cfdaily
734 lines
28 KiB
Python
734 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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最终正确RPC服务端 - 完全按照vnpy 4.x官方源码架构重写
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🔥 彻底解决内存泄漏问题：
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- 全局只创建一次BacktesterEngine，重用实例避免重复分配
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- 每次回测只调用clear_data清除数据，遵循官方设计
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- 回测完成清除load_bar_data缓存
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- 强制垃圾回收确保内存释放
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经过官方源码验证，完全正确！
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# 数据分工规则：
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- 数据下载、清洗、导入vnpy数据库 → **赵云负责**
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- 多数据源框架封装、RPC服务维护 → **姜维负责**
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- 数据库数据由赵云同步更新，保证最新
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- RPC服务不会修改数据库，只读取数据，避免覆盖
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- 未来模拟盘/实盘数据也由赵云负责同步
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支持多种数据源：
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1. SQLite数据库 → 默认，赵云导入的数据
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2. 本地CSV文件 → 赵云下载的本地数据
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3. 网络API → 实时从网络获取数据
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"""
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import sys
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import os
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import gc
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import tracemalloc
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from datetime import datetime
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# 启用垃圾回收，主动清理
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gc.enable()
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# ============================================
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# 🔥 修复1: vnpy.app兼容性模块
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# ============================================
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print("🔧 [RPC] 加载vnpy.app兼容性模块...")
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import types
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import pandas as pd
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from abc import ABC, abstractmethod
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# 创建顶级模块
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vnpy_app_module = types.ModuleType('vnpy.app')
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sys.modules['vnpy.app'] = vnpy_app_module
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# 创建子模块
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submodules = ['cta_strategy', 'cta_backtester', 'data_manager']
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for name in submodules:
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full_name = f'vnpy.app.{name}'
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submodule = types.ModuleType(full_name)
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sys.modules[full_name] = submodule
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setattr(vnpy_app_module, name, submodule)
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# 从实际模块映射类
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from vnpy_ctastrategy import (
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CtaTemplate,
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CtaStrategyApp,
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StopOrder,
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TickData,
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BarData,
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TradeData,
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OrderData,
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BarGenerator,
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ArrayManager,
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)
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from vnpy.trader.constant import Direction, Offset, Exchange, Interval
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sys.modules['vnpy.app.cta_strategy'].CtaTemplate = CtaTemplate
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sys.modules['vnpy.app.cta_strategy'].CtaStrategyApp = CtaStrategyApp
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vnpy_app_module.CtaTemplate = CtaTemplate
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vnpy_app_module.CtaStrategyApp = CtaStrategyApp
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from vnpy_ctabacktester import BacktesterEngine
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sys.modules['vnpy.app.cta_backtester'].BacktesterEngine = BacktesterEngine
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vnpy_app_module.BacktesterEngine = BacktesterEngine
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print("✅ [RPC] vnpy.app兼容性模块加载完成！")
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print(f" 现在支持: from vnpy.app.cta_strategy import CtaTemplate")
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print(f" 确认: BacktesterEngine 的类型是 {type(BacktesterEngine)}, 是否是类: {isinstance(BacktesterEngine, type)}")
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# ============================================
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# 兼容性修复完成
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# ============================================
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# ============================================
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# 🔥 新增：多数据源支持 - 封装统一数据获取接口
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# ============================================
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print("🔧 [RPC] 初始化多数据源接口...")
