FiNN-AI

This project implements an AI-powered financial news analysis system that collects and processes financial news, social media data, and financial terms to provide intelligent responses to user queries using a RAG (Retrieval-Augmented Generation) system.

Features

• Real-time financial news data collection and analysis
• Hourly automated scraping with exponential backoff and retry mechanisms
• Stock price data retrieval and technical analysis
• Stock impact analysis (which stocks are affected by news)
• Social media sentiment analysis for financial markets
• Financial knowledge base with terminology and definitions
• Multi-page Streamlit interface:
  • Market Analysis dashboard with stock charts
  • Dark-themed chat interface with AI assistant
• Advanced RAG implementation with structured reasoning chains
• Embeddings-based semantic search and retrieval
• SQLite database for data persistence
• Direct retrieval of most recent news with AI-generated summaries

Prerequisites

• Python 3.8 or higher

Installation

1. Clone the repository:

git clone https://github.com/johnson70630/FiNN-AI.git
cd FiNN-AI

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables: Copy .env.template to .env and fill in your API keys:

# OpenAI API Key
OPENAI_API_KEY=your_openai_api_key

# LangSmith (Optional)
LANGSMITH_TRACING=your_langsmith_tracing
LANGSMITH_ENDPOINT=your_langsmith_endpoint
LANGSMITH_API_KEY=your_langsmith_api_key
LANGSMITH_PROJECT=your_langsmith_project

# Twitter Playwright Login 
TWITTER_USERNAME=your_twitter_username
TWITTER_PASSWORD=your_twitter_password

# Reddit API Keys 
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret
REDDIT_USER_AGENT=your_reddit_user_agent

Running the Project

Quickstart (Recommended)

The easiest way to run the entire application:

python run.py

This will:

1. Start the backend API server
2. Launch the Streamlit frontend
3. Open the web interface in your browser
4. Show logs from both components in a single terminal

Press Ctrl+C to stop all services.

Alternative: Running Components Separately

Backend API Server

python backend/main.py

This starts the FastAPI server that provides endpoints for accessing news, social media posts, and financial terms.

Frontend Application

streamlit run frontend/app.py

This launches the Streamlit user interface that connects to the backend API.

Project Structure

.
├── run.py                # Main launcher script
├── backend/              # Backend API and core functionality
│   ├── main.py           # FastAPI entry point
│   ├── core/             # Core modules
│   │   ├── database.py   # Database connections and models
│   │   └── models.py     # Data models
│   ├── scrapers/         # Data collection modules
│   │   ├── news_scraper.py           # News article collection
│   │   ├── social_media_scraper.py   # Social media collection
│   │   ├── financial_knowledge.py    # Financial terms collection
│   │   └── scraper_service.py        # Orchestration service
│   └── services/         # Core business logic
│       ├── rag_service.py                # RAG implementation for Q&A
│       ├── chain_service.py              # Chain-based structured reasoning
│       ├── stock_analysis_service.py     # Stock analysis and sentiment
│       ├── stock_impact_service.py       # News impact on stocks
│       ├── embedding_service.py          # Vector embeddings for DB items
│       └── simple_query_service.py       # Fallback query service
├── frontend/            # User interface
│   ├── app.py           # Streamlit main page (Market Analysis)
│   └── pages/           # Multi-page Streamlit app
│       └── 01_Chat.py   # Chat interface with AI assistant
├── data/                # Data storage directory (SQLite database)
├── requirements.txt     # Project dependencies
└── README.md           # Project documentation

Usage

Data Collection

The system will automatically collect data from various sources:

• Financial news articles from major financial news websites
• Social media posts from finance-related channels
• Financial terminology and definitions
• Stock price data and market trends

Web API

1. Start the API server with python backend/main.py
2. Access the following endpoints:
   • /news: Get latest news articles
   • /social: Get social media posts
   • /terms: Get financial terms
   • /query: Submit natural language queries
   • /stocks: Get stock price information
   • /stock/{symbol}: Get detailed stock data
   • /stock-analysis/{symbol}: Get comprehensive stock analysis
   • /news/impact: Analyze news impact on stocks

User Interface

The application has a multi-page Streamlit interface:

1. Market Analysis: Stock analysis, price charts, news feed, and social media monitoring
2. Chat: Conversational interface to interact with the AI assistant about financial topics

Troubleshooting

API Keys

• Check environment variables are correctly set in .env
• Ensure all API keys are valid and have necessary permissions
• OpenAI API key is required for the RAG functionality
• Twitter and Reddit API keys are needed for social media scraping

RAG Service Errors

• If the chatbot fails to provide meaningful answers, check that:
  • Your OpenAI API key is valid and has sufficient credits
  • The database contains enough data (at least some news articles and terms)
  • The query is related to financial topics

Detailed Logs

• For detailed error messages, check the application logs in the terminal when running run.py

License

This project is licensed under the MIT License - see the LICENSE file for details.