Advanced Trading Bot Framework

🏗️ Architecture

src/trading_bot/
├── core/ # Models, enums, settings
├── data/ # ClickHouse storage & Binance client
├── strategies/ # 8+ trading strategies
├── backtesting/ # Strategy backtesting engine
├── optimization/ # Genetic algorithm optimization
├── order_book_trading/ # Real-time order book strategies
└── smart_money_detection/ # Iceberg detection & dashboard

🚀 Quick Start

Prerequisites

Setup

# Clone and install
git clone <repo-url>
cd trading-bot
poetry install
# Setup ClickHouse (recommended)
make run-db
# OR: docker run -d --name trading-clickhouse -p 8123:8123 clickhouse/clickhouse-server
# Configure environment
cp .env.example .env
# Edit .env with your settings
# Test the setup
poetry run python -c "from trading_bot.data.market_data import MarketDataManager; print('✅ Setup OK')"

Download Market Data

# Download data for backtesting
poetry run python scripts/download_data.py --symbol BTCUSDT --days 90
# Check data status
poetry run python scripts/show_data_status.py

📊 Usage Examples

1. Run a Backtest

from trading_bot.backtesting.engine import BacktestEngine
from trading_bot.strategies.ema_crossover import EMACrossoverStrategy
from trading_bot.core.models import BacktestConfig
from trading_bot.core.enums import TimeFrame
from datetime import datetime
from decimal import Decimal
# Configure backtest
config = BacktestConfig(
 symbols=["BTCUSDT"],
 timeframe=TimeFrame.FIFTEEN_MINUTES,
 since_date=datetime(2024, 1, 1),
 test_start_date=datetime(2024, 6, 1),
 initial_balance=Decimal("5000")
)
# Create and run strategy
strategy = EMACrossoverStrategy(fast_period=12, slow_period=26)
engine = BacktestEngine(config, strategy)
results = engine.run()
# Print results
for symbol, result in results.items():
 print(f"{symbol}: {result.total_return_pct:.2f}% return, {result.total_trades} trades")

2. Optimize Strategy Parameters

from trading_bot.optimization.genetic import optimize_strategy
from trading_bot.strategies.vwap_statistical import VWAPStatisticalStrategy
# Define parameter space
param_space = {
 'vwap_period': {'type': 'int', 'range': (10, 30)},
 'zscore_entry_threshold': {'type': 'float', 'range': (-2.5, -1.0)},
 'stop_loss_atr': {'type': 'float', 'range': (1.5, 3.0)}
}
# Run optimization
results = optimize_strategy(
 strategy_class=VWAPStatisticalStrategy,
 symbols=["BTCUSDT", "ETHUSDT"],
 parameter_space=param_space
)
print(f"Best parameters: {results['best_parameters']}")

3. Live Order Book Trading

# Test the order book trading system
poetry run python src/trading_bot/order_book_trading/tests/quick_test.py

4. Smart Money Detection Dashboard

# Start the web dashboard
poetry run python src/trading_bot/smart_money_detection/server.py BTCUSDT
# Open browser to: http://localhost:5000

🎯 Available Strategies

⚙️ Configuration

# Database (ClickHouse preferred)
DATABASE_TYPE=clickhouse
CLICKHOUSE_HOST=localhost
CLICKHOUSE_PORT=8123
CLICKHOUSE_USERNAME=default
CLICKHOUSE_PASSWORD=
CLICKHOUSE_DATABASE=trading_bot
# Optional: Binance API (for live data)
BINANCE_API_KEY=your_api_key
BINANCE_API_SECRET=your_api_secret

🔧 Development

Code Quality

# Format and lint
make fix-all
# Or manually:
poetry run ruff check src/ --fix
poetry run isort src/

Testing

# Run tests
poetry run pytest
# Test specific component
poetry run python -m pytest tests/strategies/

📁 Key Components

Core Framework

Trading Strategies

Real-time Trading

Optimization

🚨 Common Issues & Solutions

ClickHouse Connection Issues

# Check if ClickHouse is running
docker ps | grep clickhouse
# Restart ClickHouse
docker restart trading-clickhouse
# Check logs
docker logs trading-clickhouse

No Market Data

# Download data first
poetry run python scripts/download_data.py --popular --days 30
# Verify data
poetry run python scripts/show_data_status.py

Import Errors

# Reinstall dependencies
poetry install --no-cache
# Check Python path
poetry run python -c "import trading_bot; print('OK')"

📈 Performance Notes

🔮 Roadmap

⚠️ Disclaimers

🤝 Contributing

📄 License