246 lines
11 KiB
Python
246 lines
11 KiB
Python
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import numpy as np
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import pandas as pd
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import talib
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from collections import deque
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from typing import Optional, Any, List, Dict, Tuple
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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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# =============================================================================
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# 策略实现 (ImbalanceZFlowStrategy)
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# =============================================================================
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class ImbalanceZFlowStrategy(Strategy):
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"""
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一个基于稳态核心指标“Z-Flow”的纯粹动量策略。
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核心哲学:
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1. 根本性解决稳态问题：先将原始imbalance序列通过Z-Score转化为
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稳态序列Z_I(t)，所有后续分析都基于此坚实基础。
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2. 核心驱动 Z_Flow: 对稳态的Z_I(t)序列计算MACD，以度量“统计
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显著性”的动量，形成一个高质量的稳态振荡器。
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3. 真实的回测逻辑：所有开仓和风险计算都严格基于当前bar的open_price，
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杜绝前视偏差。
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4. 极致简洁：策略由单一指标、两个对称阈值驱动，逻辑纯粹，鲁棒性强。
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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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enable_log: bool,
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trade_volume: int,
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# --- 【霍克斯过程参数】 ---
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hawkes_lookback: int = 60,
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hawkes_alpha: float = 0.8,
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hawkes_beta: float = 0.2,
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# --- 【Z-Score 标准化参数】 ---
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z_score_period: int = 200,
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# --- 【Z-Flow 指标参数】 ---
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z_flow_fast_ema_period: int = 12,
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z_flow_slow_ema_period: int = 26,
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# --- 【交易阈值】 ---
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entry_threshold: float = 0.5,
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exit_threshold: float = 0.1,
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# --- 【风险管理】 ---
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atr_period: int = 20,
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stop_loss_atr_multiplier: float = 3.0,
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# --- 其他 ---
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order_direction: Optional[List[str]] = None,
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indicators: Optional[List[Indicator]] = None,
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):
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super().__init__(context, main_symbol, enable_log)
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if order_direction is None: order_direction = ['BUY', 'SELL']
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# --- 参数赋值 ---
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self.trade_volume = trade_volume
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self.hawkes_lookback = hawkes_lookback
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self.hawkes_alpha = hawkes_alpha
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self.hawkes_beta = hawkes_beta
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self.z_score_period = z_score_period
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self.z_flow_fast_ema_period = z_flow_fast_ema_period
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self.z_flow_slow_ema_period = z_flow_slow_ema_period
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self.entry_threshold = entry_threshold
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self.exit_threshold = exit_threshold
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self.atr_period = atr_period
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self.stop_loss_atr_multiplier = stop_loss_atr_multiplier
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self.order_direction = order_direction
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# --- 内部状态变量 ---
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self._imbalance_history: deque = deque(maxlen=self.z_score_period + self.z_flow_slow_ema_period)
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# 核心指标
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self.z_flow = 0.0
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self.prev_z_flow = 0.0
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self.position_meta: Dict[str, Any] = self.context.load_state()
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self.main_symbol = main_symbol
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self.order_id_counter = 0
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if indicators is None: indicators = [Empty(), Empty()]
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self.indicators = indicators
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self.log(f"ImbalanceZFlowStrategy Initialized")
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def on_init(self):
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super().on_init()
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self.cancel_all_pending_orders(self.main_symbol)
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self.position_meta = self.context.load_state()
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def on_open_bar(self, open_price: float, symbol: str):
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self.symbol = symbol
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bar_history = self.get_bar_history()
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required_bars = self._imbalance_history.maxlen + self.hawkes_lookback + 5
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if len(bar_history) < required_bars:
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return
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self.prev_z_flow = self.z_flow
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self._update_indicators_and_state(bar_history)
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position_volume = self.get_current_positions().get(self.symbol, 0)
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self._sync_position_state(position_volume, symbol)
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if not self.trading: return
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if position_volume != 0:
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self.manage_open_position(position_volume, bar_history[-1])
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else:
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self.evaluate_entry_signal(open_price, bar_history) # 传入open_price
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def _update_indicators_and_state(self, bar_history: List[Bar]):
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"""核心计算函数：I(t) -> Z_I(t) -> Z_Flow(t)"""
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# --- 1. 计算瞬时不均衡力量 I(t) ---
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long_events, short_events = [], []
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lookback_bars = bar_history[-self.hawkes_lookback:]
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for i, bar in enumerate(lookback_bars):
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event_age = len(lookback_bars) - 1 - i
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mark = (bar.high - bar.low) * bar.volume
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if mark > 0:
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if bar.close > bar.open:
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long_events.append({'age': event_age, 'mark': mark})
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elif bar.close < bar.open:
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short_events.append({'age': event_age, 'mark': mark})
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lambda_long = sum(self.hawkes_alpha * e['mark'] * np.exp(-self.hawkes_beta * e['age']) for e in long_events)
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lambda_short = sum(self.hawkes_alpha * e['mark'] * np.exp(-self.hawkes_beta * e['age']) for e in short_events)
