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tathya/stockdata dataset hosted on Hugging Face and contributed by the HF Datasets community
qfzcxdl/StockData dataset hosted on Hugging Face and contributed by the HF Datasets community
Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Netflix, Inc. is an American media company engaged in paid streaming and the production of films and series.
Market capitalization of Netflix (NFLX)
Market cap: $517.08 Billion USD
As of June 2025 Netflix has a market cap of $517.08 Billion USD. This makes Netflix the world's 19th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.
Revenue for Netflix (NFLX)
Revenue in 2025: $40.17 Billion USD
According to Netflix's latest financial reports the company's current revenue (TTM ) is $40.17 Billion USD. In 2024 the company made a revenue of $39.00 Billion USD an increase over the revenue in the year 2023 that were of $33.72 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.
Earnings for Netflix (NFLX)
Earnings in 2025 (TTM): $11.31 Billion USD
According to Netflix's latest financial reports the company's current earnings are $40.17 Billion USD. In 2024 the company made an earning of $10.70 Billion USD, an increase over its 2023 earnings that were of $7.02 Billion USD. The earnings displayed on this page is the company's Pretax Income.
On Jun 12th, 2025 the market cap of Netflix was reported to be:
$517.08 Billion USD by Yahoo Finance
$517.08 Billion USD by CompaniesMarketCap
$517.21 Billion USD by Nasdaq
Geography: USA
Time period: May 2002- June 2025
Unit of analysis: Netflix Stock Data 2025
Variable | Description |
---|---|
date | date |
open | The price at market open. |
high | The highest price for that day. |
low | The lowest price for that day. |
close | The price at market close, adjusted for splits. |
adj_close | The closing price after adjustments for all applicable splits and dividend distributions. Data is adjusted using appropriate split and dividend multipliers, adhering to Center for Research in Security Prices (CRSP) standards. |
volume | The number of shares traded on that day. |
This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.
https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified
The files are formatted as follows: Date, Time, Open, High, Low, Close, Volume Date – This provides the date as an integer where 20100527 would represent May 27th, 2010. Time – This gives the time as an integer where 1426 would represent 2:26PM EST. Open – The open price. High – The high price. Low – The low price. Close – The close price. Volume – The trading volume during the interval. Note that it is extremely difficult to get accurate volume information. The volume is adjusted for splits so that the total value of shares traded remains constant even if a split occurs.
This dataset was created by lianghui lu
The dataset comprises historical stock price and trading volume data from S&P 500 component stocks over a period of about 10 years (from 01/02/2009 to 12/24/2018), used to evaluate the proposed Mid-LSTM stock prediction model.
https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy
The global Stock Market API market is experiencing robust growth, driven by the increasing demand for real-time and historical financial data across various sectors. The proliferation of algorithmic trading, quantitative analysis, and the development of sophisticated financial applications are key factors fueling this expansion. The market is segmented by deployment (cloud-based and on-premises) and user type (SMEs and large enterprises), with cloud-based solutions gaining significant traction due to their scalability, cost-effectiveness, and accessibility. Large enterprises, with their extensive data processing needs and investment in advanced analytics, currently dominate the market share, but the SME segment is exhibiting impressive growth potential as access to affordable and user-friendly APIs becomes increasingly widespread. Geographic expansion is also a significant driver, with North America and Europe holding substantial market shares, while Asia-Pacific is emerging as a rapidly growing region fueled by increasing technological adoption and economic expansion. While competitive pressures from numerous providers and data security concerns present some restraints, the overall market outlook remains highly positive, projected to maintain a strong Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). The competitive landscape is characterized by a diverse range of established players and emerging startups. Established players like Refinitiv and Bloomberg offer comprehensive data solutions, while smaller companies like Alpha Vantage and Marketstack provide specialized APIs focusing on specific data sets or user needs. This competitive environment fosters innovation, driving the development of new features and capabilities within Stock Market APIs. The increasing demand for integrated data solutions—combining market data with alternative data sources—is another key trend shaping the market. Future growth will likely be fueled by the expansion of fintech, the rise of robo-advisors, and increasing adoption of APIs in academic research and financial education. The market's continued evolution necessitates ongoing adaptation and innovation from both established players and new entrants to cater to the evolving needs of a dynamic and technology-driven financial ecosystem. This ongoing innovation and increasing demand will drive the market to significant growth over the next decade.
