K线形态识别_揉搓线

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

        揉搓线是由一正一反两根T字线组成的K线组合，比喻股价像洗衣一样，被人反复揉搓。

         揉搓线表明多空双方经过两个交易日的对决，最终握手言和。单根的T字线和倒T字线已经富含操控之意，何况一前一后收出两个收盘价相同或非常接近的T字线和倒T字线呢！仅仅用巧合来解释，恐怕难以令人信服。主力在一定程度上控制开盘价和收盘价以及几日的走势，因此只有主力才可以做到收放自如。交易者见到揉搓线，一定要引起足够重视。

技术特征

1）多数出现在涨势当中。

2）由一根T字线和一根倒T字线组成，影线可长可短。

技术含义

1）在上涨初期出现揉搓线是买入信号，后市继续看涨。

2）上涨途中出现揉搓线，后市继续看涨，是持仓信号。

3）在上涨趋势末端出现揉搓线是卖出信号。

4）股价下跌，反弹过程中出现揉搓线，是卖出信号。

K线形态策略代码

def excute_strategy(daily_file_path):
    '''
    名称：揉搓线
    识别：由一根T字线和一根倒T字线组成
    自定义：
    1. 影线很短=》不超过上一交易日价格 0.5%
    2. 影线长 =》超过上一交易日价格 2%
    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 = '2015-01-01'
    end_date_str = '2016-01-01'
    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.loc[df['type']==1,'top_shadow_length'] = df['highestPrice'] - df['closePrice']
    df.loc[df['type']==-1,'top_shadow_length'] = df['highestPrice'] - df['openPrice']
    df.loc[df['type']==1,'bottom_shadow_length'] = df['openPrice'] - df['lowestPrice']
    df.loc[df['type']==-1,'bottom_shadow_length'] = df['closePrice'] - df['lowestPrice']
 
    short_len = 0.005
    long_len = 0.02
    window_len = 2
    # T
    df['ext_t'] = 0
    df.loc[(df['body_length']==0) & (df['top_shadow_length']/df['closePrice'].shift(1)'bottom_shadow_length']/df['closePrice'].shift(1)>long_len),'ext_t'] = 1
    # 倒T
    df.loc[(df['body_length'] == 0) & (df['bottom_shadow_length'] / df['closePrice'].shift(1) < short_len) & (
                df['top_shadow_length'] / df['closePrice'].shift(1) > long_len), 'ext_t'] = 2
 
    df['ext_0'] = df['ext_t'].rolling(window=window_len).sum()
    df['signal'] = 0
    df.loc[df['ext_0']==3,'signal'] = 1
    df['signal_name'] = ''
    df.loc[(df['ext_0']==3) & (df['ext_t']==2),'signal_name'] = 'T 倒T'
    df.loc[(df['ext_0']==3) & (df['ext_t']==1),'signal_name'] = '倒T T'
 
    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]
    }
    return line_data

结果