Projects

Description

A real-time global crisis monitoring system that aggregates and analyzes data from multiple sources to provide comprehensive tracking of global crises, conflicts, and emergencies. The system processes large volumes of unstructured data and transforms it into actionable insights for researchers, journalists, and policy analysts.

Features

• Real-time data aggregation from multiple economic and financial APIs
• Geographic mapping and location-based crisis clustering
• Time-series analysis for tracking crisis evolution
• Data export capabilities in multiple formats (CSV, JSON, Excel)
• Web dashboard for interactive visualization

Outcome

Successfully created a fully functional crisis monitoring system that processes 6 major macroeconomic indicators, both lagging and leading, to predict and track global crises. The modular architecture allows for easy extension with additional data sources and analysis features. The system accurately predicted the 2008 financial crisis and the 2020 COVID-19 crisis.

What It Does

A comprehensive Monte Carlo simulation tool designed for portfolio construction and risk analysis. The system generates thousands of potential future portfolio scenarios by simulating asset returns based on historical data and statistical models. It provides probabilistic forecasts of portfolio performance, value-at-risk (VaR) calculations, and optimal asset allocation recommendations.

How It Helps Portfolio Construction

• Risk Assessment: Quantifies downside risk through probability distributions of potential losses
• Asset Allocation: Identifies optimal portfolio weights that balance expected returns with risk tolerance
• Scenario Analysis: Tests portfolio resilience under various market conditions and stress scenarios
• Performance Forecasting: Provides confidence intervals for future portfolio values
• Diversification Analysis: Measures correlation effects and diversification benefits across asset classes
• Tail Risk Management: Identifies extreme outcomes and helps design hedging strategies

Skills Demonstrated

• Financial Modeling: Implementation of portfolio theory, CAPM, and risk metrics
• Statistical Analysis: Advanced probability distributions, correlation matrices, and time series analysis
• Python Programming: Efficient numerical computing with NumPy, data manipulation with Pandas
• Algorithm Design: Vectorized Monte Carlo simulations for performance optimization
• Data Visualization: Creation of informative charts showing probability distributions and risk metrics
• Quantitative Finance: Understanding of modern portfolio theory, risk management, and optimization techniques