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
解说
下跌抵抗是指下降行情中，股价或指数收出若干连续跳空低开的阴线和阳线，其中阳线收盘价也低于前一根K线收盘价。
技术特征
1）出现在下跌途中。
2）由若干阴线和阳线组合而成，但阴线大大多于阳线。
3）连续跳空低开，即使是阳线，其收盘价也比前一根K线的收盘价低。
技术含义
下跌抵抗是卖出信号，后市看跌。
下跌抵抗表明空方占据了绝对优势，后市不容乐观，持筹的交易者要清仓，持币的交易者不应入场抢反弹。
K线形态策略代码
def excute_strategy(daily_file_path):'''名称：下跌抵抗识别：收出若干连续跳空低开的阴线和阳线，其中阳线收盘价也低于前一根K线的收盘价自定义：1. 至少4根前置条件：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 pdimport osstart_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'] = 0df.loc[df['closePrice'] > df['openPrice'], 'type'] = 1df.loc[df['closePrice'] < df['openPrice'], 'type'] = -1df['gap_val'] = 0df.loc[(df['type']==1) & (df['type'].shift(1)==1),'gap_val'] = df['closePrice'] - df['openPrice'].shift(1)df.loc[(df['type']==1) & (df['type'].shift(1)==-1),'gap_val'] = df['closePrice'] - df['closePrice'].shift(1)df.loc[(df['type']==-1) & (df['type'].shift(1)==1),'gap_val'] = df['openPrice'] - df['openPrice'].shift(1)df.loc[(df['type']==-1) & (df['type'].shift(1)==-1),'gap_val'] = df['openPrice'] - df['closePrice'].shift(1)df['gap_yeah'] = 0df.loc[df['gap_val']<0,'gap_yeah'] = 1df.reset_index(inplace=True)df['i_row'] = [i for i in range(len(df))]df['ext_0'] = df['gap_yeah'] - df['gap_yeah'].shift(1)df['ext_1'] = df['gap_yeah'] - df['gap_yeah'].shift(-1)df_s = df.loc[df['ext_0']==1].copy()df_e = df.loc[df['ext_1']==1].copy()df_clear = df.loc[(df['ext_0']==1) & (df['ext_1']==1)].copy()s_row_list = df_s['i_row'].values.tolist()e_row_list = df_e['i_row'].values.tolist()clear_row_list = df_clear['i_row'].values.tolist()two_row_list = s_row_list + e_row_listtwo_row_list.sort()s_list = []e_list = []start_yeah = Falsefor item in two_row_list:if item in clear_row_list:continueif start_yeah:if item in s_row_list:s_list.append(item)if item in e_row_list:e_list.append(item)else:if item in s_row_list:s_list.append(item)start_yeah = Truepassdf['signal'] = 0df['signal_name'] = ''# 发现第一个合理的序列就继续下一对 s efor s,e in zip(s_list,e_list):if e-s < 4:continuedf.loc[(df['i_row']>=s) & (df['i_row']<=e),'signal'] = 1df.loc[(df['i_row']>=s) & (df['i_row']<=e),'signal_name'] = str(e-s)passfile_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_detail','duration_len':[],'temp':len(df.loc[df['signal']==1])}return line_data结果
