Graph and download economic data for NASDAQ Composite (NASDAQCOM) from 1971-02-05 to 2026-03-25 about composite, stock market, NASDAQ, indexes, and USA.

This dataset contains complete historical stock market data for all 100 current NASDAQ-100 index constituents, covering daily trading activity from January 2000 to February 2026. It provides over 514,000 rows of clean, split-adjusted price and volume data across 100 of the world's most valuable technology, consumer, and healthcare companies. Whether you're building an LSTM price predictor, backtesting a trading strategy, or exploring sector correlations — this dataset has you covered.

What's Inside:

📁 NASDAQ100_Historical_Data.csv — Combined file with all 100 stocks in one table (514,075 rows)

Coverage Highlights

26 years of data for legacy stocks like AAPL, MSFT, AMZN, NVDA, INTC, AMD, QCOM 64 stocks with 20+ years of history 84 stocks with 10+ years of history Newer IPOs start from their listing date (e.g., TSLA from 2010, META from 2012, PLTR from 2020, ARM from 2023) All prices are split-adjusted — no need to manually account for stock splits

Top Companies Included

AAPL · MSFT · NVDA · AMZN · GOOGL · META · TSLA · AVGO · COST · NFLX · AMD · PLTR · ADBE · INTC · QCOM · PYPL · SBUX · AMGN · ISRG · PANW · and 80 more

Use Cases:

🤖 Machine Learning — Train LSTM, Transformer, or XGBoost models for stock price prediction

📈 Technical Analysis — Compute RSI, MACD, Bollinger Bands, moving averages, and other indicators

💼 Portfolio Optimization — Build and backtest diversified portfolios across sectors

📊 Volatility Modeling — Analyze historical volatility, drawdowns, and risk metrics

🔄 Correlation Studies — Explore how tech stocks co-move during bull and bear markets

🏦 Sector Analysis — Compare performance across Technology, Consumer, Healthcare, and Communication sectors

📉 Event Studies — Examine price reactions to earnings, Fed decisions, COVID crash, and more

🎓 Education — Perfect starter dataset for learning financial data analysis with Python or R

Between 2000 and 2024, the value of the Nasdaq Industrial Index increased nearly tenfold. As of December 29, 2023, the value of this index reached *********, up from ******* in the previous year. The largest increase was recorded between 2019 and 2020.

The Nasdaq Computer Index tracks hundreds of software and hardware companies whose shares are traded on the Nasdaq stock exchange. The index grew considerably since 2000. Throught the years considered in the graph, the Nasdaq Bank index reached its lowest level at the closing of 2002, when it stood at ***** points. Since then it increased dramatically and peaked at ******** index points as of the end of 2024.

The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% 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 March of 2026.

This dataset provides a comprehensive, ML-ready view of stock market regimes from 2000 to 2026, covering multiple global equities and indices. Each trading day is labeled into interpretable market regimes—Bull, Bear, Sideways, Crisis, and High-Volatility—based on return dynamics, volatility behavior, and stress indicators.

In addition to price-based features, the dataset integrates key macroeconomic variables (interest rates, inflation, unemployment, yield curve, and VIX) to provide broader economic context for each regime.

The dataset is designed for: - Market regime detection - Algorithmic trading & portfolio allocation - Risk management & stress testing - Financial time-series modeling - ML & deep learning research

All features are constructed to avoid look-ahead bias, making the dataset suitable for real-world modeling and backtesting.

The Nasdaq Bank Index tracks hundreds of banks whose shares are traded on the Nasdaq stock exchange. The index performance fluctuated considerably since 2000. Throught the years considered in the graph, the Nasdaq Bank index reached its lowest level at the closing of 2011, when it stood at ******* points. After further fluctuations, the index recovered and peaked at ******* at the end of 2021. As of the end of 2024, the index had a value of ******* points.

Project Name & Description

Project Name: IBM Stock Price Historical Data (2000-2026) | Daily Financial Analysis Project Description: This project provides a comprehensive daily historical dataset of International Business Machines Corp. (IBM) stock prices from January 2000 to January 2026. This dataset is designed for financial analysts, data scientists, and machine learning enthusiasts to perform exploratory data analysis, time-series forecasting, and algorithmic trading strategy development.

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.

Prices for United States Stock Market Index (US2000) including live quotes, historical charts and news. United States Stock Market Index (US2000) was last updated by Trading Economics this March 27 of 2026.

Dataset is automatically gathered from different sources with APIs. Each file represents historical stock market price of related company (i.e. IBM_historic_data_2000_2024 for IBM). The data is adjusted, meaning it accounts for corporate actions such as dividends, stock splits, and other adjustments that could affect the stock price. The dataset is structured as a JSON object with a series of trading events. Below is a detailed description of each field in the dataset:

****Dataset Overview:****

• Ticker: "IBM"
  The stock ticker symbol representing the specific security. In this case, "IBM" refers to the stock of a specific company listed in the Over-the-Counter (OTC) market.

• Adjusted: true
  Indicates that the price data in the dataset is adjusted for corporate actions like dividends, stock splits, and other similar events.

• Results:
  An array of objects where each object represents the trading data for a specific time interval.

Field Descriptions:

1. v (Volume)

   • Type: Float
   • Description: The total number of shares traded during the specific time interval. For instance, a value of 200,000 indicates that 200,000 shares were traded.

