Day 89 of 90 | Sunday, January 25, 2026

Answer Block

Answer Block: 1 days remaining in our journey to build a profitable AI trading system.

1 days remaining in our journey to build a profitable AI trading system.

Every mistake is a lesson in disguise. Today we uncovered a critical flaw in our system - the kind that separates amateur traders from professionals who survive long-term.

The Hard Lessons

These are the moments that test us. Critical issues that demanded immediate attention.

CTO Ignores Surfaced RAG Lessons - Pattern Identified

Every session:

Quick Wins & Refinements

  • VIX Timing for Iron Condor Entry - LL-310: VIX Timing for Iron Condor Entry

Date: 2026-01-25 Category: Strategy / Entry Timing Status:…

  • Iron Condor Optimal Control Research - LL-309: Iron Condor Optimal Control Research

Date: 2026-01-25 Category: Research / Strategy Optimiz…

Today’s Numbers

What Count
Lessons Learned 3
Critical Issues 1
High Priority 0
Improvements 2

Tech Stack Behind the Lessons

Every lesson we learn is captured, analyzed, and stored by our AI infrastructure:

flowchart LR subgraph Learning["Learning Pipeline"] ERROR["Error/Insight
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

  1. No Lesson Lost: Every insight persists in our RAG corpus
  2. Contextual Recall: Before each trade, we query similar past situations
  3. Continuous Improvement: 200+ lessons shape every decision
  4. Transparent Journey: All learnings published publicly

Full Tech Stack Documentation

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?

3 lessons captured (1 critical, 0 high). Every mistake is a lesson in disguise. Today we uncovered a critical flaw in our system - the kind that separates amateur traders from professionals who survive…

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/2026/01/25/lessons-learned/.

Day 89/90 complete. 1 to go.