Day 88 of 90 | Saturday, January 24, 2026

Answer Block

Answer Block: 2 days remaining in our journey to build a profitable AI trading system.

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

Markets are closed, but the learning never stops. While other traders take the weekend off, we’re refining our edge.

Quick Wins & Refinements

  • CI Lint Fix - Ambiguous Variable Name (E741) - CI was failing on the Lint & Format job with error:…
  • Weekend System Hygiene Protocol - Established weekend system hygiene protocol for maintaining code quality and repository health….
  • RLHF Thompson Sampling Model for CTO Improvement - 2. Beta Distribution: α=positive+1, β=negative+1 models uncertainty…
  • PR Management and System Hygiene Protocol - During Ralph Mode iteration 18, executed comprehensive PR management and system hygiene protocol as …

Today’s Numbers

What Count
Lessons Learned 4
Critical Issues 0
High Priority 0
Improvements 4

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["legacy 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
legacy 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.

Day 88/90 complete. 2 to go.