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.

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.

This dataset was collected using a screenshot taken from the TradingView.com platform from different stock exchange markets using a 1 day as time frame (interval). The following patterns are: - Double bottom : 170 images - Double top : 170 images

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.

This horizontal bar chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is 3IZ.F and the date is the 2nd of May 2025. The data is about stocks per day.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - 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.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

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.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

This line chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is GDSI. The data is about stocks per day.

Stock Price Time Series for Daily Journal Corp. Daily Journal Corporation publishes newspapers and websites covering in California, Arizona, Utah, and Australia. It operates in two segments, Traditional Business and Journal Technologies. The company publishes 10 newspapers of general circulation, including Los Angeles Daily Journal, San Francisco Daily Journal, Daily Commerce, The Daily Recorder, The Inter-City Express, San Jose Post-Record, Orange County Reporter, Business Journal, The Daily Transcript, and The Record Reporter. It also provides specialized information services; and serves as an advertising and newspaper representative for commercial and public notice advertising. In addition, the company offers case management software systems and related products, including eCourt, eProsecutor, eDefender, and eProbation, which are browser-based case processing systems; eFile, a browser-based interface that allows attorneys and the public to electronically file documents with the court; and ePayIt, a service primarily for the online payment of traffic citations. It provides its software systems and related products to courts; prosecutor and public defender offices; probation departments; and other justice agencies, including administrative law organizations, city and county governments, and bar associations to manage cases and information electronically, to interface with other justice partners, and to extend electronic services to bar members and the public in 32 states and internationally. Daily Journal Corporation was incorporated in 1987 and is based in Los Angeles, California.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this December 2 of 2025.

This line chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is BNCM. The data is about stocks per day.

This line chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is KGDEY. The data is about stocks per day.

📁 Dataset 3: Microsoft Corp. (MSFT)

💻 Microsoft Corp. (MSFT) Stock Price Data – Last 5 Years

Description:
This dataset contains daily historical stock price data for Microsoft Corporation (Ticker: MSFT) over the past 5 years. It is sourced from reliable financial market data providers and is well-suited for:

• 📈 Time series forecasting and trend analysis
• 📊 Financial dashboards and visualizations
• 🧠 Machine learning applications in finance
• 💼 Investment research and strategy backtesting

Each entry corresponds to a single trading day and includes various price indicators and trading volume.

Columns Included:

• Date: The calendar date of the trading session (YYYY-MM-DD).
• Open: The price of the stock at the beginning of the trading day.
• High: The highest price reached during the trading day.
• Low: The lowest price recorded during the trading day.
• Close: The price of the stock at the end of the trading session.
• Adj Close: The closing price adjusted for splits and dividend distributions.
• Volume: The total number of shares traded during that day.

🔍 Beginner-Friendly Analysis Techniques:

If you're new to data analysis or finance, here are some simple but powerful techniques you can apply:

• Line Plots: Use line charts to visualize how the stock price has changed over time.
• Moving Averages: Calculate simple or exponential moving averages to identify trends and smooth out short-term fluctuations.
• Daily Returns: Compute percentage change between closing prices to analyze daily gains or losses.
• Volatility Analysis: Use rolling standard deviation to understand how volatile the stock has been.
• Volume Trends: Track trading volume to identify periods of high market activity.
• Candlestick Charts (intermediate): Create candlestick charts to represent daily open-high-low-close patterns visually.

Use Cases:
This dataset can be used to evaluate stock performance trends, calculate technical indicators, simulate investment strategies, or train predictive models on financial data.

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.

This line chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is MATH. The data is about stocks per day.

This line chart displays stocks over time by date using the aggregation count. The data is filtered where the stock is 1805.T. The data is about stocks per day.

Germany's main stock market index, the DE40, rose to 23722 points on December 2, 2025, gaining 0.56% from the previous session. Over the past month, the index has declined 1.70%, though it remains 18.51% higher than a year ago, 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 December of 2025.