Add klc and klu fx strength check

ChanKLC.py

@@ -49,7 +49,13 @@ class ChanKLC():
         self.volume_ratio = self.volume_ratio / len(self.klus)
         self.volume = self.volume / len(self.klus)
         self.macdhist = self.macdhist / len(self.klus)
-   
+    def contain_klu_fx(self):
+        if len(self.klus) > 0:
+            for klu in self.klus:
+                klu.update_realtime_analysis()
+                if klu.fx_type == self.fx and klu.fx_strength > 1.8:
+                    return True
+        return False
     def set_next(self, klc):
         self.next = klc
     def set_pre(self, klc):
@@ -1221,7 +1227,7 @@ class ChanKLC():
         # 获取分型后的几根K线数据
         subsequent_klcs = []
         temp = self.next
-        for i in range(5):  # 检查后续5根K线
+        for i in range(2):  # 检查后续5根K线
             if temp:
                 subsequent_klcs.append(temp)
                 temp = temp.next if hasattr(temp, 'next') else None

ChanKLU.py

@@ -1,3 +1,4 @@
+from ChanEnum import Chan_FX_TYPE
 class ChanKLU:
     def __init__(self, time, open, high, low, close, volume):
         # _time, _close, _open, _high, _low, _extra_info={}
@@ -21,9 +22,375 @@ class ChanKLU:
         self.ma250 = 0
         self.rsi = 0
         self.volume_ratio = 0
+        
+        # === 新增：实时分型相关属性 ===
+        self.pre = None  # 前一根K线
+        self.next = None  # 后一根K线  
+        self.fx_type = Chan_FX_TYPE.UNKNOWN      # 分型类型：0=无分型，1=顶分型，-1=底分型
+        self.fx_strength = 0  # 分型强度：0-100
+        self.fx_confirmed = False  # 分型是否确认
+        
+    def set_next(self, next):
+        self.next = next
+        self.update_realtime_analysis()
+        #if self.fx_type != Chan_FX_TYPE.UNKNOWN and self.fx_strength > 1:
+            #print(self.index, self.time, self.fx_type, self.fx_confirmed, self.fx_strength)
+    def set_pre(self, pre):
+        self.pre = pre
+    def detect_realtime_fx(self):
+        """
+        实时检测K线分型（不等待KLC确认）
+        基于原始K线的即时分型识别
+        """
+        if not self.pre or not self.next:
+            self.fx_type = Chan_FX_TYPE.UNKNOWN
+            return False
+            
+        # 顶分型检测
+        if (self.high > self.pre.high and 
+            self.high > self.next.high):
+            self.fx_type = Chan_FX_TYPE.TOP
+            self.fx_confirmed = True
+            return True
+            
+        # 底分型检测  
+        elif (self.low < self.pre.low and 
+              self.low < self.next.low):
+            self.fx_type = Chan_FX_TYPE.BOTTOM
+            self.fx_confirmed = True
+            return True
+            
+        self.fx_type = Chan_FX_TYPE.UNKNOWN
+        self.fx_confirmed = False
+        return False
+        
+    def calculate_realtime_fx_strength(self):
+        """
+        用self.pre和self.next实现分型强弱判断（与KLC中cal_fx_strength一致）
+        
+        核心缠论原理：
+        - 强分型：出现在笔的末端，能够终结当前笔，标志着趋势转折
+        - 弱分型：出现在笔的中间，是中继性质，笔还会继续延伸
+        
+        返回值：
+        3: 极强分型（笔终结+强确认）
+        2: 强分型（笔终结）
+        1: 偏强分型（可能终结笔）
+        0: 中性分型
+        -1: 偏弱分型（中继特征明显）
+        -2: 弱分型（明显中继）
+        -3: 极弱分型（无效分型）
+        """
+        # 检查是否为分型，且有前后K线数据
+        if self.fx_type == Chan_FX_TYPE.UNKNOWN:
+            return 0
+        if not self.pre or not self.next:
+            return 100
+        # === 核心判断：分型在笔中的位置 ===
+        
+        # 1. 检查这个分型是否能够终结当前笔
+        is_bi_end = self._check_if_bi_ending_fx()
+        
+        # 2. 检查分型的后续走势确认
+        post_fx_confirmation = self._check_post_fx_confirmation()
+        
+        # 3. 检查分型的标准性和强度
+        fx_quality = self._check_fx_quality()
+        
+        # === 综合评分 ===
+        base_score = 0
+        
+        # 笔位置是最重要的判断标准
+        if is_bi_end == 2:  # 强烈确认笔终结
+            base_score = 2
+        elif is_bi_end == 1:  # 可能笔终结
+            base_score = 1
+        elif is_bi_end == -1:  # 明显中继
+            base_score = -2
+        elif is_bi_end == -2:  # 强烈中继特征
+            base_score = -3
+        else:  # 不确定
+            base_score = 0
+            
+        # 后续确认调整
+        base_score += post_fx_confirmation
+        
+        # 分型质量调整
+        base_score += fx_quality
+        
+        # 限制在-3到3范围内
+        final_score = max(-3, min(3, base_score))
+        self.fx_strength = final_score
+        # 转换为0-100分制以保持接口一致性
+        #self.fx_strength = int((final_score + 3) * 100 / 6)  # -3到3映射到0-100
