1、trend + hawks 策略
This commit is contained in:
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from datetime import timedelta
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from src.analysis.result_analyzer import ResultAnalyzer
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# 导入 TqsdkEngine，而不是原来的 BacktestEngine
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from src.indicators.indicators import RSI, HistoricalRange, BollingerBandwidth
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from src.tqsdk_real_engine import TqsdkEngine
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# 导入你的策略类
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from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy
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from tqsdk import TqApi, TqBacktest, TqAuth, TqKq, TqAccount
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# --- 配置参数 ---
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# Tqsdk 的本地数据文件路径，注意 Tqsdk 要求文件名为 KQ_m@交易所_品种名_周期.csv
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# 主力合约的 symbol
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symbol = 'KQ.m@CZCE.MA'
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strategy_parameters = {
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'main_symbol': 'MA', # 根据您的数据文件中的品种名称调整
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'trade_volume': 2,
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'lag': 7,
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# 'range_factor': 1.8, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 2.8, # 示例值
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# 'range_factor': 1.6, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 2.1, # 示例值
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'range_factor_l': 1.8, # 示例值，需要通过网格搜索优化
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'profit_factor_l': 2.8, # 示例值
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'range_factor_s': 1.6, # 示例值，需要通过网格搜索优化
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'profit_factor_s': 2.1, # 示例值
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'max_position': 10,
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'enable_log': True,
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'stop_loss_points': 20,
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'use_indicator': True,
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# 'indicator': HistoricalRange(11, 25, 20),
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# 'indicator': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),
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'indicator_l': HistoricalRange(11, 25, 20),
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'indicator_s': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),
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}
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# api = TqApi(TqKq(), auth=TqAuth("emanresu", "dfgvfgdfgg"))
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api = TqApi(TqAccount('H宏源期货', '903308830', 'lzr520102'), auth=TqAuth("emanresu", "dfgvfgdfgg"))
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# --- 1. 初始化回测引擎并运行 ---
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print("\n初始化 Tqsdk 回测引擎...")
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engine = TqsdkEngine(
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strategy_class=SimpleLimitBuyStrategy,
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strategy_params=strategy_parameters,
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api=api,
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symbol=symbol,
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duration_seconds=60 * 60,
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roll_over_mode=True, # 启用换月模式检测
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history_length=50,
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close_bar_delta=timedelta(minutes=58)
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)
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engine.run() # 这是一个同步方法，内部会运行 asyncio 循环
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File diff suppressed because one or more lines are too long
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import numpy as np
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import talib
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from typing import Optional, Dict, Any, List, Tuple, Union
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from src.algo.TrendLine import calculate_latest_trendline_values
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# 假设这些是你项目中的基础模块
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from src.core_data import Bar, Order
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from src.indicators.base_indicators import Indicator
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from src.indicators.indicators import Empty
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from src.strategies.base_strategy import Strategy
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class TrendlineBreakoutStrategy(Strategy):
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"""
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趋势线突破策略 V3 (优化版):
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1. 策略逻辑与 V2 相同，但趋势线计算被重构为一个独立的、
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高性能的辅助方法。
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2. 该方法只计算最新的趋势线值，避免不必要的数组生成。
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开仓信号:
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- 做多: 上一根收盘价上穿下趋势线
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- 做空: 上一根收盘价下穿上趋势线
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平仓逻辑:
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- 采用 ATR 滑动止损 (Trailing Stop)。
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"""
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def __init__(
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self,
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context: Any,
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main_symbol: str,
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trendline_n: int = 50,
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trade_volume: int = 1,
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order_direction: Optional[List[str]] = None,
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atr_period: int = 14,
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atr_multiplier: float = 1.0,
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enable_log: bool = True,
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indicators: Union[Indicator, List[Indicator]] = None,
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):
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super().__init__(context, main_symbol, enable_log)
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self.main_symbol = main_symbol
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self.trendline_n = trendline_n
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self.trade_volume = trade_volume
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self.order_direction = order_direction or ["BUY", "SELL"]
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self.atr_period = atr_period
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self.atr_multiplier = atr_multiplier
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self.pos_meta: Dict[str, Dict[str, Any]] = {}