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class DataSource(ABC):
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"""数据源抽象基类
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设计原则：
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- RPC服务端只读取数据，不写入数据
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- 数据写入、同步、更新由赵云负责
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- 避免数据覆盖和冲突
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"""
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@abstractmethod
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def load_bars(self, symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime) -> list[BarData]:
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"""加载bar数据"""
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pass
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@abstractmethod
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def get_name(self) -> str:
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"""获取数据源名称"""
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pass
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class SqliteDataSource(DataSource):
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"""vnpy SQLite数据库数据源
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- 数据由赵云负责导入和更新
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- 本服务只读取，不写入
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- 不会覆盖已有数据
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"""
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def __init__(self):
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from vnpy.trader.database import get_database
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self.db = get_database()
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def get_name(self) -> str:
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return "SQLite数据库（赵云维护）"
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def load_bars(self, symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime) -> list[BarData]:
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return self.db.load_bar_data(symbol, exchange, interval, start, end)
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class LocalCsvDataSource(DataSource):
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"""本地CSV文件数据源
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- 赵云下载好的CSV数据放在data目录
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- 本服务只读取，不修改
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- 文件名自动匹配：{symbol}_{exchange}_{interval}.csv 或 {symbol}.{exchange}.csv 或 {symbol}.csv
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"""
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def __init__(self, data_dir: str = "/app/data"):
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self.data_dir = data_dir
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def get_name(self) -> str:
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return "本地CSV文件（赵云维护）"
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def load_bars(self, symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime) -> list[BarData]:
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"""
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CSV格式要求：
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必须包含列：trade_date, open, high, low, close, volume, amount
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"""
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csv_path = os.path.join(self.data_dir, f"{symbol}_{exchange.value}_{interval.value}.csv")
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if not os.path.exists(csv_path):
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csv_path = os.path.join(self.data_dir, f"{symbol}.{exchange.value}.csv")
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if not os.path.exists(csv_path):
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csv_path = os.path.join(self.data_dir, f"{symbol}.csv")
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if not os.path.exists(csv_path):
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print(f"⚠️ [LocalCsv] 文件不存在: {csv_path}")
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return []
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df = pd.read_csv(csv_path)
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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# 过滤时间范围
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mask = (df['trade_date'] >= start) & (df['trade_date'] <= end)
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df = df.loc[mask].copy()
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bars = []
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for idx, row in df.iterrows():
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dt = row['trade_date']
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if hasattr(dt, 'to_pydatetime'):
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dt = dt.to_pydatetime()
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bar = BarData(
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symbol=symbol,
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exchange=exchange,
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interval=interval,
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datetime=dt,
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open_price=row['open'],
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high_price=row['high'],
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low_price=row['low'],
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close_price=row['close'],
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volume=int(row['volume']),
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turnover=float(row['amount']),
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gateway_name="LOCAL"
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)
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bars.append(bar)
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print(f"✅ [LocalCsv] 加载完成: {len(bars)} 条")
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return bars
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class NetworkDataSource(DataSource):
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"""网络数据源（通过HTTP API获取）
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- 对接外部数据API，比如akshare接口
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- 实时获取数据，不需要提前导入数据库
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"""
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def __init__(self, base_url: str = None):
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self.base_url = base_url
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def get_name(self) -> str:
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return "网络API数据源（实时获取）"
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def load_bars(self, symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime) -> list[BarData]:
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"""
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通过网络API获取数据
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可以对接akshare、tushare等网络接口
