📈 Advanced Algorithmic Trading System

A professional-grade, modular algorithmic trading system built in Python. This project is designed to backtest, analyze, and optimize trading strategies using historical market data. It features a robust architecture separating data handling, strategy logic, risk management, and performance analytics.

🚀 Features

🧠 Trading Strategies

The system includes a suite of standard technical analysis strategies:

• Moving Average Crossover (SMA): Captures trends by comparing short-term and long-term averages.
• RSI (Relative Strength Index): Identifies overbought and oversold conditions for mean reversion.
• MACD (Moving Average Convergence Divergence): Momentum tracking using moving averages.
• Bollinger Bands: Volatility-based strategy trading mean reversions from band edges.
• Momentum: Trend following based on rate of change.
• Composite Strategy: A meta-strategy that combines signals from multiple indicators.

🛡️ Risk Management

A dedicated RiskManager module ensures capital preservation:

• Position Sizing: Dynamic sizing using Kelly Criterion and Volatility Scaling.
• Stop Loss & Take Profit: Automated exit rules to cap losses and secure gains.
• Drawdown Limits: Halts trading if portfolio drawdown exceeds safety thresholds.
• Diversification Checks: Prevents over-concentration in correlated assets.
• Value at Risk (VaR): Statistical risk measurement.

📊 Performance Analytics

Comprehensive PerformanceAnalyzer generating institutional-grade metrics:

• Returns: Total Return, Annualized Return.
• Risk-Adjusted: Sharpe Ratio, Sortino Ratio, Calmar Ratio, Information Ratio.
• Drawdowns: Max Drawdown, Average Drawdown Duration.
• Trade Stats: Win Rate, Profit Factor, Average Win/Loss.
• Market Comparison: Alpha and Beta against the benchmark.

📉 Visualization

Automated reporting module (Visualizer) that generates:

• Equity Curves: Strategy vs. Buy & Hold benchmark.
• Drawdown Charts: Visual representation of underwater periods.
• Reports: Auto-saved to the reports/ directory.

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� Project Structure

automated trading agent/
├── data/                   # (Updates) Caches downloaded historical data
├── logs/                   # (Updates) Runtime logs
├── reports/                # (Updates) Generated performance plots and metrics
├── tests/                  # Unit tests for system modules
│
├── main.py                 # 🚀 Entry point: Orchestrates the workflow
├── config.yaml             # ⚙️ Configuration parameters (Tickers, Risk, Dates)
├── .env                    # 🔒 Environment variables (API Keys) - excluded from git
├── requirements.txt        # 📦 Project dependencies
│
├── strategies.py           # 🧠 Strategy logic implementation (Strategy Design Pattern)
├── backtester.py           # ⚡ specialized Backtesting engine
├── risk_manager.py         # 🛡️ Risk management and position sizing logic
├── performance_metrics.py  # 📊 Financial metrics calculation
├── portfolio_optimizer.py  # ⚖️ Portfolio allocation logic
├── data_fetcher.py         # 📡 Data interface (yfinance wrapper)
└── visualization.py        # 🎨 Plotting and reporting utilities

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/Kaus-code/Automated-trading-.git
   cd Automated-trading-

2. Create a virtual environment (recommended):

   # Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r "automated trading agent/requirements.txt"

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⚙️ Configuration

The system is highly configurable via config.yaml. You can modify:

• Universes: List of stock tickers to trade (e.g., AAPL, MSFT).
• Date Range: Start and End dates for the backtest.
• Risk Parameters:
  • max_position_size: Max capital allocatable to a single trade.
  • stop_loss / take_profit: Percentage targets.
  • max_drawdown: Safety switch level.
• Strategy Parameters: Tweak lookback periods (e.g., SMA 50/200, RSI 14).

Example config.yaml snippet:

risk_management:
  max_position_size: 0.2
  stop_loss: 0.02
  take_profit: 0.05

strategies:
  sma_crossover:
    short_window: 50
    long_window: 200

---

🚀 Usage

To run the trading bot and execute the backtest pipeline:

1. Navigate to the agent directory:

   cd "automated trading agent"

2. Run the main script:

   python main.py

What happens next?

1. The system loads configuration from config.yaml.
2. DataFetcher downloads historical data (default: Yahoo Finance).
3. The engine iterates through tickers and strategies (SMA, RSI, MACD).
4. RiskManager applies sizing and constraints.
5. Backtester simulates trading execution.
6. Performance reports and charts are generated in the reports/ folder.
7. A summary table is printed to the console.

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📊 Sample Output

After execution, check the reports/ folder for visual insights:

Equity Curve	Drawdown Analysis
AAPL_SMA_Crossover_equity.png	AAPL_SMA_Crossover_drawdown.png
Shows strategy growth vs market	Shows depth and duration of losses

Console Output Example:

AAPL - SMA_Crossover Performance:
────────────────────────────────────────────────────────────
Total Return:        145.20%
Sharpe Ratio:        1.85
Max Drawdown:        -12.50%
Win Rate:            58.30%
Profit Factor:       2.10

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⚠️ Disclaimer

Educational Use Only: This software is for educational purposes. Algorithmic trading involves significant risk. The strategies provided are basic examples and should not be used for live trading with real money without extensive modification, testing, and professional financial advice.

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🤝 Contributing

Contributions are welcome!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request