import logging
import pandas as pd
from decimal import Decimal
from core.smc.SMCStruct import SMCStruct


class SMCLiquidity(SMCStruct):
    EQUAL_HIGH_COL = "equal_high"
    EQUAL_LOW_COL = "equal_low"
    LIQU_HIGH_COL = "liqu_high"
    LIQU_LOW_COL = "liqu_low"
    EQUAL_HIGH_INDEX_KEY = "equal_high_index"
    EQUAL_LOW_INDEX_KEY = "equal_low_index"
    HAS_EQ_KEY = "has_EQ"
    LIQU_HIGH_DIFF_COL = "liqu_high_diff"
    LIQU_LOW_DIFF_COL = "liqu_low_diff"
    
    # 新增等高等低点识别相关常量
    EQUAL_POINTS_TIMESTAMP_COL = "equal_points_timestamp"
    EQUAL_POINTS_INDEX_COL = "equal_points_index"
    EQUAL_POINTS_PRICE_COL = "equal_points_price"
    EQUAL_POINTS_TYPE_COL = "equal_points_type"
    EXTREME_INDEX_COL = "extreme_index"
    EXTREME_VALUE_COL = "extreme_value"
    ATR_TOLERANCE_COL = "atr_tolerance"
    IS_EXTREME_COL = "is_extreme"
    HAS_EQUAL_POINTS_COL = "has_equal_points"


    def __init__(self):
        super().__init__()
        self.logger = logging.getLogger(__name__)
        
        
    def _identify_liquidity_pivots(self, data, pivot_length=1):
        """
        识别流动性的高点和低点
        """

        df = data.copy()
        
        # 识别高点
        df[self.LIQU_HIGH_COL] = Decimal(0.0)
        for i in range(pivot_length, len(df) - pivot_length):
            if df[self.HIGH_COL].iloc[i] == max(df[self.HIGH_COL].iloc[i-pivot_length:i+pivot_length+1]):
                df.loc[df.index[i], self.LIQU_HIGH_COL] = df[self.HIGH_COL].iloc[i]        
        # 识别低点
        df[self.LIQU_LOW_COL] = Decimal(0.0)
        for i in range(pivot_length, len(df) - pivot_length):

            if df[self.LOW_COL].iloc[i] == min(df[self.LOW_COL].iloc[i-pivot_length:i+pivot_length+1]):
                df.loc[df.index[i], self.LIQU_LOW_COL] = df[self.LOW_COL].iloc[i]

        return df
        
    def find_EQH_EQL(self, data, trend, end_idx=-1, atr_offset=0.1) -> dict:
        """_summary_
        识别等高等低流动性
        Args:
            data (_type_): _description_
            trend (_type_): _description_
            end_idx (int, optional): _description_. Defaults to -1.
            atr_offset (float, optional): _description_. Defaults to 0.1.

        Returns:
            dict: _description_
        """
        
        df = data.copy() if end_idx == -1 else data.copy().iloc[:end_idx+1]

        check_columns = [self.LIQU_HIGH_COL, self.LIQU_LOW_COL]

        try:
            self.check_columns(df, check_columns) 
        except ValueError as e:
            # self.logger.warning(f"DataFrame must contain columns {check_columns} : {str(e)}")
            df = self._identify_liquidity_pivots(df)
            
        df = df[(df[self.LIQU_HIGH_COL] > 0) | (df[self.LIQU_LOW_COL] > 0)]
        # 初始化结果列
        df[self.EQUAL_HIGH_COL] = 0
        df[self.EQUAL_LOW_COL] = 0 
        atr_df = self.calculate_atr(df)
        df[self.ATR_COL] = atr_df[self.ATR_COL]
        # 跟踪前一个高点和低点
        previous_high = None
        previous_high_index = None
        previous_high_pos = -1
        previous_low = None
        previous_low_index = None  
        previous_low_pos = -1   
        for i in range(len(df)-1, -1, -1):

            offset = self.toDecimal(df[self.ATR_COL].iloc[i] * atr_offset)

            if trend == self.BULLISH_TREND:
                current_high = df[self.LIQU_HIGH_COL].iloc[i]
                if current_high == 0:
                    continue
        
                if previous_high is None:
                    previous_high = current_high
                    previous_high_index = df.index[i]
                    previous_high_pos = i
                    continue
                
                max_val = max(current_high, previous_high)
                min_val = min(current_high, previous_high)
                