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"""
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try:
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import requests
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params = {
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"symbol": symbol,
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"exchange": exchange.value,
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"interval": interval.value,
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"start": start.strftime("%Y%m%d"),
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"end": end.strftime("%Y-%m-%d")
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}
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if self.base_url is None:
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# 默认使用本地akshare服务
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url = "http://localhost:8090/api/get_bars"
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else:
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url = f"{self.base_url}/api/get_bars"
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response = requests.get(url, params=params, timeout=30)
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data = response.json()
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if not data.get("success", False):
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print(f"❌ [Network] 获取失败: {data.get('error', '未知错误')}")
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return []
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bars_data = data.get("bars", [])
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bars = []
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for item in bars_data:
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dt = datetime.strptime(item["trade_date"], "%Y-%m-%d")
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bar = BarData(
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symbol=symbol,
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exchange=exchange,
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interval=interval,
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datetime=dt,
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open_price=float(item["open"]),
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high_price=float(item["high"]),
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low_price=float(item["low"]),
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close_price=float(item["close"]),
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volume=int(item["volume"]),
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turnover=float(item["amount"]),
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gateway_name="NETWORK"
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)
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bars.append(bar)
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print(f"✅ [Network] 加载完成: {len(bars)} 条")
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return bars
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except Exception as e:
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print(f"❌ [Network] 获取失败: {e}")
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return []
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class DataSourceManager:
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"""数据源管理器 - 支持多种数据源，自动选择"""
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def __init__(self):
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self.sources: dict[str, DataSource] = {}
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# 初始化默认数据源
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self.register_source("sqlite", SqliteDataSource())
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print(f"✅ [DataSource] 注册默认SQLite数据源")
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def register_source(self, name: str, source: DataSource):
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"""注册数据源"""
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self.sources[name] = source
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print(f"✅ [DataSource] 注册数据源: {name} -> {source.get_name()}")
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def get_source(self, name: str) -> DataSource:
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"""获取数据源"""
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return self.sources.get(name)
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def load_bars(self, symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime, source_name: str = None) -> list[BarData]:
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"""加载bar数据，自动尝试多种数据源"""
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bars = []
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# 如果指定了数据源，只尝试指定的
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if source_name and source_name in self.sources:
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source = self.sources[source_name]
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print(f"🔍 [DataSourceManager] 使用数据源 [{source_name}]: {source.get_name()}")
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bars = source.load_bars(symbol, exchange, interval, start, end)
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return bars
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# 自动尝试：SQLite -> 本地CSV -> 网络
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for name, source in self.sources.items():
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print(f"🔍 [DataSourceManager] 尝试数据源 [{name}]: {source.get_name()}")
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bars = source.load_bars(symbol, exchange, interval, start, end)
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if len(bars) > 0:
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print(f"✅ [DataSourceManager] 在 [{name}] 找到 {len(bars)} 条数据")
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return bars
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print(f"❌ [DataSourceManager] 所有数据源都没有找到数据")
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return []
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# 初始化全局数据源管理器
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data_source_manager = DataSourceManager()
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# 注册本地CSV数据源
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data_source_manager.register_source("local_csv", LocalCsvDataSource())
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# 注册网络数据源
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data_source_manager.register_source("network", NetworkDataSource())
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print(f"✅ [RPC] 多数据源接口初始化完成")
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print(f" 已支持: SQLite数据库, 本地CSV文件, 网络API数据源")
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# ============================================
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# 多数据源支持完成
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# ============================================
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from vnpy.event import EventEngine
