# firmai/machine-learning-asset-management

Machine Learning in Asset Management (by @firmai)

Repository: https://github.com/firmai/machine-learning-asset-management
Canonical: https://ross.abutalabs.com/products/machine-learning-asset-management
Homepage: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, trading-strategies, assets-management, algorithmic-trading, portfolio-optimization, jupyter-notebook, google-colab, quantitative-finance, quant
Last push: 2021-12-17T07:17:49+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2610, "days_push": 1720, "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 1746, forks 467 (observed 2026-08-28T04:05:30.727821+00:00)

## What it is
A curated collection of Python Jupyter notebooks accompanying the paper 'Machine Learning in Asset Management' by David Snow, covering roughly 100 trading strategies and portfolio optimization models. It serves as an educational and research resource for quantitative finance with machine learning.

## Use cases
- learn machine learning trading strategies
- study portfolio optimization techniques
- find quantitative finance notebook examples
- explore algorithmic trading models in Python
- research ML applications in asset management
- get reproducible code for quant papers

## When to choose
- you want educational notebooks on ML trading strategies
- you are researching portfolio construction with machine learning
- you need reference implementations of quant models in Python

## When to avoid
- you need production-ready trading infrastructure
- you require a maintained library with a license and active releases
- you need guaranteed correctness for live trading decisions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, trading, data-science
- domain: fintech, machine-learning, data-science
- platform: python, cross-platform
- tags: quantitative-finance, algorithmic-trading, portfolio-optimization, jupyter-notebooks, trading-strategies, asset-management

## Member repositories
- firmai/machine-learning-asset-management (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:30.727821+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 2026-08-30T03:29:12.037108+00:00, confidence not recorded.
  - readme: https://github.com/firmai/machine-learning-asset-management (fetched 2026-08-28T04:05:30.727821+00:00, sha 106731a1e02c)
- Data as of 2026-08-30T08:39:29.467469+00:00.