whether you're a seasoned stock market analyst or a beginner in financial data science, this dataset is tailored to meet diverse needs. It features details like stock prices, trading volume, and market capitalization, enabling a deep dive into investment opportunities and market dynamics.

    For machine learning and AI enthusiasts, the Yahoo Stocks Dataset is a goldmine. It’s perfect for developing predictive models, such as stock price forecasting and sentiment analysis. The dataset's structured format ensures seamless integration into Python, R, and other analytics platforms, making data visualization and reporting effortless.

    Additionally, this dataset supports long-term trend analysis, helping investors make informed decisions. It’s also an essential resource for those conducting research in algorithmic trading and portfolio management.

    Key benefits include:

    • Historical Stock Data: Access years of trading data to analyze market behaviors.
    • Versatile Applications: Use it for financial modeling, data analytics, or academic research.
    • SEO Benefits for Finance Websites: Boost your content with insights derived from this dataset.

    Download the Yahoo Stocks Dataset today and harness the power of financial data for your projects. Whether for AI, financial reporting, or trend analysis, this dataset equips you with the tools to succeed in the dynamic world of stock markets.

  3. h

    yahoo-finance-data

    • huggingface.co
    Updated Nov 26, 2024
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    bwzheng2010 (2024). yahoo-finance-data [Dataset]. https://huggingface.co/datasets/bwzheng2010/yahoo-finance-data
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    Dataset updated
    Nov 26, 2024
    Authors
    bwzheng2010
    License

    https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/

    Description

    The Financial data from Yahoo!

      *** Key Points to Note ***
    

    All financial data is sourced from Yahoo!Ⓡ Finance, Nasdaq!Ⓡ, and the U.S. Department of the Treasury via publicly available APIs, and is intended for research and educational purposes. I will update the data regularly, and you are welcome to follow this project and use the data. Each time the data is updated, I will record the update time in spec.json.

      Data Usage Instructions
    

    Use DuckDB or… See the full description on the dataset page: https://huggingface.co/datasets/bwzheng2010/yahoo-finance-data.

  4. Stock Market Dataset

    • kaggle.com
    zip
    Updated Apr 2, 2020
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    Oleh Onyshchak (2020). Stock Market Dataset [Dataset]. http://doi.org/10.34740/kaggle/dsv/1054465
    Explore at:
    zip(547714524 bytes)Available download formats
    Dataset updated
    Apr 2, 2020
    Authors
    Oleh Onyshchak
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Overview

    This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

    It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

    Data Structure

    The date for every symbol is saved in CSV format with common fields:

    • Date - specifies trading date
    • Open - opening price
    • High - maximum price during the day
    • Low - minimum price during the day
    • Close - close price adjusted for splits
    • Adj Close - adjusted close price adjusted for both dividends and splits.
    • Volume - the number of shares that changed hands during a given day

    All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

  5. Finance, Stock, Currency / Forex, Crypto, ETF, and News Data

    • openwebninja.com
    json
    Updated Sep 18, 2024
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    OpenWeb Ninja (2024). Finance, Stock, Currency / Forex, Crypto, ETF, and News Data [Dataset]. https://www.openwebninja.com/api/real-time-finance-data
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 18, 2024
    Dataset authored and provided by
    OpenWeb Ninja
    Area covered
    Global Financial Markets
    Description

    This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

  6. Bitcoin Historical Data (2014-2025) Yahoo! Finance

    • kaggle.com
    Updated Feb 21, 2025
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    Eldintaro Farrandi (2025). Bitcoin Historical Data (2014-2025) Yahoo! Finance [Dataset]. https://www.kaggle.com/datasets/eldintarofarrandi/bitcoin-historical-data-2014-2025-yahoo-finance
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 21, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Eldintaro Farrandi
    Description

    This dataset includes daily historical price data for Bitcoin (BTC-USD) from 2014 to 2025, obtained through web scraping from the Yahoo Finance page using Selenium. The primary data source can be accessed at Yahoo Finance - Bitcoin Historical Data . The dataset contains daily information such as opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and trading volume (Volume).

    About Bitcoin: Bitcoin (BTC) is the world's first decentralized digital currency, introduced in 2009 by an anonymous creator known as Satoshi Nakamoto. It operates on a peer-to-peer network powered by blockchain technology, enabling secure, transparent, and trustless transactions without the need for intermediaries like banks. Bitcoin's limited supply of 21 million coins and its growing adoption have made it a popular asset for investment, trading, and as a hedge against inflation.

    We are excited to share this dataset and look forward to seeing the insights it can provide. We hope it will inspire collaboration and innovation within the community. By leveraging this daily data, we can explore trends, develop predictive models, and design innovative trading strategies that deepen our understanding of Bitcoin's market behavior. Together, we can unlock new opportunities and contribute to the collective advancement of cryptocurrency research and analysis.

  7. w

    Yahoo! Finance Price Fields

    • windsor.ai
    json
    Updated Feb 28, 2024
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    Windsor.ai (2024). Yahoo! Finance Price Fields [Dataset]. https://windsor.ai/data-field/yahoo_finance_price/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Feb 28, 2024
    Dataset provided by
    Windsor.ai
    Variables measured
    Today, Source, Data Source, price.chart
    Description

    Auto-generated structured data of Yahoo! Finance Price from table Fields

  8. o

    Yahoo Finance Business Information Dataset

    • opendatabay.com
    .undefined
    Updated Jun 23, 2025
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    Bright Data (2025). Yahoo Finance Business Information Dataset [Dataset]. https://www.opendatabay.com/data/premium/c7c8bf69-7728-4527-a2a2-7d1506e02263
    Explore at:
    .undefinedAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Bright Data
    Area covered
    Finance & Banking Analytics
    Description

    Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

    Use our Yahoo Finance Business Information 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.

    Dataset Features

    • name: Represents the company name.
    • company_id: Unique identifier assigned to each company.
    • entity_type: Denotes the type/category of the business entity.
    • summary: A brief description or summary of the company.
    • stock_ticker: The ticker symbol used for trading on stock exchanges.
    • currency: The currency in which financial values are expressed.
    • earnings_date: The date for the reported earnings.
    • exchange: The stock exchange on which the company is listed.
    • closing_price: The final stock price at the end of the trading day.
    • previous_close: The stock price at the close of the previous trading day.
    • open: The price at which the stock opened for the trading day.
    • bid: The current highest price that a buyer is willing to pay for the stock.
    • ask: The current lowest price that a seller is willing to accept.
    • day_range: The range between the lowest and highest prices during the trading day.
    • week_range: A broader price range over the past week.
    • volume: Number of shares that traded in the session.
    • avg_volume: Average daily share volume over a specific period.
    • market_cap: Total market capitalization of the company.
    • beta: A measure of the stock's volatility in comparison to the market.
    • pe_ratio: Price-to-earnings ratio for valuation.
    • eps: Earnings per share.
    • dividend_yield: Dividend yield percentage.
    • ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
    • target_est: The analyst's target price estimate.
    • url: The URL to more detailed company information.
    • people_also_watch: Companies frequently watched alongside this company.
    • similar: Other companies with similar profiles.
    • risk_score: A quantified risk score.
    • risk_score_text: A textual interpretation of the risk score.
    • risk_score_percentile: The risk score expressed in percentile terms.
    • recommendation_rating: Analyst recommendation ratings.
    • analyst_price_target: Analyst provided stock price target.
    • company_profile_address: Company address from the profile.
    • company_profile_website: URL for the company’s website.
    • company_profile_phone: Contact phone number.
    • company_profile_sector: The sector in which the company operates.
    • company_profile_industry: Industry classification of the company.
    • company_profile_employees: Number of employees in the company.
    • company_profile_description: A detailed profile description of the company.
    • valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
    • Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
    • financials: This column appears to provide financial statement data.
    • financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
    • earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
    • revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
    • earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
    • eps_trend: Contains information on how the EPS has trended over various recent time intervals.
    • eps_revisions: Captures recent changes in EPS forecasts.
    • growth_estimates: Offers projections related to growth prospects over different time horizons.
    • top_analysts: Intended to list the top analysts covering the company.
    • upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
    • recent_news: Meant to contain recent news articles related to the company.
    • fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
    • **company_profile_he
  9. A

    ‘Time Series Forecasting with Yahoo Stock Price ’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Time Series Forecasting with Yahoo Stock Price ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-time-series-forecasting-with-yahoo-stock-price-9e5c/d6d871c7/?iid=002-651&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

    There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

    Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

    A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

    #
    #

    https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
    #
    New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

    Content

    Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

    Dataset

    The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

    Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

    Starter Kernel(s)

    Acknowledgements

    Mining and updating of this dateset will depend upon Yahoo Finance .

    Inspiration

    Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

    Some Readings

    *If you download and find the data useful your upvote is an explicit feedback for future works*

    --- Original source retains full ownership of the source dataset ---

  10. h

    yahoo-shares

    • huggingface.co
    Updated Nov 6, 2024
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    Jonas Brahmst (2024). yahoo-shares [Dataset]. https://huggingface.co/datasets/jonas-is-coding/yahoo-shares
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 6, 2024
    Authors
    Jonas Brahmst
    Description

    Yahoo Shares

    This data set contains historical share information for the analysis and modelling of share price predictions. It can be used to train machine learning models that predict future share prices. All data was retrieved from the Yahoo Finance API.