+        if final_score > 1.8:
+            print(self.time, final_score, is_bi_end, post_fx_confirmation, fx_quality)
+        #print(self.time, final_score, is_bi_end, post_fx_confirmation, fx_quality)
+        return self.fx_strength
+    
+    def _check_if_bi_ending_fx(self):
+        """
+        检查分型是否为笔终结分型
+        返回值：
+        2: 强烈确认笔终结
+        1: 可能笔终结  
+        0: 不确定
+        -1: 明显中继
+        -2: 强烈中继特征
+        """
+        # 检查是否有足够的后续数据来判断
+        if not self.next or not hasattr(self.next, 'next'):
+            return 0
+            
+        # 获取分型后的几根K线数据
+        subsequent_klus = []
+        temp = self.next
+        for i in range(2):  # 检查后续2根K线
+            if temp:
+                subsequent_klus.append(temp)
+                temp = temp.next if hasattr(temp, 'next') else None
+            else:
+                break
+        
+        if len(subsequent_klus) < 2:
+            return 0
+            
+        if self.fx_type == Chan_FX_TYPE.TOP:
+            return self._check_top_bi_ending(subsequent_klus)
+        else:  # BOTTOM
+            return self._check_bottom_bi_ending(subsequent_klus)
+    
+    def _check_top_bi_ending(self, subsequent_klus):
+        """检查顶分型是否为笔终结"""
+        # 强烈笔终结特征：
+        # 1. 后续K线持续下跌，且跌破关键位置
+        # 2. 没有新的更高的高点出现
+        
+        broken_key_levels = 0
+        new_highs = 0
+        downward_trend = 0
+        
+        # 检查关键价位突破
+        first_low = self.pre.low
+        middle_low = self.low
+        key_support = min(first_low, middle_low)
+        
+        for i, klu in enumerate(subsequent_klus):
+            # 检查是否跌破关键支撑
+            if klu.low < key_support:
+                broken_key_levels += 1
+                
+            # 检查是否出现新高
+            if klu.high > self.high:
+                new_highs += 1
+                
+            # 检查下跌趋势
+            if i > 0 and klu.close < subsequent_klus[i-1].close:
+                downward_trend += 1
+        
+        # 强烈笔终结：跌破关键位且无新高
+        if broken_key_levels >= 1 and new_highs == 0 and downward_trend >= 2:
+            return 2
+            
+        # 可能笔终结：部分条件满足
+        if (broken_key_levels >= 1 and new_highs <= 1) or (new_highs == 0 and downward_trend >= 3):
+            return 1
+            
+        # 明显中继：出现新高且未跌破关键位
+        if new_highs >= 2 and broken_key_levels == 0:
+            return -2
+            
+        # 中继倾向：出现新高
+        if new_highs >= 1:
+            return -1
+            
+        return 0
+    
+    def _check_bottom_bi_ending(self, subsequent_klus):
+        """检查底分型是否为笔终结"""
+        # 强烈笔终结特征：
+        # 1. 后续K线持续上涨，且突破关键位置
+        # 2. 没有新的更低的低点出现
+        
+        broken_key_levels = 0
+        new_lows = 0
+        upward_trend = 0
+        
+        # 检查关键价位突破
+        first_high = self.pre.high
+        middle_high = self.high
+        key_resistance = max(first_high, middle_high)
+        
+        for i, klu in enumerate(subsequent_klus):
+            # 检查是否突破关键阻力
+            if klu.high > key_resistance:
+                broken_key_levels += 1
+                
+            # 检查是否出现新低
+            if klu.low < self.low:
+                new_lows += 1
+                
+            # 检查上涨趋势
+            if i > 0 and klu.close > subsequent_klus[i-1].close:
+                upward_trend += 1
+        
+        # 强烈笔终结：突破关键位且无新低
+        if broken_key_levels >= 1 and new_lows == 0 and upward_trend >= 2:
+            return 2
+            
+        # 可能笔终结：部分条件满足
+        if (broken_key_levels >= 1 and new_lows <= 1) or (new_lows == 0 and upward_trend >= 3):
+            return 1
+            
+        # 明显中继：出现新低且未突破关键位
+        if new_lows >= 2 and broken_key_levels == 0:
+            return -2
+            
+        # 中继倾向：出现新低
+        if new_lows >= 1:
+            return -1
+            
+        return 0
+    
+    def _check_post_fx_confirmation(self):
+        """
+        检查分型后的走势确认
+        返回值：-1到1的调整分数
+        """
+        if not self.next:
+            return 0
+            
+        score = 0
+        
+        # 检查第三根K线的确认
+        third_klu = self.next
+        
+        if self.fx_type == Chan_FX_TYPE.TOP:
+            # 顶分型：第三根K线应该走弱
+            middle_price = (self.high + self.low) / 2
+            
+            if third_klu.close < middle_price:
+                score += 0.5
