tqsdk实盘
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src/tqsdk_real_engine.py
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580
src/tqsdk_real_engine.py
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# filename: tqsdk_engine.py
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import asyncio
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from datetime import date, datetime, timedelta
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from typing import Literal, Type, Dict, Any, List, Optional
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import pandas as pd
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import uuid
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# 导入你提供的 core_data 中的类型
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from src.common_utils import is_bar_pre_close_period, is_futures_trading_time
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from src.core_data import Bar, Order, Trade, PortfolioSnapshot
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# 导入 Tqsdk 的核心类型
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import tqsdk
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from tqsdk import (
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TqApi,
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TqAccount,
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tafunc,
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TqSim,
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TqBacktest,
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TqAuth,
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TargetPosTask,
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BacktestFinished,
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)
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# 导入 TqsdkContext 和 BaseStrategy
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from src.tqsdk_real_context import TqsdkContext
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from src.strategies.base_strategy import Strategy # 假设你的策略基类在此路径
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BEIJING_TZ = "Asia/Shanghai"
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class TqsdkEngine:
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"""
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Tqsdk 回测引擎：协调 Tqsdk 数据流、策略执行、订单模拟和结果记录。
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替代原有的 BacktestEngine。
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"""
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def __init__(
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self,
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strategy_class: Type[Strategy],
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strategy_params: Dict[str, Any],
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api: TqApi,
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roll_over_mode: bool = False, # 是否开启换月模式检测
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symbol: str = None,
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duration_seconds: int = 1,
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history_length: int = 50,
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close_bar_delta: timedelta = None,
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):
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"""
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初始化 Tqsdk 回测引擎。
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Args:
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strategy_class (Type[Strategy]): 策略类。
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strategy_params (Dict[str, Any]): 传递给策略的参数字典。
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data_path (str): 本地 K 线数据文件路径，用于 TqSim 加载。
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initial_capital (float): 初始资金。
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slippage_rate (float): 交易滑点率（在 Tqsdk 中通常需要手动实现或通过费用设置）。
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commission_rate (float): 交易佣金率（在 Tqsdk 中通常需要手动实现或通过费用设置）。
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roll_over_mode (bool): 是否启用换月检测。
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start_time (Optional[datetime]): 回测开始时间。
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end_time (Optional[datetime]): 回测结束时间。
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"""
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self.strategy_class = strategy_class
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self.strategy_params = strategy_params
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self.roll_over_mode = roll_over_mode
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self.history_length = history_length
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self.close_bar_delta = close_bar_delta
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self.next_close_time = None
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# Tqsdk API 和模拟器
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# 这里使用 file_path 参数指定本地数据文件
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self._api: TqApi = api
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# 从策略参数中获取主symbol，TqsdkContext 需要知道它
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self.symbol: str = strategy_params.get("symbol")
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if not self.symbol:
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raise ValueError("strategy_params 必须包含 'symbol' 字段")
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# 获取 K 线数据（Tqsdk 自动处理）
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# 这里假设策略所需 K 线周期在 strategy_params 中，否则默认60秒（1分钟K线）
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self.bar_duration_seconds: int = strategy_params.get("bar_duration_seconds", 60)
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# self._main_kline_serial = self._api.get_kline_serial(
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# self.symbol, self.bar_duration_seconds
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# )
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# 初始化上下文
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self._context: TqsdkContext = TqsdkContext(api=self._api)
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# 实例化策略，并将上下文传递给它
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self._strategy: Strategy = self.strategy_class(
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context=self._context, **self.strategy_params
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)
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self._context.set_engine(
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self
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) # 将引擎自身传递给上下文，以便 Context 可以访问引擎属性
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self.portfolio_snapshots: List[PortfolioSnapshot] = []
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self.trade_history: List[Trade] = []
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self.all_bars: List[Bar] = [] # 收集所有处理过的Bar
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self.close_list: List[float] = []
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self.open_list: List[float] = []
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self.high_list: List[float] = []
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self.low_list: List[float] = []
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self.volume_list: List[float] = []
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self.last_processed_bar: Optional[Bar] = None
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self._is_rollover_bar: bool = False # 换月信号
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self._last_underlying_symbol = self.symbol # 用于检测主力合约换月
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self.klines = api.get_kline_serial(
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symbol, duration_seconds, data_length=history_length + 2
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)
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self.klines_1min = api.get_kline_serial(
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symbol, 60
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)
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self.now = None
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self.quote = None
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if roll_over_mode:
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self.quote = api.get_quote(symbol)
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self.kline_row = None
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print("TqsdkEngine: 初始化完成。")
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@property
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def is_rollover_bar(self) -> bool:
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"""
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属性：判断当前 K 线是否为换月 K 线（即检测到主力合约切换）。
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"""
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return self._is_rollover_bar
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def _process_queued_requests(self):
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"""
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异步处理 Context 中排队的订单和取消请求。
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"""
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# 处理订单
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while self._context.order_queue:
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order_to_send: Order = self._context.order_queue.popleft()
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print(f"Engine: 处理订单请求: {order_to_send}")
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# 映射 core_data.Order 到 Tqsdk 的订单参数
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tqsdk_direction = ""
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tqsdk_offset = ""
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if order_to_send.direction == "BUY":
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tqsdk_direction = "BUY"
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tqsdk_offset = order_to_send.offset or "OPEN" # 默认开仓
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elif order_to_send.direction == "SELL":
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tqsdk_direction = "SELL"
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tqsdk_offset = order_to_send.offset or "OPEN" # 默认开仓
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elif order_to_send.direction == "CLOSE_LONG":
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tqsdk_direction = "SELL"
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tqsdk_offset = order_to_send.offset or "CLOSE" # 平多，默认平仓
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elif order_to_send.direction == "CLOSE_SHORT":
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tqsdk_direction = "BUY"
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tqsdk_offset = order_to_send.offset or "CLOSE" # 平空，默认平仓
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else:
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print(f"Engine: 未知订单方向: {order_to_send.direction}")
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continue # 跳过此订单
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if "SHFE" in order_to_send.symbol:
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tqsdk_offset = "OPEN"
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try:
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tq_order = self._api.insert_order(
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symbol=order_to_send.symbol,
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direction=tqsdk_direction,
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offset=tqsdk_offset,
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volume=order_to_send.volume,
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# Tqsdk 市价单 limit_price 设为 None，限价单则传递价格
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limit_price=(
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order_to_send.limit_price
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if order_to_send.price_type == "LIMIT"
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# else self.quote.bid_price1 + (1 if tqsdk_direction == "BUY" else -1)
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else (
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self.quote.bid_price1
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if tqsdk_direction == "SELL"
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else self.quote.ask_price1
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)
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),
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)
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# 更新原始 Order 对象与 Tqsdk 的订单ID和状态
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order_to_send.id = tq_order.order_id
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# order_to_send.order_id = tq_order.order_id
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# order_to_send.status = tq_order.status
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order_to_send.submitted_time = pd.to_datetime(
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tq_order.insert_date_time, unit="ns", utc=True
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)
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# 等待订单状态更新（成交/撤销/报错）
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# 在 Tqsdk 中，订单和成交是独立的，通常在 wait_update() 循环中通过 api.is_changing() 检查
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# 这里为了模拟同步处理，直接等待订单状态最终确定
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# 注意：实际回测中，不应在这里长时间阻塞，而应在主循环中持续 wait_update
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# 为了简化适配，这里模拟即时处理，但可能与真实异步行为有差异。
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# 更健壮的方式是在主循环中通过订单状态回调更新
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# 这里我们假设订单会很快更新状态，或者在下一个 wait_update() 周期中被检测到
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self._api.wait_update() # 等待一次更新
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# # 检查最终订单状态和成交
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# if tq_order.status == "FINISHED":
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# # 查找对应的成交记录
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# for trade_id, tq_trade in self._api.get_trade().items():
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# if tq_trade.order_id == tq_order.order_id and tq_trade.volume > 0: # 确保是实际成交
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# # 创建 core_data.Trade 对象
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# trade = Trade(
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# order_id=tq_trade.order_id,
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# fill_time=tafunc.get_datetime_from_timestamp(tq_trade.trade_date_time) if tq_trade.trade_date_time else datetime.now(),
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# symbol=order_to_send.symbol, # 使用 Context 中的 symbol
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# direction=tq_trade.direction, # 实际成交方向
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# volume=tq_trade.volume,
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# price=tq_trade.price,
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# commission=tq_trade.commission,
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# cash_after_trade=self._api.get_account().available,
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# positions_after_trade=self._context.get_current_positions(),
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# realized_pnl=tq_trade.realized_pnl, # Tqsdk TqTrade 对象有 realized_pnl
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# is_open_trade=tq_trade.offset == "OPEN",
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# is_close_trade=tq_trade.offset in ["CLOSE", "CLOSETODAY", "CLOSEYESTERDAY"]
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# )
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# self.trade_history.append(trade)
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# print(f"Engine: 成交记录: {trade}")
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# break # 找到成交就跳出
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# order_to_send.status = tq_order.status # 更新最终状态
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except Exception as e:
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print(f"Engine: 发送订单 {order_to_send.id} 失败: {e}")
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# order_to_send.status = "ERROR"
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# 处理取消请求
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while self._context.cancel_queue:
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order_id_to_cancel = self._context.cancel_queue.popleft()
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print(f"Engine: 处理取消请求: {order_id_to_cancel}")
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tq_order_to_cancel = self._api.get_order(order_id_to_cancel)
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if tq_order_to_cancel and tq_order_to_cancel.status == "ALIVE":
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try:
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self._api.cancel_order(tq_order_to_cancel)
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self._api.wait_update() # 等待取消确认
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print(
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f"Engine: 订单 {order_id_to_cancel} 已尝试取消。当前状态: {tq_order_to_cancel.status}"
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)
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except Exception as e:
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print(f"Engine: 取消订单 {order_id_to_cancel} 失败: {e}")
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else:
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print(
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f"Engine: 订单 {order_id_to_cancel} 不存在或已非活动状态，无法取消。"
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)
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def _record_portfolio_snapshot(self, current_time: datetime):
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"""
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记录当前投资组合的快照。
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"""
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account: TqAccount = self._api.get_account()
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current_positions = self._context.get_current_positions()
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# 计算当前持仓市值
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total_market_value = 0.0
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current_prices: Dict[str, float] = {}
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for symbol, qty in current_positions.items():
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# 获取当前合约的最新价格
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quote = self._api.get_quote(symbol)
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if quote.last_price: # 确保价格是最近的
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price = quote.last_price
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current_prices[symbol] = price
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total_market_value += (
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price * qty * quote.volume_multiple
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) # volume_multiple 乘数
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else:
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# 如果没有最新价格，使用最近的K线收盘价作为估算
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# 在实盘或连续回测中，通常会有最新的行情
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print(f"警告: 未获取到 {symbol} 最新价格，可能影响净值计算。")
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# 可以尝试从 K 线获取最近价格
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kline = self._api.get_kline_serial(symbol, self.bar_duration_seconds)
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if not kline.empty:
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last_kline = kline.iloc[-2]
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price = last_kline.close
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current_prices[symbol] = price
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total_market_value += (
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price * qty * self._api.get_instrument(symbol).volume_multiple
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) # 使用 instrument 的乘数
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total_value = (
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account.available + account.frozen_margin + total_market_value
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) # Tqsdk 的 balance 已包含持仓市值和冻结资金
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# Tqsdk 的 total_profit/balance 已经包含了所有盈亏和资金
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snapshot = PortfolioSnapshot(
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datetime=current_time,
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total_value=account.balance, # Tqsdk 的 balance 包含了可用资金、冻结保证金和持仓市值
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cash=account.available,
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positions=current_positions,
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price_at_snapshot=current_prices,
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)
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self.portfolio_snapshots.append(snapshot)
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def _close_all_positions_at_end(self):
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"""
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回测结束时，平掉所有剩余持仓。
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"""
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current_positions = self._context.get_current_positions()
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if not current_positions:
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print("回测结束：没有需要平仓的持仓。")
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return
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print("回测结束：开始平仓所有剩余持仓...")
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for symbol, qty in current_positions.items():
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order_direction: Literal["BUY", "SELL"]
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if qty > 0: # 多头持仓，卖出平仓
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order_direction = "SELL"
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else: # 空头持仓，买入平仓
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order_direction = "BUY"
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TargetPosTask(self._api, symbol).set_target_volume(0)
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# # 使用市价单快速平仓
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# tq_order = self._api.insert_order(
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# symbol=symbol,
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# direction=order_direction,
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# offset="CLOSE", # 平仓
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# volume=abs(qty),
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# limit_price=self
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# )
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# print(f"平仓订单已发送: {symbol} {order_direction} {abs(qty)} 手")
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# 等待订单完成
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# while tq_order.status == "ALIVE":
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# self._api.wait_update()
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# if tq_order.status == "FINISHED":
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# print(f"订单 {tq_order.order_id} 平仓完成。")
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# else:
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# print(f"订单 {tq_order.order_id} 平仓失败或未完成，状态: {tq_order.status}")
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def _run_async(self):
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"""
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异步运行回测的主循环。
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"""
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print(f"TqsdkEngine: 开始加载历史数据，加载k线数量{self.history_length}")
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self._strategy.trading = False
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is_trading_time = is_futures_trading_time()
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for i in range(self.history_length + 1, 0 if not is_trading_time else 1, -1):
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kline_row = self.klines.iloc[-i]
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kline_dt = pd.to_datetime(kline_row.datetime, unit="ns", utc=True)
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kline_dt = kline_dt.tz_convert(BEIJING_TZ)
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self.main(kline_row, self.klines.iloc[-i - 1])
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print(f"TqsdkEngine: 加载历史k线完成, bars数量:{len(self.all_bars)},last bar datetime:{self.all_bars[-1].datetime}")
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self._strategy.trading = True
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self._last_underlying_symbol = self.quote.underlying_symbol
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print(
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f"TqsdkEngine: self._last_underlying_symbol:{self._last_underlying_symbol}, is_trading_time:{is_trading_time}"
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)
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# 初始化策略 (如果策略有 on_init 方法)
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if hasattr(self._strategy, "on_init"):
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self._strategy.on_init()
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if is_trading_time:
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print(f"TqsdkEngine: 当前是交易时间，处理最新一根k线")
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kline_row = self.klines.iloc[-1]
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kline_dt = pd.to_datetime(kline_row.datetime, unit="ns", utc=True)
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kline_dt = kline_dt.tz_convert(BEIJING_TZ)
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self.kline_row = kline_row
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self.main(self.klines.iloc[-1], self.klines.iloc[-2])
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# 迭代 K 线数据
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# 使用 self._api.get_kline_serial 获取到的 K 线是 Pandas DataFrame，
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# 直接迭代其行（Bar）更符合回测逻辑
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print(f"TqsdkEngine: 开始等待最新数据")
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while True:
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# Tqsdk API 的 wait_update() 确保数据更新
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self._api.wait_update()
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if self.roll_over_mode and (
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self._api.is_changing(self.quote, "underlying_symbol")
|
||||
or self._last_underlying_symbol != self.quote.underlying_symbol
|
||||
):
|
||||
self._last_underlying_symbol = self.quote.underlying_symbol
|
||||
|
||||
if self._api.is_changing(self.klines_1min.iloc[-1], "datetime"):
|
||||
kline_dt = pd.to_datetime(self.kline_row.datetime, unit="ns", utc=True)
|
||||
kline_dt = kline_dt.tz_convert(BEIJING_TZ)
|
||||
|
||||
is_close_bar = is_bar_pre_close_period(kline_dt, int(self.kline_row.duration), pre_close_minutes=3)
|
||||
|
||||
if is_close_bar:
|
||||
print(f'TqsdkEngine: close bar, kline_dt:{kline_dt}, now: {datetime.now()}')
|
||||
self.close_bar(kline_row)
|
||||
|
||||
if self._api.is_changing(self.klines.iloc[-1], "datetime"):
|
||||
kline_row = self.klines.iloc[-1]
|
||||
kline_dt = pd.to_datetime(kline_row.datetime, unit="ns", utc=True)
|
||||
kline_dt = kline_dt.tz_convert(BEIJING_TZ)
|
||||
self.kline_row = kline_row
|
||||
|
||||
print(
|
||||
f"TqsdkEngine: 新k线产生,k line datetime:{kline_dt}, now: {datetime.now()}"
|
||||
)
|
||||
self.main(kline_row, self.klines.iloc[-2])
|
||||
|
||||
def close_bar(self, kline_row):
|
||||
kline_dt = pd.to_datetime(kline_row.datetime, unit="ns", utc=True)
|
||||
kline_dt = kline_dt.tz_convert(BEIJING_TZ)
|
||||
if len(self.all_bars) > 0:
|
||||
# 创建 core_data.Bar 对象
|
||||
current_bar = Bar(
|
||||
datetime=kline_dt,
|
||||
symbol=self._last_underlying_symbol,
|
||||
open=kline_row.open,
|
||||
high=kline_row.high,
|
||||
low=kline_row.low,
|
||||
close=kline_row.close,
|
||||
volume=kline_row.volume,
|
||||
open_oi=kline_row.open_oi,
|
||||
close_oi=kline_row.close_oi,
|
||||
)
|
||||
self.all_bars[-1] = current_bar
|
||||
|
||||
self.close_list[-1] = current_bar.close
|
||||
self.open_list[-1] = current_bar.open
|
||||
self.high_list[-1] = current_bar.high
|
||||
self.low_list[-1] = current_bar.low
|
||||
self.volume_list[-1] = current_bar.volume
|
||||
|
||||
self.last_processed_bar = current_bar
|
||||
|
||||
if self._strategy.trading is True:
|
||||
self._strategy.on_close_bar(current_bar)
|
||||
|
||||
# 处理订单和取消请求
|
||||
self._process_queued_requests()
|
||||
|
||||
def main(self, kline_row, prev_kline_row):
|
||||
if True:
|
||||
kline_dt = pd.to_datetime(prev_kline_row.datetime, unit="ns", utc=True)
|
||||
kline_dt = kline_dt.tz_convert(BEIJING_TZ)
|
||||
if len(self.all_bars) > 0:
|
||||
# 创建 core_data.Bar 对象
|
||||
current_bar = Bar(
|
||||
datetime=kline_dt,
|
||||
symbol=self._last_underlying_symbol,
|
||||
open=prev_kline_row.open,
|
||||
high=prev_kline_row.high,
|
||||
low=prev_kline_row.low,
|
||||
close=prev_kline_row.close,
|
||||
volume=prev_kline_row.volume,
|
||||
open_oi=prev_kline_row.open_oi,
|
||||
close_oi=prev_kline_row.close_oi,
|
||||
)
|
||||
self.all_bars[-1] = current_bar
