cfdaily
157 lines
5.3 KiB
Python
157 lines
5.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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本地执行回测 - 通过RPC连接vnpy回测引擎
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SingleStockStopLossStrategy with 15% stop loss
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510300.SSE 2021-01-01 ~ 2026-03-01
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Author: Guan Yu YunChang
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Date: 2026-03-31
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"""
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import sys
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import os
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import pickle
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import zmq
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from typing import Dict, Any
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# RPC配置 - 容器外连接（当前运行在容器外 NAS Mac mini 192.168.2.153）
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RPC_ENDPOINT = "tcp://192.168.2.154:8008"
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# 回测参数
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BACKTEST_CONFIG = {
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"strategy_name": "SingleStockStopLossStrategy",
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"description": "进阶多因子动态加权 + 关羽15%单票止损风控 - 510300.SSE",
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"symbol": "510300.SSE",
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"interval": "1d",
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"start": 1609459200,
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"end": 1772515200,
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"start_date": "2021-01-01",
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"end_date": "2026-03-01",
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"initial_capital": 1000000,
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"commission_fee": 3e-5,
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"slippage": 0.002,
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"contract_size": 10000,
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"price_tick": 0.001,
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"data_source": "sqlite",
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"strategy_module_path": os.path.abspath("main_strategy_single_file.py"),
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"strategy_class": "MultiFactorDynamicStrategy",
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"stop_loss_pct": 0.15,
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"parameters": {
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"stop_loss_pct": 0.15,
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"enabled": True
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}
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}
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def main():
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"""主函数"""
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print("=" * 60)
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print("SingleStockStopLossStrategy 回测 - 本地RPC执行")
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print("标的: 510300.SSE 沪深300ETF")
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print("区间: 2021-01-01 ~ 2026-03-01")
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print("止损: 15%")
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print("初始资金: 1,000,000")
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print("=" * 60)
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# 读取策略代码
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code_path = os.path.join(os.path.dirname(__file__), "main_strategy_single_file.py")
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with open(code_path, 'r', encoding='utf-8') as f:
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strategy_code = f.read()
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BACKTEST_CONFIG["strategy_code"] = strategy_code
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# 连接RPC
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context = zmq.Context()
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socket = context.socket(zmq.REQ)
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socket.connect(RPC_ENDPOINT)
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print(f"\n连接RPC服务器: {RPC_ENDPOINT}")
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try:
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# 发送回测请求 - 使用pickle序列化匹配服务器端
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print("发送回测请求... (pickle)")
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socket.send_pyobj(BACKTEST_CONFIG)
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# 接收响应
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print("等待回测结果... (全区间回测需要几分钟，请耐心等待)")
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result = socket.recv_pyobj()
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# 检查结果
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if isinstance(result, dict):
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if result.get("code") == 200 or result.get("status") == "ok" or "metrics" in result:
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print("\n✅ 回测成功完成!")
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else:
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print(f"\n❌ 回测失败: {result.get('msg') or result.get('error')}")
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print(f"详细信息: {result}")
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sys.exit(1)
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else:
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print(f"\n❌ 返回结果不是字典: {type(result)}")
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print(f"结果: {result}")
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sys.exit(1)
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# 保存结果
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output_file = os.path.join(
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os.path.dirname(__file__),
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f"backtest_result_510300_stoploss_{int(BACKTEST_CONFIG['stop_loss_pct'] * 100)}.json"
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)
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# 保存为json方便查看
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with open(output_file, 'w', encoding='utf-8') as f:
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import json
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json.dump(result, f, indent=2, ensure_ascii=False)
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print(f"\n💾 完整结果已保存到: {output_file}")
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# 打印关键指标
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print("\n" + "=" * 60)
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print("📊 回测结果摘要")
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print("=" * 60)
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metrics = result.get('metrics') or result.get('data', {}).get('metrics', result)
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print(f"\n📋 基本信息:")
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print(f" 策略名称: {BACKTEST_CONFIG['strategy_name']}")
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print(f" 标的: {BACKTEST_CONFIG['symbol']} 沪深300ETF")
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print(f" 回测区间: {BACKTEST_CONFIG['start_date']} ~ {BACKTEST_CONFIG['end_date']}")
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print(f" 初始资金: {BACKTEST_CONFIG['initial_capital']:,}")
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print(f" 单票止损: {BACKTEST_CONFIG['stop_loss_pct'] * 100:.0f}%")
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print(f"\n📈 绩效指标:")
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def print_metric(key, name, fmt="{:.2%}"):
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if key in metrics:
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print(f" {name}: {fmt.format(metrics[key])}")
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elif key.lower() in metrics:
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print(f" {name}: {fmt.format(metrics[key.lower()])}")
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print_metric('total_return', '总收益率')
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print_metric('annual_return', '年化收益率')
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print_metric('max_drawdown', '最大回撤')
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print_metric('sharpe_ratio', '夏普比率', '{:.2f}')
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print_metric('calmar_ratio', '卡玛比率', '{:.2f}')
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print_metric('sortino_ratio', '索提诺比率', '{:.2f}')
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print_metric('win_rate', '胜率')
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print_metric('profit_loss_ratio', '盈亏比', '{:.2f}')
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print(f"\n⚠️ 交易统计:")
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if 'total_trades' in metrics:
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print(f" 总交易次数: {metrics['total_trades']} 次")
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if 'stop_loss_count' in metrics or 'stop_loss_triggered' in metrics:
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print(f" 触发止损次数: {metrics.get('stop_loss_count') or metrics.get('stop_loss_triggered', 0)} 次")
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if 'final_capital' in metrics:
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print(f" 最终资金: {metrics['final_capital']:,.2f}")
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print("\n" + "=" * 60)
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print("回测完成!")
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return result
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except Exception as e:
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print(f"\n❌ 执行异常: {type(e).__name__}: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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