# ===================================================================================== # 以下是新增的 ValueMigrationStrategy 策略代码 # ===================================================================================== from collections import deque from datetime import timedelta, time import numpy as np import pandas as pd from typing import List, Any, Optional, Dict import talib from src.core_data import Bar, Order from src.strategies.ValueMigrationStrategy.data_class import ProfileStats, calculate_profile_from_bars from src.strategies.base_strategy import Strategy # = =================================================================== # 全局辅助函数 (Global Helper Functions) # 将这些函数放在文件顶部，以便所有策略类都能调用 # ===================================================================== def compute_price_volume_distribution(bars: List[Bar], tick_size: float) -> Optional[pd.Series]: """ [全局函数] 从K线数据中计算出原始的价格-成交量分布。 """ if not bars: return None data = [] # 为了性能，我们只处理有限数量的bars，防止内存问题 # 在实际应用中，更高效的实现是必要的 for bar in bars[-500:]: # 添加一个安全限制 price_range = np.arange(bar.low, bar.high + tick_size, tick_size) if len(price_range) == 0 or bar.volume == 0: continue # 将成交量近似分布到K线覆盖的每个tick上 volume_per_tick = bar.volume / len(price_range) for price in price_range: data.append({'price': price, 'volume': volume_per_tick}) if not data: return None df = pd.DataFrame(data) if df.empty: return None return df.groupby('price')['volume'].sum().sort_index() # ===================================================================================== # 以下是最终的、以性能为首要考量的、超高速VP计算模块 # ===================================================================================== def compute_fast_volume_profile(bars: List[Bar], tick_size: float) -> Optional[pd.Series]: """ [全局核心函数] 使用“离散重心累加”法，超高速地从K线数据中构建成交量剖面图。 该方法将每根K线的全部成交量，一次性地归于其加权中心价 (HLC/3)所对齐的tick上。这在保持核心逻辑精确性的同时， 实现了计算速度的数量级提升。 Args: bars: 用于计算的K线历史数据。 tick_size: 合约的最小变动价位。 Returns: 一个代表成交量剖面图的Pandas Series，或None。 """ if not bars: return None # 使用字典进行累加，这是Python中最快的操作之一 volume_dict = {} for bar in bars: if bar.volume == 0: continue # 1. 计算K线的加权中心价 center_price = (bar.high + bar.low + bar.close) / 3 # 2. 将中心价对齐到最近的tick aligned_price = round(center_price / tick_size) * tick_size # 3. 将该Bar的全部成交量，一次性累加到对齐后的价格点上 volume_dict[aligned_price] = volume_dict.get(aligned_price, 0) + bar.volume if not volume_dict: return None # 将最终结果转换为一个有序的Pandas Series return pd.Series(volume_dict).sort_index() # 确保在文件顶部导入 from scipy.signal import find_peaks def find_hvns_with_distance(price_volume_dist: pd.Series, distance_in_ticks: int) -> List[float]: """ [全局函数] 使用峰值查找算法，根据峰值间的最小距离来识别HVNs。 Args: price_volume_dist: 价格-成交量分布序列。 distance_in_ticks: 两个HVN之间必须间隔的最小tick数量。 Returns: 一个包含所有被识别出的HVN价格的列表。 """ if price_volume_dist.empty or len(price_volume_dist) < 3: return [] # distance参数确保找到的峰值之间至少相隔N个点 peaks_indices, _ = find_peaks(price_volume_dist.values, distance=distance_in_ticks) if len(peaks_indices) == 0: return [price_volume_dist.idxmax()] # 默认返回POC hvn_prices = price_volume_dist.index[peaks_indices].tolist() return hvn_prices def find_hvns_strict(price_volume_dist: pd.Series, window_radius: int) -> List[float]: """ [全局函数] 使用严格的“滚动窗口最大值”定义来识别HVNs。 一个点是HVN，当且仅当它的成交量大于其左右各 `window_radius` 个点的成交量。 Args: price_volume_dist: 价格-成交量分布序列。 window_radius: 定义了检查窗口的半径 (即您所说的 N)。 Returns: 一个包含所有被识别出的HVN价格的列表。 """ if price_volume_dist.empty or window_radius == 0: return [price_volume_dist.idxmax()] if not price_volume_dist.empty else [] # 1. 确保价格序列是连续的，用0填充缺失的ticks full_price_range = np.arange(price_volume_dist.index.min(), price_volume_dist.index.max() + price_volume_dist.index.to_series().diff().min(), price_volume_dist.index.to_series().diff().min()) continuous_dist = price_volume_dist.reindex(full_price_range, fill_value=0) # 2. 