Trading System Diagnostics

Purpose

Use this skill when:

• No trading signals are being generated
• Need to verify AI analysis is working
• Validating technical indicator calculations
• Debugging market data issues

Diagnostic Commands

Full Diagnostic (Default)

cd /home/linuxuser/nautilus_AlgVex
source venv/bin/activate
python3 scripts/diagnose.py

Quick Diagnostic (Skip AI calls)

cd /home/linuxuser/nautilus_AlgVex
source venv/bin/activate
python3 scripts/diagnose.py --quick

With Update and Restart

python3 scripts/diagnose.py --update --restart

Expected Output

Normal Operation Signs

✅ Configuration loaded successfully
✅ Market data fetched successfully
✅ TechnicalIndicatorManager initialized
✅ Technical data retrieved
✅ Sentiment data retrieved
✅ MultiAgent 层级决策成功
   🐂 Bull Agent 分析中...
   🐻 Bear Agent 分析中...
   ⚖️ Judge Agent 判断中...
   🛡️ Risk Manager 评估中...
🎯 Judge 最终决策: BUY/SELL/HOLD

Key Checkpoints

Check	Normal Value	Abnormal Handling
RSI	0-100	Out of range = data error
MACD	Any value	NaN = insufficient data
Judge Signal	BUY/SELL/HOLD	ERROR = API failure
Winning Side	BULL/BEAR/TIE	显示辩论胜方

信号决策流程 (层级决策架构)

采用 TradingAgents 层级决策架构，Judge 决策即最终决策

决策流程:
Phase 1: Bull/Bear Debate (辩论)
  └→ 🐂 Bull Agent: 寻找做多理由
  └→ 🐻 Bear Agent: 寻找做空理由

Phase 2: Judge (Portfolio Manager) Decision
  └→ ⚖️ 评估辩论结果，做出最终决策

Phase 3: Risk Evaluation
  └→ 🛡️ 确定仓位大小和止损止盈

注意: 所有策略参数配置在 configs/base.yaml 中管理。

Common Issues

1. No Trading Signals

Possible Causes:

• Judge returns HOLD (Bull/Bear辩论无明显胜者)
• Confidence below min_confidence_to_trade
• Risk Manager 认为风险过高

Check Command:

python3 scripts/diagnose_realtime.py 2>&1 | grep -E "(Judge|Final Signal|Confidence|Winning Side)"

2. DeepSeek API Failure

Check:

grep "DEEPSEEK_API_KEY" ~/.env.algvex

3. Abnormal Technical Indicators

Check:

python3 scripts/diagnose.py 2>&1 | grep -E "(RSI|MACD|SMA)"

Key Files

File	Purpose
scripts/diagnose.py	Main diagnostic script
scripts/diagnose_realtime.py	Real-time API diagnostic
scripts/diagnostics/	Modular diagnostic system
scripts/smart_commit_analyzer.py	Regression detection (auto-evolving rules)
strategy/ai_strategy.py	Main strategy logic
configs/base.yaml	Base configuration (all parameters)
configs/production.yaml	Production environment overrides

Order Flow Simulation (v3.18)

模块化诊断系统包含 7 个订单流场景模拟。

7 Scenarios (模拟场景)

场景	描述	测试目标
1. New Position	开新仓 (无现有持仓)	Bracket 订单创建
2. Add to Position	加仓 (同方向)	SL/TP 数量更新
3. Reduce Position	减仓	SL/TP 数量减少
4. Reversal	反转仓位 (多→空/空→多)	两阶段提交逻辑
5. Close Position	平仓信号	仓位关闭 + 取消 SL/TP
6. Bracket Failure	SL/TP 订单失败	CRITICAL 告警 (不回退)
7. SL/TP Modify Failure	SL/TP 修改失败	WARNING 告警

Run Order Flow Simulation

cd /home/linuxuser/nautilus_AlgVex
source venv/bin/activate
python3 scripts/diagnose_realtime.py

诊断输出将包含 "Step 9: Order Flow Simulation (v3.18)" 显示所有 7 个场景的模拟结果。

Expected Output

============================================================
Step 9: Order Flow Simulation (v3.18)
============================================================
✅ Scenario 1: New Position - PASSED
✅ Scenario 2: Add to Position - PASSED
✅ Scenario 3: Reduce Position - PASSED
✅ Scenario 4: Reversal - PASSED
✅ Scenario 5: Close Position - PASSED
✅ Scenario 6: Bracket Failure - PASSED
✅ Scenario 7: SL/TP Modify Failure - PASSED

📊 Order Flow Simulation Summary: 7/7 scenarios passed

回归检测 (修改代码后必须运行)

# 智能回归检测 (规则自动从 git 历史生成)
python3 scripts/smart_commit_analyzer.py

# 预期结果: ✅ 所有规则验证通过