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License information was derived automatically
Analysis of ‘Google Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/google-stock-data on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware. It is considered one of the Big Five companies in the American information technology industry, along with Amazon, Facebook, Apple, and Microsoft. Google was founded on September 4, 1998, by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14% of its publicly-listed shares and control 56% of the stockholder voting power through super-voting stock. The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reorganized as a wholly-owned subsidiary of Alphabet Inc. Google is Alphabet's largest subsidiary and is a holding company for Alphabet's Internet properties and interests. Sundar Pichai was appointed CEO of Google on October 24, 2015, replacing Larry Page, who became the CEO of Alphabet. On December 3, 2019, Pichai also became the CEO of Alphabet.
This dataset provides historical data of Alphabet Inc. (GOOG). The data is available at a daily level. Currency is USD.
--- Original source retains full ownership of the source dataset ---
Algorithm-based selection among a large database of companies of a basket of stocks whose business is related to a specific theme (es. "nanotechnology"). The selection is performed by analyzing company public documents and web pages by leveraging on natural language processing and machine learning classifications and clustering techniques.
https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/
Explore the historical Whois records related to free-stock-data.com (Domain). Get insights into ownership history and changes over time.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.
The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.
The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.
The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.
All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name
I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.
This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!
Real-time stock market data including prices, volumes, and more.
https://historicaldata.net/about.html#licenseIDhttps://historicaldata.net/about.html#licenseID
Daily historical stock trading data in CSV format.
https://historicaldata.net/about.html#licenseIDhttps://historicaldata.net/about.html#licenseID
Free historical stocks data, dataset files in CSV format.
https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service
1) Data Introduction • The S&P 500 stock data is a tabular stock market dataset of daily stock price information (market, high price, low price, closing price, trading volume, etc.) for the last five years (the latest data is until February 2018) of all companies in the S&P 500 index.
2) Data Utilization (1) S&P 500 stock data has characteristics that: • Each row contains key stock metrics such as date, open, high, low, close, volume, and stock ticker name. • Data is provided as individual stock files and all stock integrated files, so it can be used for various analysis purposes. (2) S&P 500 stock data can be used to: • Stock Price Forecasting and Investment Strategy Development: Using historical stock price data, a variety of investment strategies and forecasting models can be developed, including time series forecasting, volatility analysis, and moving averages. • Market Trends and Corporate Comparison Analysis: It can be used to visualize stock price fluctuations across stocks, compare performance between stocks, analyze market trends, optimize portfolios, and more.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘MasterCard Stock Data - Latest and Updated’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kalilurrahman/mastercard-stock-data-latest-and-updated on 12 November 2021.
--- Dataset description provided by original source is as follows ---
https://upload.wikimedia.org/wikipedia/commons/thumb/a/a4/Mastercard_2019_logo.svg/195px-Mastercard_2019_logo.svg.png" alt="Mastercard">
Mastercard Inc. (stylized as MasterCard from 1979 to 2016 and MasterCard since 2016) is an American multinational financial services corporation headquartered in the Mastercard International Global Headquarters in Purchase, New York. The Global Operations Headquarters is located in O'Fallon, Missouri, a municipality of St. Charles County, Missouri. Throughout the world, its principal business is to process payments between the banks of merchants and the card-issuing banks or credit unions of the purchasers who use the "Mastercard" brand debit, credit, and prepaid cards to make purchases. Mastercard Worldwide has been a publicly traded company since 2006. Prior to its initial public offering, Mastercard Worldwide was a cooperative owned by the more than 25,000 financial institutions that issue its branded cards.
Mastercard, originally known as Interbank from 1966 to 1969 and Master Charge from 1969 to 1979, was created by an alliance of several regional bank card associations in response to the BankAmericard issued by Bank of America, which later became the Visa credit card issued by Visa Inc.
Mastercard is one of the best performing stocks of the decade of 2011-2020
--- Original source retains full ownership of the source dataset ---
This data includes the daily prices of 8 stocks from the January 2014 to December 2016. The daily opening price and closing price are collected to assist in analyzing the return of each stock. In fuzzy portfolio selection, those stock price data can be applied to estimate the fuzzy returns of each stock by using the granular computing method.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Stock Peice and Volume Data for 4 ears
Xmm/stock-data-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community