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.

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.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

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 30 September 2021.

--- 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 ---

Real-world data extracted from Yahoo Finance regarding the evolution of NVIDIA's stock market in 2023.

Great dataset for practicing ETL techniques and employing various formulas for data analysis.

Table contains values such as Months - Dates - Open - Close - ADJ Close - High - Low - Volume.

Explore how Yahoo Finance serves as a key resource for tracking soybeans, offering real-time analytics, historical insights, and expert commentary on the global soybean market's trends, supply chain dynamics, and economic impact.

Explore wheat prices on Yahoo Finance and understand the various factors influencing market trends, from global demand and weather conditions to government policies and financial analysis tools. Discover real-time data and insightful charting on the commodity's performance.

Content

Daily price data for World indices stock exchanges from all over the world (United States, China, Canada, Germany, Japan, and more). The data was all collected from Yahoo Finance, which had several decades of data available for most exchanges. Prices are quoted in terms of the USD currency of where each exchange is located.

Acknowledgement

Data collected from Yahoo Finance.

This article examines the recent modest gains in cotton prices amid a fluctuating market, highlighting insights from Yahoo Finance and IndexBox on production challenges and market dynamics.

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

According to Cognitive Market Research, the global gaming market will be USD 251269.0 million in 2024 and will expand at a compound annual growth rate (CAGR) of 9.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 100505.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 75379.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 57790.77 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.6% from 2024 to 2031.
Latin America's market will have more than 5% of the global revenue with a market size of USD 12563.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 5025.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2024 to 2031.
The smartphone held the highest share in the gaming market revenue share in 2024.

Market Dynamics of Gaming Market

Key Drivers of Gaming Market

Rise in mobile gaming fuels the gaming market

The mobile game is one of the most transformative drivers of the global gaming market, fundamentally reshaping how games developed monetized and consumed. Mobile gaming is the largest and the fast-growing segment in the gaming market, accounting for more than 50% of the total gaming industry revenue. The global penetration of smartphones has helped create a massive and always connected user base. The availability affordable smart phones, and low-cost mobile data has made gaming accessible to people all ages and incomes level even in emerging markets like India and Brazil.

For instance, the global mobile gaming market generating 30 billion installs in the first-half of 2024. 
India led with a growth of 6.6% in installs, followed by Brazil at 4.9%.

(Source:https://mobilemarketingreads.com/mobile-gaming-market-soars-with-over-30-billion-installs-in-h1-2024/ )

The rise of freemium business models in games like Candy Crush and Genshin Impact have also been highly effective. Most gaming apps are free to download and generate revenue through in-app purchases and advertisements.

(Source: https://sg.finance.yahoo.com/news/electronic-arts-ea-launches-super-120100836.html )

Restraint Factors Of Gaming Market

Addiction Issues from Intense Gaming to Restrict Market Growth

Addiction issues stemming from intense gaming have become prevalent, raising concerns about mental health and social repercussions. Despite this, the gaming market continues to expand rapidly, driven by technological advancements and a rising consumer base. However, it's imperative to exercise restraint, balancing gaming with other activities to maintain overall well-being. Moderation in gaming can safeguard against addiction-related issues, fostering healthier habits and promoting a more balanced lifestyle.

Impact of COVID-19 on the Gaming Market

The COVID-19 pandemic significantly impacted the gaming market, leading to a rise in need as people sought entertainment at home during lockdowns. With more time spent indoors, there was a notable increase in gaming hardware and software sales and online gaming subscriptions. This shift accelerated the industry's digital transformation, emphasizing the importance of virtual communities and online multiplayer experiences. Overall, COVID-19 catalyzed growth and innovation within the gaming sector. Introduction of the Gaming Market

The global gaming market covers a wide range of products and services including game development, marketing, distribution and monetization. It includes gaming across various platforms such as, gaming consoles like PlayStation, Xbox, PCs, mobile phones and online browsers. The market also includes hardware related to gaming, like consoles, hardware, VR headset and others. Games can be monetized through various methods. Most common way to monetize games include in-game purchases, game sales, subscription fees and advertising. Gaming is by far the fastest growing sector in the media industry, across the globe.

Several factors such as increased internet penetration faster processors, new hardware with improved ...

Discover where to find the most recent stock prices for Daawat, listed under L.T. Foods Ltd., on platforms like Bloomberg, Reuters, Yahoo Finance, BSE, and NSE. Learn about reliable financial data sources and tools to make informed investment decisions in the rice industry.

The Yahoo Finance Bitcoin Historical Data, spanning from 2014 to 2023, serves as a valuable resource for enthusiasts, data analysts, researchers, and traders alike. Capturing the evolution of Bitcoin's price over a decade, this dataset opens the doors to a plethora of insights and opportunities for understanding the dynamic world of cryptocurrencies.

Inspiration and Benefits: This meticulously curated dataset inspires data explorers to delve deep into the digital currency landscape and unlocks the potential to uncover hidden patterns and trends. Researchers can utilize this wealth of historical data to investigate the correlations between Bitcoin's price movements and various external factors like market sentiment, macroeconomic events, and regulatory changes.

For traders and investors, this dataset acts as a compass, providing historical context and aiding in the development of more informed trading strategies. Studying past market cycles, identifying support and resistance levels, and analyzing price volatility empowers them to make well-calculated decisions in an ever-fluctuating crypto market.