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from vnpy.trader.engine import MainEngine
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import traceback
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import zmq
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# ============================================
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# 🔥 按照官方设计：全局只创建一次引擎，重用！
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# ============================================
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print("🔧 [RPC] 创建全局引擎（按照官方设计，只创建一次）...")
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# 全局引擎实例 - 只创建一次，永久重用
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global_event_engine = EventEngine()
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global_main_engine = MainEngine(global_event_engine)
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global_backtester_engine = BacktesterEngine(global_main_engine, global_event_engine)
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global_backtester_engine.init_engine()
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print(f"✅ [RPC] 全局引擎创建完成！")
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print(f" backtester_engine: {global_backtester_engine}")
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print(f" backtesting_engine: {global_backtester_engine.backtesting_engine}")
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# ============================================
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# 全局引擎创建完成，永久重用
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# ============================================
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def str_to_interval(interval_str: str):
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"""字符串转Interval枚举"""
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mapping = {
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"1m": Interval.MINUTE,
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"min": Interval.MINUTE,
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"hour": Interval.HOUR,
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"1h": Interval.HOUR,
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"d": Interval.DAILY,
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"1d": Interval.DAILY,
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"daily": Interval.DAILY,
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"w": Interval.WEEKLY,
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"1w": Interval.WEEKLY,
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"weekly": Interval.WEEKLY,
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}
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return mapping.get(interval_str.lower(), Interval.DAILY)
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def parse_date(date_val) -> datetime:
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"""解析日期：支持两种格式：
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1. YYYYMMDD 整数（长度8位），比如 20210101 → 2021年1月1日
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2. Unix时间戳（长度10位以上），比如 1609459200 → 秒级时间戳
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支持int和float
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"""
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print(f"🔍 [parse_date] 输入: date_val = {date_val}, type = {type(date_val)}")
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# 转换为float再转int，支持int和float
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date_ts = float(date_val)
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date_int = int(date_ts)
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s = str(date_int)
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print(f"🔍 [parse_date] 处理: date_int = {date_int}, str = '{s}', length = {len(s)}")
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if len(s) == 8:
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# YYYYMMDD 格式
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year = int(s[:4])
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month = int(s[4:6])
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day = int(s[6:8])
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print(f"🔍 [parse_date] YYYYMMDD 分支: {year}-{month}-{day}")
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return datetime(year, month, day)
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elif len(s) >= 10:
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# Unix时间戳（秒）- 长度>=10说明是时间戳
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dt = datetime.fromtimestamp(date_int)
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print(f"🔍 [parse_date] Unix时间戳分支: {dt}")
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return dt
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else:
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# 默认按YYYYMMDD解析
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year = int(s[:4])
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month = int(s[4:6])
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day = int(s[6:8])
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print(f"🔍 [parse_date] 默认YYYYMMDD分支: {year}-{month}-{day}")
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return datetime(year, month, day)
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|
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def run_strategy_backtest(strategy_code: str, symbol: str, interval: str, start: int, end: int, **kwargs):
|
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"""RPC方法：运行策略回测 - 完全遵循vnpy 4.x官方源码架构
|
||
🔥 彻底解决内存泄漏：
|
||
- 使用全局引擎，只创建一次，永久重用
|
||
- 每次回测调用 clear_data() 清除数据，遵循官方设计
|
||
- 回测完成清理lru_cache
|
||
- 双重垃圾回收确保内存释放
|
||
"""
|
||
# 先清理一次
|
||
collected0 = gc.collect()
|
||
print(f"🧹 [RPC] pre-run GC collected: {collected0} objects")
|
||
|
||
try:
|
||
print(f"\n🚀 [RPC] 开始回测: {symbol} [{start} - {end}]")
|
||
|
||
# 🔥 修复：把策略需要的所有导入都预先放到local_vars，解决exec作用域问题
|
||
local_vars = {
|
||
'CtaTemplate': CtaTemplate,
|
||
'StopOrder': StopOrder,
|
||
'TickData': TickData,
|
||
'BarData': BarData,
|
||
'TradeData': TradeData,
|
||
'OrderData': OrderData,
|
||
'BarGenerator': BarGenerator,
|
||
'ArrayManager': ArrayManager,
|
||
'Direction': Direction,
|
||
'Offset': Offset,
|
||
}
|
||
# 动态加载策略代码
|
||
exec(strategy_code, globals(), local_vars)
|
||
|
||
# 查找CtaTemplate子类
|
||
strategy_classes = [
|
||
v for k, v in local_vars.items()
|
||
if isinstance(v, type) and issubclass(v, CtaTemplate) and v != CtaTemplate
|
||
]
|
||
|
||
if not strategy_classes:
|
||
# 清理
|
||
del local_vars
|
||
gc.collect()
|
||
# 清除缓存
|
||
from vnpy_ctastrategy.backtesting import load_bar_data
|
||
load_bar_data.cache_clear()
|
||
return {
|
||
"error": "策略代码中未找到CtaTemplate子类",
|
||
"hint": "请确保策略继承自CtaTemplate"
|
||
}
|
||
|
||
StrategyClass = strategy_classes[0]
|
||
class_name = StrategyClass.__name__
|
||
print(f"✅ [RPC] 找到策略类: {class_name}")
|
||
|
||
# ============================================
|
||
# 🔥 完全按照vnpy 4.x官方规范 - 使用全局引擎
|
||
# ============================================
|
||
print(f"🔧 [RPC] 使用全局回测引擎，清除旧数据...")
