precise-t-trading（精准交易）

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

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

python scripts/stock_monitor.py

Monitors stocks every 60 minutes and logs alerts.

3. Open Web Dashboard

open scripts/dashboard.html

Real-time visualization with auto-refresh every 30 seconds.

Configuration

Environment Variables

Edit scripts/config.py

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

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

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
• WeChat Group: Join our trading community
• Email: yang77160@example.com
• ⚡ Pro Tip: Use my 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 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 yang77160@example.com 或加微信**