K线形态识别_身怀六甲和十字胎

写在前面：
1. 本文中提到的“K线形态查看工具”的具体使用操作请查看该博文；
2. K线形体所处背景，诸如处在上升趋势、下降趋势、盘整等，背景内容在K线形态策略代码中没有体现；
3. 文中知识内容来自书籍《K线技术分析》by邱立波。

目录

解说

技术特征

技术含义

K线形态策略代码

结果

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解说

        身怀六甲，又称母子线、孕线，因其K线形态好像一个怀胎的孕妇而得名。身怀六甲由实体一长一短的两根K线组成，其中第二根较短的K线实体完全被第一根较长的K线实体包容。当第二根K线是十字线时，称为十字胎。

技术特征

1）既可以出现在上涨趋势中，也可以出现在下跌趋势中。

2）由两根K线组成，第一根较长的K线实体完全包容第二根较短的K线实体。

3）两根K线可以是一阴一阳，也可以是两根阳线或阴线。

4）第二根K线可以是小阴线、小阳线，也可以是十字线。

技术含义

1）大幅上涨后出现身怀六甲是见顶信号，后市看淡。

2）出现大幅下跌后是见底信号。

3）十字胎和身怀六甲的技术含义相同，是身怀六甲中信号最强的K线组合，这是因为单根的十字线本身就具有见顶和见底的意味。

K线形态策略代码

def excute_strategy(daily_file_path):
    '''
    名称：身怀六甲和十字胎
    识别：
    1. 第二根K线实体完全被第一根较长的K线实体包容，第二根K线是小阴线、小阳线
    2. 第二根K线是十字线
    前置条件：计算时间区间 2021-01-01 到 2022-01-01
    :param daily_file_path: 股票日数据文件路径
    :return:
    '''
    import pandas as pd
    import os
 
    start_date_str = '2021-01-01'
    end_date_str = '2022-01-01'
    df = pd.read_csv(daily_file_path,encoding='utf-8')
    # 删除停牌的数据
    df = df.loc[df['openPrice'] > 0].copy()
    df['o_date'] = df['tradeDate']
    df['o_date'] = pd.to_datetime(df['o_date'])
    df = df.loc[(df['o_date'] >= start_date_str) & (df['o_date']<=end_date_str)].copy()
    # 保存未复权收盘价数据
    df['close'] = df['closePrice']
    # 计算前复权数据
    df['openPrice'] = df['openPrice'] * df['accumAdjFactor']
    df['closePrice'] = df['closePrice'] * df['accumAdjFactor']
    df['highestPrice'] = df['highestPrice'] * df['accumAdjFactor']
    df['lowestPrice'] = df['lowestPrice'] * df['accumAdjFactor']
 
    # 开始计算
    df['type'] = 0
    df.loc[df['closePrice']>=df['openPrice'],'type'] = 1
    df.loc[df['closePrice']'openPrice'],'type'] = -1
 
    df['body_length'] = abs(df['closePrice']-df['openPrice'])
 
    df['second_yeah'] = 0
    df.loc[df['body_length']/df['closePrice'].shift(1)<0.015,'second_yeah'] = 1
 
    df['sort_type'] = 0
    # 阳 阳
    df.loc[(df['type']==1) & (df['type'].shift(1)==1),'sort_type'] = 1
    # 阳 阴
    df.loc[(df['type'] == 1) & (df['type'].shift(1) == -1), 'sort_type'] = 2
    # 阴 阳
    df.loc[(df['type'] == -1) & (df['type'].shift(1) == 1), 'sort_type'] = 3
    # 阴 阴
    df.loc[(df['type'] == -1) & (df['type'].shift(1) == -1), 'sort_type'] = 4
 
    df['signal'] = 0
    df['signal_name'] = ''
    df.loc[(df['sort_type']==1) & (df['second_yeah']==1) & (df['closePrice']'closePrice'].shift(1)) & (df['openPrice']>df['openPrice'].shift(1)),'signal'] = 1
    df.loc[(df['sort_type']==1) & (df['second_yeah']==1) & (df['closePrice']'closePrice'].shift(1)) & (df['openPrice']>df['openPrice'].shift(1)),'signal_name'] = '阳 阳'
    df.loc[(df['sort_type']==2) & (df['second_yeah']==1) & (df['closePrice']'openPrice'].shift(1)) & (df['openPrice']>df['closePrice'].shift(1)),'signal'] = 1
    df.loc[(df['sort_type']==2) & (df['second_yeah']==1) & (df['closePrice']'openPrice'].shift(1)) & (df['openPrice']>df['closePrice'].shift(1)),'signal_name'] = '阳 阴'
    df.loc[(df['sort_type']==3) & (df['second_yeah']==1) & (df['openPrice']'closePrice'].shift(1)) & (df['closePrice']>df['openPrice'].shift(1)),'signal'] = 1
    df.loc[(df['sort_type']==3) & (df['second_yeah']==1) & (df['openPrice']'closePrice'].shift(1)) & (df['closePrice']>df['openPrice'].shift(1)),'signal_name'] = '阴 阳'
    df.loc[(df['sort_type']==4) & (df['second_yeah']==1) & (df['openPrice']'openPrice'].shift(1)) & (df['closePrice']>df['closePrice'].shift(1)),'signal'] = 1
    df.loc[(df['sort_type']==4) & (df['second_yeah']==1) & (df['openPrice']'openPrice'].shift(1)) & (df['closePrice']>df['closePrice'].shift(1)),'signal_name'] = '阴 阴'
 
    file_name = os.path.basename(daily_file_path)
    title_str = file_name.split('.')[0]
 
    line_data = {
        'title_str':title_str,
        'whole_header':['日期','收','开','高','低'],
        'whole_df':df,
        'whole_pd_header':['tradeDate','closePrice','openPrice','highestPrice','lowestPrice'],
        'start_date_str':start_date_str,
        'end_date_str':end_date_str,
        'signal_type':'duration',
        'duration_len':[2],
        'temp':len(df.loc[df['signal']==1])
    }
    return line_data

结果