📈 Quant Trading Gym

A Robotic Playground for Algorithmic Trading Strategies 🤖➡️📊➡️💰

🏆 Hackathon-ready project for quantitative finance strategy development and testing

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✨ Features

• Three Trading Robots

  • SMA Crossover Robot: Buys when fast SMA crosses above slow SMA
  • RSI Mean Reversion Robot: Buys when oversold, sells when overbought
  • Market Mood Robot: Pairs trading using z-score between two assets

• Professional Tools

  • Interactive web dashboard (Streamlit)
  • Real-time performance metrics (Sharpe Ratio, Drawdown, Returns)
  • Customizable parameters for each strategy
  • Multi-asset support (Stocks, ETFs, Crypto)
  • Clear visualizations and comparison charts

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🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/uniquepratham/quant-trading-gym.git
cd quant-trading-gym

# Install dependencies
pip install -r requirements.txt

Run the Web App

streamlit run app.py

Open http://localhost:8501 in your browser.

Run Command Line Version

python main.py

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📊 Example Strategy Performance

Strategy	Total Return	Sharpe Ratio	Max Drawdown
SMA Crossover	-17.4%	-0.85	-19.8%
RSI Mean Reversion	+5.3%	+0.89	-1.6%
Market Mood	Customizable	Customizable	Customizable

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🎮 How to Use

1. Open the Web App: streamlit run app.py

2. Choose Your Robots: Select strategies to test

3. Set the Rules: Adjust parameters such as:

   • SMA periods (e.g., 20 vs 50 days)
   • RSI thresholds (e.g., 30 oversold, 70 overbought)
   • Z-score entry/exit levels (e.g., 2.0 entry, 0.5 exit)

4. Pick Your Assets: Backtest on QQQ, SPY, BTC-USD, etc.

5. Launch Backtest: Watch your robots trade.

6. Analyze Results: Compare performance metrics and identify the best strategy.

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🏗️ Project Structure

quant-trading-gym/
├── app.py                 # 🎪 Streamlit dashboard
├── main.py                # ⚡ CLI testing version
├── backtester.py          # 🤖 Trading simulation engine
├── utils.py               # 📦 Data handling & utilities
├── strategies/            # 🎯 Trading robots
│   ├── sma_crossover.py   # 📊 SMA crossover strategy
│   ├── rsi_meanrev.py     # 📈 RSI mean reversion
│   └── market_mood.py     # 🌐 Pairs trading strategy
├── outputs/               # 📸 Charts & results
└── requirements.txt       # 📋 Dependencies

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🏆 Hackathon Achievements

Requirement	Status
Quantitative Strategies	✅ 3+ Implemented
Backtesting Framework	✅ Metrics & engine
Multiple Timeframes	✅ Daily + customizable
Transaction Cost Modeling	✅ Commission parameter
Risk Management	✅ Position sizing
Performance Metrics	✅ Sharpe, Drawdown
Parameter Optimization	✅ Interactive tuning
Python Implementation	✅ Complete
Documentation	✅ README + comments

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🧠 Technical Details

• Data Source: Yahoo Finance API (real market data)
• Assets: Stocks (QQQ, SPY), Crypto (BTC-USD)
• Algorithms: SMA Crossover, RSI Mean Reversion, Z-Score Pairs Trading
• Metrics: Total Return, Annualized Return, Sharpe Ratio, Volatility, Max Drawdown, Calmar Ratio

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🔮 Future Enhancements

• Live trading integration (Alpaca API, Binance API)
• Machine learning strategies (LSTM, Reinforcement Learning)
• Advanced risk management (Stop-loss, position sizing)
• Multi-asset portfolio optimization
• Real-time WebSocket market data

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📜 License

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

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🙋 Need Help?

Open an issue or reach out at gargpratham71@gmail.com

⭐ Star this repo if you found it useful for your hackathon!

“The only way to win at trading is to have a better strategy — and this helps you find it.”

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