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if indicators is None:
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indicators = [Empty(), Empty()]
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self.indicators = indicators
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if self.trendline_n < 3:
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raise ValueError("trendline_n 必须大于或等于 3")
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log_message = (
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f"TrendlineBreakoutStrategy (V3 Optimized) 初始化:\n"
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f"交易标的={self.main_symbol}, 交易量={self.trade_volume}\n"
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f"趋势线周期={self.trendline_n}, ATR周期={self.atr_period}, ATR倍数={self.atr_multiplier}"
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)
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self.log(log_message)
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def _calculate_atr(self, bar_history: List[Bar]) -> Optional[float]:
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# (此函数与上一版本完全相同，保持不变)
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if len(bar_history) < self.atr_period + 1: return None
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highs = np.array([b.high for b in bar_history])
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lows = np.array([b.low for b in bar_history])
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closes = np.array([b.close for b in bar_history])
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atr = talib.ATR(highs, lows, closes, timeperiod=self.atr_period)
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return atr[-1] if not np.isnan(atr[-1]) else None
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def on_init(self):
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super().on_init()
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self.pos_meta.clear()
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def on_open_bar(self, open_price: float, symbol: str):
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bar_history = self.get_bar_history()
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min_bars_required = self.trendline_n + 2
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if len(bar_history) < min_bars_required:
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return
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self.cancel_all_pending_orders(symbol)
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pos = self.get_current_positions().get(symbol, 0)
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# 1. 优先处理平仓逻辑 (逻辑不变)
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meta = self.pos_meta.get(symbol)
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if meta and pos != 0:
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current_atr = self._calculate_atr(bar_history[:-1])
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if current_atr:
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trailing_stop = meta['trailing_stop']
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direction = meta['direction']
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last_close = bar_history[-1].close
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if direction == "BUY":
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new_stop_level = last_close - current_atr * self.atr_multiplier
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trailing_stop = max(trailing_stop, new_stop_level)
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else: # SELL
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new_stop_level = last_close + current_atr * self.atr_multiplier
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trailing_stop = min(trailing_stop, new_stop_level)
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self.pos_meta[symbol]['trailing_stop'] = trailing_stop
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if (direction == "BUY" and open_price <= trailing_stop) or \
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(direction == "SELL" and open_price >= trailing_stop):
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self.log(f"ATR滑动止损触发: 价格 {open_price:.2f} 触及止损位 {trailing_stop:.2f}")
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self.send_market_order("CLOSE_LONG" if direction == "BUY" else "CLOSE_SHORT", abs(pos))
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del self.pos_meta[symbol]
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return
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# 2. 开仓逻辑 (调用优化后的方法)
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if pos == 0:
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prices_for_trendline = np.array([b.close for b in bar_history[-self.trendline_n - 1:-1]])
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# --- 调用新的独立方法 ---
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trendline_val_upper, trendline_val_lower = calculate_latest_trendline_values(prices_for_trendline)
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if trendline_val_upper is None or trendline_val_lower is None:
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return # 无法计算趋势线，跳过
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prev_close = bar_history[-2].close
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last_close = bar_history[-1].close
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current_atr = self._calculate_atr(bar_history[:-1])
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if not current_atr:
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return
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if "BUY" in self.order_direction and last_close > trendline_val_upper and self.indicators[0].is_condition_met(*self.get_indicator_tuple()):
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self.log(f"做多信号: Close({last_close:.2f}) 上穿下趋势线({trendline_val_upper:.2f})")
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self.send_open_order("BUY", open_price, self.trade_volume, current_atr)
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elif "SELL" in self.order_direction and last_close < trendline_val_lower and self.indicators[1].is_condition_met(*self.get_indicator_tuple()):
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self.log(f"做空信号: Close({last_close:.2f}) 下穿上趋势线({trendline_val_lower:.2f})")
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self.send_open_order("SELL", open_price, self.trade_volume, current_atr)
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# is_met = self.indicators[0].is_condition_met(*self.get_indicator_tuple())
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# if "BUY" in self.order_direction and last_close > trendline_val_upper and is_met:
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# self.log(f"做多信号: Close({last_close:.2f}) 上穿下趋势线({trendline_val_upper:.2f})")
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# self.send_open_order("BUY", open_price, self.trade_volume, current_atr)
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#
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# elif "SELL" in self.order_direction and last_close < trendline_val_lower and is_met:
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# self.log(f"做空信号: Close({last_close:.2f}) 下穿上趋势线({trendline_val_lower:.2f})")
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# self.send_open_order("SELL", open_price, self.trade_volume, current_atr)
