add fx strentth
This commit is contained in:
jackyu66git
+378
-1
@@ -1130,4 +1130,381 @@ class ChanKLC():
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else:
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features['klc_star_pattern'] = 0
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return features
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# ===== 分型强度特征 =====
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# 添加分型强度相关特征
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features['klc_fx_strength'] = self.calculate_fx_strength()
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features['klc_fx_strength_level'] = self.get_fx_strength_level()
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features['klc_is_strong_fx'] = 1 if self.is_strong_fx() else 0
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# 分型强度分类特征
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fx_strength = features['klc_fx_strength']
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features['klc_fx_strength_extreme'] = 1 if fx_strength >= 80 else 0 # 极强分型
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features['klc_fx_strength_strong'] = 1 if 60 <= fx_strength < 80 else 0 # 强分型
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features['klc_fx_strength_medium'] = 1 if 40 <= fx_strength < 60 else 0 # 中等分型
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features['klc_fx_strength_weak'] = 1 if 20 <= fx_strength < 40 else 0 # 弱分型
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features['klc_fx_strength_very_weak'] = 1 if fx_strength < 20 else 0 # 极弱分型
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return features
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def calculate_fx_strength(self):
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"""
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基于专业缠论理论的分型强度评估体系
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返回值：0-100的强度分数，数值越大表示分型越强
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评分卡系统（总分29分，转换为100分制）：
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- 振幅比例：25%权重，最高5分
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- 量能配合：20%权重，最高5分
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- 均线位置：15%权重，最高5分
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- 形成速度：10%权重，最高4分
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- 次级别确认：30%权重，最高10分
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"""
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if self.fx == Chan_FX_TYPE.UNKNOWN or not self.pre or not self.next:
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return 0
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# ===== 一、基础要素确认（先决条件） =====
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if not self._verify_basic_fx_structure():
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return 0
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total_score = 0
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max_score = 29 # 5+5+5+4+10
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# ===== 二、振幅比例评估 (25%权重，最高5分) =====
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amplitude_score = self._calculate_amplitude_score()
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total_score += amplitude_score
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# ===== 三、量能配合评估 (20%权重，最高5分) =====
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volume_score = self._calculate_volume_score()
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total_score += volume_score
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# ===== 四、均线位置评估 (15%权重，最高5分) =====
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ma_score = self._calculate_ma_position_score()
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total_score += ma_score
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# ===== 五、形成速度评估 (10%权重，最高4分) =====
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speed_score = self._calculate_formation_speed_score()
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total_score += speed_score
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# ===== 六、次级别确认评估 (30%权重，最高10分) =====
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confirmation_score = self._calculate_confirmation_score()
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total_score += confirmation_score
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# 转换为100分制
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final_score = (total_score / max_score) * 100
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return round(final_score, 2)
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def _verify_basic_fx_structure(self):
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"""
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验证基础分型要素（先决条件）
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只验证最核心的分型定义，避免过度严格
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"""
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if not self.pre or not self.next:
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return False
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if self.fx == Chan_FX_TYPE.TOP:
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# 顶分型核心要素：中间K线高点必须严格高于两侧
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if not (self.high > self.pre.high and self.high > self.next.high):
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return False
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elif self.fx == Chan_FX_TYPE.BOTTOM:
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# 底分型核心要素：中间K线低点必须严格低于两侧
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if not (self.low < self.pre.low and self.low < self.next.low):
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return False
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return True
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def _calculate_amplitude_score(self):
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"""
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计算振幅比例得分 (最高5分)
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强势分型：分型区间振幅>近期平均振幅的150% = 5分
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标准分型：介于80%-150%之间 = 3分
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弱势分型：<80% = 1分
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"""
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score = 0
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# 计算分型区间振幅
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if self.fx == Chan_FX_TYPE.TOP:
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fx_amplitude = self.high - min(self.pre.low, self.next.low)
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# 加分项：右侧K线低点低于左侧K线低点（经典缠论强势特征）
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if self.next.low < self.pre.low:
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score += 1
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else: # BOTTOM
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fx_amplitude = max(self.pre.high, self.next.high) - self.low
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# 加分项：右侧K线高点高于左侧K线高点（经典缠论强势特征）
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if self.next.high > self.pre.high:
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score += 1
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# 计算近期平均振幅（前10根K线的ATR）
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avg_amplitude = self._calculate_recent_atr(lookback=10)
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if avg_amplitude <= 0:
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return max(1, score) # 确保至少有基础分
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amplitude_ratio = fx_amplitude / avg_amplitude
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if amplitude_ratio >= 1.5: # >150%
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score += 4 # 基础4分 + 可能的经典形态1分 = 最高5分
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elif amplitude_ratio >= 1.0: # 100%-150%
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score += 2 + int((amplitude_ratio - 1.0) * 4) # 2-4分线性插值
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elif amplitude_ratio >= 0.8: # 80%-100%
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score += 1 + int((amplitude_ratio - 0.8) * 5) # 1-2分线性插值
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else: # <80%
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score += 1
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return min(5, score)
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def _calculate_volume_score(self):
