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 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December of 2025.

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 New York, stock market, indexes, and USA.

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.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

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.

Graph and download economic data for Financial Market: Share Prices for Germany (SPASTT01DEM661N) from Jan 1960 to Oct 2025 about stock market and Germany.

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.

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on December of 2025.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

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.

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.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 93 companies listed on the Bucharest Stock Exchange* (XBSE) in Romania. 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.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Romania:

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.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Romania:

Bucharest Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Bucharest Stock Exchange. This index provides an overview of the overall market performance in Romania.

Bucharest Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Bucharest Stock Exchange. This index reflects the performance of international companies operating in Romania.

Company A: A prominent Romanian company with diversified operations across various sectors, such as manufacturing, technology, or finance. This company's stock is widely traded on the Bucharest Stock Exchange.

Company B: A leading financial institution in Romania, offering banking, insurance, or investment services. This company's stock is actively traded on the Bucharest Stock Exchange.

Company C: A major player in the Romanian energy or consumer goods sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Bucharest Stock Exchange.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Romania, 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.

‍

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 Romania ?

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 Romania ?

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 Romania 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 payment methods, including credit cards, direct transfers, ACH,...

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.

FTSE 100: Where to Next?

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

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.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The value of global domestic equity market increased from ***** trillion U.S. dollars in 2013 to ****** trillion U.S. dollars in 2024. The United States was by far the leading country with the largest share of total world stocks as of 2024. Global market capitalization in different regions The market capitalization of domestic companies listed varied across different regions of the world. As of Decmber 2024, the Americas region had the largest domestic equity market, totaling ** trillion U.S. dollars. This region is home to the NYSE and Nasdaq, which are the two largest stock exchange operators in the world. The market capitalization of these two exchanges alone exceeded ** billion U.S. dollars as of January 2025, larger than the total market capitalization in the Asia-Pacific, and in the EMEA regions in the same period. Largest Stock Exchanges in Latin America As of December 2024, the B3 (Brasil Bolsa Balcao) was the biggest stock exchange in Latin America in terms of market capitalization and the second-largest in terms of number of listed companies. Following the B3 were the Mexican Stock Exchange and the Santiago Stock Exchange in Chile. The most valuable company in Latin America is listed on the Mexican Stock Exchange: Fomento Económico Mexicano, a multinational beverage and retail company headquartered in Monterrey, had a market cap of *** billion U.S. dollars as of March 2025.

1) Data Introduction • The Twitter Stock Prices Dataset contains stock price data for Twitter from November 2013 to October 2022. This dataset is a time series dataset that provides daily stock trading information. • The key attributes include the stock's opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and volume (Volume).

2) Data Utilization (1) Characteristics of the Twitter Stock Prices Data • This dataset is a time series, offering daily stock price fluctuations and allows tracking of price changes over time. • It includes 7 main attributes related to stock trading, allowing for analysis of price movements (open, high, low, close) and volume, to better understand Twitter’s stock price dynamics. • This data helps analyze market trends, price volatility patterns, and price fluctuation analysis, providing insights into the dynamics of the stock market.

(2) Applications of the Twitter Stock Prices Data • Predictive Modeling: This dataset can be used to develop stock price prediction models, including predicting price increases/decreases or forecasting future stock prices using machine learning models. • Business Insights: Investment experts can use this dataset to evaluate Twitter’s stock performance, and it provides useful information for optimizing investment strategies in response to market changes. This dataset can be used for trend forecasting and investor analysis. • Trend Analysis: By analyzing stock upward/downward trends, this dataset can help evaluate the company's market performance and develop trend-based investment strategies.

This dataset contains the stock prices of Google since the COVID-19 pandemic began. There are 7 columns in this dataset: