HIGH NOON TRADER

MAC AI TRADING RESEARCH STACK — BUILD CHECKLIST

·

by

Happy Harry Huckleberry
Skip to main content
< All Topics
Print
Updated

🎯 Goal Reminder

You are building:

A local AI system that analyzes your historical trading data + backtests to evaluate strategies and improve decision-making.

No live trading. No streaming data. Fully offline research system.

🟢 PHASE 1 — FOUNDATION SETUP

📁 1. Create project structure

AI_TRADING_SYSTEM/
    01_STRATEGIES/
    02_BACKTESTS/
    03_TRADES/
    04_REGIMES/
    05_MARKET_DATA/
    06_ANALYSIS_OUTPUTS/
    07_RAG_KNOWLEDGE_BASE/
    08_MODEL_NOTES/
    CACHE/
    EMBEDDINGS/

📊 2. Load your data

  • import spreadsheets (CSV / Excel)
  • standardize columns:
    • date
    • strategy name
    • return
    • drawdown
    • trade ID
  • clean missing values

🧠 3. Define regimes (manual first)

Example:

  • bull market
  • bear market
  • high volatility
  • crisis period (COVID, etc.)

Tag backtests accordingly.

🟡 PHASE 2 — LOCAL AI SETUP

💻 1. Install tools

  • Ollama (primary runtime)
  • LM Studio (testing only)
  • Python 3.10+

🧠 2. Download models

  • 7B model → fast tasks
  • 14B model → main analysis
  • 20B model → deep reasoning

⚙️ 3. Test models

  • run simple prompts
  • confirm memory usage fits 32GB system
  • verify response quality differences

🔵 PHASE 3 — RAG SYSTEM

📦 1. Chunk your data

  • split backtests by:
    • strategy
    • regime
    • time period

🧠 2. Create embeddings

  • generate embeddings for:
    • strategy descriptions
    • backtest summaries
    • trade logs

📚 3. Store in vector DB

  • ChromaDB or FAISS
  • store embeddings + metadata

🔍 4. Test retrieval

  • query: “momentum strategy in high volatility”
  • verify correct chunks return

🟣 PHASE 4 — ANALYSIS ENGINE

🧠 1. Build retrieval pipeline

  • user question → vector search → top results

🧾 2. Build prompt template

Fixed structure:

  • summary
  • breakdown
  • regime behavior
  • risk analysis
  • comparison
  • improvements

🤖 3. Connect LLM

  • route:
    • 7B → quick
    • 14B → default
    • 20B → deep analysis

📊 4. Enforce output format

  • no freeform responses
  • always structured output

💾 5. Save results

  • write to /06_ANALYSIS_OUTPUTS/
  • include timestamp + query + response

🟠 PHASE 5 — DASHBOARD UI

🖥️ 1. Build Streamlit app

Layout:

  • left: datasets + filters
  • center: chat input
  • right: AI output

💬 2. Connect Phase 4 engine

  • every query → Phase 4 pipeline

📊 3. Add features

  • model selector (7B / 14B / 20B)
  • regime filters
  • strategy picker
  • save report button

📁 4. Display outputs

  • structured analysis sections
  • clean formatting
  • optional export to markdown

🔴 PHASE 6 — OPTIMIZATION

⚡ 1. Speed improvements

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

🧠 2. Model routing logic

  • 7B → fast tasks
  • 14B → normal analysis
  • 20B → deep reasoning only

💾 3. Storage optimization

  • cache frequent queries
  • clean temp files
  • compress embeddings if needed

🖥️ 4. UI performance

  • lazy load datasets
  • paginate tables
  • cache dashboard views

🧭 MASTER EXECUTION FLOW

This is your full system in action:

DATA (spreadsheets)
   ↓
RAG (retrieve relevant history)
   ↓
PHASE 4 (LLM analysis engine)
   ↓
STRUCTURED OUTPUT
   ↓
PHASE 5 (dashboard UI)
   ↓
SAVE TO RESEARCH ARCHIVE
   ↓
PHASE 6 (optimization improves speed)

🧠 WHAT YOU NOW HAVE

This checklist gives you:

✔ full build order
✔ no architecture confusion
✔ no extra phases
✔ direct implementation steps
✔ stable system definition

💡 FINAL ONE-LINE SUMMARY

You are building a local Mac-based AI system that turns historical trading data into structured strategy analysis using RAG + LLM reasoning + a dashboard interface.