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.

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