修改了线段中枢逻辑

2026-03-11 03:08:10 +08:00
parent 1c099be23e
commit 5226c551d5
19 changed files with 3386 additions and 324 deletions
@@ -0,0 +1,444 @@
+"""
+盘整背驰策略 (PanZhengBeiChi Strategy)
+
+基于缠论的盘整背驰进行交易：
+- 盘整背驰：同级别走势中，Ai与Ai+2比较力度减弱
+- 顶背驰（卖点）：价格创新高或接近，但MACD力度明显减弱
+- 底背驰（买点）：价格创新低或接近，但MACD力度明显减弱
+
+使用命令：
+    freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json \
+        --strategy PanZhengBeiChiStrategy --strategy-path ./user_data/Chan/strategies \
+        --timerange=20250301-
+"""
+
+import logging
+from datetime import datetime
+from typing import Optional
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval, resampled_merge
+from freqtrade.strategy import IStrategy
+
+logger = logging.getLogger(__name__)
+
+
+class PanZhengBeiChiStrategy(IStrategy):
+    """
+    盘整背驰策略
+    
+    核心逻辑：
+    1. 在5分钟级别识别同级别走势段（Ai）
+    2. 比较Ai与Ai+2的MACD力度，判断盘整背驰
+    3. 盘整顶背驰（i+2为偶数）-> 卖出
+    4. 盘整底背驰（i+2为奇数）-> 买入
+    """
+    INTERFACE_VERSION: int = 3
+
+    # === 基础配置 ===
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    
+    startup_candle_count: int = 2000  # 需要足够的数据来识别走势段
+
+    # === 止损止盈配置 ===
+    stoploss = -0.02  # 2% 硬止损
+    use_custom_stoploss = False
+
+    # Trailing stop
+    trailing_stop = True
+    trailing_stop_positive = 0.008  # 回撤 0.8% 触发退出
+    trailing_stop_positive_offset = 0.015  # 盈利 1.5% 后才开始追踪
+    trailing_only_offset_is_reached = True
+
+    # ROI - 调整止盈策略
+    minimal_roi = {
+        "0": 0.015,   # 1.5% 立即止盈（更保守）
+        "30": 0.01,   # 30分钟后 1%
+        "120": 0.008, # 2小时后 0.8%
+    }
+
+    order_types = {
+        "entry": "market",
+        "exit": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # === 盘整背驰参数 ===
+    same_level_timeframe = 5  # 5分钟级别
+    pivot_window = 4          # 转折点确认窗口（增大减少噪音）
+    min_segment_length = 5    # 最小段长度（K线数）（增大减少假信号）
+    
+    # 背驰判断参数（更严格）
+    beichi_price_threshold = 1.10   # 价格涨幅/跌幅阈值（允许10%范围内，更严格）
+    beichi_macd_threshold = 0.75    # MACD力度阈值（低于75%即背驰，更严格）
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """计算指标并识别盘整背驰"""
+        ticker = self.get_ticker_indicator()
+        
+        # Resample 到 5m 进行同级别分解
+        dataframe_5m = resample_to_interval(dataframe, ticker * self.same_level_timeframe)
+        
+        # 在 5m 上计算指标
+        dataframe_5m = self.add_indicators_5m(dataframe_5m)
+        
+        # 识别盘整背驰
+        dataframe_5m = self.identify_panzheng_beichi(dataframe_5m)
+        
+        # 合并回 1m dataframe
+        dataframe = resampled_merge(dataframe, dataframe_5m)
+        
+        # 在 1m 上也计算基础指标
+        dataframe = self.add_indicators_1m(dataframe)
+        
+        return dataframe
+
+    def add_indicators_5m(self, dataframe: DataFrame) -> DataFrame:
+        """在5m级别计算指标"""
+        # MACD 用于识别背驰
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        
+        # EMA 用于识别趋势
+        dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
+        dataframe['ema26'] = ta.EMA(dataframe, timeperiod=26)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
+        
+        # EMA趋势方向
+        dataframe['ema_trend_up'] = (dataframe['ema12'] > dataframe['ema26']) & (dataframe['ema26'] > dataframe['ema50'])
+        dataframe['ema_trend_dn'] = (dataframe['ema12'] < dataframe['ema26']) & (dataframe['ema26'] < dataframe['ema50'])
+        
+        # 价格与EMA200关系
+        dataframe['price_above_ema200'] = dataframe['close'] > dataframe['ema200']
+        dataframe['price_below_ema200'] = dataframe['close'] < dataframe['ema200']
+        
+        # 趋势强度
+        dataframe['ema12_slope'] = dataframe['ema12'].diff(5) / dataframe['ema12'].shift(5)
+        dataframe['ema26_slope'] = dataframe['ema26'].diff(5) / dataframe['ema26'].shift(5)
+        
+        # 强趋势判断
+        dataframe['strong_uptrend'] = (
+            (dataframe['ema12_slope'] > 0) & 
+            (dataframe['ema26_slope'] > 0) & 
+            (dataframe['price_above_ema200'])
+        )
+        dataframe['strong_downtrend'] = (
+            (dataframe['ema12_slope'] < 0) & 
+            (dataframe['ema26_slope'] < 0) & 
+            (dataframe['price_below_ema200'])
+        )
+        
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        
+        # ATR 用于波动率过滤
+        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
+        dataframe['atr_mean'] = dataframe['atr'].rolling(window=20).mean()
+        dataframe['volatility_ok'] = dataframe['atr'] > dataframe['atr_mean'] * 0.8
+        
+        return dataframe
+
+    def add_indicators_1m(self, dataframe: DataFrame) -> DataFrame:
+        """在1m级别计算基础指标"""
+        dataframe['rsi_1m'] = ta.RSI(dataframe, timeperiod=14)
+        dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
+        
+        # MACD 用于1m级别确认
+        macd_1m = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd_1m'] = macd_1m['macd']
+        dataframe['macdsignal_1m'] = macd_1m['macdsignal']
+        dataframe['macdhist_1m'] = macd_1m['macdhist']
+        
+        # MACD交叉
+        dataframe['macd_cross_up'] = (
+            (dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
+            (dataframe['macd_1m'].shift(1) <= dataframe['macdsignal_1m'].shift(1))
+        )
+        dataframe['macd_cross_dn'] = (
+            (dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
+            (dataframe['macd_1m'].shift(1) >= dataframe['macdsignal_1m'].shift(1))
+        )
+        
+        return dataframe
+
+    def identify_panzheng_beichi(self, dataframe: DataFrame) -> DataFrame:
+        """
+        识别盘整背驰
+        
+        核心逻辑：
+        1. 识别局部转折点（高低点）
+        2. 构建同级别走势段（Ai）
+        3. 比较Ai与Ai+2的MACD力度
+        4. 判断盘整背驰：价格涨幅相近但MACD力度减弱
+        """
+        df = dataframe.copy()
+        window = self.pivot_window
+        lookback = window + 1
+        
+        # 初始化列
+        df['ai_index'] = -1
+        df['ai_type'] = 0  # 1: 上涨, -1: 下跌
+        df['ai_high'] = np.nan
+        df['ai_low'] = np.nan
+        df['ai_macd_max'] = np.nan
+        df['ai_macd_min'] = np.nan
+        df['beichi_long'] = False   # 盘整底背驰（买入信号）
+        df['beichi_short'] = False  # 盘整顶背驰（卖出信号）
+        
+        # 识别局部高点
+        df['temp_high'] = df['high'].shift(window)
+        df['is_pivot_high'] = (
+            (df['temp_high'] == df['temp_high'].rolling(window=lookback).max()) &
+            (df['temp_high'].notna())
+        )
+        
+        # 识别局部低点
+        df['temp_low'] = df['low'].shift(window)
+        df['is_pivot_low'] = (
+            (df['temp_low'] == df['temp_low'].rolling(window=lookback).min()) &
+            (df['temp_low'].notna())
+        )
+        
+        # 逐行处理，识别走势段和背驰
+        ai_list = []
+        current_ai_start = None
+        current_ai_type = None
+        last_pivot_idx = None
+        
+        for i in range(window, len(df)):
+            # 检查新的转折点
+            is_new_pivot = False
+            pivot_type = None
+            
+            if df.iloc[i]['is_pivot_high']:
+                is_new_pivot = True
+                pivot_type = 'high'
+            elif df.iloc[i]['is_pivot_low']:
+                is_new_pivot = True
+                pivot_type = 'low'
+            
+            if is_new_pivot and last_pivot_idx is not None:
+                # 完成一个走势段
+                if current_ai_start is not None:
+                    seg_df = df.iloc[current_ai_start:last_pivot_idx]
+                    if len(seg_df) >= self.min_segment_length:
+                        high_val = seg_df['high'].max()
+                        low_val = seg_df['low'].min()
+                        macd_max = seg_df['macd'].max()
+                        macd_min = seg_df['macd'].min()
+                        
+                        # 判断走势类型
+                        if current_ai_type is None:
+                            if high_val > df.iloc[current_ai_start]['close']:
+                                current_ai_type = 1
+                            else:
+                                current_ai_type = -1
+                        
+                        ai_info = {
+                            'start': current_ai_start,
