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.

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.

The main stock market index of United States, the US500, rose to 6421 points on September 3, 2025, gaining 0.09% from the previous session. Over the past month, the index has climbed 1.44% and is up 16.33% 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 September of 2025.

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.

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.

United Kingdom's main stock market index, the GB100, fell to 9208 points on September 5, 2025, losing 0.09% from the previous session. Over the past month, the index has climbed 0.48% and is up 12.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on September of 2025.

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.

NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

Abstract copyright UK Data Service and data collection copyright owner. The dataset contains monthly indices of returns for the British equity market covering the period 1825-70. The main data source used is the Course of the Exchange, a stockbroker list for the London Stock Exchange. All common equities from this list are included apart from some stocks for which there is insufficient or missing data and stocks which listed for less than 12 months. Using monthly stock prices from the Course of the Exchange, the team computed capital appreciation, dividend yield and total return for the overall market and for the thirteen industrial/commercial sectors on the market. These returns were computed using three weighting techniques - weighted by market capitalization, weighted by paid-up capital, and equally weighted (or unweighted). Monthly total market capitalization and paid-up capital is also reported for the overall market and for each of the thirteen sectors. In an attempt to control for survivorship bias, adjustments are made to the total returns using three different strategies. Using these strategies, the lower and upper bound estimates of shareholder returns are established. Main Topics: The stock indices currently available for the 1825-1870 period are based on a small sample of equity stocks. Indeed, the best index, that of Gayer at al, only covers the period up to 1850. The aim of this study was to construct a comprehensive dataset of British equity prices for the 1825-70 period, using share price data from The Course of the Exchange and The Railway Times. As well as estimating total market capitalisation for the period mentioned, this study also shows sectoral indices. These indices will enable researchers to address three important research questions. Firstly, it will be possible to measure the growth of the market for equity and analyse which sectors contributed to the growth. Secondly, using this data will make it possible to shed some light on the extent to which uncalled capital was used and to determine the riskiness and value of uncalled capital. Thirdly, this study provides the possibility to assess the impact of liberalisation of company law on the development on the equity market.

Hong Kong's main stock market index, the HK50, rose to 25418 points on September 5, 2025, gaining 1.43% from the previous session. Over the past month, the index has climbed 2.04% and is up 47.81% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on September of 2025.

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.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2023 are final. The figures for 2024 and 2025 are provisional.

Changes as of 24 June 2025: The figures for the first quarter of 2025 are available. Figures for 2023 and 2024 have been adjusted due to updated information. The figures for 2023 are final. In the context of the revision policy of National accounts, the dividend tax has been adjusted as of the fourth quarter of 2006. The revised registration aligns more closely with the accrual principle of ESA 2010.

Changes as of 10 April 2025: Due to an error made while processing the data, the initial preliminary figures for the government financial balance sheet in 2024 were calculated incorrectly. This causes a downward revision in other accounts payable.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

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.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

The main stock market index of United States, the US500, fell to 6482 points on September 5, 2025, losing 0.32% from the previous session. Over the past month, the index has climbed 2.15% and is up 19.84% 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 September of 2025.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

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Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

According to Cognitive Market Research, the global Big Data marketsize is USD 40.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 12.9% from 2024 to 2031. Market Dynamics of Big Data Market Key Drivers for Big Data Market Increasing demand for decision-making based on data - One of the main reasons the Big Data market is growing is due to the increasing demand for decision-making based on data. Organizations understand the strategic benefit of using data insights to make accurate and informed decisions in the current competitive scenario. This change marks a break from conventional decision-making paradigms as companies depend more and more on big data analytics to maximize performance, reduce risk, and open up prospects. Real-time processing, analysis, and extraction of actionable insights from large datasets enables businesses to react quickly to consumer preferences and market trends. The increasing need to maximize performance, reduce risk, and open up prospects is anticipated to drive the Big Data market's expansion in the years ahead. Key Restraints for Big Data Market The lack of integrator and interoperability poses a serious threat to the Big Data industry. The market also faces significant difficulties because of the realization of its full potential. Introduction of the Big Data Market Big data software is a category of software used for gathering, storing, and processing large amounts of heterogeneous, dynamic data produced by humans, machines, and other technologies. It is concentrated on offering effective analytics for extraordinarily massive datasets, which help the organization obtain a profound understanding by transforming the data into superior knowledge relevant to the business scenario. Additionally, the programmer assists in identifying obscure correlations, market trends, customer preferences, hidden patterns, and other valuable information from a wide range of data sets. Due to the widespread use of digital solutions in sectors such as finance, healthcare, BFSI, retail, agriculture, telecommunications, and media, data is increasing dramatically on a worldwide scale. Smart devices, soil sensors, and GPS-enabled tractors generate massive amounts of data. Large data sets, such as supply tracks, natural trends, optimal crop conditions, sophisticated risk assessment, and more, are analyzed in agriculture through the application of big data analytics.

Unlock in-depth beauty industry insights with our Ulta Beauty Dataset, featuring 33,000 records in CSV format. This beauty cosmetics dataset provides detailed product listings, pricing, ratings, reviews, brand information, and availability, making it a powerful resource for businesses, analysts, and AI developers in the cosmetics and skincare industry.

With structured and up-to-date data, brands can analyze consumer trends, competitor pricing, product performance, and customer sentiment to optimize their marketing strategies and product offerings. Ideal for AI/ML model training, recommendation engines, price tracking, and trend forecasting, this dataset helps you stay ahead in the dynamic beauty sector.

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Japan's main stock market index, the JP225, rose to 43019 points on September 5, 2025, gaining 1.03% from the previous session. Over the past month, the index has climbed 5.45% and is up 18.21% compared to the same time last year, 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 September of 2025.

Index Time Series for Davis Select US Equity. The frequency of the observation is daily. Moving average series are also typically included. Under normal market conditions, the fund will invest at least 80% of its net assets plus any borrowings for investment purposes in equity securities issued by U.S. companies. The fund's portfolio generally contains between 15 and 35 companies. It may invest a portion of its assets in financial services companies. The fund may also invest in mid- and small-capitalization companies, which the manager considers to be those companies with less than $10 billion in market capitalization. It may invest up to 20% of net assets in non-U.S. companies. The fund is non-diversified.