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# prob = self.indicators[0].get_latest_value(*self.get_indicator_tuple())
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# if "BUY" in self.order_direction and last_close > trendline_val_upper:
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# if prob is None or prob[0] < prob[1]:
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# self.log(f"做多信号: Close({last_close:.2f}) 上穿下趋势线({trendline_val_upper:.2f})")
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# self.send_open_order("BUY", open_price, self.trade_volume, current_atr)
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#
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# elif "SELL" in self.order_direction and last_close < trendline_val_lower:
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# if prob is None or prob[2] < prob[3]:
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# self.log(f"做空信号: Close({last_close:.2f}) 下穿上趋势线({trendline_val_lower:.2f})")
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# self.send_open_order("SELL", open_price, self.trade_volume, current_atr)
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# send_open_order, send_market_order, on_rollover 等方法与上一版本完全相同，保持不变
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def send_open_order(self, direction: str, entry_price: float, volume: int, current_atr: float):
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if direction == "BUY":
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initial_stop = entry_price - current_atr * self.atr_multiplier
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else:
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initial_stop = entry_price + current_atr * self.atr_multiplier
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current_time = self.get_current_time()
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order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
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order_direction = "BUY" if direction == "BUY" else "SELL"
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order = Order(id=order_id, symbol=self.symbol, direction=order_direction, volume=volume, price_type="MARKET",
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submitted_time=current_time, offset="OPEN")
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self.send_order(order)
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self.pos_meta[self.symbol] = {"direction": direction, "volume": volume, "entry_price": entry_price,
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"trailing_stop": initial_stop}
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self.log(
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f"发送开仓订单: {direction} {volume}手 @ Market Price (执行价约 {entry_price:.2f}), 初始ATR止损位: {initial_stop:.2f}")
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def send_market_order(self, direction: str, volume: int):
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current_time = self.get_current_time()
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order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
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order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
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submitted_time=current_time, offset="CLOSE")
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self.send_order(order)
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self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
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def on_rollover(self, old_symbol: str, new_symbol: str):
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super().on_rollover(old_symbol, new_symbol)
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self.cancel_all_pending_orders(new_symbol)
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self.pos_meta.clear()
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File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
@@ -1,58 +0,0 @@
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from datetime import timedelta
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from src.analysis.result_analyzer import ResultAnalyzer
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# 导入 TqsdkEngine，而不是原来的 BacktestEngine
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from src.indicators.indicators import RSI, HistoricalRange, BollingerBandwidth
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from src.tqsdk_real_engine import TqsdkEngine
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# 导入你的策略类
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from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy
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from tqsdk import TqApi, TqBacktest, TqAuth, TqKq, TqAccount
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# --- 配置参数 ---
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# Tqsdk 的本地数据文件路径，注意 Tqsdk 要求文件名为 KQ_m@交易所_品种名_周期.csv
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# 主力合约的 symbol
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symbol = "KQ.m@CZCE.SA"
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strategy_parameters = {
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'main_symbol': "SA", # 根据您的数据文件中的品种名称调整
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'trade_volume': 1,
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# 'lag': 7,
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# 'lag': 1,
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# 'range_factor': 1.6, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 2.4, # 示例值
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# 'range_factor': 2.5, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 4.5, # 示例值
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'range_factor_l': 1.6, # 示例值，需要通过网格搜索优化
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'profit_factor_l': 2.4, # 示例值
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'range_factor_s': 2.5, # 示例值，需要通过网格搜索优化
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'profit_factor_s': 4.5, # 示例值
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'max_position': 10,
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'enable_log': True,
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'stop_loss_points': 10,
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'use_indicator': True,
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# 'indicator': RSI(window=25, down_bound=47, up_bound=67),
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# 'indicator': BollingerBandwidth(window=10, nbdev=1.5, down_bound=2.5, up_bound=6),
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'indicator_l': RSI(window=25, down_bound=47, up_bound=67),
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'indicator_s': BollingerBandwidth(window=10, nbdev=1.5, down_bound=2.5, up_bound=6),
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'lag_l': 7,
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'lag_s': 1,
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}
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# api = TqApi(TqKq(), auth=TqAuth("emanresu", "dfgvfgdfgg"))
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api = TqApi(TqAccount('H宏源期货', '903308830', 'lzr520102'), auth=TqAuth("emanresu", "dfgvfgdfgg"))
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# --- 1. 初始化回测引擎并运行 ---
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print("\n初始化 Tqsdk 回测引擎...")
