AI Trading: Day 87 - 6 Lessons Learned (January 23, 2026)

Day 87/90 - Friday, January 23, 2026

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.

6 lessons learned today (2 critical, 1 high priority)

Invalid Option Strikes Causing CALL Legs to Fail

LL-298: Invalid Option Strikes Causing CALL Legs to Fail

Date: January 23, 2026
Severity: CRITICAL
Impact: 4 consecutive days of losses (~$70 total)

Summary
Iron condor CALL legs were not executin

Ll 298 Share Churning Loss

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id: LL-298
title: "$22.61 Loss from SPY Share Churning - Crisis Workflow Failure"
date: 2026-01-23
severity: CRITICAL

category: trading

Incident
Lost $22.61 on January 23, 2026 from 49 SPY sha

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

RLHF Feedback Training Pipeline Completion

LL-301: RLHF Feedback Training Pipeline Completion

ID: LL-301
Date: 2026-01-23
Severity: IMPROVEMENT
Category: ML Infrastructure
Status: COMPLETED

What Was Missing
The RLHF feedback capture pipeli

ML/RAG Integration Analysis and Implementation

LL-302: ML/RAG Integration Analysis and Implementation

ID: LL-302
Date: 2026-01-23 (Updated: 2026-01-24)
Severity: IMPROVEMENT
Category: ML Infrastructure / Architecture
Status: IMPLEMENTED ✅

Curr

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Tech Stack Behind the Scenes

Our AI trading system uses:

• Claude Opus 4.5 - Primary reasoning engine for trade decisions
• OpenRouter - Cost-optimized LLM gateway (DeepSeek, Mistral, Kimi)
• Vertex AI RAG - Cloud semantic search with 768D embeddings
• Gemini 2.0 Flash - Retrieval-augmented generation
• MCP Protocol - Standardized tool integration layer

Every lesson is stored in our RAG corpus, enabling the system to learn from past mistakes and improve continuously.

Full Tech Stack Documentation

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Auto-generated from our AI Trading System's RAG knowledge base.

Follow our journey: AI Trading Journey on GitHub