A collection of personal applied projects in financial data analysis and quantitative methods. These projects demonstrate the practical application of Python, pandas, and financial engineering concepts to real-world market data, with a focus on the Nigerian equities market.
Comparative risk-return analysis of three major Nigerian stocks — DANGCEM, GTCO, and ZENITHBANK — over a 5-year period (2021–2026).
Key Highlights:
- DANGCEM emerged as the strongest performer on a risk-adjusted basis (highest Sharpe Ratio).
- Low similarity between DANGCEM and the banking stocks, indicating strong diversification potential.
Reusable pipeline for cleaning and preparing messy financial datasets commonly encountered in emerging markets.
Exploring the relationship between alternative data (Google Trends, social media sentiment) and stock price movements.
- Core: Python, pandas, NumPy, Matplotlib, Seaborn
- Analysis: scikit-learn (similarity measures), SciPy
- Environment: Jupyter Notebook
- Version Control: Git & GitHub
To build and showcase practical, job-ready skills in financial data analysis, risk assessment, and quantitative research by working with real African market data.
Status: In active development (Private Repository)
Last Updated: July 02, 2026
Connect with me
- LinkedIn: Olamide Emmanuel Ogundare
- GitHub: @emycodesanalytics
- Twitter/X: @emycodes