Welcome to my personal portfolio of financial analytics projects, built using Python. This repository showcases data-driven case studies in portfolio modeling, market analysis, and investment research. Each project demonstrates real-world data analysis workflows, exploratory data analysis (EDA), financial modeling, and custom visualizations using industry-standard tools.
🧠 Note: While case study prompts were generated with AI assistance, all code implementation, analysis, and interpretation are entirely my original work.
📌 My favorite and most comprehensive case study.
This project demonstrates:
- Portfolio construction using historical asset returns
- Risk-return optimization with the Efficient Frontier model
- Custom matplotlib visualizations of Sharpe ratios, volatility, and return tradeoffs
- Use of pandas for financial return calculations and cleaning
📷 Sample Visualization
- 📈 EDA of financial datasets (log returns, drawdowns, correlations)
- 🔁 Time series preprocessing (resampling, rolling averages)
- 📊 Advanced plotting with matplotlib, seaborn, and Plotly
- 🧮 Financial modeling: portfolio optimization, CAPM, value-at-risk
- 🧹 Robust data wrangling and cleaning using pandas
| Purpose | Tools & Libraries |
|---|---|
| Data Handling | pandas, numpy, yfinance, pandas-datareader |
| Visualization | matplotlib, seaborn, plotly |
| Analytics & Stats | scipy, statsmodels |
| Risk/Portfolio Modeling | pyfolio, cvxpy (where applicable) |
- Cleaning and structuring financial time series for analysis
- Creating reusable EDA workflows for return and volatility analysis
- Applying finance concepts (Sharpe ratio, diversification, correlation matrices)
- Balancing interpretability vs. accuracy in model design
Want to collaborate or chat about finance, analytics, or Python?
👉 Connect with me on LinkedIn or email me at kutil.ondra@outlook.cz.
All content is for educational and demonstration purposes only. Nothing here constitutes investment advice.