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.

The main stock market index of United States, the US500, rose to 6271 points on July 14, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 3.94% and is up 11.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-13 to 2025-07-11 about stock market, average, industry, and USA.

Context

Stock time series are a favourite among data scientists because they are easily understood and widely available - in this extensive data set you will find long-time time-series with open/close/high/min/adjusted features, as well as data regarding stock splits, trading volume and dividends.

Content

This data set includes Dow Jones member stock prices (status 01.0.1.2021) with all their historic stock performances from 01.01.2020 to 31.12.2020.

• 30 Dow Jones stocks
• 21 years of data (depending on company age)
• 1 entry per day
• 150503 data points

Please also check the corresponding Jupyter Notebook to get some basic ideas how to use this data set: https://www.kaggle.com/deeplytics/dow-jones-historic-stock-data-2000-2020

Stock Names

In the data set, all companies use their stock ticker names. If you are unfamiliar with them, please check this overview: https://www.cnbc.com/dow-30/

Inspiration

Today's free APIs and coding libraries make it relatively easy for the average user to get an understanding of stock price movements. More advanced users may even be able to find patterns, that can be incorporated into investment decisions.

Acknowledgements

Photo by Dmitry Demidko on Unsplash: https://unsplash.com/photos/eBWzFKahEaU?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

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.

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.

Dow30 Stock Prediction Dataset

  Overview

Welcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period.

  Dataset Structure

The dataset consists of the following columns:

prompt: Information about the company… See the full description on the dataset page: https://huggingface.co/datasets/descartes100/Dow30_stock_prediction.

The dataset consists of daily data from US Dow Jones for 501 large companies, over the time span 2010-2016, while monthly publicly available indexes are also used.

The main stock market index of United States, the US500, rose to 6296 points on July 15, 2025, gaining 0.44% from the previous session. Over the past month, the index has climbed 4.36% and is up 11.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Japan's main stock market index, the JP225, fell to 39519 points on July 14, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.15%, though it remains 4.25% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

The dataset consists of companies listed in the S&P500, stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United State.

The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges (including the 30 companies that compose the Dow Jones Industrial Average)

The S&P500 or SPX is the most commonly followed equity index, it covers about 80 percent of the American equity market by capitalization.

The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although called the S&P 500, the index contains 505 stocks

Dataset

This dataset was created by ElvisD

Released under Other (specified in description)

Contents

The collected tweets and Earnings Announcements (EA) cover the period of three years, from June 1, 2013 to June 3, 2016. Companies are ordered by the total number of tweets collected. For each company, there is the sentiment distribution, market capitalization, and the prevailing timing of EAs with respect to the NYSE trading hours. Each company issues four EAs per year, therefore there is a total of 360 EAs (30 companies, three years, four EAs per year)1.

This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people’s search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.

Indonesia's main stock market index, the JCI, rose to 7113 points on July 15, 2025, gaining 0.22% from the previous session. Over the past month, the index has declined 0.07% and is down 1.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on July of 2025.

This data set has been collected for "User2Vec: stock market prediction using deep learning with a novel representation of social network users" paper. Stock market prediction is an interesting and challenging problem for investors and financial analysts. Recently, recurrent neural networks like LSTM have shown good performance in the field of stock market prediction. Most current methods use historical market data and in some cases, the dominant direction of users and news for each day. In some cases, the opinions of social network members about the stocks are extracted to improve the prediction accuracy. Usually, the opinions of different users are treated in the same way and are given the same weights in these works. However, it is clear that these opinions have different values based on the accuracy of the prediction of the related user. In this study, the idea is to convert the opinion of each user about each stock into a vector (User2Vec) and then use these vectors to train a Recurrent Neural Network (RNN) and ultimately model the behavior of the users in the market. The proposed user representation is composed of the features extracted from the messages posted in a social network and the market data. Here, we consider the power of the user in predicting the future of the stock based on the social network metrics, e.g. the number of the followers of the user, and the accuracy of its previous predictions. This way, the number of training data is increased and the model is effectively learned. These data are then used to train a stacked bidirectional LSTM network used for aggregating the input data and providing the final prediction. Empirical studies of the proposed model on 30 stocks of 30 Dow Jones clearly shows the superiority of the proposed model over traditional representations. For example, the prediction accuracy is about 93% for the Apple stock which is much higher than the compared models.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 14 companies listed on the Dominican Republic Stock Exchange (XBVR) in Dominican Republic. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Dominican Republic:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Dominican Republic:

Dow Jones Dominican Republic Index: The Dow Jones Dominican Republic Index represents the performance of companies listed on the Dominican Republic Stock Exchange (Bolsa de Valores de la República Dominicana). It serves as a benchmark for tracking the overall market performance in the country.

Banco Popular Dominicano: Banco Popular Dominicano is one of the largest banks in the Dominican Republic, offering a range of banking and financial services to individuals and businesses. The securities of Banco Popular Dominicano are actively traded on the Dominican Republic Stock Exchange.

Grupo Financiero BHD León: Grupo Financiero BHD León is a financial group that operates in the Dominican Republic, providing banking, insurance, and financial services. The securities of Grupo Financiero BHD León are listed and traded on the Dominican Republic Stock Exchange.

Banco de Reservas de la República Dominicana: Banco de Reservas, also known as Banreservas, is the state-owned bank of the Dominican Republic. It offers a wide range of banking and financial services to customers. The securities of Banreservas are listed on the Dominican Republic Stock Exchange.

Altice Dominicana: Altice Dominicana is a subsidiary of Altice Group, a multinational telecommunications company. Altice Dominicana provides telecommunication services in the Dominican Republic. The securities of Altice Dominicana are listed and traded on the Dominican Republic Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Dominican Republic, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Dominican Republic ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Dominican Republic?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Dominican Republic exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Short/Long Term Stocks: Dow Jones New Zealand Index Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data