Day 87: What We Learned - January 23, 2026
Day 87 of 90 | Friday, January 23, 2026
Answer Block
Answer Block: 3 days remaining in our journey to build a profitable AI trading system.
3 days remaining in our journey to build a profitable AI trading system.
Today was a wake-up call. Two critical issues surfaced that could have derailed our entire trading operation. Here’s what went wrong and how we’re fixing it.
The Hard Lessons
These are the moments that test us. Critical issues that demanded immediate attention.
Ll 298 Share Churning Loss
Lost $22.61 on January 23, 2026 from 49 SPY share trades instead of iron condor execution.
Invalid Option Strikes Causing CALL Legs to Fail
Iron condor CALL legs were not executing because calculated strikes ($724, $729) were invalid. SPY options have $5 strike increments for OTM options, so only $720, $725, $730, etc. exist.
Important Discoveries
Not emergencies, but insights that will shape how we trade going forward.
Iron Condor Position Management System Implementation
Created dedicated iron condor position management system with proper exit rules based on LL-268/LL-277 research. This addresses a critical gap where the existing manage_positions.py used equity-base
Quick Wins & Refinements
- ML/RAG Integration Analysis and Implementation - LL-302: ML/RAG Integration Analysis and Implementation
ID: LL-302 Date: 2026-01-23 (Updated: 2026-0…
- RAG Webhook RAG Query Fix - Irrelevant Lessons Returned - Status: FIXED…
Today’s Numbers
| What | Count |
|---|---|
| Lessons Learned | 5 |
| Critical Issues | 2 |
| High Priority | 1 |
| Improvements | 2 |
Tech Stack Behind the Lessons
Every lesson we learn is captured, analyzed, and stored by our AI infrastructure:
Detected"] --> CLAUDE["Claude Opus
(Analysis)"] CLAUDE --> RAG["LanceDB RAG
(Storage)"] RAG --> BLOG["GitHub Pages
(Publishing)"] BLOG --> DEVTO["Dev.to
(Distribution)"] end
How We Learn Autonomously
| Component | Role in Learning |
|---|---|
| Claude Opus 4.5 | Analyzes errors, extracts insights, determines severity |
| LanceDB RAG | Stores lessons with 768D embeddings for semantic search |
| Gemini 2.0 Flash | Retrieves relevant past lessons before new trades |
| OpenRouter (DeepSeek) | Cost-effective sentiment analysis and research |
Why This Matters
- No Lesson Lost: Every insight persists in our RAG corpus
- Contextual Recall: Before each trade, we query similar past situations
- Continuous Improvement: 200+ lessons shape every decision
- Transparent Journey: All learnings published publicly
The Journey So Far
We’re building an autonomous AI trading system that learns from every mistake. This isn’t about getting rich quick - it’s about building a system that can consistently generate income through disciplined options trading.
Our approach:
- Paper trade for 90 days to validate the strategy
- Document every lesson, every failure, every win
- Use AI (Claude) as CTO to automate and improve
- Follow Phil Town’s Rule #1: Don’t lose money
Want to follow along? Check out the full project on GitHub.
FAQ
What did we learn today?
5 lessons captured (2 critical, 1 high). Today was a wake-up call. Two critical issues surfaced that could have derailed our entire trading operation. Here’s what went wrong and how we’re fixing it.
How do you keep these lessons from getting lost?
We index every lesson into a RAG corpus and query it before new trades and major engineering changes.
Where is the canonical version of this post?
This post’s canonical URL is https://igorganapolsky.github.io/trading/2026/01/23/lessons-learned/.
Day 87/90 complete. 3 to go.