How to build high-performing trading strategies with AI

Bryan Downing
May 29
4 min read

Retrieval-Augmented Generation RAG for Trading Applications with Langchain community LLMs

Introduction

In this article, we will explore Retrieval-Augmented Generation (RAG), a powerful AI technique that combines database retrieval with large language models (LLMs) to enhance AI-generated responses. We will walk through a basic implementation using LangChain, OpenAI’s GPT, and a PostgreSQL/SQLite database for a trading application. This helps to show you how to build high-performing trading strategies with AI

This example demonstrates how RAG can be used to fetch financial data (e.g., stock trends for Apple) and generate AI-driven insights. While we encountered some challenges with OpenAI’s API permissions, the core concepts remain valuable for developers looking to integrate RAG into their applications.

What is Retrieval-Augmented Generation (RAG)?

RAG is an AI framework that enhances LLM responses by retrieving relevant data from an external database before generating an answer. Unlike traditional LLMs that rely solely on pre-trained knowledge, RAG dynamically fetches context from a structured or unstructured data source, improving accuracy and relevance.

Key Components of RAG

Retrieval System – A database (PostgreSQL, SQLite, etc.) that stores structured or unstructured data.
LLM (Large Language Model) – A model like OpenAI’s GPT or Anthropic’s Claude that processes queries and generates responses.
Integration Layer (LangChain) – A framework that connects the retrieval system with the LLM.

Why Use RAG in Trading?

Real-time Data Integration – Fetch stock prices, trends, and financial reports dynamically.
Context-Aware Responses – Generate insights based on the latest market data.
Custom Knowledge Base – Store proprietary trading strategies and historical data.

Setting Up the RAG Application

Prerequisites

Python 3.8+
OpenAI API Key (or another LLM provider like Anthropic)
PostgreSQL/SQLite (for data storage)
LangChain (for orchestration)

Step 1: Install Required Packages

First, install the necessary Python libraries:

bash

pip install langchain openai psycopg2-binary sqlalchemy python-dotenv

Step 2: Set Up Environment Variables

Create a .env file to store your OpenAI API key:

env

OPENAI_API_KEY=your_api_key_here

Step 3: Initialize LangChain Project

Run the following command to set up a LangChain project:

bash

langchain serve

This will create the necessary project structure.

Building the RAG Application

Project Structure

basic

quant_rag/

│── app/

│ ├── init.py

│ ├── main.py

│── test_rag.py

│── requirements.txt

│── README.txt

│── .env

Step 4: Create the Retrieval Database

We’ll use SQLite for simplicity, but PostgreSQL can be used for scalability.

python

Run

from langchain.vectorstores import SQLiteVectoreStore

from langchain.embeddings import OpenAIEmbeddings

# Initialize embeddings

embeddings = OpenAIEmbeddings()

# Create a vector store

vector_store = SQLiteVectoreStore.from_texts(

texts=["Apple stock is bullish", "Tesla shows bearish trends"],

embedding=embeddings,

db_path="stocks.db"

)

Step 5: Connect to OpenAI (or Another LLM)

python

Run

from langchain.llms import OpenAI

llm = OpenAI(temperature=0.7) # Adjust for creativity vs. precision

Step 6: Implement the RAG Pipeline

python

Run

from langchain.chains import RetrievalQA

# Define the retrieval chain

qa_chain = RetrievalQA.from_chain_type(

llm=llm,

chain_type="stuff",

retriever=vector_store.as_retriever()

)

# Query the system

response = qa_chain.run("Is Apple stock showing a bullish or bearish pattern?")

print(response)

Testing the Application

Running the RAG Service

Start the LangChain server:

bash

langchain serve

Querying the System

Run the test script (test_rag.py):

python

Run

import os

from dotenv import load_dotenv

from langchain.llms import OpenAI

from langchain.vectorstores import SQLiteVectoreStore

from langchain.embeddings import OpenAIEmbeddings

load_dotenv()

# Initialize components

llm = OpenAI()

embeddings = OpenAIEmbeddings()

vector_store = SQLiteVectoreStore(embedding_function=embeddings, db_path="stocks.db")

# Query

query = "Is Apple stock bullish or bearish?"

response = qa_chain.run(query)

print(response)

Expected Output

If successful, the system should return:

Apple stock is currently showing a bullish pattern.

Common Issues & Troubleshooting

1. OpenAI API Permission Errors

Ensure your API key is valid.
Check billing/usage limits on OpenAI’s platform.

2. Prompt Rejection (Financial Queries)

Some LLMs restrict financial predictions. Modify prompts to be more general:❌ "Predict Apple’s stock price."✅ "Describe recent trends in Apple stock."

3. Database Connection Failures

Verify SQLite/PostgreSQL is running.
Check file permissions for stocks.db.

Alternative LLMs for RAG

If OpenAI’s GPT is restrictive, consider:

Anthropic Claude (better for structured financial queries)
Llama 2 (open-source, self-hosted)
Google Gemini (alternative cloud-based LLM)

Conclusion

Retrieval-Augmented Generation (RAG) is a powerful tool for enhancing AI-driven trading applications. By combining real-time data retrieval with LLM intelligence, traders can get context-aware insights without relying solely on pre-trained models.

While we faced OpenAI API limitations, the core implementation remains valid. Future improvements could include: