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.

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.

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.

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.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109AUSM293NNBR) from Jan 1897 to Sep 1916 about stock market, industry, price index, indexes, price, and USA.

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.

Complete historical financial dataset for General Dynamics Corporation

Dataset Overview

This dataset contains historical stock price data for Tesla, Inc. (TSLA) spanning from June 2010 to the present. The data includes daily records of Tesla's stock prices, covering various financial metrics for each trading day.

File Information

• File Name: TSLA.csv
• Number of Entries: 3499
• Number of Columns: 7
• File Size: 191.5 KB

Columns

1. Date (object): The date of the trading day in YYYY-MM-DD format.
2. Open (float64): The opening price of the stock on the given day.
3. High (float64): The highest price of the stock during the trading day.
4. Low (float64): The lowest price of the stock during the trading day.
5. Close (float64): The closing price of the stock on the given day.
6. Adj Close (float64): The adjusted closing price, accounting for corporate actions like stock splits and dividends.
7. Volume (int64): The total number of shares traded during the day.

Sample Data

Here are the first few rows of the dataset to give you a glimpse of the data structure:

Example Chart

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12685278%2F9479dbe596f198163a6641a5606bea6b%2FTS.png?generation=1716412477393105&alt=media" alt="">

(If you want to get the code for the chart, please access the [Tesla Stock Historical Data - Chart Examples] https://www.kaggle.com/code/girumwondemagegn/tesla-stock-historical-data-updated-may-2024) in my notebooks.)

Acknowledgements

The data has been sourced from publicly available financial records and is intended for educational and research purposes.

Complete historical financial dataset for Rekor Systems, Inc.

Graph and download economic data for Index of Stock Prices for Germany (M1123ADEM324NNBR) from Jan 1870 to Dec 1913 about stock market, Germany, and indexes.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about stock market, New York, indexes, and USA.

Finnhub is the ultimate stock api in the market, providing real-time and historical price for global stocks with Rest API and websocket. We also support a tons of other financial data like stock fundamentals, analyst estimates, fundamental data and more. Download the file to access balance sheet of Amazon.

Free historical options data, dataset files in CSV format.

ADBE Stock Prices Dataset

This dataset contains historical stock price data for Adobe Inc. (ticker: ADBE). The dataset provides valuable insights into the stock performance of Adobe Inc., making it useful for financial analysis, stock market prediction, and machine learning applications related to stock price forecasting.

Graph and download economic data for Financial Market: Share Prices for United Kingdom (SPASTT01GBM661N) from Dec 1957 to Sep 2025 about stock market and United Kingdom.

Graph and download economic data for Index of All Common Stock Prices for United States (M1125BUSM347NNBR) from Jan 1945 to Dec 1968 about stock market, indexes, and USA.

Context This dataset can be used to Predict the future Stock Prices of these companies.

Name of top Companies APPLE, AMAZON, GOOGLE, JP-MORGN, MIROSOFT, NETFLIX, NVIDIA, TESLA, VISA, WALMART

Content

This Dataset Contain data about

1.Date, 2. Opening value,

3.Closing value, 4.Lowest value,

5.Highest value, 6. Adj closing value ,

7.Volume , 8. Company

Acknowledgements Data from https://finance.yahoo.com

Inspiration ANALYSE THE STOCK DATA ,

COMPARE THE GROWTH OF THESE COMPANIES,

EDA

This study is based on the historical data for some of the indicators on the Egyptian Stock Exchange (EGX), in order to build a prediction model with high accuracy. Data used in this study are purchased from Egypt for Information Dissemination (EGID) which is a Governmental organization that provides data for EGX. The data contain six stock market indices; for example, EGX-30 index local currency is used for interest estimates and denominated in US dollars. It measures top 30 firms in liquidity and activity. The second index used in this study is EGX-30- Capped which is designed to track performance of the most traded companies in accordance with the rules set for mutual funds. The third index is EGX-70 which aims at providing wider tools for investors to monitor market performance. EGX-100 index as a forth dataset evaluates performance of the 100 active firms, including 30 of EGX- 30 index as well as 70 of EGX-70 index. NIlE index avoids concentration on one industry and therefore has a good representation of various industries/sectors in the economy, and the index is weighted by market capitalization and adjusted by free float. The last index is EGX-50-EWI which tracks top 50 companies in terms of liquidity and activity. The index is designed to balance the impact of price changes among the constituents of the index as they will have a fixed weight of 2% at each quarterly review.

ABOUT SAP:

Headquartered in Walldorf, Germany, SAP is the market leader in enterprise application software. Founded in 1972, SAP (which stands for "Systems, Applications, and Products in Data Processing") has a rich history of innovation and growth as a true industry leader.

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ABOUT DATASET:

This dataset contains detailed historical stock price data for SAP, covering the period from 09/22/1995 to 06/14/2024. The data is collected from Yahoo Finance and includes daily records of the stock's opening price, highest price, lowest price, closing price, and trading volume. Each entry in the dataset represents a single trading day, providing a comprehensive view of the stock's price movements and market activity.

PURPOSE OF DATASET

The purpose of this dataset is to provide analysts, traders, and researchers with accurate and granular historical stock price data for SAP. This data can be used for various applications, including:

• Technical Analysis: Identify trends and patterns in the stock's price movements. Calculate technical indicators such as moving averages, RSI, and Bollinger Bands.

• Market Sentiment Analysis: Analyze how the stock's price responds to market events and news. Compare the opening and closing prices to understand daily sentiment.

• Algorithmic Trading: Develop and test trading algorithms based on historical price and volume data. Use past price movements to simulate trading strategies.

• Predictive Modeling: Build models to forecast future prices and trading volumes. Use historical data to identify potential price movements and market trends.

• Educational Purposes: Serve as a teaching tool for financial education. Help students and researchers understand the dynamics of stock price changes and market behavior.

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this dataset offers a solid foundation for a wide range of financial analyses and trading applications.

With LSEG's Tokyo Stock Exchange (TSE) Data, gain full access to benchmarks, indices, reference data, market depth data, and more.