# khscience/OSkhQuant

看海量化回测系统（看海量化交易系统）开源代码，khQuant框架，实现A股可视化回测，全部开源。

Repository: https://github.com/khscience/OSkhQuant
Canonical: https://ross.abutalabs.com/products/oskhquant
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-04-18T12:14:12+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 48, longevity 43
- inputs: {"age_days": 602, "days_push": 137, "days_rel": 137, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1538, forks 417 (observed 2026-08-28T04:05:00.235041+00:00)

## What it is
KHQuant (看海量化交易系统) is a free, open-source quantitative backtesting and trading system for China's A-share market, built on MiniQMT with a graphical interface. It lets strategies run locally in Python with full access to third-party libraries, supporting backtesting, simulation, and live trading from a unified strategy file.

## Use cases
- backtest A-share stock trading strategies visually
- run quantitative strategies locally with Python libraries
- simulate and paper-trade strategies before going live
- apply machine learning and signal processing models to stock trading
- switch a strategy between backtest, simulation, and live trading
- avoid cloud quant platforms and keep strategy code private

## When to choose
- you trade China A-shares and use MiniQMT with a broker account
- you want a free, open-source, GUI-driven backtesting platform
- you need unrestricted access to Python libraries like AI/ML frameworks in strategies
- you want strategy code and data to stay on your local machine

## When to avoid
- you trade non-Chinese markets (US stocks, crypto, forex)
- you need a headless or purely programmatic backtesting library like Backtrader
- you require a fully audited, professionally signed production trading system
- you need multi-asset or futures/options support out of the box

## Facets
- artifact type: application
- maturity: active
- function: trading, data-visualization, gui, machine-learning, developer-tools
- domain: fintech, analytics, data-visualization
- platform: windows, python
- tags: quantitative-trading, backtesting, a-shares, miniqmt, stock-market, algorithmic-trading, chinese-market, automation, desktop

## Member repositories
- khscience/OSkhQuant (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.235041+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 2026-08-30T04:30:57.735973+00:00, confidence not recorded.
  - readme: https://github.com/khscience/OSkhQuant (fetched 2026-08-28T04:05:00.235041+00:00, sha 77cbe24c9640)
- Data as of 2026-08-30T08:39:29.467469+00:00.