Advanced Trading Bot Framework

A comprehensive cryptocurrency trading framework with multiple components:

• Strategy Backtesting with ClickHouse storage
• Genetic Algorithm Optimization
• Real-time Order Book Trading
• Smart Money Detection Dashboard
• Multiple Trading Strategies

🏗️ 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

• Python 3.11+
• Poetry for dependency management
• ClickHouse (preferred) or MongoDB for data storage

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

Strategy	Description	Best For
EMACrossoverStrategy	Simple moving average crossover	Trending markets
VWAPStatisticalStrategy	Statistical reversion around VWAP	Mean reversion
RSIDivergenceStrategy	Professional RSI divergence detection	Reversal points
MultiRegimeStrategy	Adapts to volatility regimes	All market conditions
TimeBasedReversionStrategy	Exploits intraday patterns	Scalping
VWAPBounceStrategy	Dynamic VWAP support/resistance	Institutional levels
MultiFactorStrategy	Combines 6 signal types	Comprehensive
SVMSlidingWindowStrategy	Machine learning approach	Complex patterns

⚙️ Configuration

Create a .env file:

# 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

• src/trading_bot/core/models.py - Data models (Position, MarketData, etc.)
• src/trading_bot/data/market_data.py - Market data management
• src/trading_bot/backtesting/engine.py - Backtesting engine

Trading Strategies

• src/trading_bot/strategies/base.py - Base strategy interface
• src/trading_bot/strategies/*.py - Individual strategy implementations

Real-time Trading

• src/trading_bot/order_book_trading/ - Live order book analysis
• src/trading_bot/smart_money_detection/ - Iceberg detection & dashboard

Optimization

• src/trading_bot/optimization/genetic.py - Genetic algorithm 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

• ClickHouse vs MongoDB: ClickHouse is ~10x faster for time-series queries
• Timeframes: 15m provides good balance of detail vs. speed
• Memory: Large datasets may require 8GB+ RAM for optimization
• Parallelization: Genetic optimization uses multiple cores

🔮 Roadmap

• Live trading execution (paper trading first)
• Additional ML strategies
• Portfolio-level backtesting
• Risk management modules
• More sophisticated order book strategies

⚠️ Disclaimers

• NOT FINANCIAL ADVICE: This is educational/research software
• USE AT YOUR OWN RISK: No warranty provided
• PAPER TRADING FIRST: Test thoroughly before risking real money
• MARKET RISKS: Past performance doesn't predict future results

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Follow the existing code style
5. Submit a pull request

📄 License

MIT License - See LICENSE file for details.