🎯 Goal

By the end of Day 3 you will have:

• your trading data split into usable chunks
• embeddings generated locally
• a vector database running (ChromaDB)
• semantic search over your strategies + backtests working

This is the first moment your system becomes “intelligent over your data.”

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🧱 STEP 1 — INSTALL RAG DEPENDENCIES

Activate your environment first:

cd AI_TRADING_SYSTEM
source venv/bin/activate

Install:

pip install chromadb sentence-transformers pandas

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📦 STEP 2 — CREATE RAG FILE STRUCTURE

mkdir -p 07_RAG_KNOWLEDGE_BASE/chunks
mkdir -p 07_RAG_KNOWLEDGE_BASE/embeddings
mkdir -p 07_RAG_KNOWLEDGE_BASE/index

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🧠 STEP 3 — DEFINE WHAT YOU ARE “CHUNKING”

Your system will NOT embed raw spreadsheets blindly.

You will convert into:

🟢 Strategy-level chunks

Example:

Strategy: Momentum_A
Regime: High Volatility
Period: 2020–2022
Sharpe: 1.2
Drawdown: -18%
Notes: performs poorly during sudden reversals

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🟡 Backtest chunks

Strategy: Mean_Reversion_B
Trade Window: 2021
Win Rate: 54%
Avg Return: 0.8%
Regime: Bull Market

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🐍 STEP 4 — CREATE EMBEDDING SCRIPT

Create file:

touch build_rag.py

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Paste:

import pandas as pd
import chromadb
from sentence_transformers import SentenceTransformer

# Load embedding model (local)
model = SentenceTransformer("all-MiniLM-L6-v2")

# Create vector DB
client = chromadb.PersistentClient(path="07_RAG_KNOWLEDGE_BASE/index")
collection = client.get_or_create_collection("trading_data")

# Example data loader (replace with your CSVs later)
data = [
    {
        "id": "momentum_1",
        "text": "Momentum strategy performs well in trending markets but fails in high volatility regimes."
    },
    {
        "id": "meanrev_1",
        "text": "Mean reversion performs best in sideways markets with low volatility."
    }
]

# Embed + store
for item in data:
    embedding = model.encode(item["text"]).tolist()

    collection.add(
        ids=[item["id"]],
        embeddings=[embedding],
        documents=[item["text"]]
    )

print("RAG database built successfully.")

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▶️ STEP 5 — RUN YOUR FIRST RAG BUILD

python build_rag.py

Expected output:

RAG database built successfully.

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🔍 STEP 6 — CREATE SEARCH TEST SCRIPT

touch query_rag.py

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Paste:

import chromadb
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

client = chromadb.PersistentClient(path="07_RAG_KNOWLEDGE_BASE/index")
collection = client.get_or_create_collection("trading_data")

query = "How does momentum perform in volatile markets?"

query_embedding = model.encode(query).tolist()

results = collection.query(
    query_embeddings=[query_embedding],
    n_results=2
)

print("\nTOP MATCHES:\n")
for doc in results["documents"][0]:
    print("-", doc)

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▶️ STEP 7 — TEST RAG SEARCH

python query_rag.py

You should see:

• relevant strategy matches
• semantic similarity results
• no keyword dependency

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🧠 STEP 8 — WHAT YOU JUST BUILT

You now have:

✔ semantic memory over trading strategies

✔ vector database of your system

✔ retrieval system for Phase 4

✔ foundation of your “trading brain”

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⚠️ IMPORTANT RULE

From now on:

Phase 4 (Analysis Engine) will ALWAYS depend on this RAG system.

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🧱 WHAT THIS ENABLES NEXT

Now your system can:

• answer questions using your real strategies
• compare historical performance intelligently
• retrieve similar regimes automatically
• feed structured context into LLMs

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💡 ONE-LINE SUMMARY

Day 3 turns your trading data into a searchable semantic memory layer using embeddings + vector retrieval.

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