+            if third_klu.low < self.pre.low:  # 跌破第一根K线低点
+                score += 0.5
+            if third_klu.close < third_klu.open and abs(third_klu.close - third_klu.open) > abs(self.close - self.open) * 0.5:
+                score += 0.3  # 明显阴线
+                
+        else:  # BOTTOM
+            # 底分型：第三根K线应该走强
+            middle_price = (self.high + self.low) / 2
+            
+            if third_klu.close > middle_price:
+                score += 0.5
+            if third_klu.high > self.pre.high:  # 突破第一根K线高点
+                score += 0.5
+            if third_klu.close > third_klu.open and abs(third_klu.close - third_klu.open) > abs(self.close - self.open) * 0.5:
+                score += 0.3  # 明显阳线
+        
+        return min(1, max(-1, score))
+    
+    def _check_fx_quality(self):
+        """
+        检查分型本身的质量
+        返回值：-1到1的调整分数
+        """
+        score = 0
+        
+        # 检查分型的标准性
+        if self.fx_type == Chan_FX_TYPE.TOP:
+            # 高点突出程度
+            high_diff1 = (self.high - self.pre.high) / self.high if self.high > 0 else 0
+            high_diff2 = (self.high - self.next.high) / self.high if self.high > 0 else 0
+            min_diff = min(high_diff1, high_diff2)
+            
+            if min_diff > 0.03:  # 非常突出
+                score += 0.5
+            elif min_diff > 0.01:  # 比较突出
+                score += 0.2
+            elif min_diff < 0.003:  # 不够突出
+                score -= 0.5
+                
+        else:  # BOTTOM
+            # 低点突出程度
+            low_diff1 = (self.pre.low - self.low) / self.pre.low if self.pre.low > 0 else 0
+            low_diff2 = (self.next.low - self.low) / self.next.low if self.next.low > 0 else 0
+            min_diff = min(low_diff1, low_diff2)
+            
+            if min_diff > 0.03:  # 非常突出
+                score += 0.5
+            elif min_diff > 0.01:  # 比较突出
+                score += 0.2
+            elif min_diff < 0.003:  # 不够突出
+                score -= 0.5
+        
+        # 检查量价配合
+        avg_volume = self._get_avg_volume(lookback=5)
+        if avg_volume > 0:
+            volume_ratio = self.volume / avg_volume
+            if volume_ratio > 1.5:
+                score += 0.3
+            elif volume_ratio < 0.7:
+                score -= 0.2
+        
+        return min(1, max(-1, score))
+        
+    def _get_avg_volume(self, lookback=5):
+        """获取前N根K线平均成交量"""
+        volumes = []
+        temp = self.pre
+        
+        for i in range(lookback):
+            if temp:
+                volumes.append(temp.volume)
+                temp = temp.pre if hasattr(temp, 'pre') else None
+            else:
+                break
+                
+        return sum(volumes) / len(volumes) if volumes else self.volume
+        
+    def get_fx_signal(self):
+        """
+        获取分型交易信号
+        返回: (信号类型, 强度, 建议)
+        """
+        if not self.fx_confirmed:
+            return ("无信号", 0, "等待分型确认")
+            
+        strength_level = "弱"
+        if self.fx_strength >= 80:
+            strength_level = "极强"
+        elif self.fx_strength >= 65:
+            strength_level = "强"
+        elif self.fx_strength >= 50:
+            strength_level = "中等"
+            
+        if self.fx_type == Chan_FX_TYPE.TOP:
+            signal_type = f"{strength_level}顶分型"
+            if self.fx_strength >= 65:
+                suggestion = "考虑减仓或止盈"
+            else:
+                suggestion = "谨慎观望"
+        else:
+            signal_type = f"{strength_level}底分型"
+            if self.fx_strength >= 65:
+                suggestion = "考虑建仓或加仓"
+            else:
+                suggestion = "谨慎观望"
+                
+        return (signal_type, self.fx_strength, suggestion)
+        
+    def update_realtime_analysis(self):
+        """
+        更新实时分析（在每根K线完成时调用）
+        """
+        self.detect_realtime_fx()
+        if self.fx_confirmed:
+            self.calculate_realtime_fx_strength()
+            
     def set_idx(self, idx):
         self.idx = idx
         self.index = idx
+        
     def set_indicators(self, item):
         self.macd = float(item['macd']) if 'macd' in item and item['macd'] else 0
         self.signal = float(item['macdsignal']) if 'macdsignal' in item and item['macdsignal'] else 0
@@ -39,6 +406,10 @@ class ChanKLU:
         
