# romanmichaelpaolucci/Quant-Guild-Library

A library of Jupyter notebooks and corresponding YouTube lectures by Roman Paolucci

Repository: https://github.com/romanmichaelpaolucci/Quant-Guild-Library
Canonical: https://ross.abutalabs.com/products/quant-guild-library
Language: Jupyter Notebook
License Family: other
Last push: 2026-08-06T14:18:40+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 35, longevity 42
- inputs: {"age_days": 592, "days_push": 27, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1404, forks 330 (observed 2026-08-28T04:04:38.057968+00:00)

## What it is
A curated library of Jupyter notebooks paired with YouTube video lectures on quantitative finance by Roman Paolucci. Topics span stochastic calculus, options pricing, trading strategies, portfolio management, and AI in finance.

## Use cases
- learn quantitative finance from scratch
- understand how to derive the Black-Scholes equation
- build an AI stock trading bot with Interactive Brokers
- learn to backtest trading strategies in Python
- study stochastic calculus and Brownian motion
- learn portfolio management and risk metrics like alpha, beta, and Sharpe ratio
- find projects to become a quant

## When to choose
- you want free, notebook-based lessons paired with video lectures on quant finance
- you are learning Python for trading, options pricing, or portfolio analytics
- you prefer a topic-by-topic curriculum from beginner to advanced quant skills

## When to avoid
- you need production-ready trading software or a maintained code library
- you require a licensed, dependency-managed package for a project
- you want a single cohesive codebase rather than standalone educational notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, trading, math, machine-learning
- domain: fintech, education, tutorials, data-science, mathematics
- platform: python, cross-platform
- tags: quantitative-finance, jupyter-notebooks, video-lectures, options-pricing, stochastic-calculus, algorithmic-trading, trading-strategies, black-scholes, portfolio-management

## Member repositories
- romanmichaelpaolucci/Quant-Guild-Library (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.057968+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:38:52.319849+00:00, confidence not recorded.
  - readme: https://github.com/romanmichaelpaolucci/Quant-Guild-Library (fetched 2026-08-28T04:04:38.057968+00:00, sha 4e90c05db77d)
- Data as of 2026-08-30T08:39:29.467469+00:00.