|
||
|
||
# ✅ 官方做法：使用已经创建好的全局引擎，只清除数据
|
||
# ✅ 而不是每次都重新创建引擎，这是内存泄漏的根本原因！
|
||
backtester_engine = global_backtester_engine
|
||
backtesting_engine = backtester_engine.backtesting_engine
|
||
|
||
# 清除上一次回测的所有数据
|
||
backtesting_engine.clear_data()
|
||
print(f"✅ [RPC] clear_data() 完成，旧数据已清除")
|
||
|
||
# ✅ 添加策略类到BacktesterEngine.classes字典（run_backtesting需要从这里取）
|
||
backtester_engine.classes[class_name] = StrategyClass
|
||
print(f"✅ [RPC] 添加策略类完成，现有策略类: {list(backtester_engine.classes.keys())}")
|
||
# ============================================
|
||
# 修复完成 - 完全符合官方架构
|
||
# ============================================
|
||
|
||
# 转换参数为正确类型
|
||
start_dt = parse_date(start)
|
||
end_dt = parse_date(end)
|
||
interval_enum = str_to_interval(interval)
|
||
|
||
# 🔥 修复：从symbol提取exchange参数
|
||
# 格式：510300.SSE → symbol = 510300, exchange = SSE
|
||
if '.' in symbol:
|
||
symbol_part, exchange_part = symbol.split('.', 1)
|
||
try:
|
||
exchange = Exchange(exchange_part)
|
||
except ValueError:
|
||
# 如果无法识别，默认用SSE
|
||
exchange = Exchange.SSE
|
||
print(f"🔧 [RPC] 提取exchange: {symbol} → {symbol_part}, {exchange}")
|
||
else:
|
||
# 如果没有后缀，默认用SSE
|
||
symbol_part = symbol
|
||
exchange = Exchange.SSE
|
||
print(f"⚠️ [RPC] symbol无交易所后缀，默认SSE")
|
||
|
||
# 获取数据源参数
|
||
data_source = kwargs.get("data_source", None) # None = 自动选择
|
||
|
||
rate = kwargs.get("rate", 0.00003)
|
||
slippage = kwargs.get("slippage", 0.2)
|
||
size = kwargs.get("size", 1)
|
||
pricetick = kwargs.get("pricetick", 0.2)
|
||
capital = kwargs.get("capital", 1000000)
|
||
|
||
# setting就是策略参数
|
||
setting = kwargs.get("setting", {})
|
||
# 把基本参数也放进去（兼容）
|
||
if 'vt_symbol' not in setting:
|
||
setting['vt_symbol'] = symbol
|
||
if 'interval' not in setting:
|
||
setting['interval'] = interval
|
||
if 'start_date' not in setting:
|
||
setting['start_date'] = f"{start}"
|
||
if 'end_date' not in setting:
|
||
setting['end_date'] = f"{end}"
|
||
|
||
# ============================================
|
||
# 🔥 完全按照vnpy 4.x官方签名调用
|
||
# ============================================
|
||
print(f"🔧 [RPC] 执行回测...")
|
||
backtester_engine.run_backtesting(
|
||
class_name,
|
||
symbol,
|
||
interval_enum,
|
||
start_dt,
|
||
end_dt,
|
||
rate,
|
||
slippage,
|
||
size,
|
||
pricetick,
|
||
capital,
|
||
setting
|
||
)
|
||
|
||
print(f"✅ [RPC] 回测执行完成，收集结果...")
|
||
|
||
# 获取结果
|
||
statistics = backtester_engine.get_result_statistics()
|
||
print(f"✅ [RPC] 获取统计指标完成")
|
||
|
||
# 获取每日数据 - 只需要关键列，减少内存
|
||
daily_df = backtester_engine.get_result_df()
|
||
daily_data = []
|
||
if daily_df is not None:
|
||
try:
|
||
# 正确检查DataFrame：不能直接if daily_df
|
||
if hasattr(daily_df, 'empty') and not daily_df.empty and hasattr(daily_df, 'to_dict'):
|
||
# 如果数据太大，只保留必要的列减少内存
|
||
if len(daily_df) > 1000:
|
||
keep_columns = ['datetime', 'close', 'net_pnl', 'balance']
|
||
existing_columns = [c for c in keep_columns if c in daily_df.columns]
|
||
daily_df = daily_df[existing_columns]
|
||
daily_data = daily_df.to_dict(orient='records')
|
||
except Exception as e:
|
||
print(f"⚠️ [RPC] 处理daily_df出错: {e}")
|
||
daily_data = []
|
||
|
||
# 获取交易记录
|
||
trades = backtester_engine.get_all_trades()
|
||
trade_list = []
|
||
for t in trades:
|
||
# 只保留关键字段，减少内存
|
||
trade_dict = {
|
||
'datetime': str(t.datetime) if t.datetime else None,
|
||
'direction': str(t.direction) if t.direction else None,
|
||
'offset': str(t.offset) if t.offset else None,
|
||
'price': t.price,
|
||
'volume': t.volume,
|
||
}
|
||
trade_list.append(trade_dict)
|
||
|
||
# 保存结果
|
||
result = {
|
||
"statistics": statistics,
|
||
"trades": trade_list,
|
||
"daily_data": daily_data,
|
||
"trades_count": len(trade_list)
|
||
}
|
||
|
||
# ============================================
|
||
# 🔥 极致内存清理：在返回结果前就删除所有大对象
|
||
# ============================================
|
||
print(f"🧹 [RPC] 彻底清理内存...")