计算滚动窗口最大值 window_size = 2 * window_radius + 1 rolling_max = continuous_dist.rolling(window=window_size, center=True).max() # 3. 找到那些自身成交量就等于其窗口最大值的点 is_hvn = (continuous_dist == rolling_max) & (continuous_dist > 0) hvn_prices = continuous_dist[is_hvn].index.tolist() # 4. 处理平顶山：如果连续多个点都是HVN，只保留中间那个 if not hvn_prices: return [price_volume_dist.idxmax()] # 如果找不到，返回POC final_hvns = [] i = 0 while i < len(hvn_prices): # 找到一个连续HVN块 j = i while j + 1 < len(hvn_prices) and (hvn_prices[j + 1] - hvn_prices[j]) < ( 2 * price_volume_dist.index.to_series().diff().min()): j += 1 # 取这个连续块的中间点 middle_index = i + (j - i) // 2 final_hvns.append(hvn_prices[middle_index]) i = j + 1 return final_hvns # 确保在文件顶部导入 from scipy.signal import find_peaks # ===================================================================================== # 以下是V2版本的、简化了状态管理的 HVNPullbackStrategy 代码 # ===================================================================================== class ValueMigrationStrategy(Strategy): """ 一个基于动态HVN突破后回测的量化交易策略。(V2: 简化状态管理) V2版本简化了内部状态管理，移除了基于order_id的复杂元数据传递， 使用更直接、更健壮的单一状态变量来处理挂单的止盈止损参数， 完美适配“单次单持仓”的策略逻辑。 """ def __init__( self, context: Any, main_symbol: str, enable_log: bool, trade_volume: int, tick_size: float = 1, profile_period: int = 100, recalc_interval: int = 4, hvn_distance_ticks: int = 20, entry_offset_atr: float = 0.0, stop_loss_atr: float = 1.0, take_profit_atr: float = 2.0, atr_period: int = 14, order_direction=None, indicators=[None, None], ): super().__init__(context, main_symbol, enable_log) if order_direction is None: order_direction = ['BUY', 'SELL'] self.trade_volume = trade_volume self.tick_size = tick_size self.profile_period = profile_period self.recalc_interval = recalc_interval self.hvn_distance_ticks = hvn_distance_ticks self.entry_offset_atr = entry_offset_atr self.stop_loss_atr = stop_loss_atr self.take_profit_atr = take_profit_atr self.atr_period = atr_period self.order_direction = order_direction self.indicator_long = indicators[0] self.indicator_short = indicators[1] self.main_symbol = main_symbol self.order_id_counter = 0 self._bar_counter = 0 self._cached_hvns: List[float] = [] # --- V2: 简化的状态管理 --- self._pending_sl_price: Optional[float] = None self._pending_tp_price: Optional[float] = None def on_open_bar(self, open_price: float, symbol: str): self.symbol = symbol self._bar_counter += 1 bar_history = self.get_bar_history() required_len = max(self.profile_period, self.atr_period) + 1 if len(bar_history) < required_len: return # --- 1. 取消所有挂单并重置挂单状态 --- self.cancel_all_pending_orders(self.symbol) # self._pending_sl_price = None # self._pending_tp_price = None # --- 2. 管理现有持仓 --- position_volume = self.get_current_positions().get(self.symbol, 0) if position_volume != 0: self.manage_open_position(position_volume, open_price) return # --- 3. 周期性地计算HVNs --- if self._bar_counter % self.recalc_interval == 1: profile_bars = bar_history[-self.profile_period:] dist = compute_price_volume_distribution(profile_bars, self.tick_size) # dist = compute_fast_volume_profile(profile_bars, self.tick_size) if dist is not None and not dist.empty: # self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks) self._cached_hvns = find_hvns_strict(dist, self.hvn_distance_ticks) self.log(f"New HVNs identified at: {[f'{p:.2f}' for p in self._cached_hvns]}") if not self._cached_hvns: return # --- 4. 评估新机会 (挂单逻辑) --- self.evaluate_entry_signal(bar_history) def manage_open_position(self, volume: int, current_price: float): """主动管理已开仓位的止盈止损。""" # # [V2 关键逻辑]: 检测是否为新持仓 # # 如果这是一个新持仓，并且我们有预设的止盈止损，就将其存入 # if self._pending_sl_price is not None and self._pending_tp_price is not None: # meta = {'sl_price': self._pending_sl_price, 'tp_price': self._pending_tp_price} # self.position_meta = meta # self.log(f"新持仓确认。已设置TP/SL: {meta}") # else: # # 这种情况理论上不应发生，但作为保护 # self.log("Error: New position detected but no pending TP/SL values found.") # self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume)) # return # [常规逻辑]: 检查止盈止损 sl_price = self._pending_sl_price tp_price = self._pending_tp_price if volume > 0: # 多头 if current_price <= sl_price or current_price >= tp_price: action = "止损" if current_price <= sl_price else "止盈" self.log(f"多头{action}触发 at {current_price:.2f}") self.close_position("CLOSE_LONG", abs(volume), current_price) elif volume < 0: # 空头 if current_price >= sl_price or current_price <= tp_price: action = "止损" if current_price >= sl_price else "止盈" self.log(f"空头{action}触发 at {current_price:.2f}") self.close_position("CLOSE_SHORT", abs(volume), current_price) def evaluate_entry_signal(self, bar_history: List[Bar]): prev_close = bar_history[-2].close current_close = bar_history[-1].close highs = np.array([b.high for b in bar_history], dtype=float) lows = np.array([b.low for b in bar_history], dtype=float) closes = np.array([b.close for b in bar_history], dtype=float) current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1] if current_atr < self.tick_size: return for hvn in sorted(self._cached_hvns): # (为了简洁，买卖逻辑合并) direction = None if "BUY" in self.order_direction and (prev_close < hvn < current_close): direction = "SELL" pass_filter = self.indicator_long is None or self.indicator_long.is_condition_met( *self.get_indicator_tuple()) elif "SELL" in self.order_direction and (prev_close > hvn > current_close): direction = "BUY" pass_filter = self.indicator_short is None or self.indicator_short.is_condition_met( *self.get_indicator_tuple()) else: continue # 没有触发穿越 if direction and pass_filter: offset = self.entry_offset_atr * current_atr limit_price = hvn + offset if direction == "BUY" else hvn - offset self.log(f"价格穿越HVN({hvn:.2f}). 在 {limit_price:.2f} 挂限价{direction}单。") # self.send_hvn_limit_order(direction, limit_price + 1 if direction == 'BUY' else -1, current_atr) self.send_hvn_limit_order(direction, limit_price, current_atr) return # 每次只挂一个单 def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float): print(limit_price, self.get_current_time()) # [V2 关键逻辑]: 直接更新实例变量 self._pending_sl_price = limit_price - self.stop_loss_atr * entry_atr if direction == "BUY" else limit_price + self.stop_loss_atr * entry_atr self._pending_tp_price = limit_price + self.take_profit_atr * entry_atr if direction == "BUY" else limit_price - self.take_profit_atr * entry_atr order_id = f"{self.symbol}_{direction}_LIMIT_{self.order_id_counter}" self.order_id_counter += 1 # order = Order( # id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume, # price_type="LIMIT", limit_price=limit_price, submitted_time=self.get_current_time(), # offset="OPEN" # ) order = Order( id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume, price_type="STOP", stop_price=limit_price, submitted_time=self.get_current_time(), offset="OPEN" ) self.send_order(order) def close_position(self, direction: str, volume: int, current_price: float): self.send_market_order(direction, volume, current_price) def send_market_order(self, direction: str, volume: int, current_price: float, offset: str = "CLOSE"): order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}" self.order_id_counter += 1 order = Order( id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET", submitted_time=self.get_current_time(), offset=offset, limit_price=current_price ) self.send_order(order)