Graph and download economic data for NASDAQ Composite (NASDAQCOM) from 1971-02-05 to 2026-04-20 about composite, NASDAQ, stock market, indexes, and USA.

Stock Market Analysis of Nasdaq 100 Stock from it's Founding / Listing Years which is 2001 to 2022

Data Dictionary

About Nasdaq 100 Stock

The Nasdaq-100 is a stock market index made up of 102 equity securities issued by 101 of the largest non-financial companies listed on the Nasdaq stock exchange. It is a modified capitalization-weighted index. The stocks' weights in the index are based on their market capitalizations, with certain rules capping the influence of the largest components. It is limited to companies from a single exchange, and it does not have any financial companies. The financial companies are in a separate index, the NASDAQ Financial-100.

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The main stock market index of United States, the US500, rose to 7126 points on April 21, 2026, gaining 0.24% from the previous session. Over the past month, the index has climbed 8.28% and is up 34.76% 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 April of 2026.

NASDAQ Stock Data with Economic Indicators

Overview

This dataset comprises historical stock price data for NASDAQ-listed companies, combined with a selection of key economic indicators. It is designed to provide a comprehensive view of market behavior, facilitating financial analysis and predictive modeling. Users can explore relationships between stock performance and various economic factors.

Features

The dataset includes the following features:

• Date: The date of the recorded stock prices (formatted as YYYY-MM-DD).

• Open: The price at which the stock opened for trading on a given day.

• High: The highest price reached by the stock during the trading day.

• Low: The lowest price recorded during the trading day.

• Close: The price at which the stock closed at the end of the trading day.

• Volume: The total number of shares traded during the day.

• Interest Rate: The prevailing interest rate, which influences economic activity and stock performance.

• Exchange Rate: The exchange rate for the USD against other currencies, reflecting international market influences.

• VIX: The Volatility Index, a measure of market risk and investor sentiment, often referred to as the "fear index."

• Gold: The price of gold per ounce, which serves as a traditional safe-haven asset and is often inversely correlated with stock prices.

• Oil: The price of crude oil, an essential commodity that influences various sectors, especially transportation and manufacturing.

• TED Spread: The difference between the interest rates on interbank loans and short-term U.S. government debt, which indicates credit risk in the banking system.

• EFFR (Effective Federal Funds Rate): The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, influencing overall economic activity.

Use Cases

This dataset is suitable for a variety of applications, including: - Financial Analysis: Evaluate historical trends in stock prices relative to economic indicators. - Predictive Modeling: Develop machine learning models to forecast stock price movements based on historical data and economic variables. - Time Series Analysis: Conduct analyses over different time frames (daily, weekly, monthly, yearly) to identify patterns and anomalies.

Data Source

The data is sourced from reputable financial APIs and databases: - Yahoo Finance: Historical stock prices. - Federal Reserve Economic Data (FRED): Economic indicators such as interest rates and VIX. - Alpha Vantage / Quandl: Commodity prices for gold and oil.

Conclusion

This dataset provides a rich foundation for analysts, researchers, and data scientists interested in the intersection of stock market performance and macroeconomic conditions. Its structured features and comprehensive nature make it a valuable resource for both academic and practical financial inquiries.

Content

Top Companies of NASDAQ 100 in 2022

Here are the top companies on the NASDAQ 100 index in 2022. NASDAQ 100 is one of the most prominent large-cap growth indices in the world.

Many companies listed in the NASDAQ 100 operate in the tech sector. That is why many investors who are focused investing in tech stocks also invest in NASDAQ index to grow their funds

What is NASDAQ 100?

NASDAQ 100 is a stock market index composed of the 100 largest and most actively traded companies in the United States of America in the non- financial sector and are segmented under technology, retail, industrial, biotechnology, health care, telecom, transportation, media and services sectors.

Acknowledgement

Data collected from Yahoo Finance.

The US Stock Market Historical Dataset contains past trading data of major companies listed on prominent American stock exchanges such as the New York Stock Exchange (NYSE) and NASDAQ. This dataset typically includes daily records of stock prices such as Open, High, Low, Close (OHLC) values, trading Volume, and sometimes Adjusted Close prices.

It provides long-term historical data that helps analysts study market trends, price movements, volatility, and investment performance over time. The dataset may cover large-cap companies, including firms listed in the S&P 500, as well as technology-focused stocks from the NASDAQ Composite.

This dataset is widely used for:

• 📈 Financial analysis and forecasting
• 🤖 Machine learning model training
• 📊 Time series analysis
• 💼 Investment strategy development
• 📉 Risk management and portfolio optimization

Researchers, students, and financial professionals use this dataset to understand historical market behavior, compare company performance, and predict future trends based on past patterns. It is a valuable resource for anyone working in finance, data science, or economic research.

The main stock market index of United States, the US500, rose to 6899 points on February 25, 2026, gaining 0.13% from the previous session. Over the past month, the index has declined 0.73%, though it remains 15.84% 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 February of 2026.

Graph and download economic data for NASDAQ-100 (NASDAQ100) from 1986-01-02 to 2026-04-20 about NASDAQ, stock market, indexes, and USA.

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, rose to 4082 points on April 20, 2026, gaining 0.76% from the previous session. Over the past month, the index has climbed 7.05% and is up 24.02% compared to the same time last year, 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 April of 2026.

Japan's main stock market index, the JP225, rose to 58825 points on April 20, 2026, gaining 0.60% from the previous session. Over the past month, the index has climbed 14.19% and is up 71.60% 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 April of 2026.

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

Germany's main stock market index, the DE40, fell to 24418 points on April 21, 2026, losing 1.15% from the previous session. Over the past month, the index has climbed 7.79% and is up 14.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on April of 2026.

Poland's main stock market index, the WIG, fell to 134072 points on April 20, 2026, losing 0.83% from the previous session. Over the past month, the index has climbed 11.79% and is up 38.04% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Poland. Warsaw Stock Exchange WIG Index - values, historical data, forecasts and news - updated on April of 2026.

Greece's main stock market index, the Athens General, fell to 2260 points on April 20, 2026, losing 2.14% from the previous session. Over the past month, the index has climbed 7.53% and is up 35.14% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Greece. Greece Stock Market (ASE) - values, historical data, forecasts and news - updated on April of 2026.

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.

Can stock prices be predicted? (NASDAQ Composite Index 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

Denmark's main stock market index, the Copenhagen, fell to 1483 points on April 20, 2026, losing 0.81% from the previous session. Over the past month, the index has climbed 9.09%, though it remains 4.64% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Denmark. Denmark Stock Market Index (Copenhagen) - values, historical data, forecasts and news - updated on April of 2026.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

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.

Nasdaq 100 Futures

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

Historical AI model predictions and analysis for Nasdaq-100 ETF stock across multiple timeframes and confidence levels