dgbit

Professional Algorithmic Trading Framework for Bybit

Build, backtest, and deploy crypto trading strategies with confidence

Documentation | Quick Start | Strategy Guide | Issues

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Why dgbit?

dgbit is a production-ready algorithmic trading framework designed for cryptocurrency traders who want to:

• Backtest strategies with historical data before risking real capital
• Execute automated trades on Bybit spot markets with confidence
• Build custom strategies using a pluggable, extensible architecture
• Monitor positions through a modern web dashboard
• Deploy anywhere with Docker support

Whether you're a quantitative trader developing new strategies or a developer building trading automation, dgbit provides the infrastructure you need.

Key Features

Feature	Description
Multi-Strategy Support	Wavelet reversal, MA crossover, RSI, Bollinger Bands, and custom strategies
Comprehensive Backtesting	In-memory simulation with detailed metrics and interactive Plotly reports
Real-time Execution	Live trading on Bybit with position tracking and risk management
Service Bus Architecture	Scalable NNG-based messaging for high-frequency operations
REST API	Full-featured FastAPI backend with WebSocket support
Web Dashboard	Vue 3 frontend for monitoring and control
Docker Ready	One-command deployment with docker-compose

Quick Start

Installation

# Install from PyPI
pip install dgbit

# Or with Docker
docker pull cryptuon/dgbit

Run Your First Backtest

from dgbit_core.backtesting import Backtester, BacktestConfig
from dgbit_core.trading.strategy import WaveletReversalStrategy
from dgbit_core.data.data_fetcher import BybitDataFetcher

# Fetch historical data
fetcher = BybitDataFetcher()
data = fetcher.get_kline_data("BTCUSDT", interval="15", limit=1000)

# Configure and run backtest
config = BacktestConfig(
    initial_capital=10000.0,
    transaction_fee=0.001,
)

backtester = Backtester(config=config)
backtester.strategy = WaveletReversalStrategy(min_signal_threshold=0.75)
result = backtester.run(data)

# View results
print(f"Total Return: {result.metrics['total_return']:.2%}")
print(f"Win Rate: {result.metrics['win_rate']:.2%}")
print(f"Max Drawdown: {result.metrics['max_drawdown']:.2%}")

Start the API Server

# Using pip installation
dgbit-api

# Or with uvicorn directly
uvicorn dgbit_api.main:app --host 0.0.0.0 --port 8000

Docker Deployment

# Clone the repository
git clone https://github.com/cryptuon/dgbit.git
cd dgbit

# Configure environment
cp dgbit-api/.env.example dgbit-api/.env
# Edit .env with your Bybit API credentials

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f api

Architecture

                           dgbit Platform
    ┌─────────────────────────────────────────────────────────────┐
    │                                                              │
    │   ┌──────────────────────────────────────────────────────┐  │
    │   │                   Vue 3 Dashboard                     │  │
    │   │    Charts | Portfolio | Strategies | Monitoring       │  │
    │   └──────────────────────────────────────────────────────┘  │
    │                           │ HTTP / WebSocket                 │
    │                           ▼                                  │
    │   ┌──────────────────────────────────────────────────────┐  │
    │   │                 FastAPI REST API                      │  │
    │   │   /backtests  /jobs  /data  /strategies  /execution   │  │
    │   └──────────────────────────────────────────────────────┘  │
    │            │                │                │               │
    │            │ NNG IPC        │ NNG IPC        │ NNG IPC       │
    │            ▼                ▼                ▼               │
    │   ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐   │
    │   │ Data Service │ │   Backtest   │ │ Strategy Service │   │
    │   │ (Market Data)│ │    Worker    │ │ (Signal Gen)     │   │
    │   └──────────────┘ └──────────────┘ └──────────────────┘   │
    │                                                              │
    │   ┌──────────────────────────────────────────────────────┐  │
    │   │               Shared Trading Core                     │  │
    │   │  Strategies | Backtesting | Position Tracking | Data  │  │
    │   └──────────────────────────────────────────────────────┘  │
    │                           │                                  │
    └───────────────────────────┼──────────────────────────────────┘
                                ▼
                         Bybit Exchange API

