test_long_etf_momentum.py

#!/usr/bin/env python3
"""
测试ETF动量策略长期回测，重现资金计算问题
"""

import asyncio
import pandas as pd
from datetime import date, datetime
import json

from src.strategies.user_strategies.etf_momentum_joinquant import ETFMomentumJoinQuantStrategy
from src.evaluation.backtesting.unified_engine import UnifiedBacktestEngine
from src.evaluation.backtesting.config import UnifiedConfig, TradeConfig, TimeConfig, FeatureFlag

async def test_long_backtest():
    """运行长期回测测试"""
    print("=" * 80)
    print("ETF动量策略长期回测测试 (重现资金计算问题)")
    print("=" * 80)
    
    # 创建策略
    strategy = ETFMomentumJoinQuantStrategy(
        strategy_id="long_test_etf_momentum",
        etf_pool=["QQQ", "SPY", "IWM", "TLT", "GLD"],
        lookback_days=20,
        r2_threshold=0.5,
    )
    
    # 创建配置 - 使用与用户报告相同的配置
    config = UnifiedConfig(
        trade=TradeConfig(
            initial_capital=100000,
            commission_rate=0.001,
            slippage_rate=0.0005,
            max_position_size=0.2,
            max_positions=1,
            stake_amount=20000,
        ),
        time=TimeConfig(
            signal_timeframe="1d",
            execution_timeframe="1d",
            warmup_days=260,
            clock_mode="daily",
        ),
        features=FeatureFlag.VECTORIZED | FeatureFlag.FREQTRADE_PROTOCOL,
    )
    
    # 创建回测引擎
    engine = UnifiedBacktestEngine(config=config)
    
    # 运行回测（1年测试）
    print("开始长期回测 (2024-01-01 到 2024-12-31)")
    
    try:
        result = await engine.run(
            strategies=[strategy],
            universe=["QQQ", "SPY", "IWM", "TLT", "GLD"],
            start=date(2024, 1, 1),
            end=date(2024, 12, 31),  # 1年测试
            auto_download=False,
        )
        
        # 分析结果
        print("\n" + "=" * 80)
        print("回测结果分析")
        print("=" * 80)
        
        print(f"初始资金: ${result.initial_capital:,.2f}")
        print(f"最终权益: ${result.final_equity:,.2f}")
        print(f"总回报: {result.total_return_pct:.2f}%")
        print(f"交易次数: {result.total_trades}")
        print(f"胜率: {result.win_rate:.2%}")
        print(f"夏普比率: {result.sharpe_ratio:.2f}")
        
        # 检查资金计算
        calculated_return = ((result.final_equity / result.initial_capital - 1) * 100)
        print(f"\n资金计算检查:")
        print(f"  基于final_equity计算的总回报: {calculated_return:.2f}%")
        print(f"  result.total_return_pct: {result.total_return_pct:.2f}%")
        
        diff = abs(calculated_return - result.total_return_pct)
        if diff > 0.01:
            print(f"  ⚠️ 资金计算不一致! 差异: {diff:.2f}%")
        
        # 分析交易频率
        print(f"\n交易频率分析:")
        print(f"  信号总数: {len(result.signals)}")
        print(f"  订单总数: {len(result.orders)}")
        print(f"  成交总数: {len(result.fills)}")
        
        # 分析持仓变化
        if result.portfolio_daily:
            print(f"\n净值曲线分析:")
            print(f"  净值记录数: {len(result.portfolio_daily)}")
            
            # 检查净值变化
            first_day = result.portfolio_daily[0]
            last_day = result.portfolio_daily[-1]
            
            print(f"  第一天净值: ${first_day.get('equity', 0):,.2f}")
            print(f"  最后一天净值: ${last_day.get('equity', 0):,.2f}")
            print(f"  第一天现金: ${first_day.get('cash', 0):,.2f}")
            print(f"  最后一天现金: ${last_day.get('cash', 0):,.2f}")
            
            # 检查现金和权益的关系
            if last_day.get('cash', 0) < 0:
                print(f"  ⚠️ 最后一天现金为负: ${last_day.get('cash', 0):,.2f}")
            
            # 计算持仓价值
            positions_value = last_day.get('equity', 0) - last_day.get('cash', 0)
            print(f"  最后一天持仓价值: ${positions_value:,.2f}")
        
        # 分析交易详情
        if result.fills:
            print(f"\n交易详情分析:")
            
            # 按交易对分组
            trades_by_symbol = {}
            for fill in result.fills:
                symbol = fill.get('symbol', 'unknown')
                if symbol not in trades_by_symbol:
                    trades_by_symbol[symbol] = []
                trades_by_symbol[symbol].append(fill)
            
            print(f"  交易对分布:")
            for symbol, trades in trades_by_symbol.items():
                print(f"    {symbol}: {len(trades)} 次交易")
            
            # 检查交易价格
            print(f"\n  交易价格检查:")
            for i, fill in enumerate(result.fills[:5]):  # 只显示前5个
                print(f"    交易 #{i+1}: {fill.get('symbol', 'N/A')} "
                      f"{fill.get('side', 'N/A')} "
                      f"{fill.get('qty', 0):.2f} @ ${fill.get('price', 0):.2f}")
        
        # 保存详细结果
        with open("long_test_result.json", "w") as f:
            result_data = {
                "initial_capital": result.initial_capital,
                "final_equity": result.final_equity,
                "total_return_pct": result.total_return_pct,
                "calculated_return": calculated_return,
                "total_trades": result.total_trades,
                "win_rate": result.win_rate,
                "sharpe_ratio": result.sharpe_ratio,
                "signals_count": len(result.signals),
                "orders_count": len(result.orders),
                "fills_count": len(result.fills),
                "portfolio_daily_summary": {
                    "first_day": result.portfolio_daily[0] if result.portfolio_daily else None,
                    "last_day": result.portfolio_daily[-1] if result.portfolio_daily else None,
                    "count": len(result.portfolio_daily),
                },
                "trades_by_symbol": {symbol: len(trades) for symbol, trades in trades_by_symbol.items()} if result.fills else {},
            }
            json.dump(result_data, f, indent=2, default=str)
        
        print(f"\n详细结果已保存到: long_test_result.json")
        
    except Exception as e:
        print(f"回测失败: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    asyncio.run(test_long_backtest())