QuantCore Trading Dashboard

A professional algorithmic trading analysis platform featuring 11+ advanced trading strategies, real-time backtesting, and AI-powered insights.

🎯 Overview

QuantCore is a full-stack trading analysis dashboard that combines traditional technical analysis, advanced time-series models, machine learning, and reinforcement learning to provide comprehensive trading signals. The platform features a unique Strategy Comparison Engine that backtests all algorithms simultaneously to identify the best performer for any asset and timeframe.

Key Features

• 11+ Trading Strategies: From basic technical indicators to advanced ML/RL algorithms
• Strategy Comparison: Backtest and rank all strategies side-by-side
• Real-time Analysis: Instant signal generation with detailed metrics
• AI Sentiment Analysis: FinBERT-powered news sentiment extraction
• Beautiful UI: Modern, responsive interface with interactive visualizations
• Production Ready: FastAPI backend with React frontend

📊 Available Strategies

Technical Indicators

• MACD Momentum - Exponential moving average crossovers
• RSI Oscillator - Overbought/oversold conditions (0-100 scale)
• Bollinger Bands - Volatility-based trading bands

Statistical Models

• Statistical Arbitrage - Pairs trading with cointegration analysis

Machine Learning

• AI Sentiment Analysis - FinBERT NLP on financial news headlines

Time Series Models

• ARIMA Forecast - AutoRegressive Integrated Moving Average
• GARCH Volatility - Conditional heteroskedasticity modeling
• VAR Multi-Asset - Vector Autoregression with Granger causality

Advanced Algorithms

• Q-Learning Portfolio - Reinforcement learning agent (LONG/NEUTRAL/SHORT)
• Ensemble Stacking - Weighted voting from 5+ sub-algorithms
• Wavelet + ML - Frequency decomposition with Random Forest

🏆 Strategy Comparison Engine

Compare all algorithms simultaneously to discover which performs best for your target asset and timeframe.

Features:

• Configurable periods: 1 week, 1 month, 3 months, 6 months, 1 year
• Performance metrics: Accuracy, win rate, total trades, correct predictions
• Visual rankings with gold/silver/bronze medals 🥇🥈🥉
• Automatic best strategy identification
• Risk level classification

Performance Metrics:

• Accuracy: % of correct directional predictions
• Win Rate: % of profitable trades
• Total Trades: Number of signals generated
• Correct Predictions: Count of accurate signals

See detailed documentation →

🚀 Quick Start

Prerequisites

# Backend
Python 3.9+
pip (Python package manager)

# Frontend
Node.js 16+
npm or yarn

Installation

1. Clone the Repository

git clone <your-repo-url>
cd quantcore-dashboard

2. Backend Setup

cd backend

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the server
python trading_api.py

The backend will run on http://localhost:8000

3. Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will run on http://localhost:5173

Verify Installation

1. Open http://localhost:5173 in your browser
2. Check for "Backend: Online" in the sidebar
3. Try running MACD analysis on AAPL
4. Click "Compare Strategies" to test the comparison feature

📖 Usage

Running Individual Strategies

1. Select a strategy from the sidebar
2. Enter ticker symbol(s) as required
3. Click "Run Analysis"
4. View detailed results with metrics and charts

Example: MACD Momentum

Ticker: AAPL
Signal: STRONG BUY
MACD: 2.45
Signal Line: 1.87
Histogram: 0.58

Comparing Strategies

1. Click "Compare Strategies" in the sidebar
2. Enter a ticker symbol (e.g., AAPL)
3. Select time period (1w to 1y)
4. Click "Compare All Strategies"
5. View rankings table with performance metrics

Example Output:

🥇 #1  Ensemble Stacking    68.5%  [16 correct / 23 trades]
🥈 #2  ARIMA Forecast       63.2%  [19 correct / 30 trades]
🥉 #3  MACD Momentum        61.8%  [21 correct / 34 trades]

Understanding Signals

Buy Signals:

• STRONG BUY: High confidence upward movement
• BUY: Moderate confidence upward movement
• LONG: Position recommendation (for RL strategy)
• BULLISH: Positive sentiment (for AI strategy)

Sell Signals:

• STRONG SELL: High confidence downward movement
• SELL: Moderate confidence downward movement
• SHORT: Position recommendation (for RL strategy)
• BEARISH: Negative sentiment (for AI strategy)

Neutral Signals:

• HOLD: No clear directional signal
• NEUTRAL: Market indecision

🏗️ Project Structure

quantcore-dashboard/
├── backend/
│   ├── trading_api.py           # Main FastAPI application
│   ├── requirements.txt         # Python dependencies
│   └── strategies/
│       ├── __init__.py
│       ├── base.py             # Base strategy classes
│       ├── ensemble.py         # Ensemble stacking
│       ├── reinforcement_learning.py  # Q-Learning
│       ├── wavelet.py          # Wavelet + ML
│       └── utils.py            # Utility functions
│
├── frontend/
│   ├── src/
│   │   ├── App.jsx             # Main application
│   │   ├── components/
│   │   │   ├── CompareView.jsx      # Strategy comparison
│   │   │   ├── RLVisualizer.jsx     # RL results display
│   │   │   ├── EnsembleVisualizer.jsx
│   │   │   ├── WaveletVisualizer.jsx
│   │   │   └── SharedComponents.jsx
│   │   ├── main.jsx
│   │   └── index.css
│   ├── package.json
│   └── index.html
│
└── README.md

