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.

Context

Visa Inc. is a stock corporation and, alongside Mastercard, one of the two major payment card companies and employed around 14,200 people in 2016. The VISA brand is a recursive acronym and stands for Visa International Service Association.

As of August 2025 Visa has a market cap of $670.57 Billion USD. This makes Visa the world's 16th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: March 2008- Aug 2025

Unit of analysis: Visa Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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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.

This dataset contains historical stock data for CVS Health, including daily opening and closing prices, highest and lowest prices of the day, adjusted closing prices, and trading volume.

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Columns Information

Date

• Description: The specific day the stock data was recorded.
• Data Type: Date (Format: YYYY-MM-DD)

Open

• Description: The price at which the stock started trading when the market opened.
• Data Type: Numeric (Format: USD)

High

• Description: The highest price at which the stock traded during the day.
• Data Type: Numeric (Format: USD)

Low

• Description: The lowest price at which the stock traded during the day.
• Data Type: Numeric (Format: USD)

Close

• Description: The price at which the stock ended trading when the market closed.
• Data Type: Numeric (Format: USD)

Adj Close

• Description: The closing price adjusted for dividends and stock splits.
• Data Type: Numeric (Format: USD)

Volume

• Description: The total volume of shares traded during the day.
• Data Type: Integer

The dataset provides valuable insights into the stock performance of CVS Health over time, starting from February 22, 1973.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-10-02 about VIX, volatility, stock market, and USA.

FNSPID: A Comprehensive Financial News Dataset in Time Series

  Description

FNSPID is a meticulously curated dataset designed to support research and applications in the field of financial news analysis within the context of time-series forecasting. Our dataset encompasses a wide range of financial news articles, providing a rich resource for developing and testing models aimed at understanding market trends, investor sentiment, and other critical financial indicators. Link… See the full description on the dataset page: https://huggingface.co/datasets/Zihan1004/FNSPID.

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.

List of companies in the NYSE, and other exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository: ...

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan

This dataset offers a detailed and organized set of financial data, enabling users to analyze company performance, conduct stock market research, and develop predictive models. It spans multiple financial aspects, such as annual and quarterly profit and loss statements, balance sheets, cash flow data, financial ratios, and market prices.

The data is structured to support time-series analysis, with datasets covering financial metrics at T0 (financial statements) and T1 (market prices).

This makes it particularly useful for applications requiring cross-temporal insights or forecasting.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie

The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)

Background research data

The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

• Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.
• LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.
• Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.
• Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague.
• Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.
• Climate Change: Computations of climate-related parameters based on KNMI climate scenarios.
• Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.

Input data

Geographic divisions

• The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service.
• District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague.
• The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.
• The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague.
• These layers have been combined in the GeographicDivisions_TheHague GeoPackage.

BAG data

• BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page.
• In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline.
• The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage.

3D BAG

• Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website.
• These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash.
• Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage.
• Ground elevation data was obtained from the Pand layer.
• Both of these layers were clipped to match The Hague's outline.
• Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building.
• The resulting data can be found in the BAG_buildings_TheHague GeoPackage.

Energy labels

• Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv.

UHI effect data

• A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff.

Output data

• The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage.
• The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:
  • SQ: Status Quo, covering the 2018-2022 reference period.
  • 2030: An average scenario projected for the year 2030.
  • 2050_L: A low-impact, best-case scenario for 2050.
  • 2050_M: A medium-impact, moderate scenario for 2050.
  • 2050_H: A high-impact, worst-case scenario for 2050.

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.