      Content of the data record
    

    Column Description

    Adj Close Adjusted closing price

    Close Closing price

    High Highest price of the day

    Low Lowest price of the day

    Open Opening price

    Volume Trading Volume… See the full description on the dataset page: https://huggingface.co/datasets/jonas-is-coding/yahoo-shares.

  11. f

    38 Global main stock indexes.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Bentian Li; Dechang Pi (2023). 38 Global main stock indexes. [Dataset]. http://doi.org/10.1371/journal.pone.0200600.t001
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bentian Li; Dechang Pi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

  12. Reddit Sentiment VS Stock Price

    • zenodo.org
    bin, csv, json, png +2
    Updated May 8, 2025
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    Will Baysingar; Will Baysingar (2025). Reddit Sentiment VS Stock Price [Dataset]. http://doi.org/10.5281/zenodo.15367306
    Explore at:
    csv, bin, png, text/x-python, txt, jsonAvailable download formats
    Dataset updated
    May 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Will Baysingar; Will Baysingar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

    Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

    To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.

  13. m

    Integrando Google Colab e Yahoo Finance (compactação e download de cotações...

    • data.mendeley.com
    Updated Aug 26, 2021
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    Bernardo Mendes (2021). Integrando Google Colab e Yahoo Finance (compactação e download de cotações em formato CSV) published at the "Open Code Community" [Dataset]. http://doi.org/10.17632/r58pyjyvbx.1
    Explore at:
    Dataset updated
    Aug 26, 2021
    Authors
    Bernardo Mendes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
  14. SnP 500 Dataset

    • kaggle.com
    Updated Jun 5, 2023
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    Yash (2023). SnP 500 Dataset [Dataset]. https://www.kaggle.com/datasets/yash16jr/snp500-dataset/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yash
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    The dataset contains the stock price information of the s&p 500 from 1927 till June 2023 with features such as Date, Open, High, Low, Close, Volume, Dividends and splits. The dataset can be used for EDA as well as Time Series Analysis.

  15. m

    Low- and High-Dimensional Asset Prices Data

    • data.mendeley.com
    Updated Oct 18, 2017
    + more versions
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    Chi Seng Pun (2017). Low- and High-Dimensional Asset Prices Data [Dataset]. http://doi.org/10.17632/ndxfrshm74.2
    Explore at:
    Dataset updated
    Oct 18, 2017
    Authors
    Chi Seng Pun
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data files contain seven low-dimensional financial research data (in .txt format) and four high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own efforts. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS).

  16. Yahoo Finance Stock Data

    • kaggle.com
    Updated May 8, 2025
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    Vatsal Lakhmani (2025). Yahoo Finance Stock Data [Dataset]. https://www.kaggle.com/datasets/watzal/yahoo-finance-stock-data/versions/1
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 8, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vatsal Lakhmani
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset

    This dataset was created by Vatsal Lakhmani

    Released under MIT

    Contents

  17. Shares of stock during COVID 19 in automotive sector

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Nov 9, 2020
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    Paula Muñoz; Abel Romero; Paula Muñoz; Abel Romero (2020). Shares of stock during COVID 19 in automotive sector [Dataset]. http://doi.org/10.5281/zenodo.4263399
    Explore at:
    binAvailable download formats
    Dataset updated
    Nov 9, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Paula Muñoz; Abel Romero; Paula Muñoz; Abel Romero
    License

    Attribution 1.0 (CC BY 1.0)https://creativecommons.org/licenses/by/1.0/
    License information was derived automatically

    Description

    This data set includes stock information for the companies Tesla, Porsche, Nio and Ferrari for each day from the date 11/08/2019 to 11/08/2020. Specifically, it shows information about the opening, closing, maximum and minimum price of the session, as well as the volume, the dividends granted to investors and the presence of stock splits generated per day. This dataste has been created with the aim to analyze how the quotes have been evolving during the COVID-19 pandemic in the automotive sector.

    The AccionesSectorAutomovil.xlsx dataset contains 4 sheets (TESLA, PAH3.DE, NIO, RACE ) and 9 variables per sheet:

    - Fecha: date in dd/MM/yyyy format
    - Abrir: value of the share at the market opening expressed in US dollars (USD)
    - Max: maximum value of the share throughout the day expressed in USD
    - Cierre*: value of the share at the close of the market expressed in USD
    - Cierre ajus.*: estimated share value at market close, expressed in USD.
    - Volumen: the amount of a specific asset invested in during a day.
    - Dividends: money received by shareholders in the form of dividends that day.
    - Stock Splits: Whether or not a stock split operation was carried out that day.

