stock/Chan241206/incremental_update.py

import pandas as pd

class IncrementalUpdater:
    def __init__(self, processor):
        """
        初始化增量更新模块
        :param processor: KlineProcessor 实例
        """
        self.processor = processor

    def update_with_new_kline(self, new_kline):
        """
        增量处理新增的 K 线
        """
        new_kline = pd.DataFrame([new_kline])
        self.processor.df = pd.concat([self.processor.df, new_kline], ignore_index=True)

        i = len(self.processor.df) - 1
        while i > 0:
            # 检查包含关系
            if (self.processor.df.loc[i, 'High'] <= self.processor.df.loc[i-1, 'High'] and
                self.processor.df.loc[i, 'Low'] >= self.processor.df.loc[i-1, 'Low']) or \
            (self.processor.df.loc[i, 'High'] >= self.processor.df.loc[i-1, 'High'] and
                self.processor.df.loc[i, 'Low'] <= self.processor.df.loc[i-1, 'Low']):
                
                # 找到更早的无包含关系的 K 线
                j = i - 1
                while j > 0 and \
                    ((self.processor.df.loc[j, 'High'] <= self.processor.df.loc[j-1, 'High'] and
                        self.processor.df.loc[j, 'Low'] >= self.processor.df.loc[j-1, 'Low']) or \
                    (self.processor.df.loc[j, 'High'] >= self.processor.df.loc[j-1, 'High'] and
                        self.processor.df.loc[j, 'Low'] <= self.processor.df.loc[j-1, 'Low'])):
                    j -= 1

                # 确定处理方向
                if self.processor.df.loc[j, 'High'] < self.processor.df.loc[i-1, 'High']:
                    # 当前包含关系为向上处理
                    self.processor.df.loc[j, 'High'] = max(self.processor.df.loc[i, 'High'],
                                                        self.processor.df.loc[j, 'High'])
                    self.processor.df.loc[j, 'Low'] = max(self.processor.df.loc[i, 'Low'],
                                                        self.processor.df.loc[j, 'Low'])
                else:
                    # 当前包含关系为向下处理
                    self.processor.df.loc[j, 'High'] = min(self.processor.df.loc[i, 'High'],
                                                        self.processor.df.loc[j, 'High'])
                    self.processor.df.loc[j, 'Low'] = min(self.processor.df.loc[i, 'Low'],
                                                        self.processor.df.loc[j, 'Low'])

                # 删除当前 K 线
                self.processor.df = self.processor.df.drop(index=i).reset_index(drop=True)
            else:
                break
            i -= 1