Market Anomaly Detector

Overview

This project is a web application designed to serve as an early warning mechanism for identifying potential financial market crashes. Utilizing a machine learning model and real-time market data, the application classifies market conditions and proposes data-driven investment strategies for risk mitigation and optimization. An integrated AI-driven chatbot explains these strategies to end users, making them accessible and actionable.

Features

• Market Data Fetching: The application retrieves real-time market data for various financial instruments.
• Anomaly Detection: A machine learning model classifies market conditions to identify potential anamolies.
• Investment Strategies: Based on the model's predictions, the application proposes tailored investment strategies to minimize losses or maximize returns.
• AI Chatbot: An integrated chatbot provides explanations and answers to user queries regarding investment strategies and market conditions.

Installation

To set up the project, ensure you have Node.js and Python installed on your machine. Then, follow these steps:

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Install the required Node.js packages:

   npm install

3. Create a virtual environment:

   python -m venv .venv

4. Activate the virtual environment:

   • On Windows (using Command Prompt):

     .venv\Scripts\activate

   • On Windows (using Git Bash):

     source .venv/Scripts/activate

   • On macOS/Linux:

     source .venv/bin/activate

5. Install the required Python packages:

   pip install -r requirements.txt

6. Create a .env file in the root directory and add your API keys:

   GROQ_API_KEY=your_groq_api_key
   

Usage

1. Start the Flask backend server:

   python backend/main.py

2. Start the Next.js frontend application:

   npm run dev

3. Open your web browser and navigate to http://localhost:3000 to access the application.

4. Interact with the chatbot to ask questions about investment strategies or market condition. Refresh page to fetch most recent market data.

File Descriptions

• backend/main.py: The server-side code containing the Flask API for handling market predictions and data fetching.
• app/api/chat/route.js: The API route for handling chat interactions with the AI assistant.
• app/api/market-data/route.js: The API route for fetching current market data.
• app/page.js: The main page of the application, integrating various components.
• components/: Contains React components for the user interface, including the chatbot, stock ticker, and investment strategy display.
• utils/strategies.js: Contains predefined investment strategies based on market conditions.

Dependencies

• Flask: For building the backend web application and handling HTTP requests.
• Flask-CORS: For enabling Cross-Origin Resource Sharing in the Flask app.
• Requests: For making HTTP requests to external APIs.
• OpenAI: For AI-generated responses and explanations.
• Next.js: For building the frontend of the web application.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.