ScottZt

ScottZt / jin-ce-zhi-suan

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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。

43
17
69% credibility
Found Mar 31, 2026 at 43 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A stock trading simulation framework that models ancient Chinese government departments to backtest and rank multiple quantitative strategies with built-in risk controls and performance analytics.

How It Works

1
🔍 Discover the Strategy Tester

You hear about a fun tool that simulates ancient government advisors competing to pick winning stock trades, like a virtual trading cabinet.

2
🚀 Start the Program

Open the main program and it welcomes you with a message about launching the stock testing cabinet.

3
📊 Pick Stocks and Dates

Choose your favorite stocks and a time period for testing, like recent months or years.

4
⚔️ Watch Strategies Battle

Hit start and see ten smart strategies analyze the data, generate trades, and compete under strict safety rules—just like ministers advising an emperor.

5
📈 Review Performance Reports

Get colorful reports for each strategy showing profits, risks, win rates, and a ranked leaderboard of the best performers.

6
Choose Next Adventure
🔄
Batch Test Many

Run tests on lists of stocks to find patterns across markets.

📡
Live Simulation

Watch strategies react to pretend live market updates.

🏆 Find Your Winners

Celebrate discovering top strategies that beat the market safely, ready for your own trading ideas.

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Star Growth

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AI-Generated Review

What is jin-ce-zhi-suan?

jin-ce-zhi-suan is a Python backtesting and live monitoring tool for A-share quant trading, pulling data from akshare, Tushare, or local databases to test strategies on minute-level history. It structures trading as a "three provinces six ministries" system—signal generation, risk gates, execution simulation, and reporting—to enforce discipline, turning gut ideas into verifiable rules with metrics like Calmar ratio and max drawdown. Users run CLI backtests (e.g., `python run_backtest.py --stock 600036.SH`), get ranked reports, or monitor live via `run_live.py`.

Why is it gaining traction?

It stands out by baking in strict risk controls—like position limits, T+1 rules, and circuit breakers—that block impulsive trades, plus batch tools for testing strategies across stocks, periods, and costs. Developers hook into it for quick iteration: define rules, backtest batches via CSV pools, and score them automatically on Sharpe, win rate, and regime consistency. No broker dependency means fast local sims on real Chinese data.

Who should use this?

Python quants prototyping mean-reversion or momentum rules for沪深 stocks, especially those tired of manual Excel backtests or broker platforms lacking custom risk layers. Retail algo traders in China validating ideas across bull/bear/震荡 phases before live deployment. Strategy farms running mass tests on 10+ rules per ticker.

Verdict

Worth forking for A-share backtesting if you code in Python—solid CLI and reports—but at 43 stars and 0.7% credibility, it's early-stage; add tests and English docs for broader appeal. Solid foundation for disciplined zhi suan.

(198 words)

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