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MelQuantLab

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.

Research philosophy

Observe → Question → Test → Measure → Reflect → Improve

Every project must answer the same six questions:

  1. The Why: Why are we doing this?
  2. What problem am I trying to solve?
  3. Why does this matter in financial markets?
  4. How did I test it?
  5. What did I learn?
  6. What would I improve next?
  7. What is the bigger picture?

This structure keeps the work understandable to humans while preserving the technical evidence needed for reproducibility.

Repository roadmap

quant-lab/
├── README.md
├── 01-trader-toolkit/
│   ├── 01-trade-calculator/
│   ├── 02-options-analytics/
│   ├── 03-relative-value-screener/
│   ├── 04-market-maker-simulator/
│   └── 05-bond-analytics/
├── 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.

Published projects

01 — Trader Toolkit

Ten-tool target

The Trader Toolkit is being developed as a defined ten-project collection:

  1. Trade & Risk Analytics Calculator — complete
  2. Black-Scholes Options Analytics — complete
  3. Credit Relative Value Screener — complete
  4. Market Maker Simulator — complete
  5. Bond Pricing, Yield & Duration — complete
  6. Value-at-Risk & Stress Testing — planned
  7. Pairs Trading & Cointegration — planned
  8. Order Book & Liquidity Analyzer — planned
  9. Portfolio Optimizer — planned
  10. Yield Curve & Forward Rate Analyzer — planned

02 — Financial Data

Standards

  • 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.

About

Quantitative finance research in Python: bias-aware backtests, credit and cross-asset analysis, portfolio risk and reproducible investment workflows

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