                
                if abs(max_val - min_val) <= offset: # EQH|EQL
            
                    df.loc[df.index[i], self.EQUAL_HIGH_COL] = previous_high_index
                    df.loc[df.index[previous_high_pos], self.EQUAL_HIGH_COL] = previous_high_index
                        
                else:
                    # 倒序遍历，等高线被高点破坏，则更新等高点位置
                    if current_high > previous_high:
                        previous_high = current_high
                        previous_high_index = df.index[i]
                        previous_high_pos = i



            else:
                current_low = df[self.LIQU_LOW_COL].iloc[i]
                if current_low == 0:
                    continue
        
                # current_low = df[self.EQUAL_LOW_COL].iloc[i]
                if previous_low is None:
                    previous_low = current_low
                    previous_low_index = df.index[i]
                    previous_low_pos = i
                    continue
                
                max_val = max(current_low, previous_low)
                min_val = min(current_low, previous_low)
                
                

                
                if abs(max_val - min_val) <= offset: # EQH|EQL
            
                    df.loc[df.index[i], self.EQUAL_LOW_COL] = previous_low_index
                    df.loc[df.index[previous_low_pos], self.EQUAL_LOW_COL] = previous_low_index
                        
                else:
                    # 倒序遍历，等高线被高点破坏，则更新等高点位置
                    if current_low < previous_low:
                        previous_low = current_low
                        previous_low_index = df.index[i]
                        previous_low_pos = i
                
        # 筛选有效结构且在prd范围内的数据
        last_EQ = {

        }
        if trend == self.BULLISH_TREND :
            mask = df[self.EQUAL_HIGH_COL] > 0
            valid_EQH_df = df[ mask ]
            if not valid_EQH_df.empty:
                last_EQ[self.HAS_EQ_KEY] = True
                last_EQ[self.EQUAL_HIGH_COL] = valid_EQH_df.iloc[-1][self.LIQU_HIGH_COL]
                last_EQ[self.EQUAL_HIGH_INDEX_KEY] = valid_EQH_df.iloc[-1][self.EQUAL_HIGH_COL]
                last_EQ[self.TIMESTAMP_COL] = valid_EQH_df.iloc[-1][self.TIMESTAMP_COL]
        else:
            mask = df[self.EQUAL_LOW_COL] > 0
            valid_EQL_df = df[ mask ]
            if not valid_EQL_df.empty:
                last_EQ[self.HAS_EQ_KEY] = True
                last_EQ[self.EQUAL_LOW_COL] = valid_EQL_df.iloc[-1][self.LIQU_LOW_COL]
                last_EQ[self.EQUAL_LOW_INDEX_KEY] = valid_EQL_df.iloc[-1][self.EQUAL_LOW_COL]
                last_EQ[self.TIMESTAMP_COL] = valid_EQL_df.iloc[-1][self.TIMESTAMP_COL]

        return last_EQ
               
    def identify_dynamic_trendlines(self, data, trend, start_idx=-1, end_idx=-1, ratio=0.8) -> tuple:
        """
        识别动态趋势线或隧道
        Args:
            data (pd.DataFrame): _description_

        Returns:
            pd.DataFrame: _description_
        """          

        df = data.copy() if start_idx == -1 or end_idx == -1 else data.copy().iloc[start_idx-1:end_idx+2] #考虑poivt值，前后各增加一个

        check_columns = [self.LIQU_HIGH_COL]

        try:
            self.check_columns(df, check_columns) 
        except ValueError as e:
            self.logger.warning(f"DataFrame must contain columns {check_columns} : {str(e)}")
            df = self._identify_liquidity_pivots(df)
        diff_ratio = 0.0
        if trend == self.BEARISH_TREND:
            # 判断Bearish趋势是高点不断升高,
            liqu_bear_df = df[df[self.LIQU_HIGH_COL] > 0]
            liqu_bear_df[self.LIQU_HIGH_DIFF_COL] = liqu_bear_df[self.LIQU_HIGH_COL].diff()
            # self.logger.info(f"dynamic_trendlines:\n {liqu_bear_df[[self.TIMESTAMP_COL,self.LIQU_HIGH_COL,self.LIQU_HIGH_DIFF_COL]]}")
            diff_ratio = self.toDecimal(liqu_bear_df[self.LIQU_HIGH_DIFF_COL].dropna().lt(0).mean(),2)
            if diff_ratio >= ratio:
               return diff_ratio,True
        else:
            # Bullish趋势是低点不断降低
            liqu_bullish_df = df[df[self.LIQU_LOW_COL] > 0]
            liqu_bullish_df[self.LIQU_LOW_DIFF_COL] = liqu_bullish_df[self.LIQU_LOW_COL].diff()
            # self.logger.info(f"dynamic_trendlines:\n {liqu_bullish_df[[self.TIMESTAMP_COL,self.LIQU_LOW_COL,self.LIQU_LOW_DIFF_COL]]}")
            diff_ratio = self.toDecimal(liqu_bullish_df[self.LIQU_LOW_DIFF_COL].dropna().gt(0).mean(),2)
            if diff_ratio >= ratio:
               return diff_ratio,True

        return diff_ratio,False

    def find_extreme_point_in_range(self, data, point_type='high'):
        """在当前K线范围内找到极值点（最高点或最低点）
        
        Args:
            data: 价格数据DataFrame
            point_type: 'high' 或 'low'
            
        Returns:
            (extreme_index, extreme_value) 极值点索引和值
            
        Raises:
            ValueError: 当输入数据无效时
        """
        # 输入验证
        if data.empty:
            self.logger.warning("输入数据为空，无法找到极值点")
            return None, None
            
        if point_type not in ['high', 'low']:
            raise ValueError("point_type必须是'high'或'low'")
            
        # 验证必需的列存在
        required_col = self.HIGH_COL if point_type == 'high' else self.LOW_COL
        if required_col not in data.columns:
            raise ValueError(f"DataFrame必须包含列: {required_col}")
            
        try:
            if point_type == 'high':
                extreme_index = data[self.HIGH_COL].idxmax()
                extreme_value = data[self.HIGH_COL].loc[extreme_index]
            else:  # point_type == 'low'
                extreme_index = data[self.LOW_COL].idxmin()
                extreme_value = data[self.LOW_COL].loc[extreme_index]
                
            # 验证找到的极值点是有效的
            if pd.isna(extreme_value):
                self.logger.warning(f"找到的{point_type}极值点包含NaN值")
                return None, None
                
            self.logger.debug(f"找到{point_type}极值点: 索引={extreme_index}, 值={extreme_value}")
            return extreme_index, extreme_value
            
        except Exception as e:
            self.logger.error(f"查找极值点时发生错误: {str(e)}")
            return None, None

    def check_equal_points_from_extreme(self, data, extreme_index, extreme_value, atr_value, atr_offset, point_type='high', min_distance=2):
        """从极值点向前搜索等点
        
        Args:
            data: 价格数据DataFrame
            extreme_index: 极值点索引
            extreme_value: 极值点价格值
            atr_value: ATR值
            atr_offset: ATR偏移量
            point_type: 'high' 或 'low'
            min_distance: 最小间隔距离，相邻节点不算等点（默认为2，即至少间隔1个节点）
            
        Returns:
            符合条件的等点列表，每个元素包含index, value, timestamp
            
        Raises:
            ValueError: 当输入参数无效时
        """
        # 输入验证
        if data.empty:
            self.logger.warning("输入数据为空，无法搜索等点")
            return []
            
        if point_type not in ['high', 'low']:
            raise ValueError("point_type必须是'high'或'low'")
            
        if extreme_index not in data.index:
            raise ValueError(f"极值点索引 {extreme_index} 不在数据范围内")
            
        if min_distance < 1:
            raise ValueError("min_distance必须大于等于1")
            
        # 计算容差
        try:
            tolerance = self.calculate_atr_tolerance(atr_value, atr_offset)
        except ValueError as e:
            self.logger.error(f"计算容差失败: {str(e)}")
            return []
            
        equal_points = []
        price_col = self.HIGH_COL if point_type == 'high' else self.LOW_COL
        
