Trading Bot - Cryptocurrency Algorithmic Trading System

A modular trading bot for cryptocurrency trading using Python, CCXT for exchange connectivity, and comprehensive backtesting capabilities.

Features

• Modular Architecture: Separate components for Strategy, Exchange Interface, and Risk Management
• Robust Backtesting Engine: Simulate trades with historical data, commission fees, and slippage
• Multiple Strategies: EMA Crossover (with more to come)
• Performance Metrics: Sharpe Ratio, Max Drawdown, Win Rate, Profit Factor
• Risk Management: Hard-coded Stop Loss and Take Profit with position sizing
• CLI Dashboard: Real-time terminal output and trade logging
• TDD Approach: Test-driven development with comprehensive test suite

Tech Stack

• Language: Python 3.10+
• Libraries:
  • ccxt - Exchange connectivity
  • pandas & pandas_ta - Data analysis and indicators
  • numpy - Numerical computations
  • tabulate - CLI formatting
  • pytest - Testing framework
• Database: SQLite (for trade logging)

Project Structure

test-tradebot/
├── src/
│   ├── config.py              # Global configuration
│   ├── data/
│   │   └── ccxt_loader.py    # OHLCV data fetching
│   ├── indicators/
│   │   └── ema_calculator.py # EMA calculations
│   ├── strategy/
│   │   └── ema_crossover.py  # EMA crossover strategy
│   ├── backtesting/
│   │   ├── backtest_engine.py      # Backtesting engine
│   │   └── performance_metrics.py  # Metrics calculation
│   ├── risk/                  # Risk management (coming soon)
│   └── database/              # Database operations (coming soon)
├── tests/
│   ├── test_indicators.py
│   ├── test_data_loader.py
│   ├── test_strategy.py
│   └── test_backtester.py
├── backtest_runner.py         # Main backtest script
├── requirements.txt
└── README.md

Installation

1. Clone the repository:

git clone <repository-url>
cd test-tradebot

2. Create a virtual environment:

python -m venv venv
source venv/Scripts/activate  # On Windows

3. Install dependencies:

pip install -r requirements.txt

Configuration

Edit src/config.py to customize:

• Trading Pair: Symbol (e.g., "ETH/USDT")
• Timeframe: Candle size (e.g., "4h")
• Backtesting Period: Number of days of historical data
• Risk Parameters: Stop Loss, Take Profit, Max Position Size
• Strategy Parameters: EMA periods (fast/slow)
• Commission & Slippage: Exchange-specific fees

Running Tests

Run the complete test suite:

pytest

Run specific test file:

pytest tests/test_backtester.py -v

Run with coverage:

pytest --cov=src tests/

Running Backtest

Run the backtester with the configured strategy:

python backtest_runner.py

This will:

1. Fetch historical OHLCV data from Binance via CCXT
2. Calculate technical indicators (EMA)
3. Generate trading signals based on strategy
4. Simulate trade execution with realistic commission/slippage
5. Calculate performance metrics
6. Display results in a formatted table

Example Output

================================================================================
TRADING BOT - BACKTESTING ENGINE
================================================================================

Configuration:
  Symbol: ETH/USDT
  Timeframe: 4h
  Backtest Period: 90 days
  Initial Capital: $1,000.00
  Strategy: EMA Crossover (9/21)
  Stop Loss: 3%
  Take Profit: 8%

[1] Loading data from binance...
    Loaded 540 candles
    Date range: 2025-11-03 to 2026-03-01

[2] Generating trading signals...
    Buy signals: 8
    Sell signals: 7

[3] Running backtest...

================================================================================
BACKTEST RESULTS
================================================================================

Performance Summary:
┌──────────────────┬─────────────────┐
│ Metric           │ Value           │
├──────────────────┼─────────────────┤
│ Initial Capital  │ $1,000.00       │
│ Final Value      │ $1,245.32       │
│ Total Return     │ $245.32         │
│ Return %         │ 24.53%          │
│ Total Trades     │ 7               │
│ Winning Trades   │ 5               │
│ Losing Trades    │ 2               │
│ Win Rate         │ 71.43%          │
│ Max Drawdown     │ -8.32%          │
│ Sharpe Ratio     │ 1.85            │
└──────────────────┴─────────────────┘

Strategy Guide

EMA Crossover Strategy

• Signal: When the fast EMA (9) crosses above the slow EMA (21), a BUY signal is generated
• Exit: When the fast EMA crosses below the slow EMA, a SELL signal is generated
• Risk Management: Every position has a hard stop loss (3%) and take profit (8%)

Next Steps

• Implement Paper Trading Mode (real-time with virtual balance)
• Add Live Trading Mode with API key support
• Implement additional strategies (RSI, Bollinger Bands, etc.)
• Add CLI dashboard for real-time monitoring
• Database logging for all trades
• Multi-symbol backtesting
• Walk-forward analysis
• Monte Carlo analysis

Risk Disclaimer

This trading bot is provided for educational purposes only. Cryptocurrency trading carries significant risk. Always:

• Use small amounts when testing with live trading
• Thoroughly backtest any strategy before live deployment
• Implement proper risk management
• Never trade capital you cannot afford to lose

Contributing

Pull requests are welcome. For major changes, please open an issue first.

License

MIT License - See LICENSE file for details