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"""
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计算量能配合得分 (最高5分)
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顶分型：第二根K线放量滞涨为强烈信号
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底分型：第三根K线放量回升为有效确认
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"""
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# 计算前5根K线平均成交量
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avg_volume = self._calculate_average_volume(lookback=5)
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if avg_volume <= 0:
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return 1
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if self.fx == Chan_FX_TYPE.TOP:
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# 顶分型：检查第二根K线（当前）是否放量滞涨
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volume_ratio = self.volume / avg_volume
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# 判断是否滞涨：收盘价位于K线下半部分
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price_position = (self.close - self.low) / (self.high - self.low) if self.high > self.low else 0.5
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if volume_ratio >= 2.0 and price_position <= 0.4: # 放量+滞涨
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return 5
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elif volume_ratio >= 1.5 and price_position <= 0.5:
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return 4
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elif volume_ratio >= 1.2:
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return 3
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else:
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return 1
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else: # BOTTOM
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# 底分型：检查第三根K线是否放量回升
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next_volume_ratio = self.next.volume / avg_volume if hasattr(self.next, 'volume') else 1
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# 判断是否回升：第三根K线收盘价相对位置较高
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if self.next.high > self.next.low:
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next_price_position = (self.next.close - self.next.low) / (self.next.high - self.next.low)
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else:
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next_price_position = 0.5
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if next_volume_ratio >= 2.0 and next_price_position >= 0.6: # 放量+回升
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return 5
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elif next_volume_ratio >= 1.5 and next_price_position >= 0.5:
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return 4
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elif next_volume_ratio >= 1.2:
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return 3
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else:
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return 1
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def _calculate_ma_position_score(self):
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"""
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计算均线位置得分 (最高5分)
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强势顶分型需在5/10均线乖离率>5%时出现
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有效底分型常伴随MACD底背离
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"""
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score = 0
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# 获取均线数据
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klu_features = self.cal_klu_features()
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if self.fx == Chan_FX_TYPE.TOP:
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# 顶分型：检查与5日和10日均线的乖离率
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ma5_bias = 0
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ma10_bias = 0
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if 'klu_ma5' in klu_features and klu_features['klu_ma5'] > 0:
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ma5_bias = (self.close - klu_features['klu_ma5']) / klu_features['klu_ma5']
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if 'klu_ma10' in klu_features and klu_features['klu_ma10'] > 0:
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ma10_bias = (self.close - klu_features['klu_ma10']) / klu_features['klu_ma10']
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# 乖离率>5%为强势信号
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if ma5_bias > 0.05 or ma10_bias > 0.05:
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score += 3
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elif ma5_bias > 0.03 or ma10_bias > 0.03:
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score += 2
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elif ma5_bias > 0 or ma10_bias > 0:
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score += 1
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else: # BOTTOM
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# 底分型：检查MACD背离和均线支撑
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# 简化处理：检查价格是否在均线附近或下方
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ma5_support = False
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ma10_support = False
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if 'klu_ma5' in klu_features and klu_features['klu_ma5'] > 0:
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ma5_bias = (self.close - klu_features['klu_ma5']) / klu_features['klu_ma5']
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if ma5_bias >= -0.05: # 在5日均线附近或上方
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ma5_support = True
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if 'klu_ma10' in klu_features and klu_features['klu_ma10'] > 0:
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ma10_bias = (self.close - klu_features['klu_ma10']) / klu_features['klu_ma10']
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if ma10_bias >= -0.05: # 在10日均线附近或上方
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ma10_support = True
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if ma5_support and ma10_support:
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score += 3
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elif ma5_support or ma10_support:
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score += 2
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else:
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score += 1
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# 检查MACD状态
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if hasattr(self, 'macdhist'):
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if self.fx == Chan_FX_TYPE.BOTTOM and self.macdhist > 0:
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score += 2 # MACD金叉附近的底分型加分
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elif self.fx == Chan_FX_TYPE.TOP and self.macdhist < 0:
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score += 2 # MACD死叉附近的顶分型加分
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return min(5, score)
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def _calculate_formation_speed_score(self):
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"""
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计算形成速度得分 (最高4分)
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强势特征：分型形成时间小于对应级别平均周期的1/3
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弱势特征：形成时间超过平均周期2倍
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"""
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# 简化处理：基于分型K线的收敛程度
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# 分型区间内的价格收敛速度越快，形成速度越快
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if self.fx == Chan_FX_TYPE.TOP:
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# 顶分型：检查左右两根K线相对于中间K线的收敛程度
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left_convergence = (self.high - self.pre.high) / self.high if self.high > 0 else 0
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right_convergence = (self.high - self.next.high) / self.high if self.high > 0 else 0
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else: # BOTTOM
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left_convergence = (self.pre.low - self.low) / self.low if self.low > 0 else 0
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right_convergence = (self.next.low - self.low) / self.low if self.low > 0 else 0
