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.

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.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

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 ---

In January 2024, Yahoo! Search had a worldwide market share of **** percent. The search engine is powered by Microsoft's Bing. Neither of these web search providers comes close to the dominance of market leader Google.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

As of March 2025, Google continued to dominate the global search engine industry by far, with an 89.62 percent market share. However, this stronghold may be showing signs of erosion, with its share across all devices dipping to its lowest point in over two decades. Bing, Google's closest competitor, currently holds a market share of 4.01 percent across, while Russia-based Yandex hikes to the third place with a share of around 2.51 percent. Competitive landscape and regional variations While Google's overall dominance persists, other search engines carve out niches in various markets and platforms. Bing holds a 12.21 percent market share across desktop devices worldwide, as Yandex and Baidu have found success inside and outside of their home markets. Yandex is used by over 63 percent of Russian internet users, but Baidu has seen its market share significantly in China As regional variations highlight the importance of local players in challenging Google's global supremacy, the company is likely to face more challenges with the AI-powered online search trend and increasing regulatory scrutiny. Search behavior and antitrust concerns Despite facing more competition, Google remains deeply ingrained in users' online habits. In 2024, "Google" itself was the most popular search query on its own platform, followed by "YouTube" - another Google-owned property. This self-reinforcing ecosystem has drawn scrutiny from regulators, with the European Commission imposing millionaire antitrust fines on the company. As its influence extends beyond search into various online services, the company's market position continues to be a subject of debate among industry watchdogs and authorities worldwide.

[Keywords] Market include Mapbox, Openstreetmap, Here, Inrix, Zenrin

Coffee fell to 288.72 USd/Lbs on July 11, 2025, down 0.41% from the previous day. Over the past month, Coffee's price has fallen 16.63%, but it is still 15.90% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on July of 2025.

[Keywords] Market include The Trade Desk, ADWORDS, Flashtalking, MediaMath, FACEBOOK BUSINESS

[Keywords] Market include Quantcast Advertise, The Trade Desk, Choozle, MediaMath, Facebook Business

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

[Keywords] Market include Microsoft, Google, SAP, Facebook., Twitter

In January 2025, online search engine Bing accounted for almost *** percent of the global tablet search market, while market leader Google had a share of over ** percent. Meanwhile, Yandex's tablet search market share was of **** percent, while Yahoo! represented little less than *** percent on these devices.

In January 2025, Google accounted for 93.82 percent of the global mobile search engine market worldwide. Yandex had 2.5 percent of the global mobile search, while, competitors like Baidu and Yahoo! accounted for less than one percent each on a global scale.

[Keywords] Market include SAP, IBM, Yahoo! Inc, Google Cloud Platform, Hewlett-Packard Development Company Layered Technologies Inc

[Keywords] Market include ESPN, Yahoo, CBS, Fox Sports Fantasy Football, DraftKings

Google is not only popular in its home country, but is also the dominant internet search provider in many major online markets, frequently generating between ** and ** percent of desktop search traffic. The search engine giant has a market share of over ** percent in India and accounted for the majority of the global search engine market, way ahead of other competitors such as Yahoo, Bing, Yandex, and Baidu. Google’s online dominance All roads lead to Rome, or if you are browsing the internet, all roads lead to Google. It is hard to imagine an online experience without the online behemoth, as the company offers a wide range of online products and services that all seamlessly integrate with each other. Google search and advertising are the core products of the company, accounting for the vast majority of the company revenues. When adding this up with the Chrome browser, Gmail, Google Maps, YouTube, Google’s ownership of the Android mobile operating system, and various other consumer and enterprise services, Google is basically a one-stop shop for online needs. Google anti-trust rulings However, Google’s dominance of the search market is not always welcome and is keenly watched by authorities and industry watchdogs – since 2017, the EU commission has fined Google over ***** billion euros in antitrust fines for abusing its monopoly in online advertising. In March 2019, European Commission found that Google violated antitrust regulations by imposing contractual restrictions on third-party websites in order to make them less competitive and fined the company *** billion euros.

In February 2025, Microsoft Sites handled **** percent of all search queries in the United States. During the same period, Verizon Media (formerly known as Yahoo and Oath) had a search market share of little less than ** percent. Market leader Google generated **** percent of all core search queries in the United States.

[Keywords] Market include Activision Blizzard, DraftKings, FanDuel, FantasyDraft, EA