         self.rsi = float(item['rsi']) if 'rsi' in item and item['rsi'] else 0
         self.volume_ratio = float(item['volume_ratio']) if 'volume_ratio' in item and item['volume_ratio'] else 0
+        
+        # 设置指标后更新实时分析
+        self.update_realtime_analysis()
+        
     def get_feature_data(self):
         features = dict()
         features['klu_close'] = self.close
@@ -58,4 +429,18 @@ class ChanKLU:
         features['klu_ma250'] = self.ma250
         features['klu_rsi'] = self.rsi
         features['klu_volume_ratio'] = self.volume_ratio
+        
+        # === 新增：实时分型特征 ===
+        # 将枚举转换为数值：UNKNOWN=0, TOP=1, BOTTOM=-1
+        if self.fx_type == Chan_FX_TYPE.TOP:
+            fx_type_value = 1
+        elif self.fx_type == Chan_FX_TYPE.BOTTOM:
+            fx_type_value = -1
+        else:
+            fx_type_value = 0
+            
+        features['klu_fx_type'] = fx_type_value
+        features['klu_fx_strength'] = self.fx_strength
+        features['klu_fx_confirmed'] = 1 if self.fx_confirmed else 0
+        
         return features

ChanLun.py

@@ -67,12 +67,12 @@ class ChanLun():
 			else:
 				print(bi.start_klc.end_time, bi.dir, bi.is_sure)
 	def check_fx(self, klc):
-		if klc.pre and klc.next:
+		if klc.pre and klc.next and klc.next.end_klu:
 			if klc.high > klc.pre.high and klc.high > klc.next.high:
 				klc.set_fx(Chan_FX_TYPE.TOP)
 				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time,klc.fx, "TOP")
 				return Chan_FX_TYPE.TOP
-		if klc.pre and klc.next:
+		if klc.pre and klc.next and klc.next.end_klu:
 			if klc.low < klc.pre.low and klc.low < klc.next.low:
 				klc.set_fx(Chan_FX_TYPE.BOTTOM)
 				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time,klc.fx, "BOTTOM")
@@ -149,9 +149,9 @@ class ChanLun():
 			klc = klc_list[klc_index]
 			if klc.end_klu and klc.end_klu.idx == index:
 				klc_index += 1
-				if klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2:
+				if (klc.klc_fx_type == Chan_KLC_FX.TOP1 or klc.klc_fx_type == Chan_KLC_FX.TOP2) and klc.contain_klu_fx():
 					fx_list.append(1)
-				elif klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+				elif (klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc.klc_fx_type == Chan_KLC_FX.BOTTOM2) and klc.contain_klu_fx():
 					fx_list.append(-1)
 				else:
 					fx_list.append(0)
@@ -235,6 +235,7 @@ class ChanLun():
 	def get_kl_data(self, dataframe:DataFrame):
 		fields = "time,open,high,low,close,volume"
 		klu_list = []
+		last_klu = None
 		for i in range(0, len(dataframe)):
 			item = dataframe.iloc[i]
 			date = item['date']
@@ -258,8 +259,13 @@ class ChanLun():
 			klu = ChanKLU(time_str, o, h, l, c, v)
 			klu.set_idx(i)
 			klu_list.append(klu)
+			if last_klu:
+				klu.set_pre(last_klu)
+				last_klu.set_next(klu)
+				last_klu.detect_realtime_fx()
 			if 'macd' in item:
 				klu.set_indicators(item)
+			last_klu = klu
 		return klu_list
 	def cal_volume_ratio(self, dataframe, window=10):
 		df = dataframe.copy()

