ScottZt/jin-ce-zhi-suan
【金策智算】➡️不靠情绪买卖、不追小道消息，专注用客观数据辅助决策。 我们仅提供本地化私有行情数据服务与历史回测工具，帮你把主观想法变成可验证的交易规则，用历史数据检验方法有效性，通过指标监控约束随意操作、控制回撤风险，让交易更有纪律、更落地。 本产品为纯量化工具，不荐股、不指导买卖、不预测行情、不承诺收益，所有决策由用户自主判断，只为你提供客观的数据支撑与AI辅助。作者【硅基流码】 observed · 2026-08-28
Health v2 · maintenance only
76/100
- Activity 93
- Release rhythm 90
- Longevity 11
Flags: young no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 7.5
- age_days: 163
- days_rel: 67
- days_push: 44
- n_releases_24m: 7
Adoption not part of the score
1466 stars · 435 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A self-hosted quantitative trading research and backtesting system for Chinese A-share markets, built with Python and FastAPI with a web dashboard. It decouples strategy generation, risk-control review, and execution into a layered 'Three Departments and Six Ministries' architecture, and supports natural-language stock screening via LLMs.
Use cases
- backtest trading strategies on historical A-share data
- convert natural language stock screening ideas into executable rules
- manage multiple trading strategies with risk controls
- convert TDX formulas and parse BLK sector pools
- run strategy evolution and performance comparison
- monitor drawdown and enforce position limits
- batch backtest a portfolio of stocks
When to choose
- you trade Chinese A-share markets and want a local, private backtesting tool
- you want to turn plain-language strategy ideas into testable rules with LLM assistance
- you need built-in risk controls like stop-loss, drawdown limits, and position constraints
- you want a web dashboard to configure and run backtests without writing code
When to avoid
- you need live trading execution or broker integration - this is backtesting and analysis only
- you trade non-Chinese markets or need global market data sources
- you expect stock picks, predictions, or guaranteed returns - the tool explicitly provides none
- you require a permissively licensed library to embed in your own product - the license is non-standard
Facets
application · maturity active
trading data-visualization web-framework machine-learning llm-inference analytics benchmarking fintech data-science analytics large-language-models self-hosted python self-hosted cross-platform quantitative-trading backtesting a-share stock-screening risk-control fastapi trading-strategy chinese-stock-market ai-stock-picking dashboard web-server
1 source
- readme: https://github.com/ScottZt/jin-ce-zhi-suan · fetched 2026-08-28 · 0fc8c03f93d7
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ScottZt/jin-ce-zhi-suan | main | 76 |
For agents
markdown · JSON · MCP: product_card(name="ScottZt/jin-ce-zhi-suan")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem