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.

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

Take a look at historical stock prices of major social media companies.

Content

Meta (Facebook), Twitter, Snap, Pinterest and Etsy historical stock prices.

2084Collective/deepstock-stock-historical-prices-dataset-processed dataset hosted on Hugging Face and contributed by the HF Datasets community

Prices for United States Stock Market Index (US500) including live quotes, historical charts and news. United States Stock Market Index (US500) was last updated by Trading Economics this December 1 of 2025.

Revelyst stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

About Dataset:

This dataset includes the daily historical stock prices for Google (GOOGL) spanning from 2020 to 2025. It features essential financial metrics such as opening and closing prices, daily highs and lows, adjusted close prices, and trading volumes. The information offers valuable insights into the stock's performance over a five-year timeframe.

Column Descriptions:

• Price: Date of the stock data (needs cleaning as the first two rows are headers).
• Adj Close: Adjusted closing price, accounting for events like dividends and splits.
• Close: Closing price of the stock at the end of the trading day.
• High: Highest price of the stock during the trading day.
• Low: Lowest price of the stock during the trading day.
• Open: Opening price of the stock at the start of the trading day.
• Volume: Number of shares traded during the day.

What Can You Achieve and Apply on This Data:

• Time Series Analysis: Examine trends and patterns over time.
• Stock Price Prediction: Use machine learning models to forecast future prices.
• Volatility Analysis: Measure the stock's price fluctuations.
• Technical Analysis: Calculate indicators like moving averages, RSI, and MACD.
• Correlation Analysis: Investigate the relationship between volume and price changes.
• Investment Strategy Backtesting: Test trading strategies like moving average crossovers.

Note: 1. This data is scraped from Yahoo Finance by me using python code. 2. Some of the About Data is generated from AI, but verified from me.

U.S. - Railroad Stock Prices - Historical chart and current data through 1937.

New York Stock Exchange - Stock Prices - Historical chart and current data through 1923.

This dataset provides comprehensive historical stock price data for Apple Inc. (AAPL) from 1980 to 2024. It offers invaluable insights for financial analysts, data scientists, and investors interested in studying Apple's stock performance, market trends, and potential investment strategies.

The data reflects Apple's stock performance over more than four decades, capturing significant events in the company's history.

The dataset includes over 10,000 daily records with the following columns:

Date: The date of the trading day. Open: The price of the stock at market open. High: The highest price reached during the trading day. Low: The lowest price reached during the trading day. Close: The price of the stock at market close. Adj Close: The closing price adjusted for dividends and stock splits. Volume: The total number of shares traded during the day.

Potential Use Cases:

-Financial Analysis: Analyzing stock price trends, volatility, and correlations.

-Predictive Modeling: Building models to forecast future stock prices, identify trading opportunities, or assess risk.

-Quantitative Trading: Developing algorithmic trading strategies based on historical data patterns.

-Research and Education: Studying market behaviour, economic indicators, and corporate events that impact stock prices.

Note: This dataset is intended for educational and research purposes. It is recommended to consult with a financial advisor before making any investment decisions based on this data.

Complete historical financial dataset for Pinterest, Inc.

Complete historical financial dataset for Adobe Inc.

Complete historical financial dataset for Boston Scientific Corporation

China Stock Market - Shanghai Composite Index - Historical chart and current data through 2025.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

Real M1 Money Stock - Historical chart and current data through 2025.

Complete historical financial dataset for Alphabet Inc.

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.

BAJFINANCE OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Bajaj Finance (BAJFINANCE), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_bajfinance_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• timestamp: The human-readable timestamp of the candle, precisely parsed to the second.
• open: The opening price of the stock during that interval.
• high: The highest price reached during that interval.
• low: The lowest price reached during that interval.
• close: The closing price of the stock during that interval.
• volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for BAJFINANCE. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to BAJFINANCE. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for BAJFINANCE. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Bajaj Finance. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

Complete historical financial dataset for ConocoPhillips