Repository Template for AI-Driven Financial and Market Analysis

Overview

This repository provides a structured framework for setting up an AI-driven financial engineering and marketing analysis agentic system using Azure AI Agent Service SDK. It includes:

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🤖 AI Agents Overview - Autogen & Azure AI foundry with Azure AI Agent Services

The system incorporates multiple AI-driven agents specialized in different aspects of financial and market analysis -
Behind the scene Autogen agents in play -

Agent Name and Flows

1️⃣ Market Analysis Agent

• Analyzes market trends and customer behavior.
• Identifies key business opportunities.
• Provides insights on geographic demand and economic patterns.

2️⃣ Financial Analysis Agent

• Evaluates company financials, balance sheets, and performance metrics.
• Assesses cost efficiency and growth opportunities.
• Conducts risk assessments and budget optimization.

3️⃣ News Analysis Agent

• Scrapes and analyzes financial news articles.
• Identifies impactful trends in the industry.
• Provides strategic implications of economic events.

4️⃣ Strategic Planning Agent

• Develops three unique business strategies based on market and financial insights.
• Creates an implementation roadmap for each strategy.
• Conducts risk-benefit analysis and final recommendations.

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• A structured Python project template
• Required dependencies (requirements.txt)
• Configuration file templates (.env, config.ini)
• Step-by-step setup guide
• Automated AI agents for data and news analysis

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📂 Project Structure

📦 Stock-Analysis-AutoGen-Multi-Agent
 ┣ 📂 src
 ┃ ┣ 📜 main.py  # Main entry point for analysis
 ┃ ┣ 📜 connectors.py  # Handles data retrieval (ERP + News API)
 ┃ ┣ 📜 agents.py  # Configures AI agents for financial & market insights
 ┃ ┣ 📜 processing.py  # Data processing & transformation
 ┃ ┗ 📜 config.py  # Loads configurations & API keys
 ┣ 📜 requirements.txt  # Dependencies
 ┣ 📜 .env.example  # Environment variables template
 ┣ 📜 README.md  # Project documentation
 ┣ 📜 setup_azure_ai_sdk.md  # Step-by-step guide for Azure AI Agent setup
 ┗ 📜 LICENSE  # License file

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🔧 Setup Instructions

1️⃣ Clone Repository

git clone https://github.com/amitlals/Stock-Analysis-AutoGen-Multi-Agent.git
cd Stock-Analysis-AutoGen-Multi-Agent

2️⃣ Setup Python Virtual Environment

python -m venv venv
source venv/bin/activate  # For Mac/Linux
venv\Scripts\activate    # For Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Up Environment Variables

1. Create a .env file (or copy from .env.example)

   cp .env.example .env

2. Add API keys & Azure configuration:

   # .env
   CURRENTS_API_KEY=your_currents_api_key
   API_KEY=your_azure_openai_api_key
   AZURE_ENDPOINT=your_azure_openai_endpoint
   MODEL_DEPLOYMENT_NAME=gpt-4
   MODEL_API_VERSION=2024-02-15-preview

📜 Azure AI Agent Service SDK Setup

Follow the steps below to configure Azure AI Agent Service SDK:

1️⃣ Install Azure SDK

pip install azure-ai-agent-sdk

2️⃣ Authenticate and Set Up Azure Service

1. Log in to Azure CLI:

   az login

2. Set your subscription:

   az account set --subscription "<your-subscription-id>"

3. Deploy AI Agent Service:

   az ai agent create --name StockAnalysisAgent --resource-group your-resource-group --location eastus --sku Standard

4. Retrieve and store your Azure AI Agent API Key in .env.

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🚀 Running the Project

To start the AI-driven financial and market analysis system, run:

python src/main.py

This will:

1. Connect to ERP system & fetch orders/products data
2. Retrieve market & financial news
3. Process the data into structured insights
4. Utilize AI agents for market & financial analysis
5. Generate strategic recommendations

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✅ Requirements (requirements.txt)

requests
dotenv
autogen
azure-ai-agent-sdk

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📌 Notes

• Keep your .env file secure and never commit it to version control.
• Follow best practices for Azure OpenAI API usage to avoid exceeding quotas.
• Modify AI agent prompts as per specific business needs.

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🏁 Next Steps

• Enhance AI Agents: Add more specialized AI agents for deeper financial analysis.
• Expand Data Sources: Integrate stock market APIs for real-time analytics.
• Deploy on Azure: Use Azure Functions or a containerized service for production deployment.

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📜 License

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

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🔗 GitHub Repository

📌 GitHub Repository Link