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.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Results of ANOVA analysis of the difference in accuracy between stock price predictions using image characteristics.

Context

This dataset provides synthetically generated financial time series data, presented as OHLCV (Open-High-Low-Close-Volume) candlestick charts. A key feature of this dataset is the inclusion of technical analysis annotations (labels) meticulously created by a human analyst for each chart.

The primary goal is to offer a resource for training and evaluating machine learning models focused on automated technical analysis and chart pattern recognition. By providing synthetic data with high-quality human labels, this dataset aims to facilitate research and development in areas like algorithmic trading and financial visualization analysis.

This is an evolving dataset. It represents the initial phase of a larger labeling effort, and future updates are planned to incorporate a greater number and variety of labeled chart patterns.

Content

The dataset is provided entirely as a collection of JSON files. Each file represents a single 300-candle chart window and contains:

1. metadata: Contains basic information related to the generation of the file (e.g., generation timestamp, version).
2. ohlcv_data: A sequence of 300 data points. Each point is a dictionary representing one time candle and includes:
   • time: Timestamp string (ISO 8601 format). Note: These timestamps maintain realistic intra-day time progression (hours, minutes), but the specific dates (Day, Month, Year) are entirely synthetic and do not align with real-world calendar dates.
   • open, high, low, close: Numerical values representing the candle's price range. Note: These values are synthetic and are not tied to any real financial instrument's price.
   • volume: A numerical value representing activity during the candle's period. Note: This is also a synthetic value.
3. labels: A dictionary containing the human-provided technical analysis annotations for the corresponding chart window:
   • horizontal_lines: A list of structures, each containing a price key. These typically denote significant horizontal levels identified by the labeler, such as support or resistance.
   • ray_lines: A list of structures, each defining a line segment via start_date, start_price, end_date, and end_price. These are used to represent patterns like trendlines, channel boundaries, or other linear formations observed by the labeler.

Data Generation Approach

The dataset features synthetically generated candlestick patterns. The generation process focuses on creating structurally plausible chart sequences. Human analysts then carefully review these sequences and apply relevant technical analysis labels (support, resistance, trendlines).

While the patterns may resemble those seen in financial markets, the underlying numerical data (price, volume, and the associated timestamps) is artificial and intentionally detached from any real-world financial data. Users should focus on the relative structure of the candles and the associated human-provided labels, rather than interpreting the absolute values as representative of any specific market or time.

Acknowledgements

This dataset is made possible through ongoing human labeling efforts and custom data generation software.

Inspiration

• Train models (e.g., CNNs, Transformers) to recognize support/resistance levels and trendlines directly from chart data.
• Develop and benchmark algorithms for automated technical analysis pattern detection.
• Use as a basis for generating further augmented chart data for ML training.
• Explore novel approaches to financial time series analysis using labeled, synthetic data.

Literature on stock price prediction.

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.

Netflix, Inc. is an American subscription streaming service and production company founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California. Initially, Netflix started as a DVD rental service, pioneering the model of online rentals with no late fees. In 2007, the company transitioned into streaming media, revolutionizing the entertainment industry by offering a vast library of movies and TV shows accessible on-demand. Netflix further expanded its influence by producing original content, beginning with the series "**House of Cards**" in 2013. Today, Netflix is a global powerhouse in entertainment, with over 200 million subscribers worldwide and a diverse portfolio of acclaimed original series, films, and documentaries.

This dataset provides a comprehensive record of Netflix's stock price changes over time. It includes essential columns such as the date, opening price, highest price of the day, lowest price of the day, closing price, adjusted closing price, and trading volume.

This data is invaluable for conducting historical analyses, forecasting future stock performance, and understanding market trends related to Netflix's stock.

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

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.

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

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

Experimental parameter settings.

Results of Chow verification for filter.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

This dataset is just for personal, non-commercial purposes.

Overview

This dataset provides a comprehensive record of NVIDIA Corporation's (NVDA) daily stock prices over the last five years. NVIDIA, a prominent technology company known for its graphics processing units (GPUs), has experienced significant market activity, making its stock price data valuable for financial analysis, trading strategies, and market trend studies.

Data Fields

The dataset includes the following columns:

1. Date: The trading date (YYYY-MM-DD format).
2. Open: The price of the stock at market opening.
3. High: The highest price of the stock during the trading day.
4. Low: The lowest price of the stock during the trading day.
5. Close: The price of the stock at market close.
6. Adj Close: The adjusted closing price, accounting for dividends and stock splits.
7. Volume: The number of shares traded on that day.

Time Period

• Start Date: Five years prior from the current date (for example, if today's date is 2024-05-23, the dataset starts from 2019-05-23).
• End Date: The most recent trading day available in the dataset.

Data Source

The data is typically sourced from reliable financial database Yahoo Finance. It is crucial to ensure data accuracy and completeness for effective analysis.

Usage

This dataset can be used for: - Historical Analysis: Studying NVIDIA's stock performance over time. - Technical Analysis: Applying various technical indicators and chart patterns. - Machine Learning: Training models for stock price prediction. - Market Research: Understanding market trends and investor behavior. - Investment Strategies: Backtesting trading strategies to assess their performance.

Example Applications

• Trend Analysis: Identifying bullish or bearish trends in NVIDIA's stock price.
• Volatility Analysis: Measuring the stock's volatility and assessing risk.
• Correlation Studies: Examining the relationship between NVIDIA's stock price and other market variables.
• Event Impact Analysis: Analyzing the impact of company announcements, earnings reports, or macroeconomic events on the stock price.

Note

It is important to handle the data responsibly, considering market hours, holidays, and any corporate actions like stock splits or dividends that might affect the stock price. Adjustments for these factors are usually reflected in the "Adj Close" column to provide a more accurate historical comparison.

This dataset is ideal for analysts, investors, researchers, and students interested in financial markets, particularly in understanding the dynamics of a leading technology company's stock over a significant period.

Thailand: Stock price volatility, percent: The latest value from 2021 is 23.2 percent, an increase from 21.62 percent in 2020. In comparison, the world average is 20.14 percent, based on data from 87 countries. Historically, the average for Thailand from 1988 to 2021 is 23.75 percent. The minimum value, 9.17 percent, was reached in 2018 while the maximum of 43.1 percent was recorded in 1999.

Finland: Stock price volatility, percent: The latest value from 2021 is 21.05 percent, a decline from 21.84 percent in 2020. In comparison, the world average is 20.14 percent, based on data from 87 countries. Historically, the average for Finland from 1987 to 2021 is 22.88 percent. The minimum value, 10.97 percent, was reached in 1989 while the maximum of 54.62 percent was recorded in 2001.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, 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 December of 2025.

Image characteristics.

CNN characteristics.