Analyze. Model. Alpha.
MelQuantLab is my growing portfolio of quantitative research, financial-data analysis and systematic-investing projects. The repository is deliberately organised as a learning and research journey: foundational programming first, then market-data analysis, followed by deeper investment research.
Observe → Question → Test → Measure → Reflect → Improve
Every project must answer the same six questions:
- The Why: Why are we doing this?
- What problem am I trying to solve?
- Why does this matter in financial markets?
- How did I test it?
- What did I learn?
- What would I improve next?
- What is the bigger picture?
This structure keeps the work understandable to humans while preserving the technical evidence needed for reproducibility.
quant-lab/
├── README.md
├── 01-python-foundations/
├── 02-financial-data/
│ └── moving-average-backtester/
├── 03-quant-research/
├── white-papers/
└── datasets/
Folders will be added as completed work becomes ready to publish. Empty categories are shown here as the intended roadmap rather than being populated with placeholder projects.
- Project 1: SPY 50/200 Moving-Average Backtester — a bias-aware, cost-aware comparison of a simple trend-following rule with continuously holding SPY.
- Research questions and assumptions are stated before conclusions.
- Signals are implemented without look-ahead bias.
- Transaction costs and limitations are made visible.
- Automated tests protect important calculations.
- Negative results are retained when they are informative.
- Plain-English explanations accompany technical outputs.
The material in this repository is historical research and education, not investment advice or a claim of future performance.