        # 验证价格列存在
        if price_col not in data.columns:
            raise ValueError(f"DataFrame必须包含列: {price_col}")
            
        try:
            # 获取极值点在DataFrame中的位置
            extreme_position = data.index.get_loc(extreme_index)
            
            # 从极值点索引向前搜索（按需求从最大值索引向前寻找）
            # 但要排除相邻的节点（距离小于min_distance的节点）
            for i in range(extreme_position):
                current_index = data.index[i]
                current_value = data[price_col].loc[current_index]
                
                # 跳过NaN值
                if pd.isna(current_value):
                    continue
                
                # 检查距离约束：相邻节点不算等点
                distance = extreme_position - i
                if distance < min_distance:
                    self.logger.debug(f"跳过相邻节点: 索引={current_index}, 距离={distance} < {min_distance}")
                    continue
                    
                # 检查是否在容差范围内
                price_diff = abs(self.toDecimal(current_value) - self.toDecimal(extreme_value))
                if price_diff <= tolerance:
                    equal_point = {
                        'index': current_index,
                        'value': current_value,
                        'timestamp': data[self.TIMESTAMP_COL].loc[current_index] if self.TIMESTAMP_COL in data.columns else None,
                        'price_diff': price_diff,
                        'tolerance_used': tolerance,
                        'distance_from_extreme': distance
                    }
                    equal_points.append(equal_point)
                    
            self.logger.debug(f"找到 {len(equal_points)} 个{point_type}等点，极值点索引: {extreme_index}，排除了相邻节点（最小距离: {min_distance}）")
            
        except Exception as e:
            self.logger.error(f"搜索等点时发生错误: {str(e)}")
            return []
                
        return equal_points

    def _validate_input_data(self, data, atr_offset):
        """验证输入数据的完整性和有效性
        
        Args:
            data: 输入的DataFrame
            atr_offset: ATR偏移量参数
            
        Raises:
            ValueError: 当数据验证失败时
        """
        if data.empty:
            raise ValueError("输入数据不能为空")
            
        required_columns = [self.HIGH_COL, self.LOW_COL, self.TIMESTAMP_COL]
        self.check_columns(data, required_columns)
        
        # 验证ATR偏移量范围
        if not (0.01 <= atr_offset <= 1.0):
            raise ValueError("atr_offset必须在0.01到1.0之间")
            
        # 验证数据类型
        for col in [self.HIGH_COL, self.LOW_COL]:
            if not pd.api.types.is_numeric_dtype(data[col]):
                raise ValueError(f"列 {col} 必须是数值类型")

    def calculate_atr_tolerance(self, atr_value, atr_offset):
        """计算基于ATR的容差值
        
        Args:
            atr_value: ATR值
            atr_offset: ATR偏移量
            
        Returns:
            计算得到的容差值
            
        Raises:
            ValueError: 当ATR值无效时
        """
        # 验证ATR值
        if pd.isna(atr_value) or atr_value <= 0:
            raise ValueError(f"ATR值必须是正数，当前值: {atr_value}")
            
        # 验证atr_offset
        if not (0.01 <= atr_offset <= 1.0):
            raise ValueError(f"atr_offset必须在0.01到1.0之间，当前值: {atr_offset}")
            
        tolerance = self.toDecimal(atr_value * atr_offset)
        self.logger.debug(f"计算ATR容差: ATR={atr_value}, offset={atr_offset}, tolerance={tolerance}")
        
        return tolerance

    def ensure_atr_column(self, data):
        """确保DataFrame包含ATR列，如果不存在则计算
        
        Args:
            data: 输入的DataFrame
            
        Returns:
            包含ATR列的DataFrame
        """
        df = data.copy()
        
        if self.ATR_COL not in df.columns:
            self.logger.info("ATR列不存在，正在计算ATR值")
            try:
                # 使用基类的ATR计算方法
                df = self.calculate_atr(df)
            except Exception as e:
                self.logger.error(f"计算ATR时发生错误: {str(e)}")
                raise ValueError(f"无法计算ATR: {str(e)}")
        
        # 验证ATR列的有效性
        if df[self.ATR_COL].isna().all():
            raise ValueError("ATR列包含全部NaN值")
            
        return df

    def collect_point_information(self, data, extreme_index, extreme_value, equal_points, point_type, atr_tolerance):
        """收集点的全面信息
        
        Args:
            data: 原始数据DataFrame
            extreme_index: 极值点索引
            extreme_value: 极值点价格值
            equal_points: 等点列表
            point_type: 点类型 ('high' 或 'low')
            atr_tolerance: 使用的ATR容差
            
        Returns:
            包含详细信息的字典列表
        """
        collected_points = []
        
        # 添加极值点信息
        extreme_point_info = {
            self.EQUAL_POINTS_TIMESTAMP_COL: data[self.TIMESTAMP_COL].loc[extreme_index] if self.TIMESTAMP_COL in data.columns else None,
            self.EQUAL_POINTS_INDEX_COL: extreme_index,
            self.EQUAL_POINTS_PRICE_COL: extreme_value,
            self.EQUAL_POINTS_TYPE_COL: f'extreme_{point_type}',
            self.EXTREME_INDEX_COL: extreme_index,
            self.EXTREME_VALUE_COL: extreme_value,
            self.ATR_TOLERANCE_COL: atr_tolerance,
            self.IS_EXTREME_COL: True,
            self.HAS_EQUAL_POINTS_COL: len(equal_points) > 0
        }
        collected_points.append(extreme_point_info)
        
        # 添加等点信息
        for equal_point in equal_points:
            equal_point_info = {
                self.EQUAL_POINTS_TIMESTAMP_COL: equal_point['timestamp'],
                self.EQUAL_POINTS_INDEX_COL: equal_point['index'],
                self.EQUAL_POINTS_PRICE_COL: equal_point['value'],
                self.EQUAL_POINTS_TYPE_COL: f'equal_{point_type}',
                self.EXTREME_INDEX_COL: extreme_index,
                self.EXTREME_VALUE_COL: extreme_value,
                self.ATR_TOLERANCE_COL: atr_tolerance,
                self.IS_EXTREME_COL: False,
                self.HAS_EQUAL_POINTS_COL: True
            }
            collected_points.append(equal_point_info)
            
        # 按时间顺序排序
        collected_points.sort(key=lambda x: x[self.EQUAL_POINTS_TIMESTAMP_COL] if x[self.EQUAL_POINTS_TIMESTAMP_COL] is not None else pd.Timestamp.min)
        
        self.logger.debug(f"收集了 {len(collected_points)} 个点的信息（包括1个极值点和{len(equal_points)}个等点）")
        
        return collected_points

    def create_result_dataframe(self, collected_points):
        """创建结果DataFrame
        
        Args:
            collected_points: 收集的点信息列表
            
        Returns:
            格式化的结果DataFrame
        """
        if not collected_points:
            # 返回空DataFrame但保持正确的列结构
            empty_df = pd.DataFrame(columns=[
                self.EQUAL_POINTS_TIMESTAMP_COL,
                self.EQUAL_POINTS_INDEX_COL,
                self.EQUAL_POINTS_PRICE_COL,
                self.EQUAL_POINTS_TYPE_COL,
                self.EXTREME_INDEX_COL,
                self.EXTREME_VALUE_COL,
                self.ATR_TOLERANCE_COL,
                self.IS_EXTREME_COL,
                self.HAS_EQUAL_POINTS_COL
            ])
            self.logger.info("未找到等点，返回空DataFrame")
            return empty_df
            
        # 创建DataFrame
        result_df = pd.DataFrame(collected_points)
        
        # 设置正确的数据类型
        result_df[self.EQUAL_POINTS_PRICE_COL] = result_df[self.EQUAL_POINTS_PRICE_COL].astype(float)
        result_df[self.EXTREME_VALUE_COL] = result_df[self.EXTREME_VALUE_COL].astype(float)
        result_df[self.ATR_TOLERANCE_COL] = result_df[self.ATR_TOLERANCE_COL].astype(float)
        result_df[self.IS_EXTREME_COL] = result_df[self.IS_EXTREME_COL].astype(bool)
        result_df[self.HAS_EQUAL_POINTS_COL] = result_df[self.HAS_EQUAL_POINTS_COL].astype(bool)
        
