🚀 Cryptocurrency Trading System (Alpha Version) - Personal Use

A sophisticated cryptocurrency trading system combining advanced volatility modeling, regime detection, and smart order execution. Built for 24/7 markets with specific adaptations for cryptocurrency dynamics.

🏗️ System Architecture

graph TD
    A[Market Data] --> B[Data Pipeline]
    B --> C[Feature Engineering]
    C --> D[Risk Engine]
    D --> E[Portfolio Optimizer]
    E --> F[Signal Generator]
    F --> G[Execution Engine]
    H[GARCH Analysis] --> D
    I[Regime Detection] --> D
    J[Whale Activity] --> B
    K[Market Sentiment] --> B
    L[Order Flow] --> B
    M[Risk Analytics] --> N[Performance Dashboard]
    G --> M

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Core Components

1. Data Pipeline

Advanced data processing pipeline for crypto markets:

from datetime import datetime, timedelta
import yfinance as yf
import pandas as pd
from typing import Dict, Optional

class CryptoDataPipeline:
    def __init__(self, config: Optional[PipelineConfig] = None):
        self.config = config or PipelineConfig()
        self.processor = DataProcessor(self.config.processor_config)
        self.data: Dict[str, pd.DataFrame] = {}

2. GARCH Analysis

Crypto-adapted GARCH modeling with regime awareness:

@dataclass
class CryptoGARCHConfig:
    p: int = 1
    q: int = 1
    dist: str = 'skewt'
    vol_targeting: bool = True
    target_vol: float = 0.50
    mean_model: str = 'Zero'
    vol_model: str = 'EGARCH'
    power: float = 2.0

3. Risk Management

Advanced risk controls with multi-factor analysis:

class RiskManager:
    def check_trade(self, symbol: str, size: float, price: float, 
                   market_data: pd.DataFrame) -> Tuple[bool, Dict]:
        checks = {
            'size_check': self._check_position_size(size),
            'var_check': self._check_portfolio_var(symbol, size, market_data),
            'liquidity_check': self._check_liquidity(symbol, size, market_data),
            'concentration_check': self._check_concentration(symbol, size),
            'drawdown_check': self._check_drawdown()
        }
        return all(checks.values()), checks

Configuration

Trading Configuration

# Trading pairs configuration
symbols:
  - BTC-USD
  - ETH-USD

# Timeframe settings
timeframes:
  - 1h
  - 4h

# Risk management parameters
max_position_size: 0.02
max_drawdown: 0.15
stop_loss: 0.02

# Exchange settings
primary_exchange: binance

Installation

Installation

# Clone repository
git clone <repository-url>
cd trading-bot

# Set up virtual environment
python3.12 -m venv venv # Python 3.12 specificly otherwise issues with dependencies
source venv/bin/activate  # Unix
.\venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

Quick Start

# Create virtual environment
python -m venv venv
source venv/bin/activate # Unix/macOS
.\venv\Scripts\activate # Windows

# Install dependencies
pip install -r requirements.txt

Development Setup

pip install -e ".[dev]"

Dependencies

Core Dependencies:

• numpy>=1.20.0
• pandas>=1.3.0
• scipy>=1.7.0
• scikit-learn>=1.0.0
• matplotlib>=3.4.0
• seaborn>=0.11.0
• python-binance>=1.0.0
• arch>=4.19.0
• statsmodels>=0.13.0
• plotly>=5.0.0
• dash>=2.0.0

Development Tools:

• pytest>=7.0.0
• pytest-asyncio>=0.18.0
• pytest-mock>=3.7.0
• black>=22.0.0
• flake8>=4.0.0
• mypy>=0.800

Testing Infrastructure

Performance Testing

def test_garch_performance(test_data):
    config = CryptoGARCHConfig(
        rescale=True,
        scale_factor=100.0,
        forecast_horizon=1,
        simulation_draws=1000
    )
    model = CryptoGARCHModel(config)
    results = run_performance_test(run_garch, test_data)
    assert results['execution_time'] < 5.0
    assert results['memory_used'] < 1024 * 1024 * 100

Real Market Data Testing

@pytest.fixture(scope="session")
def real_crypto_data():
    pairs = ['BTC-USD', 'ETH-USD', 'SOL-USD', 'DOGE-USD']
    end_date = datetime.now()
    start_date = end_date - timedelta(days=30)
    # Implementation details in tests/test_real_data.py

Monitoring Dashboard

Real-time system monitoring with Dash:

class TradingDashboard:
    def __init__(self, portfolio_manager, risk_manager, execution_router):
        self.app = dash.Dash(__name__)
        self.portfolio = portfolio_manager
        self.risk = risk_manager
        self.execution = execution_router

Project Structure

src/
├── analysis/
│   ├── garch.py    # GARCH modeling
│   └── regime.py   # Market regime detection
├── data/
│   └── pipeline.py # Data processing pipeline
├── risk/
│   └── manager.py  # Risk management system
├── monitoring/
│   └── dashboard.py # Real-time monitoring
└── utils/
    └── benchmark_utils.py # Performance testing

📄 License

Commercial Software License Copyright © 2024 Lucas Kemper All Rights Reserved

For licensing inquiries:

• Email: [email protected]

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Note: This system is under active development. Features and documentation are updated frequently.