Financial Event-Study Engine (Python)

An end-to-end Python project analysing how financial market sectors respond to major global shock events using historical price data.
The system applies event-study methodology, computes sector-level metrics, and validates results with automated testing and continuous integration.

This project is designed to demonstrate clean Python architecture, test-driven metrics validation, and CI-enabled reliability, alongside applied financial data analysis.

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Project Overview

This project investigates how different financial market sectors behave before and after major global shock events (e.g. COVID-19, the 2008 Financial Crisis, the invasion of Ukraine).

Using Python and real market data:

• Historical price data is collected programmatically
• Prices are normalised at event dates for fair comparison
• Sector-level performance metrics are computed
• Results are validated using automated unit tests

The focus is on correctness, reproducibility, and software-engineering best practices, not just exploratory analysis.

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Key Features

• Downloads real historical market data using yfinance
• Supports multiple user-defined events and time windows
• Normalises prices at event dates for consistent comparison
• Computes:
  • Pre- and post-event returns
  • Market beta relative to S&P 500
  • Recovery timing after drawdowns
• Fully unit-tested metric calculations
• Continuous Integration via GitHub Actions

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Project Structure

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├── src/                # Core analysis and strategy logic
├── tests/              # Unit tests (pytest)
├── .github/workflows/  # CI pipeline (GitHub Actions)
├── main.py             # Example execution script
├── requirements.txt    # Project dependencies
├── pytest.ini          # Pytest configuration
└── README.md