🎯 Goal
By the end of Phase 3, you will have:
- AI that reads your entire folder system automatically
- no copy/paste analysis anymore
- strategy comparison engine
- regime-aware reasoning system
- structured trading insights output
🧠 WHAT YOU ARE BUILDING
You are building a Retrieval-Augmented Generation system (RAG).
In plain English:
The AI stops guessing and starts looking up your real trading data before answering
🧱 SYSTEM FLOW (CORE IDEA)
User Question
↓
Search your trading system files
↓
Retrieve relevant:
- strategies
- backtests
- regime data
- trade history
↓
Build structured context
↓
Send to 14B or 20B model
↓
Return structured trading analysis
🧠 STEP 1 — DECIDE YOUR RAG APPROACH (IMPORTANT)
You have 3 options:
🟢 Option A (recommended start)
Use a simple Python + file-based RAG system
- easiest to build
- fully customizable
- perfect for your setup
🟡 Option B
Use tools like:
- AnythingLLM
- Open WebUI
- faster setup
- less control
🔵 Option C (advanced later)
Custom vector database system:
- FAISS / Chroma
- embeddings pipeline
👉 We will start with A
📁 STEP 2 — PREPARE YOUR DATA FOR AI
You already created:
AI_TRADING_SYSTEM/
Now we clean it for AI use.
🧠 RULE: AI ONLY READS CLEAN TEXT
Convert everything into:
- .md (markdown)
- .csv (structured tables)
- .json (metrics + summaries)
Example transformation:
BEFORE:
Excel backtest file
AFTER:
02_BACKTESTS/momentum_v1/
summary.md
trades.csv
metrics.json
🧠 STEP 3 — CREATE “AI READABLE STRATEGY CHUNKS”
Inside:
07_RAG_KNOWLEDGE_BASE/
We create chunked intelligence files
Example:
momentum_breakout_chunk_01.md
Content:
Strategy: Momentum Breakout
Core idea:
- Buy when price breaks recent high
Best conditions:
- high volatility markets
- trending regimes
Weakness:
- sideways markets
- false breakouts
Performance summary:
- Sharpe: 1.2
- Max Drawdown: -15%
👉 This is what the AI will actually “read”
🧠 STEP 4 — DEFINE YOUR RETRIEVAL LOGIC
This is the brain of Phase 3.
When you ask a question:
Example query:
“Which strategy works best in high volatility markets?”
System does:
1. Search:
- /REGIMES/high_volatility/
- /BACKTESTS/
- /STRATEGIES/
2. Retrieve:
- momentum results
- mean reversion results
- volatility metrics
3. Build context:
Momentum Strategy:
Sharpe: 1.2
Drawdown: -15%
Mean Reversion:
Sharpe: 0.9
Drawdown: -8%
4. Send to model:
- 14B (default)
- 20B (deep questions)
🧠 STEP 5 — DEFINE MODEL ROUTING LOGIC
This is key:
🟡 14B (default analyst)
Use when:
- comparing strategies
- summarizing results
- explaining performance
🔵 20B (deep reasoning layer)
Use when:
- validating conclusions
- stress-testing strategies
- asking “what could break this?”
🟢 7B (optional helper)
Use when:
- cleaning or tagging data before retrieval
🧠 STEP 6 — OUTPUT FORMAT (CRITICAL)
Every AI response must follow:
📊 STANDARD OUTPUT STRUCTURE
- Summary
- Strategy comparison
- Regime performance
- Risk analysis
- Weaknesses / failure conditions
- Final recommendation
👉 This is what turns AI into a “research analyst”
🧠 STEP 7 — FIRST WORKING RAG TEST
Once simple retrieval is working, test:
Prompt:
Compare momentum and mean reversion strategies in high volatility regimes using my stored data
Expected behavior:
AI should:
- pull real files
- compare metrics
- reference regimes
- not hallucinate
⚠️ WHAT YOU DO NOT DO YET
❌ No dashboard UI yet
❌ No automation
❌ No optimization
❌ No model fine-tuning
👉 This is still the intelligence core
🧠 WHY PHASE 3 IS THE MOST IMPORTANT
Because this is the moment where:
AI stops being a chatbot and becomes a system that understands your trading history
🏁 PHASE 3 SUCCESS CRITERIA
You are done when:
✔ AI can read your strategy files automatically
✔ AI can compare multiple backtests
✔ AI can reference regime folders
✔ AI produces structured trading analysis
✔ You no longer need copy-paste context
💡 ONE-LINE SUMMARY
Phase 3 turns your folder system into a thinking system by letting AI retrieve and reason over your real trading data.