🎯 Goal

By the end of this, you will have:

• a Python script that reads your trading files
• searches them for relevant text
• sends context to your local model (via Ollama or LM Studio API)
• returns structured analysis of your strategies

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🧠 WHAT YOU ARE BUILDING (SIMPLIFIED)

User Question
   ↓
Search local files (simple keyword match)
   ↓
Pull relevant strategy/backtest text
   ↓
Send to local LLM (14B or 20B)
   ↓
Get structured trading analysis

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🧰 STEP 1 — REQUIREMENTS (VERY LIGHT)

You only need:

✔ Python (built into Mac usually)

Check:

python3 --version

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✔ Install one package

We’ll use requests for calling the model:

pip3 install requests

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⚙️ STEP 2 — CHOOSE YOUR MODEL ENDPOINT

We will use Ollama because it’s easiest for automation.

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Make sure Ollama is running:

ollama serve

(or just open the app normally — it runs in background)

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Pull a model (if not already):

ollama run qwen:14b

or

ollama run mistral

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📁 STEP 3 — YOUR FIRST RAG SCRIPT

Create a file:

rag_query.py

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Paste this code:

import os
import requests

# -------- CONFIG --------
MODEL = "qwen:14b"  # or mistral / 20b model if installed
BASE_URL = "http://localhost:11434/api/generate"

DATA_FOLDER = "AI_TRADING_SYSTEM"

# -------- SIMPLE FILE SEARCH --------
def search_files(query):
    results = []

    for root, dirs, files in os.walk(DATA_FOLDER):
        for file in files:
            if file.endswith(".md") or file.endswith(".txt"):
                path = os.path.join(root, file)

                try:
                    with open(path, "r", encoding="utf-8") as f:
                        content = f.read()

                        if any(word.lower() in content.lower() for word in query.split()):
                            results.append(content[:1500])  # limit size
                except:
                    pass

    return results[:3]  # top 3 matches


# -------- CALL LOCAL LLM --------
def ask_llm(context, question):
    prompt = f"""
You are a professional trading analyst.

Use the context below to answer the question.

CONTEXT:
{context}

QUESTION:
{question}

Provide:
1. Summary
2. Strategy analysis
3. Risk notes
4. Recommendation
"""

    response = requests.post(BASE_URL, json={
        "model": MODEL,
        "prompt": prompt,
        "stream": False
    })

    return response.json()["response"]


# -------- MAIN FUNCTION --------
def main():
    question = input("Ask your trading question: ")

    print("\n🔎 Searching your trading system...\n")
    docs = search_files(question)

    if not docs:
        print("No relevant data found.")
        return

    context = "\n\n---\n\n".join(docs)

    print("\n🧠 Analyzing with local AI...\n")
    result = ask_llm(context, question)

    print("\n📊 RESULT:\n")
    print(result)


if __name__ == "__main__":
    main()

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🚀 STEP 4 — RUN IT

In terminal:

python3 rag_query.py

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Example input:

Which strategy performs best in high volatility regimes?

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🧠 WHAT YOU JUST BUILT

You now have:

✔ File-based search
✔ Context injection
✔ Local LLM reasoning
✔ Structured trading output

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⚖️ WHAT THIS IS (AND IS NOT)

✔ This IS:

• real RAG (simple version)
• working AI analyst
• your first trading intelligence engine

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❌ This is NOT yet:

• vector database
• semantic search
• dashboard UI
• automation system

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🧠 WHY THIS VERSION IS IMPORTANT

Because it proves:

AI can already reason over YOUR data, not generic internet knowledge

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🔥 NEXT STEP AFTER THIS

Once this works, Phase 3.2 upgrades it to:

• smarter retrieval (embeddings)
• better ranking of documents
• regime-aware filtering
• cleaner structured outputs

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🏁 PHASE 3.1 SUCCESS CRITERIA

You are done when:

✔ Script runs without errors
✔ It finds relevant strategy files
✔ It sends context to model
✔ It returns structured analysis
✔ You can ask multiple questions

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💡 ONE-LINE TAKEAWAY

Phase 3.1 turns your folders into an AI-readable knowledge base using the simplest possible working pipeline.

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