AI Investment Committee Platform

This is an institutional-style portfolio intelligence MVP inspired by BlackRock's investment workflows. It fetches live market data, identifies macroeconomic regimes, generates AI-powered investment recommendations via the Groq LLM API, and persists all historical intelligence to MongoDB for change-tracking and comparative analysis.

Architecture

The platform follows a clean service-oriented architecture:

• Market Agent: Fetches live pricing, market cap, and calculates volatility using yfinance.
• Macro Agent: Provides macroeconomic data (inflation, interest rates, GDP growth).
• Regime Service: Detects market regimes based on macroeconomic conditions.
• Portfolio Agent: Aggregates and analyzes multi-asset portfolios for sector exposure and concentration risks.
• Committee Agent: Leverages a specified Groq LLM (e.g., Llama 3.3) to formulate structured, qualitative investment recommendations.
• Comparison Service: A historical intelligence engine that tracks rating, confidence, and regime changes over time.
• MongoDB Service: Persists every analysis securely to MongoDB Atlas or a local instance, enabling historical memory.

Prerequisites

• Python 3.9+
• A valid Groq API Key
• A MongoDB Atlas cluster (or local MongoDB running on port 27017)

Setup Instructions

1. Create a Virtual Environment

   python -m venv venv
   source venv/bin/activate

2. Install Dependencies

   pip install -r backend/requirements.txt

3. Configure Environment Variables Create a .env file in the root directory (you can copy .env.example) and add your Groq API key and MongoDB URI:

   GROQ_API_KEY=your_groq_api_key_here
   MONGODB_URI=mongodb+srv://<user>:<password>@cluster.mongodb.net/

4. Run the Application Start the FastAPI development server:

   uvicorn backend.main:app --reload

   The API will be accessible at http://localhost:8000. You can access the interactive Swagger UI at http://localhost:8000/docs.

API Endpoints

1. Analysis Endpoints

• POST /analyze: Generates a complete market, macro, and AI committee analysis for a single ticker (e.g., RELIANCE.NS).
• POST /analyze-portfolio: Generates an aggregated analysis for a multi-asset portfolio, including sector exposure and concentration risk.

2. Historical Intelligence Endpoints

Every time an analysis is generated, it is persisted to MongoDB along with a tracked comparison against its previous run.

• GET /history/stocks: Retrieves the most recent stock analyses.
• GET /history/stocks/{ticker}: Retrieves the entire timeline of analyses for a specific asset.
• GET /history/stocks/{ticker}/latest: Retrieves the absolute latest analysis for a specific asset.
• GET /history/portfolios: Retrieves the history of portfolio analyses.
• GET /history/portfolios/latest: Retrieves the absolute latest portfolio run.

Included Test Scripts

The repository includes several bash scripts to quickly validate the platform without needing a frontend:

• test_api.sh: Spins up the server and tests the single-asset /analyze endpoint.
• test_portfolio_api.sh: Spins up the server and tests the multi-asset /analyze-portfolio endpoint.
• test_script.py: Tests the underlying Python agents directly without the web server.
• test_models.py: Pings the Groq API to list available LLMs.