# BlackArbsCEO/Adv_Fin_ML_Exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Repository: https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises
Canonical: https://ross.abutalabs.com/products/adv_fin_ml_exercises
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2022-12-08T01:39:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3052, "days_push": 1365, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1954, forks 657 (observed 2026-08-28T04:05:58.992529+00:00)

## What it is
A collection of experimental Jupyter notebook solutions to selected exercises from the book 'Advances in Financial Machine Learning' by Marcos Lopez De Prado. It includes reusable Python source modules like bars.py and snippets.py for financial ML techniques.

## Use cases
- solving exercises from Advances in Financial Machine Learning
- learning financial machine learning techniques
- implementing information-driven bars like dollar and volume bars
- studying sample weighting and cross-validation for financial data
- exploring quant trading ML workflows in Python

## When to choose
- you are reading the AFML book and want worked exercise solutions
- you want Python reference implementations of financial ML concepts
- you are a quant learning ML applied to market data

## When to avoid
- you need production-ready, tested financial ML libraries
- you want a maintained framework with active development
- you need guaranteed correctness of solutions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, trading
- domain: fintech, machine-learning, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, quantitative-finance, financial-machine-learning, book-exercises, education

## Member repositories
- BlackArbsCEO/Adv_Fin_ML_Exercises (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:58.992529+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 2026-08-30T03:06:31.117351+00:00, confidence not recorded.
  - readme: https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises (fetched 2026-08-28T04:05:58.992529+00:00, sha 1f842a33d954)
- Data as of 2026-08-30T08:39:29.467469+00:00.