# FinHackCN/finhack

FinHack®，一个易于拓展的量化金融框架，它在当前版本中集成了数据采集、因子计算、因子挖掘、因子分析、机器学习、策略编写、量化回测、实盘接入等全流程的量化投研工作。

Repository: https://github.com/FinHackCN/finhack
Canonical: https://ross.abutalabs.com/products/finhack
Homepage: https://github.com/FinHackCN/finhack/wiki/1%E3%80%81%E5%BF%AB%E9%80%9F%E5%85%A5%E9%97%A8
Language: Python
License: NOASSERTION
License Family: other
Topics: investment, quant, quantitative, quantitative-finance, trading
Last push: 2026-08-19T03:01:18+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 98
- inputs: {"age_days": 1382, "days_push": 14, "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 1145, forks 224 (observed 2026-08-28T04:03:45.523277+00:00)

## What it is
FinHack is an extensible Python quantitative finance framework covering the full quant research workflow: data collection, factor computation and mining, factor analysis, machine learning, strategy development, backtesting, and live trading integration. It includes an Alpha101/Alpha191 formula-based factor engine, A-share backtesting rules (price limits, T+1), dynamic adjustment mechanisms, and multiprocess backtesting and model training.

## Use cases
- backtest A-share trading strategies with T+1 and price limit rules
- compute Alpha101 and Alpha191 factors from a formula engine
- mine and analyze stock factors with machine learning
- collect market data from tushare into MySQL
- run multiprocess backtests and model training on a server
- connect strategies to live trading
- organize multiple strategy projects with isolated environments

## When to choose
- you need an end-to-end open-source quant research pipeline for Chinese A-share markets
- you want extensible factor computation, mining, and backtesting in Python
- you need multiprocess backtesting and ML training on your own hardware

## When to avoid
- you need US stocks, futures, forex, or crypto support (planned but not implemented)
- you require a stable, production-ready system - the project is mid-refactor and currently broken
- you need Windows support or a lightweight install without MySQL/Redis
- you cannot accept the GPL-3.0 dual license for commercial use

## Facets
- artifact type: framework
- maturity: experimental
- function: machine-learning, data-science, etl, caching, database, workflow-automation, trading
- domain: fintech, machine-learning
- platform: python, cli
- tags: quantitative-finance, backtesting, factor-mining, alpha-factors, tushare, a-share, live-trading, quant-research, mysql, redis, data-engineering, automation, linux, docker

## Member repositories
- FinHackCN/finhack (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:45.523277+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 2026-08-30T06:34:25.378008+00:00, confidence not recorded.
  - readme: https://github.com/FinHackCN/finhack (fetched 2026-08-28T04:03:45.523277+00:00, sha 36f17883dfb1)
  - homepage: https://github.com/FinHackCN/finhack/wiki/1%E3%80%81%E5%BF%AB%E9%80%9F%E5%85%A5%E9%97%A8 (fetched 2026-08-29T12:39:35.672518+00:00, sha bb13a04cf85a)
- Data as of 2026-08-30T08:39:29.467469+00:00.