""" 分型识别模块：识别顶分型和底分型 分型定义：至少需要3根K线，中间K线的高点（或低点）比两侧都高（或低） 增强版：包含多维度强弱程度评估 """ import pandas as pd import numpy as np from typing import List, Tuple, Optional, Dict from dataclasses import dataclass import logging logger = logging.getLogger(__name__) @dataclass class FractalPoint: """分型点数据类""" index: int # 在原始数据中的位置 timestamp: pd.Timestamp # 时间戳 price: float # 分型价格（高点或低点） fractal_type: str # 'top' 或 'bottom' strength: int # 基础分型强度（左右确认的K线数量） enhanced_strength: float # 增强强度评分（0-100） price_dominance: float # 价格优势度（0-100） volume_strength: float # 成交量强度（0-100） trend_position: float # 趋势位置强度（0-100） confirmed: bool = False # 是否已确认 class Fractal: """分型识别类（增强版）""" def __init__(self, kline_data: pd.DataFrame, min_strength: int = 1): """ 初始化分型识别器 Args: kline_data: 处理包含关系后的K线数据 min_strength: 最小分型强度（左右各需多少根K线确认） """ self.data = kline_data.copy() self.min_strength = max(1, min_strength) # 至少为1 self.top_fractals = [] self.bottom_fractals = [] self.all_fractals = [] # 计算一些辅助指标 self._calculate_auxiliary_indicators() def _calculate_auxiliary_indicators(self): """计算辅助技术指标""" # 成交量移动平均 self.data['volume_ma'] = self.data['volume'].rolling(window=20, min_periods=1).mean() # 价格振幅 self.data['range'] = self.data['high'] - self.data['low'] self.data['range_ma'] = self.data['range'].rolling(window=20, min_periods=1).mean() # 相对位置（高点在整个区间的位置） window = 20 self.data['highest'] = self.data['high'].rolling(window=window, min_periods=1).max() self.data['lowest'] = self.data['low'].rolling(window=window, min_periods=1).min() # 趋势强度（简单移动平均斜率） self.data['close_ma'] = self.data['close'].rolling(window=10, min_periods=1).mean() self.data['trend_slope'] = self.data['close_ma'].diff(5) # 5期斜率 def _calculate_price_dominance(self, idx: int, fractal_type: str, strength: int) -> float: """ 计算价格优势度：分型价格相对于周围价格的优势程度 Args: idx: 分型位置 fractal_type: 分型类型 strength: 基础强度 Returns: 价格优势度（0-100） """ try: # 扩展检查范围 check_range = max(strength * 2, 10) start_idx = max(0, idx - check_range) end_idx = min(len(self.data), idx + check_range + 1) if fractal_type == 'top': center_price = self.data.iloc[idx]['high'] range_data = self.data.iloc[start_idx:end_idx] max_around = range_data['high'].max() second_max = range_data['high'].nlargest(2).iloc[1] if len(range_data) > 1 else center_price # 计算相对优势 if max_around == center_price: price_gap = center_price - second_max avg_range = self.data.iloc[idx]['range_ma'] dominance = min((price_gap / avg_range) * 50, 100) if avg_range > 0 else 50 else: dominance = 0 else: # bottom center_price = self.data.iloc[idx]['low'] range_data = self.data.iloc[start_idx:end_idx] min_around = range_data['low'].min() second_min = range_data['low'].nsmallest(2).iloc[1] if len(range_data) > 1 else center_price # 计算相对优势 if min_around == center_price: price_gap = second_min - center_price avg_range = self.data.iloc[idx]['range_ma'] dominance = min((price_gap / avg_range) * 50, 100) if avg_range > 0 else 50 else: dominance = 0 return max(0, dominance) except Exception as e: logger.warning(f"计算价格优势度失败: {e}") return 30 # 默认值 def _calculate_volume_strength(self, idx: int, fractal_type: str, strength: int) -> float: """ 计算成交量强度：分型形成时的成交量特征 Args: idx: 分型位置 fractal_type: 分型类型 strength: 基础强度 Returns: 成交量强度（0-100） """ try: center_volume = self.data.iloc[idx]['volume'] volume_ma = self.data.iloc[idx]['volume_ma'] # 