Commodities Tracker with Weather Integration

Overview

This project builds a commodities tracker that uses both commodity data and weather information to help inform investment decisions. It includes modules for data acquisition, analysis, forecasting, decision making, and risk management.

Features

• Data Acquisition: Fetch commodity and weather data via APIs with robust error handling and logging.
• Analysis: Explore correlations between commodity prices and weather patterns.
• Forecasting: Use Prophet for time-series price forecasting with uncertainty intervals.
• Decision Engine: Generate buy/sell/hold signals based on forecasted data, integrated weather anomalies, and computed risk metrics.
• Risk Management: Incorporates risk metrics (e.g., Value-at-Risk, volatility), backtesting, and scenario analyses.
• Dashboard: A Flask-based dashboard to visualize signals and key metrics.

Additional Considerations

• Data Quality & Frequency: Ensures high-resolution data and proper alignment between commodity and weather datasets.
• Model Calibration & Uncertainty: Implements continuous model recalibration and provides confidence intervals for forecasts.
• Risk Management: Features backtesting, risk metrics computation, and scenario analyses for robust decision-making.
• Scalability & Reliability: Designed to be scalable with cloud integration in mind, using scheduled ETL and real-time monitoring.
• Transparency & Explainability: Provides logging and clear outputs to explain how decisions are derived, and ensures compliance with regulatory considerations.

Setup

1. Install dependencies using pip install -r requirements.txt.
2. Configure API keys in the modules/data_acquisition.py file.
3. Run the dashboard with python dashboard/app.py.

Notes

This project is a blueprint. In a production environment, further refinements, extensive backtesting, and integration with multiple data sources would be required.