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Igor Ganapolsky
Igor Ganapolsky

Posted on • Originally published at github.com

AI Trading: Lesson Learned #117: Trust Audit - Full System Review (Jan 8, 2026)

Lesson Learned #117: Trust Audit - Full System Review (Jan 8, 2026)

ID: LL-117
Date: January 8, 2026
Severity: HIGH
Category: System Audit, Trust Verification

Summary

CEO requested comprehensive trust audit covering Phil Town compliance, risk mitigation, RAG recording, and operational readiness.

Key Findings

Phil Town Rule #1 Compliance

  • Status: PARTIALLY COMPLIANT
  • Trailing stops: IMPLEMENTED in daily-trading.yml
  • Previous violations: ll_106 (unprotected positions lost $93.69)
  • Current risk: $0 (no open positions)

Risk Mitigation

  • Status: AUTOMATED
  • Trailing stops: 10% equities, 20% options
  • Position limits: Max 2 positions
  • Stop-loss on workflow failure: exit 1 (fails entire workflow)

RAG Trade Recording

  • Status: BIDIRECTIONAL PIPELINE ACTIVE
  • Pre-trade: query_vertex_rag.py
  • Post-trade: sync_trades_to_rag.py
  • Local backup: JSON files always work

$100/Day North Star

  • Reality: Requires $50,000+ capital
  • Current: $30 live, $5K paper
  • Path: Compounding + $10/day deposits = ~Jun 2026 for $5K

Dashboard & Blog

  • Status: CURRENT (Jan 8, 2026)
  • Day 71/90 displayed correctly
  • Paper: $5K fresh start after CEO reset

Gaps Identified

  1. YouTube/Blog Learning: Analyzer exists but not automated in workflow
  2. CI Trigger from Sandbox: No GitHub token available
  3. First Paper Trade: $5K sitting idle since Jan 7 reset

#1 Action for Operational Security

Execute first paper trade with full protection validation:

  1. Pre-trade RAG query
  2. CSP execution on F or SOFI ($5 strike)
  3. Automatic trailing stop
  4. Post-trade RAG sync

Tags

trust_audit, phil_town, risk_mitigation, rag, operational_readiness


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

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