+                            'end': last_pivot_idx,
+                            'type': current_ai_type,
+                            'high': high_val,
+                            'low': low_val,
+                            'macd_max': macd_max,
+                            'macd_min': macd_min,
+                        }
+                        ai_list.append(ai_info)
+                        
+                        # 标记该段
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_index')] = len(ai_list) - 1
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_type')] = current_ai_type
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_high')] = high_val
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_low')] = low_val
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_max')] = macd_max
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_min')] = macd_min
+                        
+                        # 判断背驰（Ai与Ai+2比较）
+                        if len(ai_list) >= 3:
+                            ai = ai_list[-3]      # Ai
+                            ai_plus_2 = ai_list[-1]  # Ai+2
+                            
+                            if ai['type'] == ai_plus_2['type']:
+                                # 上涨段：比较向上力度
+                                if ai['type'] == 1:
+                                    price_chg = (ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low'] if ai_plus_2['low'] > 0 else 0
+                                    price_chg_prev = (ai['high'] - ai['low']) / ai['low'] if ai['low'] > 0 else 0
+                                    macd_chg = ai_plus_2['macd_max']
+                                    macd_chg_prev = ai['macd_max']
+                                    
+                                    # 顶背驰：价格涨幅相近但MACD力度减弱
+                                    if price_chg <= price_chg_prev * self.beichi_price_threshold and \
+                                       macd_chg < macd_chg_prev * self.beichi_macd_threshold:
+                                        idx = len(ai_list) - 1  # i+2的索引
+                                        if idx % 2 == 0:  # 偶数 -> 卖出
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
+                                        else:  # 奇数 -> 买入
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
+                                
+                                # 下跌段：比较向下力度
+                                else:
+                                    price_chg = abs((ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low']) if ai_plus_2['low'] > 0 else 0
+                                    price_chg_prev = abs((ai['high'] - ai['low']) / ai['low']) if ai['low'] > 0 else 0
+                                    macd_chg = abs(ai_plus_2['macd_min'])
+                                    macd_chg_prev = abs(ai['macd_min'])
+                                    
+                                    # 底背驰：价格跌幅相近但MACD力度减弱
+                                    if price_chg <= price_chg_prev * self.beichi_price_threshold and \
+                                       macd_chg < macd_chg_prev * self.beichi_macd_threshold:
+                                        idx = len(ai_list) - 1
+                                        if idx % 2 == 0:  # 偶数 -> 卖出
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
+                                        else:  # 奇数 -> 买入
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
+                
+                # 更新当前段信息
+                if pivot_type == 'high':
+                    current_ai_type = -1  # 高点后向下
+                else:
+                    current_ai_type = 1   # 低点后向上
+                current_ai_start = last_pivot_idx
+            
+            if is_new_pivot:
+                last_pivot_idx = i
+        
+        return df
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        ticker = self.get_ticker_indicator()
+        resample_col = f"resample_{ticker * self.same_level_timeframe}_"
+        
+        # 5m 级别指标列名
+        beichi_long_col = f"{resample_col}beichi_long"