        # 如果有时间戳列，确保是datetime类型
        if self.EQUAL_POINTS_TIMESTAMP_COL in result_df.columns:
            result_df[self.EQUAL_POINTS_TIMESTAMP_COL] = pd.to_datetime(result_df[self.EQUAL_POINTS_TIMESTAMP_COL])
            
        self.logger.info(f"创建结果DataFrame，包含 {len(result_df)} 行数据")
        
        return result_df

    def recursive_extreme_search(self, data, start_index=0, point_type='high', atr_offset=0.1, max_depth=10, min_distance=2):
        """递归搜索后续极值点并判断等点
        
        Args:
            data: 价格数据DataFrame
            start_index: 开始搜索的索引位置（在DataFrame中的位置，不是索引值）
            point_type: 'high' 或 'low'
            atr_offset: ATR偏移量
            max_depth: 最大递归深度，防止无限递归
            min_distance: 最小间隔距离，相邻节点不算等点
            
        Returns:
            包含所有识别出的极值点和等点信息的DataFrame
            
        Raises:
            ValueError: 当输入参数无效时
        """
        # 输入验证
        self._validate_input_data(data, atr_offset)
        
        if point_type not in ['high', 'low']:
            raise ValueError("point_type必须是'high'或'low'")
            
        if max_depth <= 0:
            raise ValueError("max_depth必须大于0")
            
        # 确保数据包含ATR列
        df = self.ensure_atr_column(data)
        
        all_collected_points = []
        current_start = start_index
        depth = 0
        
        self.logger.info(f"开始递归搜索{point_type}极值点，起始位置: {current_start}")
        
        while depth < max_depth and current_start < len(df):
            # 获取当前搜索范围的数据
            search_data = df.iloc[current_start:]
            
            if search_data.empty:
                self.logger.debug(f"搜索数据为空，递归结束，深度: {depth}")
                break
                
            # 在当前范围内找到极值点
            extreme_index, extreme_value = self.find_extreme_point_in_range(search_data, point_type)
            
            if extreme_index is None or extreme_value is None:
                self.logger.debug(f"未找到{point_type}极值点，递归结束，深度: {depth}")
                break
                
            # 获取ATR值
            atr_value = df[self.ATR_COL].loc[extreme_index]
            if pd.isna(atr_value) or atr_value <= 0:
                self.logger.warning(f"极值点 {extreme_index} 的ATR值无效: {atr_value}")
                # 尝试使用附近的ATR值
                atr_value = self._get_valid_atr_value(df, extreme_index)
                if atr_value is None:
                    break
                    
            # 搜索等点
            equal_points = self.check_equal_points_from_extreme(
                search_data, extreme_index, extreme_value, atr_value, atr_offset, point_type, min_distance
            )
            
            # 计算容差
            tolerance = self.calculate_atr_tolerance(atr_value, atr_offset)
            
            # 收集点信息
            points_info = self.collect_point_information(
                df, extreme_index, extreme_value, equal_points, point_type, tolerance
            )
            
            all_collected_points.extend(points_info)
            
            # 更新下一次搜索的起始位置
            # 找到极值点在原始DataFrame中的位置
            extreme_position_in_original = df.index.get_loc(extreme_index)
            current_start = extreme_position_in_original + 1
            
            depth += 1
            self.logger.debug(f"完成第 {depth} 层递归，找到极值点: {extreme_index}，等点数量: {len(equal_points)}")
            
        self.logger.info(f"递归搜索完成，总深度: {depth}，总点数: {len(all_collected_points)}")
        
        # 创建结果DataFrame
        result_df = self.create_result_dataframe(all_collected_points)
        
        return result_df

    def _get_valid_atr_value(self, data, target_index, search_range=5):
        """获取有效的ATR值
        
        Args:
            data: 数据DataFrame
            target_index: 目标索引
            search_range: 搜索范围
            
        Returns:
            有效的ATR值或None
        """
        try:
            target_position = data.index.get_loc(target_index)
            
            # 在目标位置附近搜索有效的ATR值
            for offset in range(1, search_range + 1):
                # 向前搜索
                if target_position - offset >= 0:
                    atr_val = data[self.ATR_COL].iloc[target_position - offset]
                    if not pd.isna(atr_val) and atr_val > 0:
                        self.logger.debug(f"使用位置 {target_position - offset} 的ATR值: {atr_val}")
                        return atr_val
                        
