# cedricporter/funcat

Funcat 将同花顺、通达信、文华财经麦语言等的公式写法移植到了 Python 中。

Repository: https://github.com/cedricporter/funcat
Canonical: https://ross.abutalabs.com/products/funcat
Homepage: http://funcat.org
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: quant, python, trade
Last push: 2019-08-04T02:29:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3528, "days_push": 2587, "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 1033, forks 439 (observed 2026-08-28T04:03:18.511307+00:00)

## What it is
Funcat is a Python library (built on numpy) that ports the formula languages of Chinese trading platforms like Tonghuashun, Tongdaxin, and Wenhua into Python. It provides a concise DSL for technical analysis indicators, cross signals, and stock screening.

## Use cases
- write stock technical indicator formulas in python
- compute moving average crossovers like tonghuashun
- screen stocks for limit-up days
- reimplement KDJ or DMI indicators matching Chinese trading software
- translate tongdaxin formulas to python

## When to choose
- you want concise, formula-style technical analysis in Python
- you need results matching Tonghuashun/Tongdaxin indicator calculations
- you're doing quant research with Chinese market data

## When to avoid
- you need a full backtesting or live trading framework
- you want actively maintained software (last release 2019)
- you need indicators beyond the currently implemented set

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, math, parser
- domain: fintech, data-science
- platform: python
- tags: quant, technical-analysis, stock-formula, dsl, numpy, trading, tonghuashun, tongdaxin, algorithms

## Member repositories
- cedricporter/funcat (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.511307+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 2026-08-30T07:06:39.705203+00:00, confidence not recorded.
  - readme: https://github.com/cedricporter/funcat (fetched 2026-08-28T04:03:18.511307+00:00, sha ee45c32e89c0)
  - homepage: http://funcat.org (fetched 2026-08-29T13:06:40.786462+00:00, sha 966654459b18)
  - registry_pypi: https://pypi.org/pypi/funcat/json (fetched 2026-08-29T13:06:40.791009+00:00, sha bb6bd2bee8d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.