2. vw (Volume-Weighted Average Price)

   • Type: Float
   • Description: The volume-weighted average price of the stock during the time interval. It is calculated by taking the average price of the stock, weighted by the volume of trades at each price level. A value of 0.0566 means that the average price of the stock, weighted by the trading volume, was $0.0566.

3. o (Opening Price)

   • Type: Float
   • Description: The price at which the stock first traded during the time interval. For example, a value of 0.0545 indicates that the stock opened at $0.0545.

4. c (Closing Price)

   • Type: Float
   • Description: The price at which the stock last traded during the time interval. A value of 0.0575 indicates that the stock closed at $0.0575.

5. h (High Price)

   • Type: Float
   • Description: The highest price at which the stock traded during the time interval. For example, a value of 0.0584 means the highest trade occurred at $0.0584.

6. l (Low Price)

   • Type: Float
   • Description: The lowest price at which the stock traded during the time interval. For instance, a value of 0.0545 indicates that the lowest trade occurred at $0.0545.

7. t (Timestamp)

   • Type: Integer (Epoch Time)
   • Description: The Unix timestamp in milliseconds representing the start of the time interval for this specific trading data. A value of 1661778000000 corresponds to a specific date and time (in UTC).

8. n (Number of Trades)

   • Type: Integer
   • Description: The total number of individual trades executed during the time interval. For example, 4 indicates that there were four trades.

9. otc (Over-the-Counter)

   • Type: Boolean
   • Description: Indicates whether the trading data is from an Over-the-Counter (OTC) market. A value of true means the trades were executed in the OTC market, which is typically less regulated and more decentralized than major exchanges.

Use Cases:

****Price Trend Analysis:**** Analyze the stock's price movements over time by looking at the opening, closing, high, and low prices.
****Volume Analysis:**** Investigate trading volumes to understand market interest and liquidity during specific periods.
****VWAP Utilization:**** Use the VWAP to determine the average price at which most traders executed their trades.
****Trade Frequency:**** The number of trades (n) can indicate the level of trading activity, with higher numbers potentially signaling more significant market interest.

This detailed dataset can be utilized for various financial analyses, including predicting future stock prices, evaluating trading strategies, or understanding market behavior during specific intervals.

United States US: Index: Share Price: NASDAQ Composite data was reported at 86.473 2010=100 in 2001. This records a decrease from the previous number of 160.920 2010=100 for 2000. United States US: Index: Share Price: NASDAQ Composite data is updated yearly, averaging 15.784 2010=100 from Dec 1972 (Median) to 2001, with 30 observations. The data reached an all-time high of 160.920 2010=100 in 2000 and a record low of 3.254 2010=100 in 1974. United States US: Index: Share Price: NASDAQ Composite data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Share Price Index: Annual.

This statistic presents the year-end closing values of the NASDAQ telecommunications index from 2010 to 2018. The value of NASDAQ telecommunications index amounted to 349 points in 2018.

The Nasdaq Biotech Index has shown remarkable growth over the past two decades, reflecting the expanding influence of biotechnology in the pharmaceutical industry. From a value of *** in 2005, the index surged to ***** points by the end of 2020, demonstrating the sector's rapid development and increasing market capitalization. After peaking in 2020, the index fluctuated in the following years and closed 2024 at ******** points.

This dataset contains firm-level, daily observations of capital flow activity for approximately 2,000 publicly traded companies listed on the NASDAQ, Russell 3000, and S&P 500 indices between 2015 and 2025. Each record corresponds to one trading day per firm and reports the following variables: date – trading day in YYYY-MM-DD format buy_flow_usd – total value of buy-side transactions (USD) sell_flow_usd – total value of sell-side transactions (USD) net_flow_usd – difference between buy and sell flows (positive = inflow; negative = outflow) trades – number of executed trades during the trading day The data were constructed from aggregated trade-level information using intraday transaction records obtained from official exchange data feeds. All flows are expressed in nominal USD. This dataset can be used to study market liquidity, institutional trading behavior, investor sentiment, and capital movement patterns across major U.S. indices. It supports replication of research on daily capital flows, market microstructure, and cross-sectional determinants of liquidity.

Lithuania's main stock market index, the Vilnius, fell to 1351 points on March 27, 2026, losing 0.22% from the previous session. Over the past month, the index has declined 1.31%, though it remains 16.63% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Lithuania. Lithuania Stock Market Index (Vilnius) - values, historical data, forecasts and news - updated on March of 2026.

Graph and download economic data for OMX Stockholm Consumer Products and Services Price Index (NASDAQSX4020PI) from 2000-01-03 to 2026-03-19 about NASDAQ, production, consumer, services, indexes, and USA.

Introduction

These data files are generated by my python script.

Source

The data is collected from Yahoo Finance.

Graph and download economic data for OMX Copenhagen Banks Price Index (NASDAQCX3010PI) from 2000-01-03 to 2026-03-26 about NASDAQ, banks, depository institutions, indexes, and USA.

Context

Needed some data for my own personal project so figured I might as well publish it to make it easy for people to explore financial data.

Content

Columns:

• Stock: Acronym of stock
• Timestamp: Unix time of date
• Open, high, low, adj_close: Price of stock at beginning of day, peak, lowest and end of day
• Amount of stock avaliable

Acknowledgements

Dataset scraped with personal script from investopedia.com!

Inspiration

Can this dataset be enough to make some sort of prediction about a stock?