algorithm_comparison_test.py

@@ -0,0 +1,223 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+KLU与KLC分型强度算法一致性测试
+验证两种算法在相同数据下是否产生一致的结果
+"""
+
+from ChanKLU import ChanKLU
+from ChanKLC import ChanKLC
+from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR
+import pandas as pd
+from datetime import datetime, timedelta
+
+def create_test_data():
+    """创建测试用的K线数据"""
+    test_cases = [
+        # 测试用例1：标准顶分型
+        {
+            'name': '标准顶分型',
+            'data': [
+                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
+                {'open': 101, 'high': 105, 'low': 100, 'close': 103, 'volume': 1500}, # K2 (顶分型中心)
+                {'open': 103, 'high': 104, 'low': 98, 'close': 99, 'volume': 1200},   # K3
+            ]
+        },
+        # 测试用例2：标准底分型
+        {
+            'name': '标准底分型',
+            'data': [
+                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
+                {'open': 101, 'high': 103, 'low': 95, 'close': 97, 'volume': 1500},   # K2 (底分型中心)
+                {'open': 97, 'high': 104, 'low': 96, 'close': 102, 'volume': 1200},    # K3
+            ]
+        },
+        # 测试用例3：强势顶分型（放量+下影线）
+        {
+            'name': '强势顶分型',
+            'data': [
+                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
+                {'open': 101, 'high': 108, 'low': 100, 'close': 102, 'volume': 2500}, # K2 (强顶分型)
+                {'open': 102, 'high': 103, 'low': 95, 'close': 96, 'volume': 1800},   # K3 (大阴线确认)
+            ]
+        }
+    ]
+    return test_cases
+
+def setup_klu_chain(data_list):
+    """设置KLU链"""
+    klus = []
+    base_time = datetime.now()
+    
+    for i, data in enumerate(data_list):
+        time_str = (base_time + timedelta(minutes=i)).strftime("%Y-%m-%d %H:%M:%S")
+        klu = ChanKLU(time_str, data['open'], data['high'], data['low'], data['close'], data['volume'])
+        klu.set_idx(i)
+        
+        # 设置基础技术指标
+        indicators = {
+            'ma5': data['close'] + (i-1) * 0.1,
+            'ma10': data['close'] + (i-1) * 0.05,
+            'rsi': 50 + (i % 3 - 1) * 15,
+            'macd': (i % 3 - 1) * 0.01,
+            'macdhist': (i % 2) * 0.005,
+            'volume_ratio': 1.0 + (i % 2) * 0.3
+        }
+        klu.set_indicators(indicators)
+        klus.append(klu)
+    
+    # 建立前后关系
+    for i in range(len(klus)):
+        if i > 0:
+            klus[i].set_pre(klus[i-1])
+        if i < len(klus) - 1:
+            klus[i].set_next(klus[i+1])
+    
+    return klus
+
+def setup_klc_chain(data_list):
+    """设置KLC链（基于KLU）"""
+    klus = setup_klu_chain(data_list)
+    klcs = []
+    
+    # 为简化测试，假设每个KLU对应一个KLC（无包含关系处理）
+    for i, klu in enumerate(klus):
+        klc = ChanKLC(klu, i, Chan_KLINE_DIR.UP)
+        klc.set_end_klu(klu)
+        klcs.append(klc)
+    
+    # 建立前后关系
+    for i in range(len(klcs)):
+        if i > 0:
+            klcs[i].set_pre(klcs[i-1])
+        if i < len(klcs) - 1:
+            klcs[i].set_next(klcs[i+1])
+    
+    # 设置分型类型
+    if len(klcs) >= 3:
+        middle_klc = klcs[1]
+        if (middle_klc.high > klcs[0].high and middle_klc.high > klcs[2].high):
+            middle_klc.set_fx(Chan_FX_TYPE.TOP)
+        elif (middle_klc.low < klcs[0].low and middle_klc.low < klcs[2].low):
+            middle_klc.set_fx(Chan_FX_TYPE.BOTTOM)
+    
+    return klcs
+
+def compare_algorithms(test_cases):
+    """对比KLU和KLC算法"""
+    
+    print("=" * 80)
+    print("KLU与KLC分型强度算法一致性测试")
+    print("=" * 80)
+    
+    for case in test_cases:
+        print(f"\n🔍 测试用例: {case['name']}")
+        print("-" * 50)
+        
+        # 准备数据
+        klus = setup_klu_chain(case['data'])
+        klcs = setup_klc_chain(case['data'])
+        
+        if len(klus) >= 3 and len(klcs) >= 3:
