Quantitative Finance Lab

Project-based research + a reusable Python library for quantitative finance

A living lab of reproducible quant-finance projects (notes, math, experiments) backed by a growing Python package: quantfinlab.
Author

Ramtin Asadi

Note

This site is the published companion to the GitHub repository:
https://github.com/ramtin-asadi/Quantitative-Finance-Lab

What this is

Quantitative-Finance-Lab is a project series where each topic is developed end-to-end:

  • Clear narrative: intuition → math → implementation
  • Reproducible experiments: consistent metrics, plots, and comparisons
  • Library-first engineering: reusable components are extracted into quantfinlab

The goal is to build a portfolio that is both research-grade (well explained) and engineering-grade (reusable, testable, maintainable).

Start here

  • Read the Overview for conventions, reproducibility notes, and how projects are structured.
  • Then open a project notebook and follow it top-to-bottom.

Projects

01 — Yield Curve, Bond Pricing & Risk

  • Yield curve construction (discount factors / zero rates)
  • Bond pricing and key risk measures
  • Practical fixed-income utilities you can reuse elsewhere

Open Project 01

02 — Portfolio Optimization (Mean–Variance)

  • Mean–variance optimization pipeline
  • Covariance estimators and model comparisons
  • Backtest-style evaluation and performance diagnostics

Open Project 02

The quantfinlab library

As the series grows, reusable code is consolidated into quantfinlab:

  • shared fixed-income helpers
  • portfolio utilities
  • plotting/diagnostic helpers
  • common conventions (metrics, configs, I/O)

The intent is simple: notebooks explain and experiment; the library implements.

Reproducibility

  • This repo follows a reproducibility-first workflow (formatting/linting/testing/CI).
  • Large datasets are typically not committed. If a notebook expects data files, place them under a local data/ directory (gitignored) and follow any project-specific notes.

Disclaimer

This repository is for research and educational purposes. Nothing here is investment advice, and results may vary by dataset, assumptions, costs, constraints, and implementation details.