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engine = TqsdkEngine(
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strategy_class=SimpleLimitBuyStrategy,
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strategy_params=strategy_parameters,
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api=api,
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symbol=symbol,
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duration_seconds=60 * 60,
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roll_over_mode=True, # 启用换月模式检测
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history_length=300,
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close_bar_delta=timedelta(minutes=58)
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)
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engine.run() # 这是一个同步方法，内部会运行 asyncio 循环
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@@ -1,59 +0,0 @@
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from datetime import timedelta
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from src.analysis.result_analyzer import ResultAnalyzer
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# 导入 TqsdkEngine，而不是原来的 BacktestEngine
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from src.indicators.indicators import RSI, HistoricalRange, BollingerBandwidth, NormalizedATR
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from src.tqsdk_real_engine import TqsdkEngine
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# 导入你的策略类
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from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy
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from tqsdk import TqApi, TqBacktest, TqAuth, TqKq, TqAccount
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# --- 配置参数 ---
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# Tqsdk 的本地数据文件路径，注意 Tqsdk 要求文件名为 KQ_m@交易所_品种名_周期.csv
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# 主力合约的 symbol
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symbol = "KQ.m@SHFE.fu"
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strategy_parameters = {
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'main_symbol': "fu", # 根据您的数据文件中的品种名称调整
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'trade_volume': 3,
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'lag': 7,
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# 'lag': 7,
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# 'range_factor': 0.7, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 0.3, # 示例值
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# 'range_factor': 1.9, # 示例值，需要通过网格搜索优化
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# 'profit_factor': 0.2, # 示例值
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'range_factor_l': 0.7, # 示例值，需要通过网格搜索优化
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'profit_factor_l': 0.3, # 示例值
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'range_factor_s': 1.9, # 示例值，需要通过网格搜索优化
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'profit_factor_s': 0.2, # 示例值
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'max_position': 10,
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'enable_log': True,
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'stop_loss_points': 20,
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'use_indicator': True,
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# 'indicator': HistoricalRange(shift_window=0, down_bound=5, up_bound=20),
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# 'indicator': RSI(window=5, down_bound=40, up_bound=70),
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# 'indicator': HistoricalRange(shift_window=0, down_bound=20, up_bound=100),
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'indicator_l': HistoricalRange(shift_window=0, down_bound=5, up_bound=20),
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'indicator_s': HistoricalRange(shift_window=0, down_bound=20, up_bound=100),
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# 'lag_l': 1,
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# 'lag_s': 7,
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}
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# api = TqApi(TqKq(), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
api = TqApi(TqAccount('H宏源期货', '903308830', 'lzr520102'), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
|
||||
# --- 1. 初始化回测引擎并运行 ---
|
||||
print("\n初始化 Tqsdk 回测引擎...")
|
||||
engine = TqsdkEngine(
|
||||
strategy_class=SimpleLimitBuyStrategy,
|
||||
strategy_params=strategy_parameters,
|
||||
api=api,
|
||||
symbol=symbol,
|
||||
duration_seconds=60 * 60,
|
||||
roll_over_mode=True, # 启用换月模式检测
|
||||
history_length=50,
|
||||
close_bar_delta=timedelta(minutes=58)
|
||||
)
|
||||
engine.run() # 这是一个同步方法，内部会运行 asyncio 循环
|
||||
@@ -1,55 +0,0 @@
|
||||
from datetime import timedelta
|
||||
from src.analysis.result_analyzer import ResultAnalyzer
|
||||
|
||||
# 导入 TqsdkEngine，而不是原来的 BacktestEngine
|
||||
from src.indicators.indicators import RSI, HistoricalRange
|
||||
from src.tqsdk_real_engine import TqsdkEngine
|
||||
|
||||
# 导入你的策略类
|
||||
from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy
|
||||
|
||||
from tqsdk import TqApi, TqBacktest, TqAuth, TqKq, TqAccount
|
||||
|
||||
# --- 配置参数 ---
|
||||
# Tqsdk 的本地数据文件路径，注意 Tqsdk 要求文件名为 KQ_m@交易所_品种名_周期.csv
|
||||
|
||||
# 主力合约的 symbol
|
||||
main_symbol = "KQ.m@DCE.jm"
|
||||
strategy_parameters = {
|
||||
'symbol': 'jm', # 根据您的数据文件中的品种名称调整
|
||||
'trade_volume': 1,
|
||||
'lag': 1,
|
||||
# 'range_factor': 1.3, # 示例值，需要通过网格搜索优化
|
||||
# 'profit_factor': 4.8, # 示例值
|
||||
# 'range_factor': 1.1, # 示例值，需要通过网格搜索优化
|
||||
# 'profit_factor': 4.9, # 示例值
|
||||
'range_factor_l': 1.3, # 示例值，需要通过网格搜索优化
|
||||
'profit_factor_l': 4.8, # 示例值
|
||||
'range_factor_s': 1.1, # 示例值，需要通过网格搜索优化
|
||||
'profit_factor_s': 4.9, # 示例值
|
||||
'max_position': 10,
|
||||
'enable_log': True,
|
||||
'stop_loss_points': 20,
|
||||
'use_indicator': True,
|
||||
# 'indicator': RSI(5, 63, 95),
|
||||
# 'indicator': RSI(5, 5, 25),
|
||||
'indicator_l': RSI(5, 63, 95),
|
||||
'indicator_s': RSI(5, 0, 100),
|
||||
}
|
||||
|
||||
# api = TqApi(TqKq(), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
api = TqApi(TqAccount('H宏源期货', '903308830', 'lzr520102'), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
|
||||
# --- 1. 初始化回测引擎并运行 ---
|
||||
print("\n初始化 Tqsdk 回测引擎...")