+            middle_klu = klus[1]
+            middle_klc = klcs[1]
+            
+            # KLU分析
+            middle_klu.update_realtime_analysis()
+            klu_fx_type = middle_klu.fx_type
+            klu_strength = middle_klu.fx_strength
+            klu_confirmed = middle_klu.fx_confirmed
+            
+            # KLC分析 
+            klc_fx_type = middle_klc.fx
+            klc_strength_raw = middle_klc.cal_fx_strength()  # -3到3
+            klc_strength_converted = int((klc_strength_raw + 3) * 100 / 6)  # 转换为0-100
+            
+            # 输出对比结果
+            print(f"K线数据: {case['data'][1]}")
+            print(f"\nKLU算法结果:")
+            print(f"  分型类型: {klu_fx_type}")
+            print(f"  分型强度: {klu_strength}")
+            print(f"  是否确认: {klu_confirmed}")
+            
+            print(f"\nKLC算法结果:")
+            print(f"  分型类型: {klc_fx_type}")
+            print(f"  分型强度(原始): {klc_strength_raw}")
+            print(f"  分型强度(转换): {klc_strength_converted}")
+            
+            # 一致性检查
+            type_consistent = (klu_fx_type == klc_fx_type)
+            strength_diff = abs(klu_strength - klc_strength_converted)
+            strength_consistent = strength_diff <= 10  # 允许10分以内的差异
+            
+            print(f"\n一致性检查:")
+            print(f"  分型类型一致: {'✅' if type_consistent else '❌'}")
+            print(f"  强度差异: {strength_diff}分 {'✅' if strength_consistent else '❌'}")
+            
+            if not type_consistent or not strength_consistent:
+                print(f"  ⚠️  算法结果不一致!")
+            else:
+                print(f"  ✅ 算法结果一致")
+        else:
+            print("❌ 数据不足，无法进行对比")
+
+def detailed_strength_analysis():
+    """详细的强度分析对比"""
+    print("\n" + "=" * 80)
+    print("详细强度分析对比")
+    print("=" * 80)
+    
+    # 创建一个明确的强分型案例
+    strong_top_data = [
+        {'open': 100, 'high': 101, 'low': 99, 'close': 100, 'volume': 1000},
+        {'open': 100, 'high': 110, 'low': 99, 'close': 102, 'volume': 3000},  # 强顶分型
+        {'open': 102, 'high': 103, 'low': 92, 'close': 93, 'volume': 2000},   # 强确认
+        {'open': 93, 'high': 94, 'low': 90, 'close': 91, 'volume': 1500},     # 继续下跌
+        {'open': 91, 'high': 92, 'low': 88, 'close': 89, 'volume': 1200},     # 进一步确认
+    ]
+    
+    klus = setup_klu_chain(strong_top_data)
+    
+    if len(klus) >= 5:
+        target_klu = klus[1]  # 目标分型K线
+        
+        print(f"分析目标: 第2根K线 (索引1)")
+        print(f"K线数据: {strong_top_data[1]}")
+        
+        # 更新分析
+        target_klu.update_realtime_analysis()
+        
+        print(f"\n分型检测结果:")
+        print(f"  分型类型: {target_klu.fx_type}")
+        print(f"  分型确认: {target_klu.fx_confirmed}")
+        print(f"  最终强度: {target_klu.fx_strength}")
+        
+        # 显示中间计算过程（需要重新调用以获取详细信息）
+        if target_klu.fx_confirmed:
+            print(f"\n强度计算过程:")
+            is_bi_end = target_klu._check_if_bi_ending_fx()
+            post_confirmation = target_klu._check_post_fx_confirmation()
+            fx_quality = target_klu._check_fx_quality()
+            
+            print(f"  笔终结判断: {is_bi_end}")
+            print(f"  后续确认: {post_confirmation}")
+            print(f"  分型质量: {fx_quality}")
+            
+            raw_score = is_bi_end + post_confirmation + fx_quality
+            final_raw = max(-3, min(3, raw_score))
+            converted_score = int((final_raw + 3) * 100 / 6)
+            
+            print(f"  原始总分: {raw_score} -> {final_raw}")
+            print(f"  转换分数: {converted_score}")
+
+if __name__ == "__main__":
+    # 运行测试
+    test_cases = create_test_data()
+    compare_algorithms(test_cases)
+    
+    # 详细分析
+    detailed_strength_analysis()
+    
+    print("\n" + "=" * 80)
+    print("测试完成！")
+    print("=" * 80) 

