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.

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.

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.

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.

Complete historical financial dataset for General Dynamics Corporation

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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.

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.

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MetaData

Crude Oil Stock Price Data (2000-2024)

Dataset Description

This dataset contains historical stock price data for Crude Oil from 2000 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Crude Oil's stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Crude Oil stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Crude Oil stock at the end of each trading day.

High: The highest price reached by Crude Oil stock during the trading day.

Low: The lowest price reached by Crude Oil stock during the trading day.

Open: The price of Crude Oil stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

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.

Free historical options data, dataset files in CSV format.

STOCK narxlar tarixini kuzatish kriptovalyuta investorlariga o'z investitsiyalari samaradorligini osongina kuzatish imkonini beradi. Vaqt o'tishi bilan STOCK uchun ochilish qiymati, yuqori va yopilishini hamda savdo hajmini qulay tarzda kuzatishingiz mumkin. Bundan tashqari, siz kunlik o'zgarishlarni bir zumda foiz sifatida ko'rishingiz mumkin, bu esa sezilarli tebranishlar bo'lgan kunlarni aniqlashni osonlashtiradi. Bizning STOCK narxlari tarixiy ma'lumotlariga ko'ra, uning qiymati 2025-10-19da misli ko'rilmagan cho'qqigacha ko'tarilib, -- AQSh dollaridan oshib ketdi. Boshqa tomondan, STOCK narxlari traektoriyasidagi eng past nuqta, odatda “STOCK barcha vaqtlardagi eng past” deb ataladigan nuqta 2025-10-19 da sodir bo'ldi. Agar kimdir shu vaqt ichida STOCK xarid qilgan bo'lsa, u hozirda 0% miqdorida ajoyib foyda olishi mumkin edi. Maqsadga ko'ra 999,987,611.46 STOCK yaratiladi. Hozirda STOCK aylanma ta'minoti taxminan 999,987,600 ni tashkil qiladi. Ushbu sahifada keltirilgan barcha narxlar ishonchli manba Bitgetdan olingan. Investitsiyalaringizni tekshirish uchun bitta manbaga tayanish juda muhim, chunki qiymatlar turli sotuvchilar orasida farq qilishi mumkin. Tarixiy STOCK narxlari ma'lumotlar to'plamimiz 1 daqiqa, 1 kun, 1 hafta va 1 oy oralig'idagi ma'lumotlarni o'z ichiga oladi (ochiq/yuqori/past/yopiq/hajm). Ushbu ma'lumotlar to'plamlari izchillik, to'liqlik va aniqlikni ta'minlash uchun qattiq sinovdan o'tkazildi. Ular maxsus savdo simulyatsiyasi va test sinovlari uchun mo'ljallangan bo'lib, ularni bepul yuklab olish mumkin va real vaqt rejimida yangilanadi.

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

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