FX & Precious Metals Market Analytics Engine

Run this project interactively in Google Colab: https://colab.research.google.com/drive/1RzQ1R4Nkqu7GBCM6FkR1kbTosGGlgQXS?usp=sharing

An automated quantitative pipeline built in Python to model Foreign Exchange (FX) volatility, calculate parametric Value-at-Risk (VaR), and analyze market correlations between G10 currency pairs and precious metals (Gold spot prices).

Designed for treasury operations, risk management, and Capital Markets decision-making workflows.

Key Capabilities & Features

• Dynamic Financial Data Ingestion: Fetches real-time daily historical rates for major global currencies (EUR, USD, CAD, GBP, JPY, AUD, CHF, NZD, BRL) and precious metals via yfinance.
• Risk & Volatility Modeling: Computes annualized volatility and parametric Value-at-Risk (95% 1-day VaR) to quantify daily market exposure.
• Commodity Correlation Analytics: Analyzes relationship patterns between custom currency pairs and precious metals (Gold, Silver, Platinum).
• Interactive Visualization: Dynamic time-series engine that plots dual-axis performance charts based on user-selected parameters.

Tech Stack & Tools

• Language: Python 3
• Data Manipulation: Pandas, NumPy
• Data Querying & Storage: SQL / SQLite, SQLAlchemy
• Market Data APIs: Yahoo Finance (yfinance)
• Visualization: Matplotlib

Sample Output & Risk Metrics

The model outputs structured institutional summaries including:

• Annualized Volatility: Quantifies market price fluctuation intensity over a 252-trading-day baseline.
• 95% Daily VaR: Establishes maximum expected daily drawdown under standard market conditions.

How to Run in Google Colab

1. Open the interactive notebook: https://colab.research.google.com/drive/1RzQ1R4Nkqu7GBCM6FkR1kbTosGGlgQXS?usp=sharing
2. Run all cells sequentially.
3. Select desired currency tickers from the interactive control menu to generate real-time reports and visual charts.