Building an autonomous AI trading system means things break. Here's what we discovered, fixed, and learned today.
LL-309: Iron Condor Optimal Control Research
The Problem: Date: 2026-01-25 Category: Research / Strategy Optimization Source: arXiv:2501.12397 - "Stochastic Optimal Control of Iron Condor Portfolios"
What We Did: - Finding: "Asymmetric, left-biased Iron Condor portfolios with τ = T are optimal in SPX markets" - Meaning: Put spread should be closer to current price than call spread - Why: Markets have negative skew (crashes more likely than rallies)
The Takeaway: - Left-biased portfolios: Hold to expiration (τ = T) is optimal - Non-left-biased portfolios: Exit at 50-75% of duration
Code Changes
These commits shipped today (view on GitHub):
| Commit | Description |
|---|---|
| ab3cc781 | docs(ralph): Auto-publish discovery blog post |
| 6a3fc7c6 | chore(ralph): CI iteration ✅ |
| 13a6b928 | docs(ralph): Auto-publish discovery blog post |
| e9f050c3 | feat(safety): Add regime-based iron condor entry gating |
| 40698072 | feat(rag): Add LL-310 lesson - Iteration 7 RAG learning |
Why We Share This
Every bug is a lesson. Every fix makes the system stronger. We're building in public because:
- Transparency builds trust - See exactly how an autonomous trading system evolves
- Failures teach more than successes - Our mistakes help others avoid the same pitfalls
- Documentation prevents regression - Writing it down means we won't repeat it
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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