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.

Stock Charts

This dataset is a collection of a sample of images from tweets that I scraped using my Discord bot that keeps track of financial influencers on Twitter. The data consists of images that were part of tweets that mentioned a stock. This dataset can be used for a wide variety of tasks, such as image classification or feature extraction.

  FinTwit Charts Collection

This dataset is part of a larger collection of datasets, scraped from Twitter and labeled by a… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/stock-charts.

Stock market index in the USA, September, 2025 The most recent value is 199.97 points as of September 2025, an increase compared to the previous value of 194.61 points. Historically, the average for the USA from January 1960 to September 2025 is 46.08 points. The minimum of 2.98 points was recorded in June 1962, while the maximum of 199.97 points was reached in September 2025. | TheGlobalEconomy.com

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.

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.

Prices for United States Stock Market Index (US5000) including live quotes, historical charts and news. United States Stock Market Index (US5000) was last updated by Trading Economics this December 2 of 2025.

Stock market index in Mexico, September, 2025 The most recent value is 139.61 points as of September 2025, an increase compared to the previous value of 132.58 points. Historically, the average for Mexico from January 1970 to September 2025 is 36.41 points. The minimum of 0 points was recorded in January 1970, while the maximum of 139.61 points was reached in September 2025. | TheGlobalEconomy.com

Graph and download economic data for Stock Market Total Value Traded to GDP for United States (DDDM02USA156NWDB) from 1975 to 2019 about market cap, stock market, trade, GDP, and USA.

Graph and download economic data for Index of Stock Prices (General) for Germany (M1123BDEM334NNBR) from Jan 1924 to Dec 1935 about stock market, Germany, and indexes.

Stock market index in Israel, September, 2025 The most recent value is 186.14 points as of September 2025, an increase compared to the previous value of 183.94 points. Historically, the average for Israel from January 1980 to September 2025 is 47.87 points. The minimum of 0 points was recorded in January 1980, while the maximum of 186.2 points was reached in July 2025. | TheGlobalEconomy.com

Dataset extracted from the post Stock Market Today : Technical Analysis with Charts in Details: Data Backed Research on Nifty: Updated 6th October 2025 on Smart Investello.

The USA: Stock market capitalization, billion USD: The latest value from 2024 is 62185.69 billion U.S. dollars, an increase from 48979.4 billion U.S. dollars in 2023. In comparison, the world average is 1647.20 billion U.S. dollars, based on data from 68 countries. Historically, the average for the USA from 1975 to 2024 is 14957.77 billion U.S. dollars. The minimum value, 703.8 billion U.S. dollars, was reached in 1975 while the maximum of 62185.69 billion U.S. dollars was recorded in 2024.

France: Stock market capitalization, billion USD: The latest value from 2018 is 2365.95 billion U.S. dollars, a decline from 2749.31 billion U.S. dollars in 2017. In comparison, the world average is 918.86 billion U.S. dollars, based on data from 76 countries. Historically, the average for France from 1975 to 2018 is 966.7 billion U.S. dollars. The minimum value, 27.4 billion U.S. dollars, was reached in 1976 while the maximum of 2749.31 billion U.S. dollars was recorded in 2017.

India: Stock market capitalization, billion USD: The latest value from 2024 is 5131.4 billion U.S. dollars, an increase from 4340.13 billion U.S. dollars in 2023. In comparison, the world average is 1647.20 billion U.S. dollars, based on data from 68 countries. Historically, the average for India from 2000 to 2024 is 1822.81 billion U.S. dollars. The minimum value, 148.77 billion U.S. dollars, was reached in 2001 while the maximum of 5131.4 billion U.S. dollars was recorded in 2024.

Greece's main stock market index, the Athens General, rose to 2108 points on December 2, 2025, gaining 0.41% from the previous session. Over the past month, the index has climbed 4.15% and is up 47.48% 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 December of 2025.

Oil stock price graphs are crucial tools for analyzing the historical performance of oil stocks. They help investors and financial analysts identify trends, patterns, support and resistance levels, and key chart patterns.

This dataset contains several daily features of NASDAQ Composite, Dow Jones Industrial Average, and NYSE Composite from 2010 to 2024. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables" published at Expert Systems with Applications. This dataset has been used in "SAMBA: A Graph-Mamba Approach for Stock Price Prediction" published at ICASSP 2025. Link to Code: https://github.com/Ali-Meh619/SAMBA

Stock Chart

## Overview

Stock Chart is a dataset for object detection tasks - it contains Stocks annotations for 80 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).