🌟 Multi-Agent Collaboration for Financial Analysis

Welcome to the Multi-Agent Collaboration for Financial Analysis repository! This project showcases how multiple intelligent agents can work together to solve complex financial tasks. 🚀

📋 Prerequisites

Before running the code, make sure you have the following set up:

🔑 API Keys

1. Anthropic API Key:

   • Visit [Anthropic's website](https://console.anthropic.com/ to create an account and generate an API key.
   • Add the key to a .env file as ANTHROPIC_API_KEY.

2. Serper API Key (or other search APIs):

   • Sign up at Serper to obtain an API key.
   • Add the key to the .env file as SERPER_API_KEY.

3. Optional: Other LLMs

   • While this repository uses Anthropic Claude, you can integrate other large language models (LLMs) by modifying the code accordingly. Ensure the appropriate API keys are added to the .env file.

🛠 Environment Setup

• Install the required libraries:

  pip install crewai crewai_tools anthropic litellm langchain_community

• Ensure Python 3.8+ is installed.

• Use either:

  • Jupyter Notebook: Install Jupyter Notebook and open the provided .ipynb file.
  • Google Colab: Upload the .ipynb file to Google Colab for an easy-to-use cloud environment.
  • Amazon SageMaker: Use Amazon SageMaker Studio for a managed and scalable machine learning environment.

📝 Solution Overview

This project focuses on:

1. 🤝 Multi-Agent Collaboration: Leverages multiple agents to collaborate and exchange data for enhanced financial analysis.

2. 🧠 Anthropic Claude Integration: Utilizes Claude for natural language processing and reasoning tasks.

3. 🔄 Extensibility: Supports integration with other LLMs or APIs for customized workflows.

4. 🧰 Utilities: The utils module includes helper functions for loading API keys and managing environment variables.

🤖 Agents in Action

Here are the 4 agents designed to collaborate in this project:

1. 📊 Data Analyst: Gathers and processes financial data to extract meaningful insights.

2. 📈 Trading Strategy Agent: Develops and evaluates trading strategies based on data trends and predictive models.

3. 💼 Trade Advisory Agent: Provides recommendations on trade executions, taking into account market conditions and strategies.

4. ⚠️ Risk Advisor Agent: Assesses potential risks and ensures strategies align with risk management protocols.

🚀 Running the Solution

1. Clone the repository:

   git clone https://github.com/draqai/financial-analysis.git
   cd financial-analysis

2. Create a .env file in the project directory:

   ANTHROPIC_API_KEY=your_anthropic_key
   SERPER_API_KEY=your_serper_key

3. Run the Jupyter Notebook:

   jupyter notebook Collaboration_Financial_Analysis_wClaude.ipynb

4. Follow the notebook steps to execute the multi-agent collaboration workflow.

✨ Key Features

• Flexibility: Swap out Anthropic Claude for other LLMs like Llama or DeepSeek.
• Scalable Workflows: Easily extend the solution to handle different domains beyond financial analysis.
• Modular Utilities: Customize the utils module for additional functionality.

📜 License

This project is licensed under the MIT License.

🤗 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve the solution.