+        beichi_short_col = f"{resample_col}beichi_short"
+        ai_type_col = f"{resample_col}ai_type"
+        rsi_5m_col = f"{resample_col}rsi"
+        ema_trend_up_col = f"{resample_col}ema_trend_up"
+        ema_trend_dn_col = f"{resample_col}ema_trend_dn"
+        volatility_ok_col = f"{resample_col}volatility_ok"
+        strong_uptrend_col = f"{resample_col}strong_uptrend"
+        strong_downtrend_col = f"{resample_col}strong_downtrend"
+        price_above_ema200_col = f"{resample_col}price_above_ema200"
+        price_below_ema200_col = f"{resample_col}price_below_ema200"
+        
+        # === 做多入场 ===
+        # 条件：盘整底背驰 + 强上升趋势确认
+        dataframe.loc[
+            (
+                # 核心信号：盘整底背驰
+                (dataframe[beichi_long_col] == True) &
+                
+                # 强上升趋势确认（更严格）
+                (dataframe[strong_uptrend_col] == True) &
+                
+                # RSI 确认（更严格：只在大趋势中操作）
+                (dataframe[rsi_5m_col] > 40) &
+                (dataframe[rsi_5m_col] < 60) &
+                
+                # 1m 指标确认
+                (dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
+                
+                # 成交量确认
+                (dataframe['volume'] > dataframe['volume_mean'] * 1.5)
+            ),
+            ['enter_long', 'enter_tag']
+        ] = (1, "pzbc_long")
+        
+        # === 做空入场 ===
+        # 条件：盘整顶背驰 + 强下降趋势确认（更严格）
+        dataframe.loc[
+            (
+                # 核心信号：盘整顶背驰
+                (dataframe[beichi_short_col] == True) &
+                
+                # 强下降趋势确认
+                (dataframe[strong_downtrend_col] == True) &
+                
+                # RSI 确认
+                (dataframe[rsi_5m_col] > 40) &
+                (dataframe[rsi_5m_col] < 60) &
+                
+                # 1m 指标确认
+                (dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
+                
+                # 成交量确认
+                (dataframe['volume'] > dataframe['volume_mean'] * 1.5)
+            ),
+            ['enter_short', 'enter_tag']
+        ] = (1, "pzbc_short")
+        
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        出场逻辑
+        
+        多头出场：
+        1. 出现盘整顶背驰
+        2. 趋势转弱
+        
+        空头出场：
+        1. 出现盘整底背驰
+        2. 趋势转弱
+        """
+        ticker = self.get_ticker_indicator()
+        resample_col = f"resample_{ticker * self.same_level_timeframe}_"
+        
+        beichi_long_col = f"{resample_col}beichi_long"
+        beichi_short_col = f"{resample_col}beichi_short"
+        ai_type_col = f"{resample_col}ai_type"
+        rsi_5m_col = f"{resample_col}rsi"
+        ema_trend_dn_col = f"{resample_col}ema_trend_dn"
+        strong_downtrend_col = f"{resample_col}strong_downtrend"
+        strong_uptrend_col = f"{resample_col}strong_uptrend"
+        
+        # === 多头出场 ===
+        dataframe.loc[
+            (
+                # 出现盘整顶背驰 -> 退出多头
+                (dataframe[beichi_short_col] == True) |
+                
+                # 趋势转弱
+                (
+                    (dataframe[ai_type_col] == -1) &
+                    (dataframe[rsi_5m_col] > 55)
+                ) |
+                
+                # 强下跌趋势
+                (dataframe[strong_downtrend_col] == True)
+            ),
+            ['exit_long', 'exit_tag']
+        ] = (1, "pzbc_exit_long")
+        
+        # === 空头出场 ===
+        dataframe.loc[
+            (
+                # 出现盘整底背驰 -> 退出空头
+                (dataframe[beichi_long_col] == True) |
+                
+                # 趋势转弱
+                (
+                    (dataframe[ai_type_col] == 1) &
+                    (dataframe[rsi_5m_col] < 45)
+                ) |
+                
+                # 强上涨趋势
+                (dataframe[strong_uptrend_col] == True)
+            ),
+            ['exit_short', 'exit_tag']
+        ] = (1, "pzbc_exit_short")
+        
+        return dataframe
+
+    def get_ticker_indicator(self) -> int:
+        """获取 timeframe 的分钟数"""
+        return int(self.timeframe[:-1])
+