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imbalance = lambda_long - lambda_short
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self._imbalance_history.append(imbalance)
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if len(self._imbalance_history) < self.z_score_period:
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self.z_flow = 0.0
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return
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imbalance_series = pd.Series(list(self._imbalance_history))
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# --- 2. Z-Score 标准化，得到稳态序列 Z_I(t) ---
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rolling_mean = imbalance_series.rolling(window=self.z_score_period).mean()
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rolling_std = imbalance_series.rolling(window=self.z_score_period).std()
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# 避免除以零，并处理初始NaN值
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if rolling_std.iloc[-1] > 1e-9:
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z_score_series = (imbalance_series - rolling_mean) / rolling_std
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else:
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z_score_series = pd.Series(0.0, index=imbalance_series.index)
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z_score_series.fillna(0.0, inplace=True)
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# --- 3. 对稳态序列 Z_I(t) 计算MACD，得到 Z_Flow ---
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fast_ema = z_score_series.ewm(span=self.z_flow_fast_ema_period, adjust=False).mean()
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slow_ema = z_score_series.ewm(span=self.z_flow_slow_ema_period, adjust=False).mean()
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self.z_flow = fast_ema.iloc[-1] - slow_ema.iloc[-1]
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def manage_open_position(self, volume: int, current_bar: Bar):
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"""由Z-Flow驱动的统一平仓逻辑，并辅以价格止损。"""
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meta = self.position_meta.get(self.symbol)
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if not meta: return
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is_long = volume > 0
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# 1. 价格硬止损
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stop_loss_price = meta['stop_loss_price']
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if (is_long and current_bar.low <= stop_loss_price) or \
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(not is_long and current_bar.high >= stop_loss_price):
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self.log(f"ATR Stop Loss Hit at {stop_loss_price:.4f}")
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self.close_position("CLOSE_LONG" if is_long else "CLOSE_SHORT", abs(volume))
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return
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# 2. Z_Flow 驱动的平仓 (动能耗散)
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exit_triggered = False
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if is_long and self.z_flow < self.exit_threshold:
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exit_triggered = True
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elif not is_long and self.z_flow > -self.exit_threshold:
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exit_triggered = True
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if exit_triggered:
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self.log(f"Z-Flow Dissipation Exit. Direction: {'LONG' if is_long else 'SHORT'}. "
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f"Z-Flow ({self.z_flow:.2f}) returned to neutral zone.")
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self.close_position("CLOSE_LONG" if is_long else "CLOSE_SHORT", abs(volume))
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def evaluate_entry_signal(self, open_price: float, bar_history: List[Bar]):
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"""当Z-Flow穿越入场阈值时，在open_price开仓。"""
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direction = None
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if "BUY" in self.order_direction and self.prev_z_flow < self.entry_threshold <= self.z_flow:
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direction = "BUY"
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elif "SELL" in self.order_direction and self.prev_z_flow > -self.entry_threshold >= self.z_flow:
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direction = "SELL"
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if direction:
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self.log(
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f"Z-Flow Signal: {direction}. Z-Flow: {self.z_flow:.2f} crossed threshold. Entry on Open: {open_price}")
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# 使用完整的bar_history计算ATR
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highs = np.array([b.high for b in bar_history], dtype=float)
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lows = np.array([b.low for b in bar_history], dtype=float)
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closes = np.array([b.close for b in bar_history], dtype=float)
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current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
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if current_atr <= 0: return
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# ** 关键修正：基于 open_price 计算止损 **
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stop_loss_price = open_price - self.stop_loss_atr_multiplier * current_atr if direction == "BUY" \
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else open_price + self.stop_loss_atr_multiplier * current_atr
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# ** 关键修正：记录 open_price 为入场价 **
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meta = {'entry_price': open_price, 'stop_loss_price': stop_loss_price}
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self.send_market_order(direction, self.trade_volume, "OPEN", meta)
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self.save_state(self.position_meta)
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# ... (辅助函数保持不变) ...
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def _sync_position_state(self, position_volume, symbol):
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meta = self.position_meta.get(symbol)
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if position_volume != 0 and not meta:
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self.log(f"警告：持仓({position_volume})与策略状态不一致！将强制平仓。", level='WARNING')
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self.close_position("CLOSE_LONG" if position_volume > 0 else "CLOSE_SHORT", abs(position_volume))
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return
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if position_volume == 0 and meta:
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self.log(f"信息：清理过时的策略状态。", level='INFO')
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self.position_meta.pop(symbol, None)
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self.save_state(self.position_meta)
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def close_position(self, direction: str, volume: int):
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self.send_market_order(direction, volume, offset="CLOSE")
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if self.symbol in self.position_meta:
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self.position_meta.pop(self.symbol, None)
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self.save_state(self.position_meta)
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def send_market_order(self, direction: str, volume: int, offset: str, meta: Optional[Dict] = None):
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if offset == "OPEN" and meta: self.position_meta[self.symbol] = meta
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order_id = f"{self.symbol}_{direction}_MARKET_{self.order_id_counter}"
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self.order_id_counter += 1
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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=self.get_current_time(), offset=offset)
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self.send_order(order)
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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.position_meta = {}
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self._imbalance_history.clear()
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self.z_flow = 0.0
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self.prev_z_flow = 0.0
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self.log("Rollover detected. All strategy states have been reset.")
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