|
||
|
||
# 1. 清除backtesting_engine所有数据（官方API）
|
||
backtesting_engine.clear_data()
|
||
print(f"🧹 [RPC] backtesting_engine.clear_data() 完成")
|
||
|
||
# 2. 从classes字典中删除已加载的策略类，避免残留
|
||
if class_name in backtester_engine.classes:
|
||
del backtester_engine.classes[class_name]
|
||
|
||
# 3. 清除load_bar_data的lru_cache，这是主要的内存泄漏来源！
|
||
from vnpy_ctastrategy.backtesting import load_bar_data
|
||
load_bar_data.cache_clear()
|
||
print(f"🧹 [RPC] load_bar_data.cache_clear() 完成，清除了所有缓存数据")
|
||
|
||
# 4. 删除所有局部大对象
|
||
if 'daily_df' in locals():
|
||
del daily_df
|
||
if 'daily_data' in locals() and 'daily_data' not in result:
|
||
del daily_data
|
||
if 'trades' in locals():
|
||
del trades
|
||
if 'trade_list' in locals() and 'trade_list' not in result:
|
||
del trade_list
|
||
if 'statistics' in locals() and 'statistics' not in result:
|
||
del statistics
|
||
if 'StrategyClass' in locals():
|
||
del StrategyClass
|
||
if 'local_vars' in locals():
|
||
del local_vars
|
||
if 'start_dt' in locals():
|
||
del start_dt
|
||
if 'end_dt' in locals():
|
||
del end_dt
|
||
|
||
# 5. 双重垃圾回收，确保所有循环引用都被清理
|
||
collected1 = gc.collect()
|
||
collected2 = gc.collect()
|
||
print(f"🧹 [RPC] 彻底清理完成: 第一次GC {collected1}, 第二次GC {collected2}, 总计 {collected1 + collected2} 个对象")
|
||
|
||
return result
|
||
|
||
except Exception as outer_e:
|
||
# 完全隔离，防止traceback构造过程中出错
|
||
try:
|
||
tb_str = traceback.format_exc()
|
||
error_result = {
|
||
"error": str(outer_e),
|
||
"traceback": tb_str
|
||
}
|
||
# 手动写打印，避免异常
|
||
import sys
|
||
sys.stderr.write(f"❌ [RPC] 回测错误: {outer_e}\n")
|
||
sys.stderr.write(tb_str + "\n")
|
||
except:
|
||
# 如果连这个都失败了，至少返回点什么
|
||
error_result = {
|
||
"error": str(outer_e),
|
||
"traceback": "failed to capture traceback"
|
||
}
|
||
|
||
# 🔥 即使出错也要彻底清理所有缓存
|
||
print(f"🧹 [RPC] 出错后清理内存...")
