Real-Time Financial Advisor LLM System

Overview

The Real-Time Financial Advisor LLM System is an open-source AI-powered Q&A system built using GPT-3.5. It provides real-time answers and insights to financial queries, assisting investors, financial analysts, and enthusiasts with on-the-spot analysis and recommendations based on live data.

Technology Stack

The system is built using:

• GPT-3.5 for natural language processing and financial query answering.
• Comet ML for tracking experiments and fine-tuning the model.
• ByteWax for streaming live financial data.
• LangChain for developing natural language processing pipelines.
• Vector Databases for efficient data retrieval and analysis.

System Architecture

The system integrates a three-pipeline architecture for real-time financial data processing:

1. Feature Pipeline

• Collects real-time financial news using Alpaca API.
• Stores historical financial datasets for analysis.
• Provides both streaming and structured financial news data.

2. Training Pipeline

• Uses QLora for training a Large Language Model (LLM) with financial data.
• Includes a Q&A dataset tailored for financial analysis.
• Fine-tunes the model to enhance accuracy and relevancy.

3. Inference Pipeline

• Exposes a RESTful API for external integrations.
• Features a Financial Assistance Bot to process user queries.
• Includes a UI for interaction with the financial advisory system.

System Diagram

Key Challenges Overcome

• Balancing real-time performance and accuracy: Optimized model responses to ensure rapid yet accurate financial insights.
• Handling complex financial queries: Developed custom tokenization and fine-tuning strategies to process diverse financial topics.
• Efficient data retrieval: Integrated vector databases for high-speed search and retrieval of relevant financial information.

Impact and Results

• Significantly improves decision-making speed by providing accurate, real-time financial insights.
• Enables dynamic responses by integrating live financial news with AI analysis.
• Helps users adapt quickly to market changes, enhancing investment strategies.

Future Enhancements

• Expanding data sources: Integrate additional financial APIs for broader market coverage.
• Advanced ML techniques: Improve forecasting capabilities with more sophisticated models.
• User personalization: Develop a custom financial advisory experience based on user behavior and preferences.

Installation & Usage

Prerequisites

• Python 3.8+
• OpenAI API Key
• Comet ML Account (Optional for tracking)
• Alpaca API Key (For real-time financial news)

Installation Steps

1. Clone the repository:

   git clone https://github.com/yourusername/real-time-financial-advisor-llm.git
   cd real-time-financial-advisor-llm

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   export OPENAI_API_KEY='your-api-key'
   export ALPACA_API_KEY='your-alpaca-api-key'

4. Run the system:

   python main.py

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-branch).
5. Open a Pull Request.

Contact

For any questions, feel free to reach out:

• GitHub Issues: Open an issue in the repository.