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avg_convergence = (left_convergence + right_convergence) / 2
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if avg_convergence >= 0.03: # 快速形成
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return 4
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elif avg_convergence >= 0.02:
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return 3
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elif avg_convergence >= 0.01:
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return 2
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else:
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return 1
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def _calculate_confirmation_score(self):
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"""
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计算次级别确认得分 (最高10分)
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- 笔破坏检测：真实强势分型会破坏前一笔的趋势
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- 观察分型后3根K线能否站稳分型区间1/2以上
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- 结合技术指标确认
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"""
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score = 0
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# 1. 检查分型后确认（如果有next的next数据）
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if hasattr(self.next, 'next'):
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next2 = self.next.next
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if next2:
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if self.fx == Chan_FX_TYPE.TOP:
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# 顶分型：检查后续2根K线是否持续走弱
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fx_mid_level = (self.high + min(self.pre.low, self.next.low)) / 2
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if self.next.close < fx_mid_level and next2.close < fx_mid_level:
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score += 5 # 强确认
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elif self.next.close < fx_mid_level:
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score += 3 # 中等确认
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else: # BOTTOM
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# 底分型：检查后续2根K线是否持续走强
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fx_mid_level = (max(self.pre.high, self.next.high) + self.low) / 2
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if self.next.close > fx_mid_level and next2.close > fx_mid_level:
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score += 5 # 强确认
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elif self.next.close > fx_mid_level:
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score += 3 # 中等确认
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# 2. 技术指标确认
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if hasattr(self, 'rsi'):
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if self.fx == Chan_FX_TYPE.TOP and self.rsi > 70:
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score += 2 # 超买区顶分型
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elif self.fx == Chan_FX_TYPE.BOTTOM and self.rsi < 30:
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score += 2 # 超卖区底分型
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# 3. 分型强度自身确认（K线形态）
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if self.fx == Chan_FX_TYPE.TOP:
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# 长上影线确认
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upper_shadow = self.high - max(self.open, self.close)
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candle_range = self.high - self.low
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if candle_range > 0 and upper_shadow / candle_range > 0.5:
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score += 2
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else: # BOTTOM
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# 长下影线确认
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lower_shadow = min(self.open, self.close) - self.low
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candle_range = self.high - self.low
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if candle_range > 0 and lower_shadow / candle_range > 0.5:
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score += 2
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# 4. 与前一个分型的关系
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if self.pre and hasattr(self.pre, 'fx') and self.pre.fx != Chan_FX_TYPE.UNKNOWN:
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# 检查是否形成有效的笔结构
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if self.fx != self.pre.fx: # 分型类型相反
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score += 1
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return min(10, score)
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def _calculate_recent_atr(self, lookback=10):
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"""
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计算近期ATR（平均真实波动范围）
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"""
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tr_values = []
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temp = self
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for i in range(lookback):
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if temp and temp.pre:
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tr = max(
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temp.high - temp.low,
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abs(temp.high - temp.pre.close),
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abs(temp.low - temp.pre.close)
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)
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tr_values.append(tr)
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temp = temp.pre
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else:
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break
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return sum(tr_values) / len(tr_values) if tr_values else 0
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def _calculate_average_volume(self, lookback=5):
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"""
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计算平均成交量
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"""
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volumes = []
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temp = self.pre # 从前一根K线开始计算
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for i in range(lookback):
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if temp:
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volumes.append(temp.volume)
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temp = temp.pre
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else:
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break
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return sum(volumes) / len(volumes) if volumes else 0
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def get_fx_strength_level(self):
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"""
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获取分型强度等级
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根据专业评分标准：≥80分为有效强势分型，≤40分建议忽略
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"""
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strength = self.calculate_fx_strength()
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if strength >= 80:
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return "极强"
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elif strength >= 65:
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return "强"
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elif strength >= 50:
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return "中等"
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elif strength >= 40:
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return "弱"
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else:
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return "极弱"
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def is_strong_fx(self, threshold=65):
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"""
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判断是否为强分型
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根据专业标准调整阈值为65分
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"""
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return self.calculate_fx_strength() >= threshold
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