Igor Ganapolsky

Posted on Jan 11 • Originally published at github.com

AI Trading: Lesson Learned #130: Comprehensive Investment Strategy Review (Jan 11, 2026)

#ai #trading #python #machinelearning

Lesson Learned #130: Comprehensive Investment Strategy Review (Jan 11, 2026)

ID: LL-130
Date: January 11, 2026 (Sunday - Markets Closed)
Severity: CRITICAL
Category: strategy-audit, trust-verification, risk-management

CEO Questions Answered

The CEO asked 12 comprehensive questions about the trading system. This lesson documents the findings.

Key Findings

1. Phil Town Rule #1 Status: PARTIALLY IMPLEMENTED

Code Fixed: Jan 6, 2026 - execute_phil_town_csp() added to scripts/rule_one_trader.py:125
NOT VERIFIED: No trades since Jan 6 - Monday must confirm execution works
Action Required: Verify trade file data/trades_2026-01-13.json is created Monday

2. Why Losing Money: Root Causes Identified

Issue	Old Value	New Value	Status
Stop Loss	200%	50%	FIXED Jan 9
Strategy	MACD/RSI direction	Phil Town CSPs	FIXED Jan 6
Sample Size	5 trades	Need 30+	INSUFFICIENT
Avg Return	-6.97%	Target +2%	NOT ACHIEVED

3. Risk Mitigation: Configured But Unverified

Stop loss: 50% (was 200%)
Max delta: 30 (70% probability OTM)
Position size: Max 10% of portfolio
Trailing stops: Configured in CI, needs live verification

4. $100/day North Star Reality

Requires: ~$50,000 capital
Current: $30 (brokerage), $5,000 (paper)
Timeline: Jun 2026 for $5K → $15-20/day achievable
Compounding Advantage: +93% more capital vs deposits alone

5. RAG Learning System: WORKING

153 lessons learned files exist
Weekend Learning Pipeline runs Sat/Sun 8 AM ET
Sources: Phil Town, Bogleheads, Option Alpha, InTheMoney
Vertex AI sync via CI (sandbox cannot connect directly)

6. Dashboard & Blog: OPERATIONAL

GitHub Pages: https://igorganapolsky.github.io/trading/ - WORKING
Shows Day 74/90 R&D challenge
Paper account: $5,000, Live: $30

Most Important Monday Action

VERIFY PHIL TOWN CSP EXECUTION IN PRODUCTION

Checklist:

[ ] Trade file created: data/trades_2026-01-13.json
[ ] CSP order placed via execute_phil_town_csp()
[ ] Stop loss order accompanies trade
[ ] Order visible in Alpaca paper account

Prevention Measures

Never claim strategy "works" without production verification
Always show evidence with claims (file paths, line numbers)
Check data freshness before reporting (system_state.json last_updated)
Run full trust audit monthly (last: Jan 10, 2026)

Files Referenced

scripts/rule_one_trader.py - Phil Town CSP execution
data/system_state.json - Current state
.github/workflows/weekend-learning.yml - RAG learning
ll_128_trust_audit_jan10_comprehensive.md - Previous audit

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