📈 Investment Portfolio Risk Assessment AI Agent

🌟 Project Overview

An AI-powered investment analysis tool that leverages simple ReAct AI agent flow framework and financial analysis techniques to provide comprehensive portfolio insights. This intelligent agent helps investors make data-driven decisions by offering deep portfolio risk assessment, stock profiling, and personalized recommendations.

🏗️ Key Features

• Stock Risk Profiling: Detailed risk assessment for individual stocks, including volatility, market cap, and return potential.
• Portfolio Diversification Analysis: Breakdown of sector allocation and risk levels within a given portfolio.
• Expected Return Calculation: Estimation of annual returns based on historical stock performance.
• Intelligent Portfolio Adjustments: Personalized recommendations aligned with user's risk tolerance.
• Interactive Web Interface: User-friendly Streamlit application for easy interaction with the AI agent.

👨‍🏫 How ReAct Agent works

The Investment Portfolio Analysis AI Agent is built on the ReAct (Reasoning and Action) framework, which combines the strength of large language models with a structured approach to problem-solving. Here's the workflow:

1. Thought: The agent analyzes the user's query and formulates a plan of action.
2. Action: Based on the thought, the agent selects and executes an appropriate tool or API call.
3. Observation: The agent observes and interprets the results of the action.
4. Repeat: This cycle continues until the agent has gathered enough information to provide a comprehensive answer.

Extension to Tool Calling

The ReAct framework can be extended to incorporate specialized tools:

• Tool Definition: Each tool (e.g., stock risk profiler, portfolio analyzer) is defined with clear inputs and outputs.
• Tool Selection: The agent learns to choose the most appropriate tool based on the current context and user query.
• Tool Execution: The selected tool is called with the necessary parameters.
• Result Integration: The agent incorporates tool outputs into its reasoning process for the final response.

This extension allows the agent to leverage specific financial analysis functions while maintaining a flexible, language-model-driven interaction flow.

🚀 Technologies Used

• Backend: Python
• AI Model: Groq API
• Frontend: Streamlit
• Key Libraries:
  • Groq
  • python-dotenv
  • Logging
  • JSON processing
  • Dataclasses

🔧 Installation

Prerequisites

• Python 3.8+
• Groq API Key

Setup Steps

1. Clone the repository

git clone https://github.com/yourusername/investment-portfolio-ai.git
cd investment-portfolio-ai

2. Create a virtual environment

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables

• Create a .env file
• Add your Groq API key: GROQ_API_KEY=your_groq_api_key
• Optionally you can add file paths to stock risk json file and system prompt

STOCK_DATA_PATH=your_stock_data_json_file_path SYS_PROMPT_DATA_PATH=your_system_prompt_data_path

🖥️ Usage

Streamlit Web Application

streamlit run streamlit_app.py

Example Queries

• "Get risk profile for NVDA stock"
• "Analyze a portfolio with 40% AAPL, 30% GOOGL, 30% SPY"
• "Calculate expected return for a portfolio"
• "Recommend adjustments for a portfolio with specific risk tolerance"

📊 How It Works

The AI agent uses a ReAct (Reasoning and Acting) framework to:

1. Understand the user's investment query
2. Choose appropriate financial analysis tools
3. Generate data-driven insights and recommendations

Key Components

• FinancialTools: Comprehensive analysis methods
• Agent: Intelligent interaction and tool selection
• Streamlit Interface: User-friendly web application

🛡️ Risk Disclaimer

Investment involves risks. This tool provides insights and should not be considered definitive financial advice. Always consult with a financial professional before making investment decisions.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📜 License

Distributed under the MIT License. See LICENSE file for more information.

💌Acknowledgments

• Groq for providing the API used in this project.
• Streamlit for the excellent web app framework.
• Contributors and maintainers of the open-source libraries used in this project.

👋 Connect with Me

Feel free to reach out for collaboration, questions, or potential opportunities! I'm always excited to discuss

• 🧠 Intelligent AI Agents
• 📊 Risk Analysis & Financial Technology
• 🤖 Machine Learning Solutions
• 🚀 AI-Driven Decision Support Systems