SavantCapital/MagSeven-ML-Ready-High-Frequency-Anomaly-Dataset-January-2026-sample

MagSeven High-Frequency Anomaly Dataset (Sample)

This repository provides a public sample dataset extracted from a larger, production-ready financial time series dataset.

The goal of this sample is to demonstrate:

• —Data structure
• —Feature engineering style
• —Cleanliness and usability for modeling

📦 Dataset Overview

This dataset contains normalized and relative time-series features derived from raw OHLCV market data.

Instead of absolute prices, the data focuses on relative movements anchored to an open price, making it more suitable for:

• —Machine learning models
• —Cross-asset generalization
• —Regime-agnostic pattern discovery

📊 Event-Level Data Schema

Each JSON object corresponds to a single detected anomaly event and follows the schema below:

{
  "ticker": "AAPL",
  "timestamp_utc": "2026-01-06 14:30:00+00:00",
  "metrics": {
    "market_phase": "OPEN_AUCTION",
    "event_type": "MARKET_LIQUIDITY",
    "anomaly_strength": "Level_2",
    "volume_z_score": 6.32,
    "volatility_z_score": 2.15,
    "price_change_from_window_open": 0.003,
    "outcomes": {
      "future_return_30m": 0.0045,
      "max_upside_30m": 0.008,
      "max_drawdown_30m": -0.001,
      "label": "BULLISH"
    }
  },
  "data_1m": [ ... ],
  "data_5m": [ ... ]
}

🧱 Field Overview

Top-Level Fields

• —ticker: Stock symbol associated with the anomaly event.
• —timestamp_utc: UTC timestamp corresponding to the anchor (trigger) bar.
• —metrics: Event-level summary statistics, detection logic outputs, and forward-looking outcome labels.
• —data_1m: High-resolution 1-minute microstructure window centered around the anomaly event.
• —data_5m: Lower-frequency 5-minute context window capturing short-term trend behavior.

🛠 Usage Notes

• —All price-related fields are normalized to protect data source agreements and emphasize relative price action dynamics.
• —Absolute price levels are intentionally excluded.
• —The dataset is optimized for direct ingestion into ML pipelines without additional preprocessing.

🧪 Example: Loading the Dataset in Python

import pandas as pd

# Load anomaly data for a single ticker
df = pd.read_json("AAPL_anomaly_package.jsonl", lines=True)

# Inspect event-level metrics
print(df.iloc[0]["metrics"])

# Convert the 1-minute window into a DataFrame
event_1m = pd.DataFrame(df.iloc[0]["data_1m"])
print(event_1m.head())

📚 Feature Dictionary (data1m & data5m)

Both data1m (microstructure) and data5m (trend context) arrays share the same feature definitions.

🧠 Intended Use Cases

This dataset format is suitable for:

• —Time-series forecasting
• —Anomaly detection
• —Pattern discovery
• —Quantitative research
• —Feature engineering benchmarks
• —ML / DL pipelines (LSTM, Transformer, etc.)

🧪 About This Sample

This Hugging Face repository contains only a small subset of the full dataset.

It is intended for:

• —Inspection
• —Experimentation
• —Pipeline testing

The full dataset includes:

• —Larger time coverage
• —Multiple instruments
• —Extended metadata
• —Ready-to-train splits

🔗 Full Dataset (Gumroad)

The complete dataset is available for purchase on Gumroad:

👉 [Get the full dataset on Gumroad](https://xiaoyaoblob.gumroad.com/l/rlnpv)

📄 License

This sample dataset is released under the CC-BY-4.0 License.

You are free to use it for research and experimentation, with attribution.

📬 Contact

If you have questions, feedback, or custom data requests, feel free to reach out via Gumroad.

📬 Contact & Support

If you have any questions about this dataset, licensing, or access to the full version, feel free to reach out:

📧 Email: quantalpha.global@gmail.com

Please note that this email is intended for dataset-related inquiries only. We aim to respond within 1–2 business days.