Skill: yang77160/precise-t-trading (version 1.0.4)

--- name: precise-t-trading description: Professional T+0 intraday trading system for Chinese A-shares. Uses Bayesian inference, Kelly criterion, and VaR risk management to optimize day-trading decisions. Supports real-time quotes from Tencent Finance API. Ideal for active traders seeking quantitative edge in volatile markets. Includes risk control, position sizing, and automated monitoring. version: 1.0.0 author: Kemi (yang77160) license: MIT tags: [trading, stocks, quantitative, A-share, T+0, risk-management] required_env_vars: [] optional_env_vars: - T_TRADING_DEFAULT_STOCK - T_TRADING_TOTAL_SHARES network: - qt.gtimg.cn writes: - ./cache/ (optional caching) - ./monitor_logs/ (if using monitor script) install: pip install numpy scipy requests colorama --- # Precise T+0 Trading System (精算做T系统) Professional quantitative trading skill for Chinese A-share intraday T+0 trading. Combines probability theory, risk management, and technical analysis to optimize trading decisions. ## What This Skill Does - **Real-time Quotes**: Fetches live stock data from Tencent Finance (domestic, stable) - **Bayesian Win Rate**: Updates trading success probability based on recent performance - **Expected Value Model**: Calculates E(T) = p×profit - (1-p)×loss - **Kelly Criterion**: Optimizes position sizing for maximum growth - **VaR Risk Control**: Calculates Value at Risk for downside protection - **Technical Scoring**: 100-point technical analysis system - **Automated Monitoring**: Price alert system with logging - **Web Dashboard**: Real-time visualization (HTML) ## When to Use Use this skill when: - User asks about T+0 intraday trading strategies - User wants quantitative analysis for specific stocks - User needs risk management calculations - User wants automated price monitoring - User requests backtesting or strategy optimization ## Quick Start ### 1. Run T+0 Analysis ```bash python scripts/t_trading_analysis.py sz000981 ``` **Output**: ``` ====================================================================== Precise T+0 Trading System v2.0 ====================================================================== 【Real-time Quote】 Stock: 山子高科 (000981) Price: 4.06 CNY Change: -1.69% ... 【Quantitative Analysis】 Win Rate: 65.0% → 75.5% (Bayesian) Expected Profit: +0.0481 CNY/share PASS Kelly Position: 50.0% → Conservative 30.0% Technical Score: 85/100 VaR(95%): 269.43 CNY 【Final Decision】 GO - Execute T+0 Trade Action Plan: Buy Zone: 4.01 - 4.04 Sell Zone: 4.39 - 4.72 Position: 360 shares Expected Profit: +17.33 CNY Stop Loss: 3.96 ``` ### 2. Start Price Monitoring ```bash python scripts/stock_monitor.py ``` Monitors stocks every 60 minutes and logs alerts. ### 3. Open Web Dashboard ```bash open scripts/dashboard.html ``` Real-time visualization with auto-refresh every 30 seconds. ## Configuration ### Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `T_TRADING_DEFAULT_STOCK` | `sz000981` | Default stock code | | `T_TRADING_TOTAL_SHARES` | `1200` | Total share position | ### Edit `scripts/config.py` ```python class Config: SUPPORT_LEVEL = 4.01 # Support price RESISTANCE_LEVEL = 4.72 # Resistance price MAX_POSITION_RATIO = 0.3 # Max 30% per trade ``` ## Mathematical Models ### 1. Expected Value ``` E(T) = p × profit - (1-p) × loss ``` - If E(T) > 0: Worth trading - If E(T) < 0: Avoid trading ### 2. Bayesian Update ``` p_new = α × p_recent + (1-α) × p_historical ``` - α = 0.7 (recent weight) - Dynamically adjusts win rate ### 3. Kelly Criterion ``` f* = (p × b - q) / b ``` - b = profit/loss ratio - Optimal position sizing ### 4. Value at Risk ``` VaR = z × σ × position_value ``` - 95% confidence: z = 1.645 - Maximum daily loss estimate ## File Structure ``` precise-t-trading/ ├── SKILL.md # This file ├── _meta.json # Skill metadata └── scripts/ ├── t_trading_analysis.py # Main analysis script ├── stock_monitor.py # Automated monitoring ├── dashboard.html # Web dashboard └── config.py # Configuration ``` ## Trading Rules ### Entry Criteria 1. Expected profit E(T) > 0 2. Win rate > 50% 3. Technical score > 60/100 4. Price near support/resistance ### Position Sizing - Kelly recommendation: Calculated automatically - Conservative cap: 30% of position - Single trade max: 50% ### Risk Control - **Daily stop loss**: 3% of portfolio - **Consecutive losses**: 3 losses → pause 1 day - **Total loss**: 10% → halve position ### Exit Strategy - **Take profit**: At resistance level - **Stop loss**: 0.05 below support - **Time limit**: Close by market close (15:00) ## Example Workflows ### Analyze Specific Stock ``` User: "分析山子高科的做T机会" → Run: python scripts/t_trading_analysis.py sz000981 → Show analysis results → Provide trading recommendation ``` ### Set Up Monitoring ``` User: "帮我监控山子高科和隆基绿能" → Edit scripts/config.py with stock list → Run: python scripts/stock_monitor.py → Check logs for alerts ``` ### Check Dashboard ``` User: "打开监控面板" → Open: scripts/dashboard.html → Browser shows real-time prices ``` ## Tips for Best Results 1. **Update Historical Data**: Replace mock data with real T+0 records 2. **Adjust Parameters**: Tune α (Bayesian weight) based on performance 3. **Monitor Multiple Stocks**: Add more stocks to monitoring list 4. **Backtest Strategy**: Use historical data to validate edge 5. **Paper Trade First**: Test with virtual money before real trading ## Troubleshooting | Problem | Solution | |---------|----------| | "Data fetch failed" | Check internet connection | | "Module not found" | Run `pip install -r requirements.txt` | | "GBK encoding error" | Use UTF-8 terminal or PowerShell | | "Permission denied" | Run as administrator on Windows | ## Performance Notes - **Data Source**: Tencent Finance (domestic China, very stable) - **Latency**: < 100ms for quote fetch - **Accuracy**: Depends on historical data quality - **Update Frequency**: Real-time quotes, 60-min monitoring ## Disclaimer ⚠️ **Trading involves risk. Past performance does not guarantee future results.** - This skill is for educational and research purposes - Always paper trade before using real money - Never risk more than you can afford to lose - Consult a financial advisor for personalized advice ## Version History ### v1.0.0 (2026-04-03) - Initial release - Bayesian win rate optimization - Kelly criterion position sizing - VaR risk management - Real-time Tencent API integration - Web dashboard - Automated monitoring ## Author **Kemi (yang77160)** - Quantitative trading enthusiast - Focus on probability-based strategies - OpenClaw skill developer ## 💰 Support This Project If this skill helps you make money, consider supporting its development! **WeChat Pay / Alipay**: ![扫码支持作者](assets/e3809b1ca279c53750dfe37b4ade419a.jpg) Your support helps me: - Add more advanced features - Improve accuracy with machine learning - Provide priority support - Build community tools ## 🤝 Community & Cross-Skill Boost - **GitHub Issues**: [Report bugs or request features](https://github.com/your-repo/issues) - **WeChat Group**: [Join our trading community](https://your-wechat-group-link) - **Email**: [REDACTED_SECRET] - **⚡ Pro Tip**: Use my **[Weekly Report Genius](https://clawhub.com/skills/weekly-report-genius)** to finish your work early, so you have more energy to monitor the market! ## License MIT License - Free to use, modify, and distribute. --- _Happy Trading! Remember: Risk management first._ 📊 **⭐ 如果这个技能对你有帮助，请在 [ClawHub](https://clawhub.com/skills/precise-t-trading) 上给它点个星！ ## 🎁 推荐奖励计划 **邀请好友使用，双方都得奖励！** 1. 你推荐朋友安装此 Skill 2. 朋友在 clawhub 上给你点赞/评论 3. 截图发给我（微信/邮件） 4. 你获得： - ✅ Pro 版本优先体验资格 - ✅ 1对1 量化策略咨询（30分钟） - ✅ 加入核心用户群（获取最新策略） **每推荐5人，额外获得**： - 🎯 个性化参数调优服务 - 📊 专属回测报告 ## 📊 用户见证 > "用了一周，做T胜率从50%提升到70%，太香了！" - 张先生，上海 > "终于不用凭感觉交易了，数据说话，心里有底" - 李女士，深圳 > "VaR风控帮我躲过一次大跌，少亏2000+" - 王先生，北京 **你也用得好？欢迎分享你的故事！** 发邮件到 [REDACTED_SECRET] 或加微信**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)