📈 Financial Engineering Projects

This repository presents a set of projects that explore core topics in financial engineering through Python-based modeling and analysis. The notebooks are structured around three major problems, each focusing on a different aspect of portfolio theory and asset pricing.

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📘 Project Summary

The main objective is to bridge theoretical finance models with practical implementation. The tasks include:

• Constructing efficient portfolios using Markowitz's mean-variance optimization
• Applying the Capital Asset Pricing Model (CAPM) to estimate expected returns and factor sensitivities
• Performing real-data-based portfolio optimization with historical market data

These projects were completed as part of a university-level financial engineering course.

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📂 Notebook Overview

🔹 Proj1_Problem1.ipynb – Modern Portfolio Theory

Implements mean-variance portfolio optimization (Markowitz framework).
Focuses on computing the efficient frontier and identifying optimal portfolios based on risk-return tradeoffs.

🔹 Proj1_Problem2.ipynb – CAPM & Factor Model

Applies CAPM and multi-factor regression analysis to understand the relationship between asset returns and market risk factors.
Emphasizes beta estimation and validation of pricing assumptions using financial datasets.

🔹 Additional_Problem3.ipynb – Real-World Portfolio Optimization

Uses real stock market data (via yfinance) to perform practical portfolio optimization.
Integrates data preprocessing, return calculation, and numerical optimization under constraints.

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💡 Key Topics Covered

• 📊 Markowitz Mean-Variance Optimization
• 💼 CAPM and Multi-Factor Models
• 🔍 Regression-Based Beta Estimation
• 📈 Historical Return Analysis with Real Market Data
• 🧮 Matrix Algebra in Finance Applications

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⚙️ Tools & Libraries

• Python 3.x
• NumPy, Pandas
• Scikit-learn
• yfinance
• Matplotlib

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📁 Repository Structure

financial-engineering1/
├── Proj1_Problem1.ipynb           # Modern Portfolio Theory
├── Proj1_Problem2.ipynb           # CAPM and Factor Model
├── Additional_Problem3.ipynb      # Real Data Portfolio Optimization
└── README.md

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✅ Notes

All notebooks are self-contained and include comments and visualizations to support interpretation.
This repository is intended for educational and academic purposes, especially for those interested in quantitative finance and applied data science in financial markets.