realtime_fx_example.py

@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+实时K线分型强弱判断示例
+解决KLC滞后问题，提供即时的分型信号
+"""
+
+from ChanKLU import ChanKLU
+from ChanEnum import Chan_FX_TYPE
+import pandas as pd
+from datetime import datetime, timedelta
+
+class RealtimeFxAnalyzer:
+    """实时分型分析器"""
+    
+    def __init__(self):
+        self.klu_list = []
+        self.latest_signals = []
+        
+    def add_kline(self, time, open_price, high, low, close, volume, indicators=None):
+        """
+        添加新的K线数据并进行实时分析
+        
+        Args:
+            time: 时间
+            open_price, high, low, close, volume: K线数据
+            indicators: 技术指标字典 {'macd': xx, 'rsi': xx, 'ma5': xx, ...}
+        """
+        # 创建新的KLU对象
+        new_klu = ChanKLU(time, open_price, high, low, close, volume)
+        
+        # 设置技术指标
+        if indicators:
+            new_klu.set_indicators(indicators)
+        
+        # 设置索引
+        new_klu.set_idx(len(self.klu_list))
+        
+        # 建立前后关系链
+        if len(self.klu_list) >= 1:
+            prev_klu = self.klu_list[-1]
+            new_klu.set_pre(prev_klu)
+            prev_klu.set_next(new_klu)
+            
+        # 如果有足够的数据，设置前一根K线的next关系
+        if len(self.klu_list) >= 2:
+            prev_prev_klu = self.klu_list[-2]
+            prev_prev_klu.set_next(self.klu_list[-1])
+            
+        self.klu_list.append(new_klu)
+        
+        # 实时分析最近的K线分型
+        self._analyze_recent_fractals()
+        
+        return new_klu
+        
+    def _analyze_recent_fractals(self):
+        """分析最近的分型情况"""
+        if len(self.klu_list) < 3:
+            return
+            
+        # 检查倒数第二根K线的分型（因为需要左右两根K线确认）
+        target_idx = len(self.klu_list) - 2
+        if target_idx >= 1:
+            target_klu = self.klu_list[target_idx]
+            
+            # 进行实时分型分析
+            target_klu.update_realtime_analysis()
+            
+            # 如果发现分型，记录信号
+            if target_klu.fx_confirmed:
+                signal = target_klu.get_fx_signal()
+                signal_info = {
+                    'time': target_klu.time,
+                    'price': target_klu.close,
+                    'signal_type': signal[0],
+                    'strength': signal[1],
+                    'suggestion': signal[2],
+                    'fx_type': target_klu.fx_type
+                }
+                
+                self.latest_signals.append(signal_info)
+                
+                # 保持最近20个信号
+                if len(self.latest_signals) > 20:
+                    self.latest_signals.pop(0)
+                    
+                print(f"🔔 分型信号: {signal_info['time']} - {signal_info['signal_type']} "
+                      f"(强度: {signal_info['strength']}) - {signal_info['suggestion']}")
+    
+    def get_latest_signal(self):
+        """获取最新的分型信号"""
+        return self.latest_signals[-1] if self.latest_signals else None
+        
+    def get_current_fx_status(self):
+        """获取当前分型状态统计"""
+        if len(self.klu_list) < 10:
+            return {"status": "数据不足"}
+            
+        recent_10 = self.klu_list[-10:]
+        
+        top_fx_count = sum(1 for klu in recent_10 if klu.fx_type == Chan_FX_TYPE.TOP)
+        bottom_fx_count = sum(1 for klu in recent_10 if klu.fx_type == Chan_FX_TYPE.BOTTOM)
+        
+        strong_fx_count = sum(1 for klu in recent_10 if klu.fx_strength >= 65)
+        
+        return {
+            "最近10根K线": len(recent_10),
+            "顶分型数量": top_fx_count,
+            "底分型数量": bottom_fx_count,
+            "强分型数量": strong_fx_count,
+            "最新K线时间": recent_10[-1].time,
+            "最新信号": self.get_latest_signal()
+        }
+
+def simulate_realtime_trading():
+    """模拟实时交易场景"""
+    
+    print("=== 实时K线分型分析示例 ===\n")
+    
+    # 创建分析器
+    analyzer = RealtimeFxAnalyzer()
+    
+    # 模拟实时K线数据流
+    base_time = datetime.now()
+    base_price = 100.0
+    
+    print("开始接收K线数据...\n")
+    
+    for i in range(20):
+        # 模拟价格波动
+        if i < 5:  # 上涨阶段
+            price_change = 0.5
+        elif i < 10:  # 下跌阶段  
+            price_change = -0.8
+        elif i < 15:  # 震荡阶段
+            price_change = 0.3 * ((-1) ** i)
+        else:  # 再次上涨
+            price_change = 0.6
+            
+        current_price = base_price + price_change
+        
+        # 构造K线数据
+        open_price = base_price
+        high = max(open_price, current_price) + abs(price_change) * 0.2
+        low = min(open_price, current_price) - abs(price_change) * 0.2
+        close = current_price
+        volume = 1000 + i * 50
+        
+        # 模拟技术指标
+        indicators = {
+            'ma5': base_price + (i - 10) * 0.1,
+            'ma10': base_price + (i - 10) * 0.05,
+            'rsi': 50 + (i % 7 - 3) * 10,
+            'macd': (i % 6 - 3) * 0.01,
+            'macdhist': (i % 4 - 2) * 0.005,
+            'volume_ratio': 1.0 + (i % 3 - 1) * 0.2
+        }
+        
+        # 添加K线数据
+        kline_time = base_time + timedelta(minutes=i)
+        analyzer.add_kline(
+            time=kline_time.strftime("%Y-%m-%d %H:%M:%S"),
+            open_price=open_price,
+            high=high,
+            low=low,
+            close=close,
+            volume=volume,
+            indicators=indicators
+        )
+        
+        base_price = current_price
+        
+        # 每5根K线显示一次状态
+        if (i + 1) % 5 == 0:
+            status = analyzer.get_current_fx_status()
+            print(f"\n--- 第{i+1}根K线后的状态 ---")
+            for key, value in status.items():
+                if key != "最新信号":
+                    print(f"{key}: {value}")
+            
+            if "最新信号" in status and status["最新信号"]:
+                signal = status["最新信号"]
+                print(f"最新信号: {signal['signal_type']} (强度: {signal['strength']})")
+            print()
+    
+    print("\n=== 所有分型信号汇总 ===")
+    for signal in analyzer.latest_signals:
+        print(f"{signal['time']} | {signal['signal_type']} | 强度: {signal['strength']} | {signal['suggestion']}")
+
+def compare_latency():
+    """对比KLC和KLU方法的延迟差异"""
+    
+    print("\n=== 延迟对比分析 ===")
+    print("假设场景：连续包含关系的K线序列")
+    print("原始K线: K1, K2(包含K1), K3(包含K2), K4(突破), K5, K6")
+    print()
+    
+    print("KLC方法:")
+    print("- 需要等待K4确认包含关系结束")
+    print("- KLC1 = [K1+K2+K3], 在K4完成时才确定")
+    print("- 分型检测: 需要等待KLC1, KLC2, KLC3")
+    print("- 实际延迟: 可能6-8根原始K线")
+    print()
+    
+    print("KLU实时方法:")
+    print("- 每根K线完成时立即检测")
+    print("- K3完成时就能检测K2的分型状态")
+    print("- 实际延迟: 最多1根K线")
+    print()
+    
+    print("延迟改善: 从6-8根K线缩短到1根K线")
+    print("时间价值: 在5分钟K线下，可节省25-40分钟的反应时间")
+
+if __name__ == "__main__":
+    # 运行模拟
+    simulate_realtime_trading()
+    
+    # 显示延迟对比
+    compare_latency() 

strategies/ChanLun_BTC_15.py

@@ -14,16 +14,19 @@ import talib.abstract as ta
 from pandas import DataFrame
 from datetime import datetime, timedelta
 from freqtrade.persistence import Trade
-from typing import Optional
+from typing import Optional, List, Dict
 import logging
 logger = logging.getLogger(__name__)
+from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
 ### Now you can use logger.info('asfd') to log
 # freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
 
 # freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --export none --strategy-path ./user_data/Chan/strategies --timerange=20250525-
 # freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
-# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
-# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250401
+# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs SOL/USDT:USDT --timerange=20250405-
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250501
+# freqtrade live-backtest -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
 
 # sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
 # sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
@@ -35,10 +38,10 @@ class ChanLun_BTC_15(IStrategy):
 	# This attribute will be overridden if the config file contains "minimal_roi"
 	# 30m and 1h
 	minimal_roi = {
-		"0": 0.60,
-		"360": 0.2,
-		"640": 0.1,
-		"1200": 0
+		"0": 0.15,
+		"240": 0.1,
+		"480": 0.02,
+		"960": 0
 	}
 	# 5m and 15m
 	minimal_roi_1 = {
@@ -61,14 +64,13 @@ class ChanLun_BTC_15(IStrategy):
 		"3600": 0
 	}
 	can_short = True
-	lev = 50.0
-	stoploss = -0.3
+	lev = 10
+	stoploss = -0.8
 	trailing_stop = False
 	trailing_stop_positive = 0.025
 	trailing_stop_positive_offset = 0.045
 	trailing_only_offset_is_reached = False
 
-	position_adjustment_enable = True
 	startup_candle_count = 600
 
 	time5 = 5
@@ -174,8 +176,8 @@ class ChanLun_BTC_15(IStrategy):
 		dataframe.loc[
 			(
 				#(dataframe['state'] == "-30")
-				(dataframe[state_str].shift(self.time5) > 1.0) &
-				(dataframe[fx_str].shift(self.time5) == -1)
+				(dataframe[state_str].shift(self.time5*2) > 0) &
+				(dataframe[fx_str].shift(self.time5*2) == -1)
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
@@ -185,8 +187,8 @@ class ChanLun_BTC_15(IStrategy):
 		dataframe.loc[
 			(
 				#(dataframe['state'] == "-30")
-				(dataframe[state_str].shift(self.time5) > 1.0) &
-				(dataframe[fx_str].shift(self.time5) == 1)
+				(dataframe[state_str].shift(self.time5*2) > 0) &
+				(dataframe[fx_str].shift(self.time5*2) == 1)
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
@@ -200,8 +202,8 @@ class ChanLun_BTC_15(IStrategy):
 		dataframe.loc[
 			(
 				#(dataframe['state']== "30")
-				(dataframe[state_str].shift(self.time5) > 1.0) &
-				(dataframe[fx_str].shift(self.time5) == 1)
+				(dataframe[state_str].shift(self.time5*2) > 0) &
+				(dataframe[fx_str].shift(self.time5*2) == 1)
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
 			),
@@ -209,8 +211,8 @@ class ChanLun_BTC_15(IStrategy):
 		dataframe.loc[
 			(
 				#(dataframe['state']== "30")
-				(dataframe[state_str].shift(self.time5) > 1.0) &
-				(dataframe[fx_str].shift(self.time5) == -1)
+				(dataframe[state_str].shift(self.time5*2) > 0) &
+				(dataframe[fx_str].shift(self.time5*2) == -1)
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
 				#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
 			),

web/nginx/logs/error.log

@@ -0,0 +1,18 @@
+2025/05/27 01:57:54 [notice] 1#1: using the "epoll" event method
+2025/05/27 01:57:54 [notice] 1#1: nginx/1.27.5
+2025/05/27 01:57:54 [notice] 1#1: built by gcc 12.2.0 (Debian 12.2.0-14) 
+2025/05/27 01:57:54 [notice] 1#1: OS: Linux 6.10.14-linuxkit
+2025/05/27 01:57:54 [notice] 1#1: getrlimit(RLIMIT_NOFILE): 1048576:1048576
+2025/05/27 01:57:54 [notice] 1#1: start worker processes
+2025/05/27 01:57:54 [notice] 1#1: start worker process 20
+2025/05/27 01:57:54 [notice] 1#1: start worker process 21
+2025/05/27 01:57:54 [notice] 1#1: start worker process 22
+2025/05/27 01:57:54 [notice] 1#1: start worker process 23
+2025/05/27 01:57:54 [notice] 1#1: start worker process 24
+2025/05/27 01:57:54 [notice] 1#1: start worker process 25
+2025/05/27 01:57:54 [notice] 1#1: start worker process 26
+2025/05/27 01:57:54 [notice] 1#1: start worker process 27
+2025/05/27 01:57:54 [notice] 1#1: start worker process 28
+2025/05/27 01:57:54 [notice] 1#1: start worker process 29
+2025/05/27 01:57:54 [notice] 1#1: start worker process 30
+2025/05/27 01:57:54 [notice] 1#1: start worker process 31

web/templates/index.html

@@ -2941,7 +2941,7 @@
                                 is_strong_fx: fx.is_strong_fx
                             });
                             const displayText = `${fx.fx_strength_level} ${fx.fx_strength.toFixed(1)}`;
-                            if (fx.fx_strength < 1) {
+                            if (fx.fx_strength < 1.4) {
                                 displayText = ''
                             }
                             console.log('显示文本:', displayText);