xqtrader 0.1.1

Quant trading engine toolkit

XQTrader

中文文档

A quantitative trading engine toolkit for strategy development, backtesting, paper trading, and live trading.

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Features

• Strategy Framework: Modular strategy system with hot-reload support
• Multi-Exchange Support: 100+ exchanges via CCXT
• Technical Indicators: 60+ indicators powered by talipp
• Futures Trading: Perpetual futures with hedge mode
• Event Contracts: Binary options style trading
• Risk Management: Multi-level risk control (WARNING/CRITICAL)
• Smart Caching: LRU + TTL caching with circuit breaker

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Installation

Requirements

• Python >= 3.12
• uv (recommended) or pip

Use as a dependency

Add XQTrader to your project with uv:

uv add "xqtrader @ git+https://github.com/Aixtrade/QuantTrader.git@main"

Or pin to a specific version tag:

uv add "xqtrader @ git+https://github.com/Aixtrade/QuantTrader.git@v0.1.0"

Or with pip:

pip install "xqtrader @ git+https://github.com/Aixtrade/QuantTrader.git@main"

Development install

git clone https://github.com/Aixtrade/QuantTrader.git
cd XQTrader

# Install dependencies with uv
uv sync --dev

# Or with pip
pip install -e ".[dev]"

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Quick Start

1. Create a Strategy

from xqtrader.strategies.base import (
    BaseStrategy,
    StrategyContext,
    StrategyResult,
)

class MyStrategy(BaseStrategy):
    def __init__(self):
        super().__init__(name="my_strategy", version="1.0.0")

    def execute(self, context: StrategyContext) -> StrategyResult:
        close_prices = context.market_data.get("close", [])
        if not close_prices:
            return StrategyResult(
                signals=[self.create_signal("HOLD", context.symbol)],
                indicators={},
                metadata={},
                execution_time=0.0,
                success=True,
            )

        # Your trading logic here
        signal = self.create_signal("LONG", context.symbol, confidence=0.8)
        return StrategyResult(
            signals=[signal],
            indicators={},
            metadata={},
            execution_time=0.0,
            success=True,
        )

2. Run Backtest

import asyncio
from datetime import datetime, timedelta, timezone
from xqtrader.engine.backtest import BacktestConfig, BacktestEngine

async def main():
    strategy = MyStrategy()
    engine = BacktestEngine()

    now = datetime.now(timezone.utc)
    config = BacktestConfig(
        symbol="BTC/USDT",
        interval="1h",
        initial_capital=10000.0,
        start_time=int((now - timedelta(days=7)).timestamp() * 1000),
        end_time=int(now.timestamp() * 1000),
    )

    async for event in engine.run(strategy, config):
        if event.event_type == "trade":
            print(f"Trade: {event.data}")
        elif event.event_type == "complete":
            print(f"Final balance: {event.data.get('final_balance')}")

asyncio.run(main())

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Signal Conventions

Contract Type	Signals
Perpetual/Futures	LONG, SHORT, CLOSE_LONG, CLOSE_SHORT, CLOSE
Event Contracts	UP, DOWN, HOLD

Event trader also accepts LONG/SHORT/BUY/SELL and auto-maps to UP/DOWN.

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Architecture

xqtrader/
├── strategies/     # Strategy system - BaseStrategy and dynamic loader
├── engine/         # Execution engines - BacktestEngine / RealtimeEngine
├── accounts/       # Account management - SimulatedAccount / FuturesSimulatedAccount
├── traders/        # Trade executors - EventsTrader / FuturesTrader
├── data/           # Data services - DataCenterService + CCXT adapters
│   └── adapters/   # Exchange adapters - CCXTAdapter / BinanceAdapter
├── indicators/     # Technical indicators - 60+ indicators via talipp
├── risk/           # Risk management - RiskManager (WARNING/CRITICAL levels)
├── reports/        # Report generation - BacktestReport / TradeRecord
└── config/         # Configuration management

Data Flow

Market Data (OHLCV) → Indicator Engine → Strategy Context → Strategy Execution
     ↓                                                            ↓
DataCenterService                                          StrategyResult
                                                                  ↓
Account Update ← Trade Execution ← Risk Check ← Trading Signals
     ↓
BacktestReport

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Examples

See the examples/ directory for complete examples:

• MACD Strategy: examples/macd_strategy/
  • macd_strategy.py - Strategy implementation
  • run_backtest_futures.py - Futures backtest
  • run_backtest_events.py - Event contracts backtest

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Development

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_data_center.py -v

# Run specific test class
uv run pytest tests/test_data_center.py::TestLRUCache -v

# Run examples
uv run python examples/macd_strategy/run_backtest_futures.py

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Risk Rules

Rule	Warning	Critical
Daily Loss	3.5%	5%
Max Drawdown	10%	15%

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Roadmap

• MVP v0: Single-asset backtesting
• MVP v1: Real-time paper trading
• MVP v2: Funding rate optimization
• MVP v3: Multi-asset portfolio
• MVP v4: Live trading interface

See docs/trading_system_roadmap.md for details.

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Dependencies

Package	Purpose
ccxt	Exchange connectivity
talipp	Technical indicators
numpy	Numerical computing
pydantic	Data validation
httpx	Async HTTP client

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License

MIT License - see LICENSE for details.

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Contributing

Contributions are welcome! Please read the AGENTS.md for coding conventions.