|
||||
|
||||
self.close_list[-1] = current_bar.close
|
||||
self.open_list[-1] = current_bar.open
|
||||
self.high_list[-1] = current_bar.high
|
||||
self.low_list[-1] = current_bar.low
|
||||
self.volume_list[-1] = current_bar.volume
|
||||
|
||||
self.last_processed_bar = current_bar
|
||||
|
||||
# if self._strategy.trading is True:
|
||||
# self._strategy.on_close_bar(current_bar)
|
||||
|
||||
# # 处理订单和取消请求
|
||||
# self._process_queued_requests()
|
||||
|
||||
# on open bar --------------------------------------
|
||||
# 创建 core_data.Bar 对象
|
||||
kline_dt = pd.to_datetime(kline_row.datetime, unit="ns", utc=True)
|
||||
kline_dt = kline_dt.tz_convert(BEIJING_TZ)
|
||||
current_bar = Bar(
|
||||
datetime=kline_dt,
|
||||
symbol=self._last_underlying_symbol,
|
||||
open=kline_row.open,
|
||||
high=kline_row.high,
|
||||
low=kline_row.low,
|
||||
close=kline_row.close,
|
||||
volume=kline_row.volume,
|
||||
open_oi=kline_row.open_oi,
|
||||
close_oi=kline_row.close_oi,
|
||||
)
|
||||
|
||||
# 设置当前 Bar 到 Context
|
||||
self._context.set_current_bar(current_bar)
|
||||
|
||||
# Tqsdk 的 is_changing 用于判断数据是否有变化，对于回测遍历 K 线，每次迭代都算作新 Bar
|
||||
# 如果 kline_row.datetime 与上次不同，则认为是新 Bar
|
||||
if (
|
||||
self.roll_over_mode
|
||||
and self.last_processed_bar is not None
|
||||
and self._last_underlying_symbol != self.last_processed_bar.symbol
|
||||
and self._strategy.trading is True
|
||||
):
|
||||
self._is_rollover_bar = True
|
||||
print(
|
||||
f"TqsdkEngine: 检测到换月信号！从 {self._last_underlying_symbol} 切换到 {self.quote.underlying_symbol}"
|
||||
)
|
||||
self._close_all_positions_at_end()
|
||||
|
||||
self._strategy.cancel_all_pending_orders()
|
||||
|
||||
self._strategy.on_rollover(
|
||||
self.last_processed_bar.symbol, self._last_underlying_symbol
|
||||
)
|
||||
else:
|
||||
self._is_rollover_bar = False
|
||||
|
||||
self.all_bars.append(current_bar)
|
||||
|
||||
self.close_list.append(current_bar.close)
|
||||
self.open_list.append(current_bar.open)
|
||||
self.high_list.append(current_bar.high)
|
||||
self.low_list.append(current_bar.low)
|
||||
self.volume_list.append(current_bar.volume)
|
||||
|
||||
self.last_processed_bar = current_bar
|
||||
|
||||
# 调用策略的 on_bar 方法
|
||||
self._strategy.on_open_bar(current_bar)
|
||||
|
||||
# 处理订单和取消请求
|
||||
if self._strategy.trading is True:
|
||||
self._process_queued_requests()
|
||||
|
||||
# 记录投资组合快照
|
||||
self._record_portfolio_snapshot(current_bar.datetime)
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
同步调用异步回测主循环。
|
||||
"""
|
||||
try:
|
||||
self._run_async()
|
||||
except KeyboardInterrupt:
|
||||
print("\n回测被用户中断。")
|
||||
finally:
|
||||
self._api.close()
|
||||
print("TqsdkEngine: API 已关闭。")
|
||||
|
||||
def get_results(self) -> Dict[str, Any]:
|
||||
"""
|
||||
返回回测结果数据，供结果分析模块使用。
|
||||
"""
|
||||
final_portfolio_value = 0.0
|
||||
if self.portfolio_snapshots:
|
||||
final_portfolio_value = self.portfolio_snapshots[-1].total_value
|
||||
# else:
|
||||
# final_portfolio_value = self.initial_capital # 如果没有快照，则净值是初始资金
|
||||
|
||||
# total_return_percentage = (
|
||||
# (final_portfolio_value - self.initial_capital) / self.initial_capital
|
||||
# ) * 100 if self.initial_capital != 0 else 0.0
|
||||
|
||||
return {
|
||||
"portfolio_snapshots": self.portfolio_snapshots,
|
||||
"trade_history": self.trade_history,
|
||||
# "initial_capital": self.initial_capital,
|
||||
"all_bars": self.all_bars,
|
||||
"final_portfolio_value": final_portfolio_value,
|
||||
# "total_return_percentage": total_return_percentage,
|
||||
}
|
||||
|
||||
def get_bar_history(self):
|
||||
return self.all_bars
|
||||
|
||||
def get_price_history(self, key: str):
|
||||
if key == "close":
|
||||
return self.close_list
|
||||
elif key == "open":
|
||||
return self.open_list
|
||||
elif key == "high":
|
||||
return self.high_list
|
||||
elif key == "low":
|
||||
return self.low_list
|
||||
elif key == "volume":
|
||||
return self.volume_list
|
||||
Reference in New Issue
Block a user