# datawhalechina/whale-quant

本项目为量化开源课程，可以帮助人们快速掌握量化金融知识以及使用Python进行量化开发的能力。

Repository: https://github.com/datawhalechina/whale-quant
Canonical: https://ross.abutalabs.com/products/whale-quant
Language: Jupyter Notebook
License Family: other
Last push: 2026-01-15T13:02:15+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 35, longevity 88
- inputs: {"age_days": 1243, "days_push": 230, "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 2749, forks 319 (observed 2026-08-28T04:07:17.843547+00:00)

## What it is
WhaleQuant is an open-source quantitative finance course by Datawhale that teaches quantitative investing concepts and Python-based quant development. It provides a full pipeline of Jupyter notebook lessons covering market data, stock selection, timing, rebalancing strategies, backtesting, and machine learning for trading.

## Use cases
- learn quantitative finance from scratch
- learn how to build trading strategies in Python
- get stock market data with Python
- backtest a quant trading strategy
- apply machine learning to stock selection
- study quant portfolio rebalancing strategies

## When to choose
- you want a structured, free introduction to quantitative trading
- you prefer learning through runnable Jupyter notebooks
- you want an end-to-end pipeline from data to backtesting to live trading concepts

## When to avoid
- you need production-grade trading infrastructure or a maintained trading library
- you require permissively licensed code for commercial use (CC BY-NC-SA)
- you need content in a language other than Chinese

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, trading
- domain: fintech, tutorials, data-science, education
- platform: python, cross-platform
- tags: quantitative-finance, quant-trading, open-course, jupyter-notebooks, backtesting, chinese-language, datawhale

## Member repositories
- datawhalechina/whale-quant (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:17.843547+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 2026-08-30T08:18:58.496141+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/whale-quant (fetched 2026-08-28T04:07:17.843547+00:00, sha fe8e4e2e3efe)
- Data as of 2026-08-30T08:39:29.467469+00:00.