apilot 0.1.1

AI-Driven Qutan, Open to All

APilot - AI-Driven Quantitative Trading Platform

Overview

APilot is a high-performance quantitative trading framework focused on cryptocurrency and stock markets, developed by the AlphaPilot.tech team. The framework supports both strategy backtesting and live trading, providing a comprehensive solution for quantitative traders.

Official website: www.alphapilot.tech

Key Features

• Event-driven architecture: Built for high-performance, real-time trading systems
• Multiple trading strategies: Price Action strategies, Factor strategies (in development)
• Professional execution algorithms: BestLimit, TWAP algorithms
• Comprehensive backtesting: Accurate simulation with detailed performance analytics
• Multi-exchange support: Currently focusing on Binance, with more to come
• Live trading capability: Execute strategies in real-time with risk management
• Extensible framework: Easy to add new strategies, data sources, and exchanges

Strategy Types

• Price Action (PA) strategies: Support for trend following, mean reversion, and other classic price action strategies
• Factor strategies: Quantitative strategies based on multi-factor models (in development)

Technical Architecture

Design Principles

• Core Module: Contains all abstract interfaces and core data structures

  • Abstract base classes (BaseEngine, BaseGateway, etc.)
  • Data models (OrderData, TickData, etc.)
  • Constant definitions (Direction, Interval, etc.)
  • Basic event system

• Feature Modules: Specific implementations for different domains

  • execution/gateway/ - Exchange API implementations
  • engine/ - Specific engine implementations
  • strategy/ - Trading strategy templates and implementations
  • performance/ - Performance calculation and reporting

Installation

# Clone the repository
git clone https://github.com/yourusername/apilot.git
cd apilot

# Install the package in development mode
pip install -e .

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

Quick Start

Backtesting a Strategy

from apilot.engine import BacktestingEngine
from apilot.strategy import SampleStrategy

# Create a backtesting engine
engine = BacktestingEngine()
engine.set_parameters(
    start="2023-01-01",
    end="2023-12-31",
    initial_capital=10000,
    commission_rate=0.001
)

# Add a strategy
engine.add_strategy(SampleStrategy, "MyStrategy", "BTC/USDT.BINANCE", {
    "param1": 10,
    "param2": 20
})

# Run backtest
engine.run_backtest()

# Analyze performance
engine.show_performance()

Running Live Trading

from apilot.engine import PAEngine
from apilot.strategy import SampleStrategy

# Create a live trading engine
engine = PAEngine(main_engine, event_engine)

# Add a strategy
engine.add_strategy(SampleStrategy, "MyStrategy", "BTC/USDT.BINANCE", {
    "param1": 10,
    "param2": 20
})

# Initialize and start the strategy
engine.init_strategy("MyStrategy")
engine.start_strategy("MyStrategy")

Getting Started with Development

For detailed documentation on developing with APilot, please refer to our Development Guide.

Testing

# Run all tests
python -m pytest tests/

# Run specific test file
python -m pytest tests/test_bar_generator.py

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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