Sunday, January 25, 2026 (Eastern Time)
Building an autonomous AI trading system means things break. Here's what we discovered, fixed, and learned today.
LL-262: Data Sync Infrastructure Improvements
The Problem: - Max staleness during market hours: 15 min (was 30 min) - Data integrity check: Passes on every health check - Sync health visibility: Full history available
What We Did: - Peak hours (10am-3pm ET): Every 15 minutes - Market open/close: Every 30 minutes - Added manual trigger option with force_sync parameter Added to src/utils/staleness_guard.py:
The Takeaway: Risk reduced and system resilience improved
LL-266: OptiMind Evaluation - Not Relevant to Our System
The Problem: 3. Single ticker strategy - SPY ONLY per CLAUDE.md; no portfolio allocation needed 4. Simplicity is a feature - Phil Town Rule #1 achieved through discipline, not optimization 5. Massive overhead - 20B model for zero benefit - Multi-asset portfolio with allocation constraints - Supply chain / logistics optimization
What We Did: Applied targeted fix based on root cause analysis
The Takeaway: Not every impressive technology is relevant to our system. Our $5K account with simple rules doesn't need mathematical optimization. The SOFI disaster taught us: complexity ≠ profitability. - evaluation - microsoft-research - optimization - not-applicable
This is part of our journey building an AI-powered iron condor trading system targeting financial independence.
Resources:
- Source Code
- Strategy Guide
- The Silent 74 Days - How we built a system that did nothing
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