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.

Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

During the coronavirus (COVID-19) pandemic, AT&T have suffered the largest drop in share prices, falling from ***** U.S. dollars per share to ***** U.S. dollars. T-Mobile's share prices were boosted by the successful merger with Sprint Corp. on 1 April 2020.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The main stock market index of United States, the US500, fell to 6734 points on October 21, 2025, losing 0.02% from the previous session. Over the past month, the index has climbed 0.60% and is up 15.09% 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 October of 2025.

MercadoLibre (MELI) Stock Price Dataset (2007-2025)

📌 Dataset Overview

This dataset contains historical stock market data for MercadoLibre (MELI) from August 10, 2007, to March 16, 2025. It provides key financial indicators such as Open, High, Low, Close (OHLC) prices, Adjusted Close prices, and Trading Volume for each trading day.

📊 Features

The dataset includes the following columns:

📈 Possible Use Cases

This dataset is valuable for: - Stock Market Analysis: Analyze trends in MercadoLibre's stock performance over time. - Time Series Forecasting: Build machine learning models to predict future stock prices. - Technical Analysis: Identify patterns using OHLC data for trading strategies. - Financial Research: Study the impact of macroeconomic factors on stock prices.

🔍 Data Source

The dataset is compiled from stock market historical data sources and is updated Weekly.

🏷️ Keywords (SEO Optimized)

• MercadoLibre stock data
• MELI stock price history
• Stock market dataset
• Financial time series data
• OHLC stock prices
• Trading volume dataset
• Machine learning stock prediction

📥 Download & Usage

You can download the dataset and use it for research, trading analysis, and machine learning models. If you find this dataset useful, consider giving it a ⭐ on Kaggle!

Contect info:

You can contect me for more data sets

-E_mail

-Linkdin

-Kaggle

-X

-Github

📢 Note: This dataset is for educational and research purposes only. It should not be considered financial advice.

The Netflix Stock Price Dataset provides historical trading data including date, opening, high, low, closing, adjusted closing prices, and trading volume. It is ideal for financial analysis, forecasting, and machine learning applications.

Overview

20 years of historical US stock prices from AMEX, Nasdaq and NYSE markets with data for 7000+ stocks.

Content

There are four files in this dataset:

1. dataset_summary.csv: Summary table for all stocks in this dataset.
2. earnings_latest.csv: Earnings data for each stock with earnings date, estimated EPS and reported EPS.
3. stock_prices.csv: Daily stock price data with open, close, low, high, trading volumes and adjusted closing prices as well as stock split coefficient.
4. dividends_latest.csv: Dividend data for each stock with dividend date.

Updates

I'm updating the data in batches, so don't expect that all stocks will have prices up to the same date. It could take up to two weeks for individual stocks to catch up. I'll try to upload updated data at least once a month.

Data sources

The data was scraped from different data sources:

• Nasdaq
• Yahoo Finance
• Zacks
• Alpha Vantage

1) Data Introduction • The Twitter Stock Prices Dataset contains stock price data for Twitter from November 2013 to October 2022. This dataset is a time series dataset that provides daily stock trading information. • The key attributes include the stock's opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and volume (Volume).

2) Data Utilization (1) Characteristics of the Twitter Stock Prices Data • This dataset is a time series, offering daily stock price fluctuations and allows tracking of price changes over time. • It includes 7 main attributes related to stock trading, allowing for analysis of price movements (open, high, low, close) and volume, to better understand Twitter’s stock price dynamics. • This data helps analyze market trends, price volatility patterns, and price fluctuation analysis, providing insights into the dynamics of the stock market.

(2) Applications of the Twitter Stock Prices Data • Predictive Modeling: This dataset can be used to develop stock price prediction models, including predicting price increases/decreases or forecasting future stock prices using machine learning models. • Business Insights: Investment experts can use this dataset to evaluate Twitter’s stock performance, and it provides useful information for optimizing investment strategies in response to market changes. This dataset can be used for trend forecasting and investor analysis. • Trend Analysis: By analyzing stock upward/downward trends, this dataset can help evaluate the company's market performance and develop trend-based investment strategies.

1) Data Introduction • The S&P 500 stock data is a tabular stock market dataset of daily stock price information (market, high price, low price, closing price, trading volume, etc.) for the last five years (the latest data is until February 2018) of all companies in the S&P 500 index.

2) Data Utilization (1) S&P 500 stock data has characteristics that: • Each row contains key stock metrics such as date, open, high, low, close, volume, and stock ticker name. • Data is provided as individual stock files and all stock integrated files, so it can be used for various analysis purposes. (2) S&P 500 stock data can be used to: • Stock Price Forecasting and Investment Strategy Development: Using historical stock price data, a variety of investment strategies and forecasting models can be developed, including time series forecasting, volatility analysis, and moving averages. • Market Trends and Corporate Comparison Analysis: It can be used to visualize stock price fluctuations across stocks, compare performance between stocks, analyze market trends, optimize portfolios, and more.

This line chart displays closing price by date using the aggregation sum. The data is filtered where the stock is IPO. The data is about stocks per day.

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.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

This dataset are used to predict stock price movement project that contains of historical stock price and sentiment analysis result (sentiment score). The project aims to improve stock price prediction analysis result by integrating historical stock price and sentiment analysis result. This dataset has been preprocessed. The following of data description detail below :

• Price = Closing stock price
• Open = Opening Price
• High = Highest stock price of the day
• Low = Lowest stock price of the day
• mean_comp = The average value of the compound sentiment score calculated based on all comments that appear in one day.

Japan TRI: Closing Price: TOPIX Small data was reported at 2,754.700 JPY in Apr 2020. This records an increase from the previous number of 2,635.370 JPY for Mar 2020. Japan TRI: Closing Price: TOPIX Small data is updated monthly, averaging 2,599.060 JPY from Jul 2013 (Median) to Apr 2020, with 82 observations. The data reached an all-time high of 3,523.880 JPY in Jan 2018 and a record low of 1,652.640 JPY in Aug 2013. Japan TRI: Closing Price: TOPIX Small data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z016: Tokyo Stock Exchange: Total Return Index.

This dataset contains comprehensive historical trading data for Amazon, including daily open, high, low, and close prices, as well as trading volume, dividends, and stock splits. The data spans a significant time frame, offering insights into Amazon's stock performance over time. Ideal for investors, financial analysts, and data scientists, this dataset can be used for trend analysis, backtesting trading strategies, and understanding market behavior. Whether you're studying Amazon's stock history or developing predictive models, this dataset provides the essential data you need

Data Overview

1. Datetime: This column records the date and time when the stock prices were observed.

2. Open: This is the opening price of the stock for the given time period.

3. High: This represents the highest price at which the stock is traded during the specified time period

4. Low: This is the lowest price at which the stock is traded during the specified time period.

5. Close: This is the closing price of the stock for the given time period.

6. Volume: This column records the total number of shares of the stock that were traded during the specified time period.

7. Dividends: This column records any dividend payments that occurred on the specified date. Dividends are distributions of a company's earnings to its shareholders.

8. Stock Splits: This column records any stock splits that occurred on the specified date. A stock split is a corporate action in which a company increases the number of its outstanding shares by issuing more shares to its current shareholders.

This line chart displays closing price by date using the aggregation sum. The data is filtered where the stock is CUT. The data is about stocks per day.