基础成交量比率 volume_ratio = center_volume / volume_ma if volume_ma > 0 else 1 base_score = min(volume_ratio * 30, 60) # 最高60分 # 检查分型形成过程中的成交量模式 pattern_score = 0 if strength >= 2: # 检查左右成交量是否递减（表示力度衰竭） left_volumes = [self.data.iloc[idx - i]['volume'] for i in range(1, strength + 1)] right_volumes = [self.data.iloc[idx + i]['volume'] for i in range(1, strength + 1)] # 中心成交量应该相对突出 center_prominence = sum([1 for v in left_volumes + right_volumes if center_volume > v]) total_compared = len(left_volumes + right_volumes) pattern_score = (center_prominence / total_compared) * 40 if total_compared > 0 else 20 total_score = base_score + pattern_score return max(0, min(total_score, 100)) except Exception as e: logger.warning(f"计算成交量强度失败: {e}") return 40 # 默认值 def _calculate_trend_position_strength(self, idx: int, fractal_type: str) -> float: """ 计算趋势位置强度：分型在整体趋势中的位置优势 Args: idx: 分型位置 fractal_type: 分型类型 Returns: 趋势位置强度（0-100） """ try: current_price = self.data.iloc[idx]['high' if fractal_type == 'top' else 'low'] highest = self.data.iloc[idx]['highest'] lowest = self.data.iloc[idx]['lowest'] trend_slope = self.data.iloc[idx]['trend_slope'] # 计算在价格区间中的相对位置 price_range = highest - lowest if price_range > 0: if fractal_type == 'top': # 顶分型：越接近高点越强 position_ratio = (current_price - lowest) / price_range else: # 底分型：越接近低点越强 position_ratio = (highest - current_price) / price_range else: position_ratio = 0.5 position_score = position_ratio * 60 # 位置得分最高60分 # 趋势方向得分 trend_score = 0 if trend_slope is not None and not np.isnan(trend_slope): if fractal_type == 'top' and trend_slope < 0: # 顶分型形成在下降趋势中更有效 trend_score = min(abs(trend_slope) * 1000, 40) elif fractal_type == 'bottom' and trend_slope > 0: # 底分型形成在上升趋势中更有效 trend_score = min(abs(trend_slope) * 1000, 40) else: trend_score = 20 # 趋势方向不匹配给予中等分数 total_score = position_score + trend_score return max(0, min(total_score, 100)) except Exception as e: logger.warning(f"计算趋势位置强度失败: {e}") return 50 # 默认值 def _calculate_enhanced_strength(self, idx: int, fractal_type: str, basic_strength: int) -> Dict[str, float]: """ 计算增强强度评分 Args: idx: 分型位置 fractal_type: 分型类型 basic_strength: 基础强度 Returns: 包含各维度强度的字典 """ # 计算各维度强度 price_dominance = self._calculate_price_dominance(idx, fractal_type, basic_strength) volume_strength = self._calculate_volume_strength(idx, fractal_type, basic_strength) trend_position = self._calculate_trend_position_strength(idx, fractal_type) # 基础强度转换为评分（强度越高，分数越高） basic_score = min(basic_strength * 20, 60) # 基础强度最高60分 # 计算综合强度（加权平均） enhanced_strength = ( basic_score * 0.3 + # 基础强度 30% price_dominance * 0.4 + # 价格优势 40% volume_strength * 0.2 + # 成交量强度 20% trend_position * 0.1 # 趋势位置 10% ) return { 'enhanced_strength': enhanced_strength, 'price_dominance': price_dominance, 'volume_strength': volume_strength, 'trend_position': trend_position } def is_top_fractal(self, idx: int, strength: int = None) -> Tuple[bool, int]: """ 判断指定位置是否为顶分型 Args: idx: 检查的位置索引 strength: 检查强度，如果为None则使用类默认值 Returns: (是否为顶分型, 实际强度) """ if strength is None: strength = self.min_strength data_len = len(self.data) # 检查边界 if idx < strength or idx >= data_len - strength: return False, 0 center_high = self.data.iloc[idx]['high'] # 检查左侧K线 left_valid = True for i in range(1, strength + 1): if self.data.iloc[idx - i]['high'] >= center_high: left_valid = False break # 检查右侧K线 right_valid = True for i in range(1, strength + 1): if self.data.iloc[idx + i]['high'] >= center_high: right_valid = False break is_fractal = left_valid and right_valid actual_strength = strength if is_fractal else 0 return is_fractal, actual_strength def is_bottom_fractal(self, idx: int, strength: int = None) -> Tuple[bool, int]: """ 判断指定位置是否为底分型 Args: idx: 检查的位置索引 strength: 检查强度，如果为None则使用类默认值 Returns: (是否为底分型, 实际强度) """ if strength is None: strength = self.min_strength data_len = len(self.data) # 检查边界 if idx < strength or idx >= data_len - strength: return False, 0 center_low = self.data.iloc[idx]['low'] # 检查左侧K线 left_valid = True for i in range(1, strength + 1): if self.data.iloc[idx - i]['low'] <= center_low: left_valid = False break # 检查右侧K线 right_valid = True for i in range(1, strength + 1): if self.data.iloc[idx + i]['low'] <= center_low: right_valid = False break is_fractal = left_valid and right_valid actual_strength = strength if is_fractal else 0 return is_fractal, actual_strength def find_max_strength_fractal(self, idx: int, fractal_type: str, max_strength: int = 5) -> Tuple[bool, int]: """ 寻找指定位置的最大强度分型 Args: idx: 检查位置 fractal_type: 'top' 或 'bottom' max_strength: 最大检查强度 Returns: (是否为分型, 最大强度) """ max_valid_strength = 0 for strength in range(self.min_strength, max_strength + 1): if fractal_type == 'top': is_valid, _ = self.is_top_fractal(idx, strength) else: is_valid, _ = self.is_bottom_fractal(idx, strength) if is_valid: max_valid_strength = strength else: break # 一旦失败就停止，因为更高强度也不会成功 return max_valid_strength > 0, max_valid_strength def detect_fractals(self, use_max_strength: bool = True) -> List[FractalPoint]: """ 检测所有分型 Args: use_max_strength: 是否使用最大强度检测 Returns: 所有分型点列表 """ fractals = [] data_len = len(self.data) logger.info(f"开始检测分型，数据长度: {data_len}") # 遍历所有可能的分型位置 for idx in range(self.min_strength, data_len - self.min_strength): timestamp = self.data.index[idx] # 检测顶分型 if use_max_strength: is_top, top_strength = self.find_max_strength_fractal(idx, 'top') else: is_top, top_strength = self.is_top_fractal(idx) if is_top: # 计算增强强度 strength_metrics = self._calculate_enhanced_strength(idx, 'top', top_strength) fractal = FractalPoint( index=idx, timestamp=timestamp, price=self.data.iloc[idx]['high'], fractal_type='top', strength=top_strength, enhanced_strength=strength_metrics['enhanced_strength'], price_dominance=strength_metrics['price_dominance'], volume_strength=strength_metrics['volume_strength'], trend_position=strength_metrics['trend_position'], confirmed=True # 简化处理，认为都已确认 ) fractals.append(fractal) self.top_fractals.append(fractal) # 检测底分型 if use_max_strength: is_bottom, bottom_strength = self.find_max_strength_fractal(idx, 'bottom') else: is_bottom, bottom_strength = self.is_bottom_fractal(idx) if is_bottom: # 计算增强强度 strength_metrics = self._calculate_enhanced_strength(idx, 'bottom', bottom_strength) fractal = FractalPoint( index=idx, timestamp=timestamp, price=self.data.iloc[idx]['low'], fractal_type='bottom', strength=bottom_strength, enhanced_strength=strength_metrics['enhanced_strength'], price_dominance=strength_metrics['price_dominance'], volume_strength=strength_metrics['volume_strength'], trend_position=strength_metrics['trend_position'], confirmed=True ) fractals.append(fractal) self.bottom_fractals.append(fractal) # 