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.

Experience a decade of NASDAQ market dynamics with this comprehensive historical price dataset from 2014 to 2024.

The NASDAQ Composite is a benchmark index representing the performance of more than 2,500 stocks listed on the NASDAQ stock exchange, encompassing various sectors including technology, healthcare, and finance. This dataset, sourced meticulously from Yahoo Finance, offers daily insights into the index's opening, highest, lowest, and closing prices, along with adjusted close prices and daily volume.

We offer historical price data for equity indexes, ETFs and individual stocks in a Open/High/Low/Close (OHLC) format and can add almost any other required metric. We cover all major markets and many minor markets. Available for one-time purchase or with regular updates. Real-time/near-time (usually anything quicker than a 15min delay) requires an additional licence from the respective exchange, anything slower does not.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The dataset contains historical technical data of Dhaka Stock Exchange (DSE). The data was collected from different sources found in the internet where the data was publicly available. The data available here are used for information and research purposes and though to the best of our knowledge, it does not contain any mistakes, there might still be some mistakes. It is not encourages to use this dataset for portfolio management purposes and use this dataset out of your own interest. The contributors do not hold any liability if it is used for any purposes.

Update

Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.

Content

This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart

Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.

Acknowledgements

List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average

Thanks to https://iextrading.com for providing this data for free!

Terms of Use

Data provided for free by IEX. View IEX’s Terms of Use.

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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.
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The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

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