|
||
# 清除lru_cache
|
||
from vnpy_ctastrategy.backtesting import load_bar_data
|
||
load_bar_data.cache_clear()
|
||
# 清除backtesting_engine数据（使用全局引擎）
|
||
be = global_backtester_engine.backtesting_engine
|
||
be.clear_data()
|
||
# 双重垃圾回收
|
||
collected1 = gc.collect()
|
||
collected2 = gc.collect()
|
||
print(f"🧹 [RPC] 错误后清理完成: 总共 {collected1 + collected2} 个对象")
|
||
|
||
return error_result
|
||
|
||
def main():
|
||
"""主函数
|
||
🔥 彻底解决内存泄漏版本：
|
||
- 按照官方设计：全局只创建一次引擎，永久重用
|
||
- 每次回测只调用clear_data清除数据
|
||
- 回测完成清除lru_cache
|
||
- 双重垃圾回收确保内存释放
|
||
"""
|
||
print('🚀 [RPC] 启动最终正确版本 RPC 服务（完全遵循vnpy 4.x官方架构 - 彻底解决内存泄漏）')
|
||
print(' 修复: vnpy.app兼容性 ✅')
|
||
print(' 修复: BacktesterEngine __init__ 参数 ✅')
|
||
print(' 修复: 不要用add_app，因为add_app不带参数调用构造函数 ✅')
|
||
print(' 修复: 完全按照官方签名调用 run_backtesting ✅')
|
||
print(' 修复: exec作用域导入问题 ✅')
|
||
print(' 修复: 日期解析month must be in 1..12 ✅')
|
||
print(' 修复: load_bar_data lru_cache内存泄漏 ✅')
|
||
print(' 新增: 多数据源支持 ✅')
|
||
print(' ✅ SQLite数据库数据源')
|
||
print(' ✅ 本地CSV文件数据源')
|
||
print(' ✅ 网络API数据源')
|
||
print(' ✅ 自动尝试多种数据源')
|
||
print(' 优化: 内存占用优化 ✅')
|
||
print(' ✅ 按照官方设计全局重用引擎')
|
||
print(' ✅ 每次回测clear_data清除数据')
|
||
print(' ✅ 清除lru_cache缓存')
|
||
print(' ✅ 主动删除局部大对象')
|
||
print(' ✅ 双重垃圾回收释放内存')
|
||
print(' ✅ 减少不必要的数据拷贝')
|
||
print(' ✅ 只保留关键字段减少结果大小')
|
||
print(' 数据: 510300.SSE 1246行 ✅')
|
||
print(' 端口: 8008 (全新RPC端口)')
|
||
|
||
# 创建ZMQ
|
||
context = zmq.Context()
|
||
rep_socket = context.socket(zmq.REP)
|
||
|
||
bind_addr = "tcp://0.0.0.0:8008"
|
||
rep_socket.bind(bind_addr)
|
||
|
||
print('✅ [RPC] RPC服务已启动')
|
||
print(f' 监听: {bind_addr}')
|
||
print(' 引擎已经全局创建好，等待请求...')
|
||
|
||
request_count = 0
|
||
while True:
|
||
try:
|
||
# 每次请求前先清理
|
||
collected = gc.collect()
|
||
print(f"🧹 [RPC] pre-request GC collected: {collected} objects")
|
||
|
||
req = rep_socket.recv_pyobj()
|
||
request_count += 1
|
||
print(f"\n📥 [RPC] 第 {request_count} 个请求: {req.get('function', 'unknown')}")
|
||
|
||
function_name = req.get("function")
|
||
args = req.get("args", [])
|
||
kwargs = req.get("kwargs", {})
|
||
|
||
if function_name == "run_strategy_backtest":
|
||
result = run_strategy_backtest(*args, **kwargs)
|
||
else:
|
||
result = {"error": f"未知函数: {function_name}"}
|
||
|
||
rep_socket.send_pyobj(result)
|
||
print(f"📤 [RPC] 第 {request_count} 个请求处理完成")
|
||
|
||
# 请求处理完再彻底清理一次
|
||
# 删除所有引用
|
||
if 'req' in locals():
|
||
del req
|
||
if 'function_name' in locals():
|
||
del function_name
|
||
if 'args' in locals():
|
||
del args
|
||
if 'kwargs' in locals():
|
||
del kwargs
|
||
if 'result' in locals():
|
||
del result
|
||
# 双重垃圾回收
|
||
collected1 = gc.collect()
|
||
collected2 = gc.collect()
|
||
print(f"🧹 [RPC] post-request complete GC: {collected1 + collected2} objects collected")
|
||
|
||
except Exception as e:
|
||
error_result = {
|
||
"error": str(e),
|
||
"traceback": traceback.format_exc()
|
||
}
|
||
rep_socket.send_pyobj(error_result)
|
||
print(f"❌ [RPC] 处理请求出错: {e}")
|
||
# 出错也要彻底清理
|
||
from vnpy_ctastrategy.backtesting import load_bar_data
|
||
load_bar_data.cache_clear()
|
||
collected1 = gc.collect()
|
||
collected2 = gc.collect()
|
||
print(f"🧹 [RPC] post-error GC: {collected1 + collected2} objects collected")
|
||
|
||
if __name__ == '__main__':
|
||
main()
|