按时间排序 fractals.sort(key=lambda x: x.index) self.all_fractals = fractals logger.info(f"检测完成：顶分型 {len(self.top_fractals)} 个，底分型 {len(self.bottom_fractals)} 个") return fractals def filter_fractals_by_strength(self, min_strength: int) -> List[FractalPoint]: """ 按强度过滤分型 Args: min_strength: 最小强度要求 Returns: 过滤后的分型列表 """ return [f for f in self.all_fractals if f.strength >= min_strength] def get_fractal_sequence(self) -> List[FractalPoint]: """ 获取交替的分型序列（顶-底-顶-底...） Returns: 交替分型序列 """ if not self.all_fractals: return [] sequence = [] last_type = None for fractal in self.all_fractals: if fractal.fractal_type != last_type: sequence.append(fractal) last_type = fractal.fractal_type return sequence def validate_fractal_sequence(self, sequence: List[FractalPoint]) -> bool: """ 验证分型序列的有效性 Args: sequence: 分型序列 Returns: 是否有效 """ if len(sequence) < 2: return True for i in range(1, len(sequence)): prev_fractal = sequence[i-1] curr_fractal = sequence[i] # 检查类型是否交替 if prev_fractal.fractal_type == curr_fractal.fractal_type: return False # 检查价格关系是否合理 if prev_fractal.fractal_type == 'top': # 顶分型后应该是底分型，且价格应该更低 if curr_fractal.price >= prev_fractal.price: return False else: # 底分型后应该是顶分型，且价格应该更高 if curr_fractal.price <= prev_fractal.price: return False return True def get_fractal_statistics(self) -> Dict: """ 获取分型统计信息（增强版） Returns: 统计信息字典 """ if not self.all_fractals: return {} top_count = len(self.top_fractals) bottom_count = len(self.bottom_fractals) # 基础统计 top_strengths = [f.strength for f in self.top_fractals] bottom_strengths = [f.strength for f in self.bottom_fractals] # 增强强度统计 enhanced_strengths = [f.enhanced_strength for f in self.all_fractals] price_dominances = [f.price_dominance for f in self.all_fractals] volume_strengths = [f.volume_strength for f in self.all_fractals] trend_positions = [f.trend_position for f in self.all_fractals] # 分级统计（按增强强度） strong_fractals = [f for f in self.all_fractals if f.enhanced_strength >= 70] medium_fractals = [f for f in self.all_fractals if 40 <= f.enhanced_strength < 70] weak_fractals = [f for f in self.all_fractals if f.enhanced_strength < 40] stats = { 'basic_info': { 'total_fractals': len(self.all_fractals), 'top_fractals': top_count, 'bottom_fractals': bottom_count, 'avg_basic_strength': np.mean([f.strength for f in self.all_fractals]), 'max_basic_strength': max([f.strength for f in self.all_fractals]), }, 'enhanced_strength': { 'avg_enhanced_strength': np.mean(enhanced_strengths), 'max_enhanced_strength': max(enhanced_strengths), 'min_enhanced_strength': min(enhanced_strengths), 'strong_count': len(strong_fractals), # 强势分型数量 'medium_count': len(medium_fractals), # 中等分型数量 'weak_count': len(weak_fractals), # 弱势分型数量 }, 'dimension_analysis': { 'avg_price_dominance': np.mean(price_dominances), 'avg_volume_strength': np.mean(volume_strengths), 'avg_trend_position': np.mean(trend_positions), }, 'top_fractals_detail': { 'count': top_count, 'avg_basic_strength': np.mean(top_strengths) if top_strengths else 0, 'avg_enhanced_strength': np.mean([f.enhanced_strength for f in self.top_fractals]) if self.top_fractals else 0, 'strong_tops': len([f for f in self.top_fractals if f.enhanced_strength >= 70]), }, 'bottom_fractals_detail': { 'count': bottom_count, 'avg_basic_strength': np.mean(bottom_strengths) if bottom_strengths