📈 AI Investment Risk Analyzer

A full-stack ML web application that analyzes stock portfolio risk using real-time data, Monte Carlo simulation, LSTM volatility forecasting, and AI-powered rebalancing advice.

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📸 Screenshots

Full Dashboard

Monte Carlo Simulation

Correlation Matrix & LSTM Volatility Forecast

Sector Concentration & AI Portfolio Advisor

Portfolio Builder

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🚀 What It Does

Enter any stock tickers (e.g. AAPL, GOOGL, TSLA) and the app will:

• 📊 Download 5 years of real stock data from Yahoo Finance
• ⚡ Compute professional risk metrics (Sharpe Ratio, Beta, VaR, Max Drawdown)
• 🎲 Run 1000 Monte Carlo simulations to forecast future portfolio value
• 🤖 Train an LSTM neural network to forecast stock volatility
• 🧩 Generate AI-powered rebalancing advice using LLaMA 3.3 via Groq
• 📉 Display everything in a beautiful interactive dark-theme dashboard

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🛠️ Tech Stack

Layer	Technology
Backend API	Python 3.13 + FastAPI
ML Models	TensorFlow/Keras (LSTM) + scikit-learn
Risk Engine	NumPy + Pandas
AI Advisor	LangChain + Groq (LLaMA 3.3-70b)
Data Source	yfinance (Yahoo Finance)
Frontend	React 18 + Recharts
Styling	Custom CSS (dark theme)

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📁 Project Structure

portfolio-analyzer/
├── backend/
│   ├── main.py              # FastAPI server & endpoints
│   ├── risk_engine.py       # Sharpe, Beta, VaR, Monte Carlo, Correlation
│   ├── lstm_model.py        # 2-layer LSTM volatility forecaster
│   ├── ai_advisor.py        # LangChain + Groq AI advisor
│   └── requirements.txt     # Python dependencies
├── frontend/
│   └── src/
│       ├── App.jsx
│       └── components/
│           ├── PortfolioInput.jsx
│           ├── RiskScoreCard.jsx
│           ├── MonteCarloChart.jsx
│           ├── HeatmapChart.jsx
│           ├── VolatilityChart.jsx
│           └── AIAdvisorPanel.jsx
├── screenshots/             # App screenshots
└── README.md

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⚙️ Setup & Installation

Prerequisites

• Python 3.10+
• Node.js 18+
• Groq API key (free at console.groq.com)

Backend Setup

cd backend
python -m venv venv
venv\Scripts\activate        # Windows
pip install -r requirements.txt

Environment Variables

Create a .env file in the backend/ folder:

GROQ_API_KEY=your-groq-key-here

Run the App

# Terminal 1 — Backend
cd backend
venv\Scripts\activate
uvicorn main:app --reload --port 8000

# Terminal 2 — Frontend
cd frontend
npm install
npm start

Open http://localhost:3000 in your browser.

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🔬 Key Features Explained

LSTM Volatility Forecasting

A 2-layer LSTM neural network trained on 5 years of rolling volatility data predicts future volatility for each stock. Architecture: Input(60 days) → LSTM(50) → Dropout(0.2) → LSTM(50) → Dropout(0.2) → Dense(1)

Monte Carlo Simulation

Runs 1000 stochastic simulations using Geometric Brownian Motion (GBM) to model the range of possible future portfolio values over 252 trading days.

Risk Metrics

• Sharpe Ratio: (Return - Risk Free Rate) / Std Dev
• Beta: Portfolio covariance with SPY / SPY variance
• VaR (95%): 5th percentile of historical daily returns
• Max Drawdown: Maximum peak-to-trough decline

AI Advisor

Uses LangChain to format risk metrics into a structured prompt and sends it to LLaMA 3.3-70b via Groq API, returning 3-4 specific actionable rebalancing recommendations.

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📊 Sample Output

For a portfolio of AAPL (12 shares), GOOGL (3 shares), TSLA (9 shares):

Sharpe Ratio:    0.945  (Moderate)
Portfolio Beta:  1.673  (Very High)
VaR (95%):       3.49%  (High Risk)
Max Drawdown:   -22.62% (Moderate)

AI Advice:

• Reduce TSLA weight to 30% to decrease overall portfolio beta • Reallocate the reduced TSLA portion to GOOGL to increase diversification • Consider adding a low-beta stock or bond to reduce Value at Risk • Limit Technology sector exposure to 20% to reduce concentration risk

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📄 License

MIT License — free to use for personal and educational purposes.

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👩‍💻 Author

Prerna Pandit

• GitHub: @prernapandit1

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