HIGH NOON TRADER

MAC AI TRADING SYSTEM — BUILD PLAN (DAY-BY-DAY)

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Happy Harry Huckleberry
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🎯 Goal Reminder

By the end you will have:

  • local LLMs running (7B / 14B / 20B)
  • your trading data structured
  • a working RAG system
  • an analysis engine (Phase 4)
  • a dashboard UI (Phase 5)
  • basic optimization (Phase 6)

🟢 DAY 1 — PROJECT FOUNDATION

🧱 1. Create folder system

Set up:

AI_TRADING_SYSTEM/

Create all subfolders:

  • 01_STRATEGIES
  • 02_BACKTESTS
  • 03_TRADES
  • 04_REGIMES
  • 05_MARKET_DATA
  • 06_ANALYSIS_OUTPUTS
  • 07_RAG_KNOWLEDGE_BASE
  • 08_MODEL_NOTES
  • CACHE
  • EMBEDDINGS
---## 📊 2. Import your data- move all spreadsheets in
- standardize formats (CSV preferred)
- ensure each file has:
  - strategy name
  - date
  - returns
  - drawdown---## 🧠 3. Define regimes (manual)Create simple file:```text
04_REGIMES/regimes.csv

Include:

  • bull
  • bear
  • high volatility
  • crisis periods

🟡 DAY 2 — LOCAL AI SETUP

💻 1. Install tools

  • Ollama
  • Python 3.10+
  • VS Code

(Optional: LM Studio just for testing)

🧠 2. Download models

Run:

  • 7B model (fast)
  • 14B model (main workhorse)
  • 20B model (deep reasoning only)

⚙️ 3. Test models

Run simple prompts:

  • “Summarize this strategy”
  • “Compare momentum vs mean reversion”

Confirm:

  • speed is acceptable
  • memory usage stable (32GB system)

🔵 DAY 3 — RAG SYSTEM (FIRST REAL INTELLIGENCE)

📦 1. Chunk your data

Split into:

  • strategy summaries
  • backtest results
  • regime-specific slices

🧠 2. Create embeddings

Use Python:

  • sentence-transformers or similar
  • generate embeddings for all chunks

📚 3. Store in vector DB

  • install ChromaDB (or FAISS)
  • store:
    • embeddings
    • metadata (strategy, regime, date)

🔍 4. Test retrieval

Example queries:

  • “high volatility momentum performance”
  • “best strategies in bear markets”

🟣 DAY 4 — PHASE 4 (ANALYSIS ENGINE)

🧠 1. Build retrieval pipeline

Flow:

  • user question → vector search → top results

🧾 2. Create prompt template

Fixed structure:

  1. Summary
  2. Strategy Breakdown
  3. Regime Behavior
  4. Risk Analysis
  5. Comparison
  6. Improvements

🤖 3. Connect LLM

Route logic:

  • 7B → fast tasks
  • 14B → default analysis
  • 20B → deep reasoning

📊 4. Test full pipeline

Run:

“Why did momentum fail in volatile markets?”

Confirm:

  • retrieval works
  • output is structured
  • no hallucinated data

🟠 DAY 5 — PHASE 5 DASHBOARD

🖥️ 1. Build Streamlit app

Layout:

  • left: data + filters
  • center: chat input
  • right: results panel

💬 2. Connect Phase 4 engine

  • every query goes into analysis engine

📊 3. Add controls

  • model selector
  • regime filter
  • dataset selector
  • save report button

📁 4. Save outputs

Store:

/06_ANALYSIS_OUTPUTS/

🔴 DAY 6 — OPTIMIZATION

⚡ 1. Speed improvements

  • cache embeddings
  • reuse retrieval results
  • avoid full dataset loads

🧠 2. Model routing

Rule:

  • simple → 7B
  • normal → 14B
  • complex → 20B

💾 3. Storage cleanup

  • cache folder cleanup
  • compress embeddings
  • avoid duplicate retrievals

🖥️ 4. UI performance

  • lazy loading
  • paginate tables
  • cache dashboard results

🧭 END STATE (AFTER DAY 6)

You now have:

✔ working local LLM stack
✔ structured trading dataset
✔ semantic RAG system
✔ analysis engine (Phase 4)
✔ dashboard interface (Phase 5)
✔ optimized system (Phase 6)

💡 ONE-LINE SUMMARY

In 6 days you build a local Mac-based AI system that turns historical trading data into structured strategy analysis using RAG + LLM reasoning + a research dashboard.