1、vp策略-v2

src/strategies/ValueMigrationStrategy/ValueMigrationStrategy.py

@@ -276,13 +276,3 @@ class ValueMigrationStrategy(Strategy):
         )
         self.send_order(order)
 
-
-def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
-    # ... (与之前版本相同) ...
-    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
-    )
-    self.send_order(order)

src/strategies/ValueMigrationStrategy/ValueMigrationStrategy2.py

@@ -0,0 +1,315 @@
+# =====================================================================================
+#  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()
+
+
+# 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)
+            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))
+        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))
+
+    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 = "BUY"
+                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 = "SELL"
+                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, current_atr)
+                return  # 每次只挂一个单
+
+    def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
+        # [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"
+        )
+        self.send_order(order)
+
+    def close_position(self, direction: str, volume: int):
+        self.send_market_order(direction, volume)
+
+
+    def send_market_order(self, direction: str, volume: int, 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
+        )
+        self.send_order(order)

src/strategies/ValueMigrationStrategy/ValueMigrationStrategy3.py

@@ -0,0 +1,317 @@
+# =====================================================================================
+#  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()
+
+
+# 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
+
+
+# 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
+# =====================================================================================
+
+# 引入必要的类型，确保代码清晰
+from typing import Any, Dict, Optional, List
+import numpy as np
+import talib
+
+
+class ValueMigrationStrategy(Strategy):
+    """
+    一个基于动态HVN突破后回测的量化交易策略。(V3: 集成上下文状态管理)
+
+    V3版本完全集成BacktestContext的状态管理功能，实现了策略重启后的状态恢复。
+    - 状态被简化为两个核心变量：_pending_sl_price 和 _pending_tp_price。
+    - 在策略初始化时安全地加载状态，并兼容空状态或旧版状态。
+    - 在下单或平仓时立即持久化状态，确保数据一致性。
+    - 增加了逻辑检查，处理重启后可能出现的状态与实际持仓不一致的问题。
+    """
+
+    def __init__(
+            self,
+            context: Any,  # 通常会是 BacktestContext
+            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 = 1,
+            entry_offset_atr: float = 0.0,
+            stop_loss_atr: float = 1.0,
+            take_profit_atr: float = 1.0,
+            atr_period: int = 14,
+            order_direction=None,
+            indicators=[None, None],
+    ):
+        super().__init__(context, main_symbol, enable_log)
+        # --- 参数初始化 (保持不变) ---
+        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] = []
+
+        # --- 新增: 初始化时加载状态 ---
+        self._pending_sl_price: Optional[float] = None
+        self._pending_tp_price: Optional[float] = None
+        self._load_state_from_context()
+
+    def _get_state_dict(self) -> Dict[str, Any]:
+        """一个辅助函数，用于生成当前需要保存的状态字典。"""
+        return {
+            "_pending_sl_price": self._pending_sl_price,
+            "_pending_tp_price": self._pending_tp_price,
+        }
+
+    def _load_state_from_context(self):
+        """
+        [新增] 从上下文中加载状态，并进行健壮性处理。
+        """
+        loaded_state = self.context.load_state()
+        if not loaded_state:
+            self.log("未找到历史状态，进行全新初始化。")
+            return
+
+        # 使用 .get() 方法安全地读取，即使key不存在或state为空也不会报错。
+        # 这完美解决了“读取的state的key不一样”的问题。
+        self._pending_sl_price = loaded_state.get("_pending_sl_price")
+        self._pending_tp_price = loaded_state.get("_pending_tp_price")
+
+        if self._pending_sl_price is not None:
+            self.log(f"成功从上下文加载状态: SL={self._pending_sl_price}, TP={self._pending_tp_price}")
+        else:
+            self.log("加载的状态为空或格式不兼容，视为全新初始化。")
+
+    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
+
+        # 取消所有挂单，这符合原逻辑，确保每根bar都是新的开始
+        self.cancel_all_pending_orders(self.symbol)
+
+        position_volume = self.get_current_positions().get(self.symbol, 0)
+
+        # --- 新增: 状态一致性检查 ---
+        # 场景：策略重启后，加载了之前的止盈止损状态，但发现实际上并没有持仓
+        # (可能因为上次平仓后、清空状态前程序就关闭了)。
+        # 这种情况下，状态是无效的“幽灵状态”，必须清除。
+        if position_volume == 0 and self._pending_sl_price is not None:
+            self.log("检测到状态与实际持仓不符 (有状态但无持仓)，重置本地状态。")
+            self._pending_sl_price = None
+            self._pending_tp_price = None
+            self.context.save_state(self._get_state_dict())  # 立即同步清除后的状态
+
+        # --- 1. 管理现有持仓 (如果存在) ---
+        if position_volume != 0:
+            self.manage_open_position(position_volume, open_price)
+            return
+
+
+        # 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)
+            if dist is not None and not dist.empty:
+                self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks)
+                self.log(f"识别到新的高价值节点: {[f'{p:.2f}' for p in self._cached_hvns]}")
+
+        if not self._cached_hvns: return
+
+        # 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):
+        """
+        [修改] 主动管理已开仓位的止盈止损。
+        不再使用 position_meta，直接依赖实例变量。
+        """
+        # [关键安全检查]: 如果有持仓，但却没有止盈止损状态，这是一个危险的信号。
+        # 可能是状态文件损坏或逻辑错误。为控制风险，应立即平仓。
+        if self._pending_sl_price is None or self._pending_tp_price is None:
+            self.log("风险警告：存在持仓但无有效的止盈止损价格，立即市价平仓！")
+            self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume))
+            return
+
+        sl_price = self._pending_sl_price
+        tp_price = self._pending_tp_price
+
+        # 止盈止损逻辑 (保持不变)
+        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}触发于 {current_price:.2f} (SL: {sl_price}, TP: {tp_price})")
+                self.close_position("CLOSE_LONG", abs(volume))
+        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}触发于 {current_price:.2f} (SL: {sl_price}, TP: {tp_price})")
+                self.close_position("CLOSE_SHORT", abs(volume))
+
+    def evaluate_entry_signal(self, bar_history: List[Bar]):
+        # [修改] 在挂单前，先重置旧的挂单状态，虽然on_open_bar开头也做了，但这里更保险
+        self._pending_sl_price = None
+        self._pending_tp_price = None
+
+        # ... 原有挂单信号计算逻辑保持不变 ...
+        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):
+            if "BUY" in self.order_direction and (prev_close < hvn < current_close):
+                direction = "BUY"
+                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 = "SELL"
+                pass_filter = self.indicator_short is None or self.indicator_short.is_condition_met(
+                    *self.get_indicator_tuple())
+            else:
+                continue
+
+            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, current_atr)
+                return
+
+    def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
+        # 1. 设置实例的止盈止损状态
+        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
+
+        # 2. [新增] 状态已更新，立即通过上下文持久化
+        self.context.save_state(self._get_state_dict())
+        self.log(f"状态已更新并保存: SL={self._pending_sl_price}, TP={self._pending_tp_price}")
+
+        # 3. 发送订单
+        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"
+        )
+        self.send_order(order)
+
+    def close_position(self, direction: str, volume: int):
+        """[修改] 平仓时，必须清空状态并立即保存。"""
+        # 1. 发送平仓市价单
+        self.send_market_order(direction, volume)
+
+        # 2. 清空本地的止盈止损状态
+        self._pending_sl_price = None
+        self._pending_tp_price = None
+
+        # 3. [新增] 状态已清空，立即通过上下文持久化这个“空状态”
+        self.context.save_state(self._get_state_dict())
+        self.log("持仓已平，相关的止盈止损状态已清空并保存。")
+
+    def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
+        # ... 此辅助函数保持不变 ...
+        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
+        )
+        self.send_order(order)