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.

This dataset contains historical stock price data for major banks from the year 2014 to 2024. The dataset includes daily stock prices, trading volume, and other relevant financial metrics for prominent banks. The stock prices are provided in IDR (Indonesian Rupiah) currency.

PT Bank Central Asia Tbk (BBCA.JK), more commonly recognized as Bank Central Asia (BCA). As one of Indonesia's largest privately-owned banks, BCA was founded in 1955 and provides a diverse array of banking services encompassing consumer banking, corporate banking, investment banking, and asset management. With a widespread presence throughout Indonesia, including numerous branches and ATMs, BCA is esteemed for its robust financial achievements, inventive banking offerings, and dedication to customer satisfaction.

Dataset Variables:

1. Date: The date of the stock price data.
2. Open Price: The opening price of the bank's stock on the given date.
3. Close Price: The closing price of the bank's stock on the given date.
4. High Price: The highest price reached by the bank's stock during the trading day.
5. Low Price: The lowest price reached by the bank's stock during the trading day.
6. Adjusted Low Price: The closing price on a given trading day, adjusted to reflect any corporate actions, such as stock splits, dividends, rights offerings, or other adjustments that may affect the stock price.
7. Volume: The number of shares traded on the given date.

Data Sources: The dataset is compiled from reliable financial sources, including stock exchanges, financial news websites, and reputable financial data providers. Data cleaning and preprocessing techniques have been applied to ensure accuracy and consistency. More info: https://finance.yahoo.com/quote/BBCA.JK/history/

Use Case: This dataset can be utilized for various purposes, including financial analysis, stock market forecasting, algorithmic trading strategies, and academic research. Researchers, analysts, and data scientists can explore the trends, patterns, and relationships within the data to derive valuable insights into the performance of the banking sector over the specified period. Additionally, this dataset can serve as a benchmark for evaluating the performance of machine learning models and quantitative trading strategies in the banking industry.

Gain access to LSEG's National Stock Exchange of India data, India's largest stock exchange with more than 180,000 terminals across 600 districts.

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.

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Comprehensive dataset containing 26 verified Stock Market locations in India with complete contact information, ratings, reviews, and location data.

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 main stock market index of United States, the US500, rose to 6664 points on October 17, 2025, gaining 0.53% from the previous session. Over the past month, the index has climbed 0.48% and is up 13.63% 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.

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

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View LSEG's ICE Data Pricing and Reference Data, and find real-time market data, time-sensitive pricing, and reference data for securities trading.

Comprehensive dataset containing 65 verified Stock exchange building businesses in United States with complete contact information, ratings, reviews, and location data.

India's main stock market index, the SENSEX, rose to 83952 points on October 17, 2025, gaining 0.58% from the previous session. Over the past month, the index has climbed 1.13% and is up 3.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

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.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 35 companies listed on the Trinidad & Tobago Stock Exchange (XTRN) in Trinidad and Tobago. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Trinidad and Tobago :

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Trinidad and Tobago:

Trinidad and Tobago Composite Index (TTCI): The main index that tracks the performance of companies listed on the Trinidad and Tobago Stock Exchange (TTSE). TTCI provides insights into the overall market performance in Trinidad and Tobago.

Trinidad and Tobago All T&T Index (TTAI): An index that includes all companies listed on the TTSE, providing a comprehensive view of the entire stock market in Trinidad and Tobago.

Republic Financial Holdings Limited: A prominent financial institution in Trinidad and Tobago, offering banking and financial services. Republic Financial Holdings is one of the major players in the country's financial sector.

Guardian Holdings Limited: A leading insurance and financial services company in Trinidad and Tobago. Guardian Holdings offers a range of insurance products and financial solutions.

Trinidad Cement Limited (TCL): A company engaged in the production and sale of cement and building materials in Trinidad and Tobago. TCL plays a significant role in the construction industry in the country.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Trinidad and Tobago, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Trinidad and Tobago ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Trinidad and Tobago?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Trinidad and Tobago exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various paymen...

Comprehensive dataset containing 47 verified Stock exchange building businesses in Iran with complete contact information, ratings, reviews, and location data.

United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Should You Buy NSE ITC Right Now? (Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data