                # 向后搜索
                if target_position + offset < len(data):
                    atr_val = data[self.ATR_COL].iloc[target_position + offset]
                    if not pd.isna(atr_val) and atr_val > 0:
                        self.logger.debug(f"使用位置 {target_position + offset} 的ATR值: {atr_val}")
                        return atr_val
                        
        except Exception as e:
            self.logger.error(f"获取有效ATR值时发生错误: {str(e)}")
            
        return None

    def identify_equal_points_in_range(self, data, atr_offset=0.1, point_type=None, max_depth=10, min_distance=2):
        """在当前K线范围内识别极值点并判断是否存在等点（主要集成方法）
        
        Args:
            data: 包含OHLC和ATR数据的DataFrame
            atr_offset: ATR偏移量用于容差计算
            point_type: 指定搜索类型 ('high', 'low', 或 None 表示两者都搜索)
            max_depth: 最大递归深度
            min_distance: 最小间隔距离，相邻节点不算等点（默认为2，即至少间隔1个节点）
            
        Returns:
            包含等点信息的DataFrame
            
        Raises:
            ValueError: 当输入参数无效时
        """
        # 输入验证
        self._validate_input_data(data, atr_offset)
        
        if point_type is not None and point_type not in ['high', 'low']:
            raise ValueError("point_type必须是'high'、'low'或None")
            
        # 确保数据包含ATR列
        df = self.ensure_atr_column(data)
        
        all_results = []
        
        # 根据point_type决定搜索类型
        search_types = [point_type] if point_type else ['high', 'low']
        
        for search_type in search_types:
            self.logger.info(f"开始识别{search_type}等点")
            
            try:
                # 执行递归搜索
                result_df = self.recursive_extreme_search(
                    df, 
                    start_index=0, 
                    point_type=search_type, 
                    atr_offset=atr_offset,
                    max_depth=max_depth,
                    min_distance=min_distance
                )
                
                if not result_df.empty:
                    all_results.append(result_df)
                    
            except Exception as e:
                self.logger.error(f"识别{search_type}等点时发生错误: {str(e)}")
                continue
                
        # 合并所有结果
        if all_results:
            combined_result = pd.concat(all_results, ignore_index=True)
            # 按时间戳排序
            if self.EQUAL_POINTS_TIMESTAMP_COL in combined_result.columns:
                combined_result = combined_result.sort_values(self.EQUAL_POINTS_TIMESTAMP_COL)
                combined_result = combined_result.reset_index(drop=True)
                
            self.logger.info(f"识别完成，总共找到 {len(combined_result)} 个点")
            return combined_result
        else:
            # 返回空DataFrame但保持正确的列结构
            empty_result = self.create_result_dataframe([])
            self.logger.info("未找到任何等点")
            return empty_result

    def get_equal_points_summary(self, result_df):
        """获取等点识别结果的摘要信息
        
        Args:
            result_df: identify_equal_points_in_range方法返回的结果DataFrame
            
        Returns:
            包含摘要信息的字典
        """
        if result_df.empty:
            return {
                'total_points': 0,
                'extreme_points': 0,
                'equal_high_points': 0,
                'equal_low_points': 0,
                'has_equal_points': False
            }
            
        summary = {
            'total_points': len(result_df),
            'extreme_points': len(result_df[result_df[self.IS_EXTREME_COL] == True]),
            'equal_high_points': len(result_df[result_df[self.EQUAL_POINTS_TYPE_COL] == 'equal_high']),
            'equal_low_points': len(result_df[result_df[self.EQUAL_POINTS_TYPE_COL] == 'equal_low']),
            'has_equal_points': len(result_df[result_df[self.IS_EXTREME_COL] == False]) > 0
        }
        
        # 添加时间范围信息
        if self.EQUAL_POINTS_TIMESTAMP_COL in result_df.columns and not result_df[self.EQUAL_POINTS_TIMESTAMP_COL].isna().all():
            summary['time_range'] = {
                'start': result_df[self.EQUAL_POINTS_TIMESTAMP_COL].min(),
                'end': result_df[self.EQUAL_POINTS_TIMESTAMP_COL].max()
            }
            
        return summary

        

