# Auquan/Tutorials

Ipython notebooks for math and finance tutorials

Repository: https://github.com/Auquan/Tutorials
Canonical: https://ross.abutalabs.com/products/auquan-tutorials
Language: Jupyter Notebook
License Family: other
Topics: tutorials, trading, math
Last push: 2020-08-01T17:03:34+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": 3511, "days_push": 2223, "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 1134, forks 578 (observed 2026-08-28T04:03:43.095342+00:00)

## What it is
A collection of IPython/Jupyter notebooks teaching quantitative trading strategies and the underlying math, including time series analysis, mean reversion, momentum, and pairs trading. It is educational material meant to be run locally with the Auquan toolbox.

## Use cases
- learn quantitative trading strategies with python notebooks
- understand mean reversion and momentum trading basics
- learn time series analysis ARIMA GARCH with examples
- study pairs trading and cointegration
- learn statistics for finance random variables covariance
- avoid overfitting when backtesting trading models

## When to choose
- you want hands-on Jupyter notebook tutorials for quant trading and financial math
- you are learning time series analysis with practical examples
- you use the Auquan toolbox ecosystem

## When to avoid
- you need production trading software or a maintained library
- you require an actively updated resource - last release was 2020
- you need a license permitting redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, trading, math
- domain: tutorials, fintech, mathematics, data-science
- platform: python, cross-platform
- tags: jupyter-notebooks, quantitative-finance, time-series-analysis, trading-strategies, education

## Member repositories
- Auquan/Tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:43.095342+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 2026-08-30T06:36:49.382939+00:00, confidence not recorded.
  - readme: https://github.com/Auquan/Tutorials (fetched 2026-08-28T04:03:43.095342+00:00, sha 90c89206c66f)
- Data as of 2026-08-30T08:39:29.467469+00:00.