🔧 API Documentation

Base URL

http://localhost:8000

Endpoints

Get Available Strategies

GET /strategies

Response:

{
  "strategies": [
    {
      "id": "macd",
      "name": "MACD Momentum",
      "category": "technical",
      "inputs": 1,
      "risk": "Low"
    },
    ...
  ]
}

Execute Strategy

POST /execute
Content-Type: application/json

{
  "strategy_id": "macd",
  "tickers": ["AAPL"]
}

Response:

{
  "strategy_name": "MACD Momentum",
  "signal": "STRONG BUY",
  "metrics": {
    "macd_value": 2.45,
    "signal_value": 1.87,
    "histogram": 0.58,
    "current_price": 178.32
  },
  "chart_data": { ... }
}

Compare Strategies

POST /compare
Content-Type: application/json

{
  "ticker": "AAPL",
  "period": "1m"
}

Response:

{
  "ticker": "AAPL",
  "period": "1m",
  "current_price": 178.32,
  "period_return": 5.3,
  "results": [
    {
      "strategy_id": "ensemble",
      "strategy_name": "Ensemble Stacking",
      "accuracy": 68.5,
      "win_rate": 68.5,
      "total_trades": 23,
      "correct_predictions": 16,
      "risk_level": "Medium"
    },
    ...
  ],
  "best_strategy": "Ensemble Stacking",
  "best_accuracy": 68.5
}

Interactive API Docs

Visit http://localhost:8000/docs for interactive Swagger documentation.

🎨 Technology Stack

Backend

• FastAPI - Modern Python web framework
• yfinance - Yahoo Finance data fetching
• pandas - Data manipulation
• numpy - Numerical computing
• statsmodels - Statistical models (ARIMA, VAR, GARCH)
• scikit-learn - Machine learning (Random Forest)
• transformers - Hugging Face (FinBERT)
• PyTorch - Deep learning backend
• pywavelets - Wavelet transform

Frontend

• React - UI framework
• Vite - Build tool
• Tailwind CSS - Styling
• Lucide React - Icons
• Fetch API - HTTP requests

⚠️ Important Disclaimers

Trading Risk

• Past performance does not guarantee future results
• This platform is for analysis and research only
• Not financial or investment advice
• Always validate signals with your own research
• Consider transaction costs, slippage, and market conditions

Backtesting Limitations

• Simplified logic compared to real trading
• No transaction costs included
• No slippage modeling
• Perfect information assumption
• Market regime changes not captured

Responsible Use

• Use for educational purposes
• Combine multiple strategies
• Consider risk tolerance
• Never invest more than you can afford to lose
• Consult financial professionals for advice

📈 Performance Notes

Speed

• Individual strategy analysis: 1-5 seconds
• Strategy comparison: 10-30 seconds (8 strategies)
• Data caching improves subsequent runs
• First run slowest (data fetching)

Optimization Tips

1. Use shorter periods for faster comparisons
2. Historical data is cached via @lru_cache
3. Start with simple tickers (AAPL, MSFT)
4. Backend runs CPU-intensive tasks
5. GPU support available for FinBERT

🐛 Troubleshooting

Backend Issues

Error: "No module named 'fastapi'"

pip install -r requirements.txt

Error: "Port 8000 already in use"

# Find and kill the process
lsof -ti:8000 | xargs kill -9

Error: "FinBERT model loading failed"

• Check internet connection
• Model downloads on first run (~400MB)
• CPU mode works but slower

Frontend Issues

Error: "Cannot connect to backend"

• Verify backend is running on port 8000
• Check API_BASE_URL in App.jsx
• Disable browser CORS extensions

Error: "Module not found"

npm install
npm run dev

Data Issues

Error: "No data found for ticker"

• Check ticker symbol is valid
• Yahoo Finance may be rate limiting
• Try different ticker or wait

Error: "Not enough historical data"

• Choose shorter time period
• Different ticker may have limited history
• Requires 100+ days for most strategies

🔮 Future Enhancements

Planned Features

• Portfolio backtesting
• Walk-forward analysis
• Monte Carlo simulation
• Multi-asset comparison
• Export results (CSV/PDF)
• Historical performance charts
• Strategy correlation analysis
• Real-time alerts
• Paper trading integration
• Custom strategy builder

Community Contributions

Pull requests welcome! Areas for contribution:

• Additional trading strategies
• UI/UX improvements
• Performance optimizations
• Documentation
• Testing coverage

📄 License

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

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

📞 Support

Issues

Report bugs or request features via GitHub Issues

Documentation

• Strategy Comparison Guide
• API Documentation
• Integration Guide

🙏 Acknowledgments

• Yahoo Finance for market data
• Hugging Face for FinBERT model
• FastAPI framework
• React and Tailwind CSS communities
• Open source contributors

⭐ Show Your Support

Give a ⭐️ if this project helped you!

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Built with ❤️ for algorithmic traders

Disclaimer: This software is provided "as is" without warranty. Use at your own risk.