else 0, 'avg_enhanced_strength': np.mean([f.enhanced_strength for f in self.bottom_fractals]) if self.bottom_fractals else 0, 'strong_bottoms': len([f for f in self.bottom_fractals if f.enhanced_strength >= 70]), } } return stats def to_dataframe(self) -> pd.DataFrame: """ 将分型转换为DataFrame Returns: 包含分型信息的DataFrame """ if not self.all_fractals: return pd.DataFrame() data = [] for fractal in self.all_fractals: data.append({ 'timestamp': fractal.timestamp, 'index': fractal.index, 'price': fractal.price, 'type': fractal.fractal_type, 'strength': fractal.strength, 'enhanced_strength': fractal.enhanced_strength, 'price_dominance': fractal.price_dominance, 'volume_strength': fractal.volume_strength, 'trend_position': fractal.trend_position, 'confirmed': fractal.confirmed }) df = pd.DataFrame(data) df.set_index('timestamp', inplace=True) return df def update_fractal_confirmation(self, current_idx: int): """ 更新分型确认状态 Args: current_idx: 当前处理到的K线位置 """ for fractal in self.all_fractals: if not fractal.confirmed: # 检查是否已经过了足够的确认期 required_confirmation = fractal.strength time_passed = current_idx - fractal.index if time_passed >= required_confirmation: fractal.confirmed = True def get_confirmed_fractals(self, current_idx: int) -> List[FractalPoint]: """ 获取已确认的分型列表 Args: current_idx: 当前处理到的K线位置 Returns: 已确认的分型列表 """ confirmed_fractals = [] for fractal in self.all_fractals: required_confirmation = fractal.strength time_passed = current_idx - fractal.index if time_passed >= required_confirmation: confirmed_fractals.append(fractal) return confirmed_fractals def detect_real_time_fractals(self, current_idx: int, lookback_periods: int = 50) -> List[FractalPoint]: """ 实时分型检测（避免使用未来数据） Args: current_idx: 当前K线位置 lookback_periods: 回看周期数 Returns: 实时可用的分型列表 """ real_time_fractals = [] data_len = len(self.data) # 只检测到当前位置之前的分型 end_idx = min(current_idx, data_len - self.min_strength) start_idx = max(self.min_strength, end_idx - lookback_periods) for idx in range(start_idx, end_idx): timestamp = self.data.index[idx] # 检测顶分型（但只能检测已确认的） is_top, top_strength = self.find_max_strength_fractal(idx, 'top') if is_top and current_idx - idx >= top_strength: # 已确认 strength_metrics = self._calculate_enhanced_strength(idx, 'top', top_strength) fractal = FractalPoint( index=idx, timestamp=timestamp, price=self.data.iloc[idx]['high'], fractal_type='top', strength=top_strength, enhanced_strength=strength_metrics['enhanced_strength'], price_dominance=strength_metrics['price_dominance'], volume_strength=strength_metrics['volume_strength'], trend_position=strength_metrics['trend_position'], confirmed=True ) real_time_fractals.append(fractal) # 检测底分型（但只能检测已确认的） is_bottom, bottom_strength = self.find_max_strength_fractal(idx, 'bottom') if is_bottom and current_idx - idx >= bottom_strength: # 已确认 strength_metrics = self._calculate_enhanced_strength(idx, 'bottom', bottom_strength) fractal = FractalPoint( index=idx, timestamp=timestamp, price=self.data.iloc[idx]['low'], fractal_type='bottom', strength=bottom_strength, enhanced_strength=strength_metrics['enhanced_strength'], price_dominance=strength_metrics['price_dominance'], volume_strength=strength_metrics['volume_strength'], trend_position=strength_metrics['trend_position'], confirmed=True ) real_time_fractals.append(fractal) return real_time_fractals