    For more information about the project visit the link on [Github](https://github.com/paulamlago/Financial-Web-Scrapping)

  18. Integrated Cryptocurrency Historical Data for a Predictive Data-Driven...

    • cryptodata.center
    Updated Dec 4, 2024
    + more versions
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    cryptodata.center (2024). Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm - Dataset - CryptoData Hub [Dataset]. https://cryptodata.center/dataset/integrated-cryptocurrency-historical-data-for-a-predictive-data-driven-decision-making-algorithm
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    Dataset updated
    Dec 4, 2024
    Dataset provided by
    CryptoDATA
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

  19. D

    Stock Values and Earnings Call Transcripts: a Sentiment Analysis Dataset

    • dataverse.nl
    • huggingface.co
    csv, pdf, txt
    Updated Mar 9, 2021
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    Francesco Lelli; Francesco Lelli (2021). Stock Values and Earnings Call Transcripts: a Sentiment Analysis Dataset [Dataset]. http://doi.org/10.34894/TJE0D0
    Explore at:
    txt(45664), txt(69108), txt(61227), txt(73316), txt(60920), txt(68500), txt(64752), txt(73514), txt(75606), txt(71254), txt(72974), txt(57886), txt(64498), txt(44335), txt(62220), txt(52783), txt(74174), txt(43275), txt(64053), txt(59372), txt(78693), txt(63476), txt(64148), txt(64647), txt(60250), txt(66812), txt(53732), txt(76227), txt(61396), txt(67286), txt(59405), txt(68587), txt(44829), txt(68705), txt(64377), txt(69060), txt(66945), csv(92207), txt(71700), txt(70104), txt(58409), txt(68889), txt(71982), txt(63147), txt(69094), txt(66329), txt(61005), txt(70116), txt(64506), txt(64737), txt(68918), txt(73882), txt(64056), txt(63766), txt(73253), txt(62646), txt(76549), txt(65563), txt(60342), txt(68642), txt(74732), csv(87977), txt(74962), txt(70291), txt(62521), txt(62619), txt(73775), csv(83755), txt(73634), txt(72021), txt(67537), txt(51920), txt(64742), txt(42513), txt(66225), csv(98369), txt(70699), txt(72528), txt(80646), txt(45126), txt(69705), txt(82716), txt(68239), txt(69210), txt(60996), txt(62169), txt(65434), txt(65037), csv(84780), txt(48140), txt(64708), txt(55715), txt(69516), csv(82610), txt(60858), txt(74035), txt(65396), txt(40439), txt(62663), txt(69286), txt(69692), txt(67626), txt(65733), txt(66492), txt(64582), txt(68179), txt(96840), csv(92396), txt(70806), txt(70780), txt(60676), txt(72204), txt(68102), txt(86406), txt(68455), txt(62869), txt(65384), txt(68140), txt(66143), txt(68343), txt(62529), txt(83466), txt(53543), txt(61310), txt(41758), txt(68387), txt(61074), txt(63610), txt(61719), txt(37429), txt(63281), txt(68593), txt(43034), txt(68046), txt(65280), txt(43381), txt(77087), txt(73435), txt(59982), txt(75674), txt(71903), txt(61820), txt(59633), txt(74108), txt(39394), txt(57223), txt(59172), txt(61593), txt(46097), pdf(241665), txt(73121), txt(65844), txt(60797), txt(71421), txt(71067), txt(67940), txt(71441), txt(58016), txt(41635), txt(73532), txt(74062), txt(60550), txt(67906), txt(73854), txt(64807), txt(60863), txt(67247), csv(83749), txt(81321), txt(61965), txt(54538), txt(62678), txt(66619), txt(65102), txt(62603), csv(86996), txt(58972), txt(61306), txt(65727), txt(68768), csv(86612), csv(83716), txt(65538), txt(70659), txt(62600), txt(78098), txt(69221), txt(59002), txt(60376), txt(67164), txt(72955), txt(69814), txt(72770), txt(60037), txt(45817), txt(62345), txt(63555), txt(64762), txt(70490)Available download formats
    Dataset updated
    Mar 9, 2021
    Dataset provided by
    DataverseNL
    Authors
    Francesco Lelli; Francesco Lelli
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure. Contact at Tilburg University: Francesco Lelli

  20. i

    SZI

    • ieee-dataport.org
    Updated Jul 8, 2024
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    Yi Li (2024). SZI [Dataset]. https://ieee-dataport.org/documents/stock-index-price-ssec-szi-and-spx
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    Dataset updated
    Jul 8, 2024
    Authors
    Yi Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    2023

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Bright Data (2023). Yahoo Finance Dataset [Dataset]. https://brightdata.com/products/datasets/yahoo-finance
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Yahoo Finance Dataset

Explore at:
.json, .csv, .xlsxAvailable download formats
Dataset updated
Feb 21, 2023
Dataset authored and provided by
Bright Datahttps://brightdata.com/
License

https://brightdata.com/licensehttps://brightdata.com/license

Area covered
Worldwide
Description

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.

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