AI Trading: Lesson Learned #128: Comprehensive Trust Audit (Jan 10, 2026)

Lesson Learned #128: Comprehensive Trust Audit (Jan 10, 2026)

ID: LL-128
Date: January 10, 2026
Severity: CRITICAL
Category: trust, verification, risk-management, strategy-execution

CEO Questions Answered

The CEO asked 18 critical questions about system trustworthiness. This lesson documents the findings.

Key Findings

1. Phil Town Strategy Was Broken for 69 Days

• Root cause: rule_one_trader.py analyzed stocks but never executed trades
• Fix: Added execute_phil_town_csp() function (Jan 6, 2026)
• Verification needed: Monday's trading session must place actual orders

2. System Was Losing Money Despite 80% "Win Rate"

• Average return: -6.97% (NEGATIVE)
• Sample size: Only 5 closed trades (statistically meaningless)
• Stop loss was 200%: Let losers run, cut winners short

3. Risk Rules Were Inadequate Until Jan 9, 2026

• Before: 200% stop loss (could lose 2x position)
• After: 25% stop loss, 15% trailing stop, 2% max position risk

4. $100/day Goal Requires ~$50,000 Capital

• Current capital: $30 (live), $5,000 (paper)
• Realistic timeline: Jun 2026 for $5K, much longer for $50K
• Must use compounding strategy (adds 93% to capital vs deposits alone)

5. RAG Learning System Works But Sandbox Can't Sync Directly

• 147 lesson learned files exist locally
• CI workflows handle Vertex AI sync
• Pretrade RAG query runs in CI before each trade

Prevention Measures

1. Daily verification: Check that trades_YYYY-MM-DD.json files are created
2. Win rate honesty: Always report sample size alongside win rate
3. Stop loss enforcement: Workflow now sets trailing stops automatically
4. Capital tier awareness: System knows what strategies are available at each capital level

Trust Verification Checklist

• [ ] Monday's trade executes with Phil Town strategy
• [ ] Trade file created: data/trades_2026-01-13.json
• [ ] Stop loss order placed with trade
• [ ] Dashboard updates automatically
• [ ] RAG sync runs in CI

Files Changed/Referenced

• scripts/rule_one_trader.py - Now executes trades
• data/system_state.json - Contains risk rules
• .github/workflows/daily-trading.yml - Enforces Phil Town strategy

Tags

trust-audit, phil-town, risk-management, verification, honesty, ceo-directive

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This lesson was auto-published from our AI Trading repository.

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