🎯 Goal

By the end of this phase, your system will:

• understand meaning (not just keywords)
• retrieve the right strategies even if wording differs
• rank relevance intelligently
• dramatically improve analysis quality
• still run locally on your Mac

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🧠 WHAT CHANGED FROM 3.1 → 3.2

❌ Before (Phase 3.1)

• “momentum” only finds files containing the word momentum
• weak matching
• misses important context

✅ Now (Phase 3.2)

• understands similarity in meaning
• finds relevant strategies even if wording differs
• behaves like a real research assistant

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🧱 CORE UPGRADE: EMBEDDINGS

We introduce:

text → vector → similarity search → best matches

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⚙️ STEP 1 — INSTALL EMBEDDING TOOLING

Run:

pip3 install sentence-transformers faiss-cpu numpy

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🧠 WHAT THIS DOES

Tool	Purpose
sentence-transformers	converts text into “meaning vectors”
faiss	fast similarity search
numpy	math support

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📁 STEP 2 — CREATE NEW FILE

rag_semantic.py

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🧠 STEP 3 — FULL WORKING SEMANTIC RAG SYSTEM

Paste this:

import os
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
import requests

# -------- CONFIG --------
DATA_FOLDER = "AI_TRADING_SYSTEM"
MODEL_NAME = "all-MiniLM-L6-v2"  # lightweight but strong
LLM_MODEL = "qwen:14b"
OLLAMA_URL = "http://localhost:11434/api/generate"

# -------- LOAD EMBEDDING MODEL --------
embedder = SentenceTransformer(MODEL_NAME)

documents = []
file_paths = []

# -------- LOAD FILES --------
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:
                    text = f.read()
                    documents.append(text[:2000])
                    file_paths.append(path)
            except:
                pass

print(f"Loaded {len(documents)} documents")

# -------- CREATE EMBEDDINGS --------
print("Creating embeddings...")
embeddings = embedder.encode(documents)

dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings))

# -------- SEARCH FUNCTION --------
def search(query, k=3):
    query_vec = embedder.encode([query])
    distances, indices = index.search(np.array(query_vec), k)

    results = []
    for i in indices[0]:
        results.append(documents[i])

    return results

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

Use the context to analyze the question.

CONTEXT:
{context}

QUESTION:
{question}

Return:
1. Summary
2. Strategy comparison
3. Risk analysis
4. Recommendation
"""

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

    return response.json()["response"]

# -------- MAIN --------
def main():
    while True:
        question = input("\nAsk trading question: ")

        print("\n🔎 Semantic search running...")
        docs = search(question)

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

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

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

if __name__ == "__main__":
    main()

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🚀 STEP 4 — RUN IT

python3 rag_semantic.py

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🧠 WHAT YOU NOW HAVE

You now upgraded from:

Phase 3.1:

• keyword search ❌

Phase 3.2:

• semantic understanding ✅
• intelligent retrieval ✅
• real AI research behavior ✅

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🔥 WHY THIS IS A BIG MOMENT

This is the point where your system starts behaving like:

a real quantitative research assistant

not a chatbot.

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🧠 WHAT IT CAN NOW DO

You can ask:

• “Which strategies fail in sideways markets?”
• “What performs best during volatility spikes?”
• “Compare all momentum variants in my dataset”
• “What hidden risks exist in my best strategy?”

And it will:

• find meaning-based matches
• not just keyword matches
• produce structured analysis

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⚖️ LIMITATIONS (important)

This version still:

• loads everything into memory (ok for now)
• no dashboard yet
• no streaming UI
• no multi-user architecture

👉 That comes later

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🏁 PHASE 3.2 SUCCESS CRITERIA

You are done when:

✔ system loads your files
✔ embeddings are created
✔ semantic search returns better results than keyword search
✔ AI gives meaningful strategy comparisons

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💡 ONE-LINE TAKEAWAY

Phase 3.2 is where your AI stops “searching words” and starts “understanding ideas.”

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