# QuantFans/quantdigger

基于python的量化交易平台

Repository: https://github.com/QuantFans/quantdigger
Canonical: https://ross.abutalabs.com/products/quantdigger
Language: Python
License Family: other
Last push: 2020-05-02T09:52:06+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": 4295, "days_push": 2314, "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 1811, forks 653 (observed 2026-08-28T04:05:39.548471+00:00)

## What it is
QuantDigger is a Python-based quantitative trading backtesting framework for stocks and futures, with strategy syntax inspired by commercial platforms like TB and Pyramid. It includes a matplotlib-based candlestick charting interface and supports stock selection, arbitrage, timing, and portfolio strategies.

## Use cases
- backtest stock trading strategies in python
- backtest futures strategies
- run portfolio trading strategy backtests
- plot candlestick charts with strategy signals
- backtest arbitrage and stock-picking strategies

## When to choose
- you want a python backtesting framework with trader-friendly strategy syntax
- you need simple stock or futures backtesting with built-in charting
- you prefer python over proprietary scripting languages of commercial platforms

## When to avoid
- you need live trading or broker connectivity - trading interfaces were never added
- you need an actively maintained project - it is explicitly unmaintained since 2020
- you need production-grade performance or modern python version support

## Facets
- artifact type: library
- maturity: abandoned
- function: trading, data-visualization, analytics
- domain: fintech, data-science
- platform: python, cross-platform
- tags: quantitative-finance, backtesting, algorithmic-trading, stocks, futures, kline-charting, unmaintained, algorithms

## Member repositories
- QuantFans/quantdigger (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:39.548471+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 2026-08-30T03:21:14.660645+00:00, confidence not recorded.
  - readme: https://github.com/QuantFans/quantdigger (fetched 2026-08-28T04:05:39.548471+00:00, sha 3c1578577ef7)
  - registry_pypi: https://pypi.org/pypi/quantdigger/json (fetched 2026-08-29T11:00:05.273011+00:00, sha 1bde30ac3803)
- Data as of 2026-08-30T08:39:29.467469+00:00.