Python Financial Data Analysis Mini Project

This project performs an exploratory data analysis (EDA) on a synthetic financial dataset containing daily closing prices for four exchange-traded funds (ETFs): ETF_A, ETF_B, ETF_C, ETF_D.
The goal is to demonstrate skills in Python, data cleaning, visualization, time-series analysis, returns, and correlations.

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📁 Files in This Repository

• python_financial_data_analysis.ipynb — Main Jupyter Notebook performing the analysis
• financial_data.csv — Synthetic price dataset (250 trading days × 4 tickers)
• README.md — Project explanation and documentation

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🔧 Tools & Libraries

• Python
• Pandas
• NumPy
• Matplotlib
• (Optional) Seaborn

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📊 Analysis Performed

• Importing and cleaning financial price data
• Pivoting data into time-series format
• Visualizing ETF price trends
• Calculating daily percentage returns
• Return summary statistics
• Correlation matrix (heatmap)
• Distribution of returns
• Cumulative return comparison
• Identifying best-performing ETF

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📈 Example Insights

(You can modify these after running the notebook.)

• Highest-return ETF over the full period
• Lowest volatility ETF
• Strongest correlations between asset returns
• ETF performance divergence over time

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🧩 Purpose of This Project

This project is designed to showcase:

• Python proficiency
• Data analysis workflow
• Time-series concepts
• Visualization and reporting skills
• Finance-oriented quantitative thinking

Perfect for use in:

• CV / Resume Projects Section
• Internship applications
• Master’s programs (Finance, Analytics, IE, OR)

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Visualizations

📌 Author

Rahmi Berkay Alp
Sabancı University