Built-in Trading Strategies

Strategy	Type	Description
Wavelet Reversal	Mean Reversion	Daubechies wavelet decomposition for trend reversal detection
MA Crossover	Trend Following	Classic moving average crossover signals
RSI	Momentum	Relative Strength Index overbought/oversold signals
Bollinger Bands	Volatility	Breakout detection using Bollinger Band boundaries

Creating Custom Strategies

from dgbit_core.trading.strategy import (
    BaseStrategy, 
    StrategyMetadata, 
    SignalType,
    strategy_registry
)

class MyMomentumStrategy(BaseStrategy):
    """Custom momentum-based trading strategy."""
    
    metadata = StrategyMetadata(
        name="my_momentum",
        description="Custom momentum strategy with volume confirmation",
        author="Your Name",
        version="1.0.0",
        signal_type=SignalType.MOMENTUM,
        parameters={
            "lookback_period": {"type": "int", "default": 14},
            "volume_threshold": {"type": "float", "default": 1.5},
        },
    )
    
    def generate_signal(self, data):
        # Your strategy logic here
        momentum = data['close'].pct_change(self.lookback_period).iloc[-1]
        volume_ratio = data['volume'].iloc[-1] / data['volume'].mean()
        
        if momentum > 0.02 and volume_ratio > self.volume_threshold:
            return 0.8  # Strong buy signal
        elif momentum < -0.02 and volume_ratio > self.volume_threshold:
            return 0.2  # Strong sell signal
        return 0.5  # Neutral

# Register your strategy
strategy_registry.register(MyMomentumStrategy)

API Reference

REST Endpoints

Endpoint	Method	Description
/api/health	GET	Service health and stats
/api/backtests	POST	Schedule a backtest job
/api/jobs	GET	List all jobs
/api/jobs/{uuid}	GET	Get job status and results
/api/data/klines	GET	Fetch OHLCV data
/api/data/symbols	GET	List available trading pairs
/api/strategies	GET	List available strategies
/api/strategies/{name}/signal	POST	Generate trading signal
/api/execution/orders	POST	Place an order
/api/execution/positions	GET	Get open positions

WebSocket Events

// Connect to event stream
const ws = new WebSocket('ws://localhost:8000/api/ws/events');

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log('Event:', data.type, data.payload);
};

// Event types: job.created, job.completed, job.failed, 
//              trade.entered, trade.exited, signal.generated

Configuration

Create a .env file with your settings:

# Bybit API (required for live trading)
BYBIT_API_KEY=your_api_key
BYBIT_API_SECRET=your_api_secret
BYBIT_TESTNET=true

# Application settings
ENVIRONMENT=development
LOG_LEVEL=INFO

# Default trading parameters
DEFAULT_SYMBOL=BTCUSDT
DEFAULT_INTERVAL=1

# Service bus addresses
NNG_COMMAND_ADDRESS=ipc:///tmp/dgbit_cmd.ipc
NNG_EVENT_ADDRESS=ipc:///tmp/dgbit_evt.ipc

Documentation

Comprehensive documentation is available at docs.cryptuon.com/dgbit:

• Installation Guide
• Quick Start Tutorial
• Strategy Development
• Backtesting Guide
• API Reference
• Docker Deployment

Development

# Clone repository
git clone https://github.com/cryptuon/dgbit.git
cd dgbit

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest dgbit-api/tests/

# Run linting
ruff check .

# Start development server
cd dgbit-api
uvicorn dgbit_api.main:app --reload

Contributing

Contributions are welcome! Please read our Contributing Guide for details on:

• Code style and standards
• Pull request process
• Development setup

License

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

Disclaimer

Trading cryptocurrencies involves significant risk. This software is provided for educational and research purposes only. Past performance does not guarantee future results. Always test strategies thoroughly with paper trading before using real funds. The authors are not responsible for any financial losses incurred while using this software.

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Made with care by Cryptuon