|
||||
engine = TqsdkEngine(
|
||||
strategy_class=SimpleLimitBuyStrategy,
|
||||
strategy_params=strategy_parameters,
|
||||
api=api,
|
||||
symbol=main_symbol,
|
||||
duration_seconds=60 * 60,
|
||||
roll_over_mode=True, # 启用换月模式检测
|
||||
history_length=50,
|
||||
close_bar_delta=timedelta(minutes=58)
|
||||
)
|
||||
engine.run() # 这是一个同步方法，内部会运行 asyncio 循环
|
||||
@@ -1,58 +0,0 @@
|
||||
from datetime import timedelta
|
||||
from src.analysis.result_analyzer import ResultAnalyzer
|
||||
|
||||
# 导入 TqsdkEngine，而不是原来的 BacktestEngine
|
||||
from src.indicators.indicators import RSI, HistoricalRange, BollingerBandwidth, NormalizedATR
|
||||
from src.tqsdk_real_engine import TqsdkEngine
|
||||
|
||||
# 导入你的策略类
|
||||
from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy
|
||||
|
||||
from tqsdk import TqApi, TqBacktest, TqAuth, TqKq, TqAccount
|
||||
|
||||
# --- 配置参数 ---
|
||||
# Tqsdk 的本地数据文件路径，注意 Tqsdk 要求文件名为 KQ_m@交易所_品种名_周期.csv
|
||||
|
||||
# 主力合约的 symbol
|
||||
symbol = "KQ.m@SHFE.rb"
|
||||
strategy_parameters = {
|
||||
'main_symbol': 'rb', # 根据您的数据文件中的品种名称调整
|
||||
'trade_volume': 2,
|
||||
# 'lag': 1,
|
||||
# 'lag': 7,
|
||||
# 'range_factor': 2.1, # 示例值，需要通过网格搜索优化
|
||||
# 'profit_factor': 1.6, # 示例值
|
||||
# 'range_factor': 0.2, # 示例值，需要通过网格搜索优化
|
||||
# 'profit_factor': 3.8, # 示例值
|
||||
'range_factor_l': 2.1, # 示例值，需要通过网格搜索优化
|
||||
'profit_factor_l': 1.6, # 示例值
|
||||
'range_factor_s': 0.2, # 示例值，需要通过网格搜索优化
|
||||
'profit_factor_s': 3.8, # 示例值
|
||||
'max_position': 10,
|
||||
'enable_log': True,
|
||||
'stop_loss_points': 20,
|
||||
'use_indicator': True,
|
||||
# 'indicator': RSI(14, 44, 70),
|
||||
# 'indicator': NormalizedATR(window=5, down_bound=0.62, up_bound=0.76),
|
||||
'indicator_l': RSI(14, 44, 70),
|
||||
'indicator_s': NormalizedATR(window=5, down_bound=0.62, up_bound=0.76),
|
||||
'lag_l': 1,
|
||||
'lag_s': 7,
|
||||
}
|
||||
|
||||
# api = TqApi(TqKq(), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
api = TqApi(TqAccount('H宏源期货', '903308830', 'lzr520102'), auth=TqAuth("emanresu", "dfgvfgdfgg"))
|
||||
|
||||
# --- 1. 初始化回测引擎并运行 ---
|
||||
print("\n初始化 Tqsdk 回测引擎...")
|
||||
engine = TqsdkEngine(
|
||||
strategy_class=SimpleLimitBuyStrategy,
|
||||
strategy_params=strategy_parameters,
|
||||
api=api,
|
||||
symbol=symbol,
|
||||
duration_seconds=60 * 60,
|
||||
roll_over_mode=True, # 启用换月模式检测
|
||||
history_length=50,
|
||||
close_bar_delta=timedelta(minutes=58)
|
||||
)
|
||||
engine.run() # 这是一个同步方法，内部会运行 asyncio 循环
|
||||
Reference in New Issue
Block a user