Futures Trading: Satellite & Geopolitical Data Connected to Commodity Markets

Democratizing Bloomberg Terminal-level data for everyday people. This project connects satellite imagery, weather data, crop yields, and geopolitical events to commodity futures markets through an automated data pipeline and interactive dashboard.

Live dashboard: https://promeos.github.io/futures-trading/

Features

• Weather monitoring — Temperature, precipitation, and drought indices from NOAA/ERA5 for major growing regions
• Crop analytics — USDA crop production, yield, and acreage data with historical trends
• Satellite indices — Vegetation health (NDVI) and soil moisture from satellite imagery
• Futures prices — CME/CBOT commodity price data for corn, wheat, soybeans, cotton, sugar, coffee, crude oil, and natural gas
• Geopolitical tracking — Trade policy, sanctions, and geopolitical events affecting commodity markets
• Cross-dataset signals — Correlation analysis, anomaly detection, and signal generation across all data sources

Architecture

pipeline/               # Python data pipeline
  config.py             # Commodity definitions, API endpoints, region bounds
  fetch_weather.py      # NOAA/ERA5 weather data
  fetch_crops.py        # USDA crop production and yield data
  fetch_satellites.py   # Satellite vegetation indices
  fetch_futures.py      # CME/CBOT futures price data
  fetch_geopolitical.py # Geopolitical events and trade policy data
  process.py            # Correlation analysis, anomaly detection, signal generation
  export.py             # Exports JSON to docs/data/

docs/                   # Static frontend (GitHub Pages)
  index.html            # Dashboard
  css/style.css         # Dark theme design system
  js/dashboard.js       # Chart rendering with Plotly.js
  data/                 # Pipeline JSON output consumed by frontend

Data Flow

fetch_weather.py ──────┐
fetch_crops.py ────────┤
fetch_satellites.py ───┼─→ process.py ─→ export.py ─→ docs/data/*.json ─→ dashboard
fetch_futures.py ──────┤
fetch_geopolitical.py ─┘

Getting Started

Prerequisites

• Python 3.9+

Installation

git clone https://github.com/Promeos/futures-trading.git
cd futures-trading
pip install -r requirements.txt

Run the Pipeline

python -m pipeline.export        # Run full pipeline (fetch + process + export)

Individual pipeline stages can be run independently:

python -m pipeline.fetch_weather
python -m pipeline.fetch_crops
python -m pipeline.fetch_satellites
python -m pipeline.fetch_futures
python -m pipeline.fetch_geopolitical
python -m pipeline.process

Each fetcher includes a synthetic fallback — no API credentials are needed for development.

Serve the Dashboard Locally

python -m http.server 8000 --directory docs

Then open http://localhost:8000 in your browser.

Run Tests

pip install -r requirements-dev.txt
python -m pytest tests/ -v

Data Sources & Attribution

Source	Data	Usage
USDA NASS	Crop production, yield, acreage	Subject to NASS API Terms of Service
NOAA / ERA5	Temperature, precipitation, drought indices	Weather monitoring for growing regions
CME/CBOT	Commodity futures prices	Historical and current market data

This product uses the NASS API but is not endorsed or certified by NASS. Any processed or derived data (correlations, signals, anomaly detection) is not representative of original NASS data.

Citation

If you use this project in your research, please cite it:

@software{ortiz_futures_trading_2026,
  author       = {Ortiz, Christopher Logan},
  title        = {Futures Trading: Satellite \& Geopolitical Data Connected to Commodity Markets},
  year         = {2026},
  url          = {https://github.com/Promeos/futures-trading}
}

See CITATION.cff for machine-readable citation metadata.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

See LICENSE for the full license text.

Disclaimer

This project is for educational and research purposes only. It is not financial advice. Data is provided "as is" with no guarantees of accuracy or completeness.