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.

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.

Amazon inc and Intel inc.

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.

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

Dataset

This dataset was created by Aditya Rajuladevi

Released under CC0: Public Domain

Contents

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.

This dataset contains historical daily prices for all tickers currently trading on NASDAQ (stocks and ETFs). 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.

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.

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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)

• Simple LSTM Regression

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

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*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 ---

A collective dataset derived from Yahoo Finance for:

1. Stock prices
2. Raw material prices
3. Volatility indices

For multiple historical scenarios.

Dataset

This dataset was created by Valentina C

Released under CC0: Public Domain

Contents

A survey conducted between 2022 and 2024 among consumers in the United States found that most of Yahoo! users visit the platform every day. In 2024, over 20 percent of respondents reported accessing Yahoo! services such as Yahoo Mail and Yahoo Finance daily. This represents a marginal increase compared to the usage recorded in the previous years. While approximately 40 percent of respondents reporting to have never used Yahoo! websites, daily and weekly usage remained more common than monthly access.

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.

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.

Analysis of ‘Yahoo Finance Apple Inc. (AAPL)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/achintyatripathi/yahoo-finance-apple-inc-aapl on 27 August 2021.

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

Context

This is Historical Data which contains data that tells the onening and closing price of the market. The highest and lowest points and also tells about VWAP . It have data of one whole year, which is divided into 3 parts, 1.Daily updates 2. Weekly updates, 3. Monthly Updates.

Inspiration

The idea came from whether we can actually predict what will be the opening or closing price of the market, or what will be the higgest and lowest price of the market.

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

2023

Material published at "https://opencodecom.net/post/2021-07-22-como-baixar-e-zipar-csv-utilizando-python/"

Explore the intricacies of wheat as a global commodity on Yahoo Finance, offering live price updates, historical data, and market insights. Discover how geopolitical events, weather conditions, and supply chain logistics influence wheat prices and affect various economic sectors. Stay informed with expert analyses and community discussions, providing comprehensive resources for both novice and seasoned investors in the agricultural markets.

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.

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)