Exploring the Data Data explorers can visualize Bitcoin's price charts over the years, applying technical indicators and conducting in-depth statistical analyses. They can explore the impact of halving events, examine market reactions to major news events, and compare Bitcoin's performance against other assets, revealing the cryptocurrency's potential role as a diversification tool in investment portfolios.

Questions to Solve and Problems to Address: The dataset encourages researchers to seek answers to intriguing questions. For instance, they can investigate whether Bitcoin's price movements follow any discernible patterns or if there are long-term trends indicative of the asset's maturation. Addressing challenges, such as Bitcoin's correlation with traditional markets during times of economic turmoil, can provide valuable insights into its role as a potential safe-haven asset.

Moreover, researchers can analyze Bitcoin's adoption trends, its use in cross-border transactions, and the impact of regulatory developments on its price and acceptance in the global financial ecosystem.

Overview This dataset provides a comprehensive collection of daily stock price data for Nvidia Corporation (NVDA), spanning a 20-year period from January 2, 2004, to January 1, 2024. Nvidia, a global leader in graphics processing units (GPUs) and AI technologies, has experienced exponential growth, particularly in recent years as it became a major player in artificial intelligence, gaming, and autonomous vehicles. This dataset captures key market movements and trends during Nvidia’s significant rise to prominence.

About the Dataset The dataset contains crucial financial data for Nvidia's stock, including opening, high, low, and closing prices, as well as trading volume for each day in the 20-year period. This data is ideal for conducting a variety of financial analyses, ranging from simple trend observation to complex predictive modeling using machine learning algorithms such as LSTM (Long Short-Term Memory). Traders, financial analysts, and data scientists can use this dataset to backtest trading strategies, develop stock market prediction models, and perform time series analysis on stock price movements.

Attribute Information Date: The date of the stock price record. Open: The stock price at the beginning of the trading day. High: The highest price Nvidia stock reached during the day. Low: The lowest price Nvidia stock reached during the day. Close: The stock price at the end of the trading day. Volume: The total number of Nvidia shares traded during the day.

Key Insights from the Dataset Over the past two decades, Nvidia has gone through various phases of growth, most notably its dramatic rise after the 2010s, which was fueled by the growing demand for GPUs in the gaming industry, as well as Nvidia’s breakthroughs in artificial intelligence (AI) and deep learning. The dataset captures periods of market volatility, growth spurts, and consolidations, providing ample opportunities for in-depth analysis of Nvidia's market behavior.

Usage This dataset can be used for various types of financial analysis and machine learning tasks, including:

Trend Analysis: Track Nvidia’s stock price trends over time and identify key inflection points. Technical Analysis: Use the provided historical data to calculate various technical indicators such as Moving Averages, RSI (Relative Strength Index), Bollinger Bands, MACD (Moving Average Convergence Divergence), and more, allowing for a deeper understanding of price patterns.

Stock Price Prediction: Leverage machine learning algorithms like LSTM and ARIMA to predict future stock price movements based on the historical data. Backtesting Trading Strategies: Test and optimize trading strategies using Nvidia’s historical price data to simulate real-world trading conditions.

Data Collection Methodology The data was collected from Yahoo Finance using the yfinance Python library, which provides easy access to historical stock price data. This dataset covers the daily stock prices of Nvidia Corporation (NVDA) from January 2, 2004, to January 1, 2024, with each entry representing a single trading day. The data includes fields for open, high, low, and close prices, along with the trading volume for each day.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

This dataset contains historical price data for Bitcoin (BTC) against the U.S. Dollar (USD), spanning from June 2010 to November 2024. The data is organized on a daily basis and includes key market metrics such as the opening price, closing price, high, low, volume, and market capitalization for each day.

Columns: The dataset consists of the following columns:

Date: The date of the recorded data point (format: YYYY-MM-DD). Open: The opening price of Bitcoin on that day. High: The highest price Bitcoin reached on that day. Low: The lowest price Bitcoin reached on that day. Close: The closing price of Bitcoin on that day. Volume: The total trading volume of Bitcoin during that day. Market Cap: The total market capitalization of Bitcoin on that day (calculated by multiplying the closing price by the circulating supply of Bitcoin at the time). Source: The data is sourced from Yahoo Finance.

Time Period: The data spans from June 2010, when Bitcoin first began trading, to November 2024. This provides a comprehensive view of Bitcoin’s historical price movements, from its early days of trading at a fraction of a cent to its more recent valuation in the thousands of dollars.

Use Cases:

This dataset is valuable for a variety of purposes, including:

Time Series Analysis: Analyze Bitcoin price movements, identify trends, and develop predictive models for future prices. Financial Modeling: Use the dataset to assess Bitcoin as an asset class, model its volatility, or simulate investment strategies. Machine Learning: Train machine learning algorithms to forecast Bitcoin’s future price or predict market trends based on historical data. Economic Research: Study the impact of global events on Bitcoin’s price, such as regulatory changes, technological developments, or macroeconomic factors. Visualization: Generate visualizations of Bitcoin price trends, trading volume, and market capitalization over time.

Baltic Dry fell to 2,024 Index Points on September 1, 2025, down 0.05% from the previous day. Over the past month, Baltic Dry's price has risen 2.74%, and is up 5.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on September of 2025.