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README.md

📈 Stock Portfolio Analyst

Powered by DeepSeek-V4-Flash via NVIDIA API for fast, cost-efficient reasoning and real-time financial analysis

Stock Portfolio Analyst Demo

Overview

An AI-powered portfolio analysis agent that evaluates stock holdings using live market data. Enter your tickers, share counts, and average purchase prices, and the agent fetches real-time pricing and fundamentals via YFinance, searches for recent news via DuckDuckGo, and generates a comprehensive portfolio review, streamed live in the UI as it builds.

The report covers P&L per position, portfolio-level metrics, concentration and valuation risk flags, sector breakdown, and specific rebalancing recommendations.

Features

  • Live Market Data: Fetches real-time prices, fundamentals, and analyst ratings via YFinance for every holding
  • P&L Calculations: Unrealized gain/loss per position (absolute and percentage) plus portfolio totals
  • Risk Assessment: Flags concentration risk (positions > 20%), valuation extremes (P/E > 40), and negative news sentiment
  • Sector Breakdown: Visualizes portfolio weight across sectors to identify diversification gaps
  • Rebalancing Recommendations: Specific, data-driven suggestions on what to trim, hold, or add
  • Streaming Output: Analysis streams in real time as the agent works through each holding
  • Pre-loaded Example Portfolio: A 7-stock portfolio is ready to analyze on first launch
  • Quick Question Buttons: One-click common analysis queries in the sidebar

Tech Stack

Framework & Tools:

  • Agno: Agent framework orchestrating tool use and LLM reasoning
  • YFinanceTools: Live stock prices, fundamentals, analyst recommendations, company news
  • DuckDuckGoTools: Real-time web search for market context and headlines
  • CalculatorTools: Precise P&L and concentration calculations

Model:

  • DeepSeek-V4-Flash (deepseek-ai/deepseek-v4-flash): fast and cost-efficient reasoning via the NVIDIA API

UI:

  • Gradio: Interactive web interface with streaming markdown output

Prerequisites

  • Python 3.10 or higher
  • uv (recommended) or pip
  • An NVIDIA API key (free tier available)

Installation

1. Clone the Repository

git clone https://github.com/Sumanth077/Hands-On-AI-Engineering.git
cd Hands-On-AI-Engineering/ai_agents/stock_portfolio_analyst

2. Set Up Environment Variables

cp .env.example .env

Open .env and add your NVIDIA API key:

NVIDIA_API_KEY=your_nvidia_api_key_here

Get your API key at build.nvidia.com.

3. Install Dependencies

uv sync

Usage

uv run python app.py

Navigate to http://localhost:7860 in your browser.

  1. Edit the portfolio table — update tickers, share counts, and average purchase prices
  2. Type a question or leave it blank for a full portfolio review
  3. Click Analyze Portfolio — the report streams in real time as the agent fetches data
  4. Use the Quick Question buttons for common one-click analyses
  5. Click Reset to restore the example portfolio

How It Works

User Portfolio Input
        │
        ▼
  YFinanceTools          ← live price, fundamentals, analyst ratings per ticker
        │
        ▼
  DuckDuckGoTools        ← recent news and market context
        │
        ▼
  CalculatorTools        ← P&L, weight, concentration calculations
        │
        ▼
  DeepSeek-V4-Flash      ← synthesizes data into structured markdown report
        │
        ▼
  Streamed Report        ← Summary · Holdings · Risk Flags · Rebalancing

Project Structure

stock_portfolio_analyst/
├── app.py            # Gradio UI and streaming interface
├── agent.py          # Agno agent, DeepSeek-V4-Flash model, tool config, prompt
├── assets/
│   └── demo.png      # App screenshot
├── .env.example      # API key template
├── pyproject.toml    # Project dependencies (uv)
└── README.md

Disclaimer

This tool is for educational and informational purposes only. Nothing in this application constitutes financial advice. Always consult a qualified financial advisor before making investment decisions.

Contributing

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

License

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

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