Crypto Algo Trading System

Overview

A high-performance cryptocurrency trading system that combines AI-driven analysis with automated execution. Built on a microservice architecture, it features LSTM neural networks for price prediction, real-time market data processing, and advanced risk management capabilities. The platform supports multiple exchanges and includes LLM-powered news sentiment analysis for comprehensive market insights.

Project Structure

crypto-quant-trading-system/
   ├── src/
   │   ├── api/                      # API Layer
   │   │   ├── endpoints/            # API Endpoints
   │   │   ├── routes/               # Route Definitions
   │   │   └── websocket/            # WebSocket Handlers
   │   │
   │   ├── core/                     # Core Functionality
   │   │   ├── config/               # Configuration Management
   │   │   ├── events/               # Event Handling
   │   │   └── utils/                # Utility Functions
   │   │
   │   ├── data/                     # Data Layer
   │   │   ├── collectors/           # Data Collectors
   │   │   │   ├── market_data/      # Exchange Market Data
   │   │   │   └── news_data/        # News & Social Data
   │   │   ├── processors/           # Data Processors
   │   │   └── storage/              # Data Storage
   │   │
   │   ├── models/                   # Model Layer
   │   │   ├── evaluation/           # Model Evaluation
   │   │   ├── lstm/                 # LSTM Price Models
   │   │   └── sentiment/            # Sentiment Models
   │   │
   │   ├── services/                 # Services Layer
   │   │   ├── monitoring/           # Monitoring Services
   │   │   ├── nacos/                # Service Registration
   │   │   └── newsbot/              # News Analysis Service
   │   │       ├── llm/              # LLM Integration
   │   │       ├── sentiment/        # Sentiment Analysis
   │   │       └── signals/          # Trading Signal Generation
   │   │
   │   └── trading/                  # Trading Layer
   │       ├── execution/            # Trade Execution
   │       │   ├── order_router/     # Smart Order Routing
   │       │   └── position/         # Position Management
   │       ├── risk/                 # Risk Management
   │       │   ├── limits/           # Trading Limits
   │       │   └── metrics/          # Risk Metrics
   │       └── strategy/             # Trading Strategies
   │           ├── arbitrage/        # Arbitrage Strategies
   │           ├── mean_reversion/   # Mean Reversion
   │           └── trend/            # Trend Following
   │
   ├── tests/                        # Tests Directory
   │   ├── unit/                     # Unit Tests
   │   └── integration/              # Integration Tests
   ├── docs/                         # Documentation
   └── scripts/                      # Deployment Scripts

Key Features

1. Event-Driven Data Pipeline

• Data Integration Layer:
  • Real-time market data websocket streams
  • Custom data normalization and preprocessing
  • Multi-exchange data aggregation
• Event Processing:
  • Event-driven architecture for real-time data handling
  • Message queue integration for event distribution
  • Scalable event processing framework

2. Trading System

• Mid-Low Frequency Trading:
  • Statistical arbitrage strategies
  • Mean reversion models
  • Trend following implementations
• Risk Management:
  • Position sizing algorithms
  • Stop-loss automation
  • Volatility-based risk adjustment
  • Maximum drawdown controls
• Order Management:
  • Smart order routing
  • Order splitting algorithms
  • Multi-exchange execution

3. Price Engine

• LSTM Price Prediction:
  • Multi-layer LSTM implementation (4 layers with 50 units each)
  • Dropout layers (0.2) for preventing overfitting
  • Time-series prediction optimization
• Traditional Pricing Models:
  • Black-Scholes model implementation
  • Volatility surface calibration
  • Greeks calculation and risk metrics

4. Newsbot Intelligence

• LLM Integration:
  • Deepseek API implementation for market analysis
  • Custom prompt engineering for financial context
• Information Gathering:
  • Google Search API integration
  • Real-time news aggregation
  • Sentiment analysis pipeline
• Trading Signal Generation:
  • News-based event detection
  • Sentiment-driven trading signals
  • Market impact analysis

Development Setup

Prerequisites

• Python 3.7+
• Docker & Docker Compose
• MongoDB
• Redis (for caching)
• CUDA-compatible GPU (recommended for model training)

Quick Start

1. Clone & Setup

git clone https://github.com/yourusername/crypto-quant-trading-system.git
cd crypto-quant-trading-system
python -m venv venv
source venv/bin/activate  # Windows: .\venv\Scripts\activate
pip install -r requirements.txt

2. Configuration

• Copy .env.example to .env and configure environment variables
• Update service configurations in src/core/config.py
• Configure exchange API keys in config/exchanges.yaml

3. Launch Services

docker-compose up -d  # Start infrastructure services
python src/main.py    # Launch main application

System Configuration

Service Discovery

• Nacos Configuration
  • Server: localhost:8848 (default)
  • Namespace: crypto-trading
  • Authentication: JWT-based

Model Parameters

• LSTM Configuration (src/models/lstm/config.py)

  SEQUENCE_LENGTH = 60
  LSTM_UNITS = [50, 50, 50, 50]
  DROPOUT_RATE = 0.2
  OPTIMIZER = 'adam'
  LOSS = 'mse'

LLM & Search API Configuration

• Deepseek Configuration (src/services/newsbot/llm/config.py)

  DEEPSEEK_API_KEY = "your_api_key"
  DEEPSEEK_MODEL = "deepseek-chat-v3"
  MAX_TOKENS = 2048
  TEMPERATURE = 0.7
  TOP_P = 0.95
  CONTEXT_WINDOW = 16384  # Maximum context length

• Google Search API (src/services/newsbot/config.py)

  GOOGLE_API_KEY = "your_api_key"
  GOOGLE_CSE_ID = "your_custom_search_engine_id"
  SEARCH_QUOTA_PER_DAY = 10000
  MAX_RESULTS_PER_QUERY = 10
  REGION = "US"  # Search region

Development Workflow

1. Fork the repository
2. Create feature branch (git checkout -b feature/xyz)
3. Commit changes (git commit -m 'Add feature xyz')
4. Push to branch (git push origin feature/xyz)
5. Submit Pull Request

Code Standards

• Follow PEP 8 style guide
• Maintain test coverage >80%
• Document all public APIs
• Add type hints to new code

Contact

• https://www.linkedin.com/in/dylan-chen-684a52249/

License

MIT License - See LICENSE file for details