K线形态识别_绵绵阴跌
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
绵绵阴跌是指在下跌初期或盘整后期，股价或指数收出若干夹着一些小阳线、十字线的小阴线（一般不少于8根），整体走势略向下倾斜。
技术特征
1）出现在下跌初期或盘整后期。
2）由若干小K线组成（一般不少于8根）。
3）一般以小阴线居多，中间也可夹着一些小阳线、十字线。
4）整个K线组合排列略呈向下倾斜状态。
技术含义
绵绵阴跌是卖出信号，后市看跌。
绵绵阴跌每天的跌幅都不大，但却像黄梅天的阴雨下个不停。股市中有谚语说：不怕急跌，最怕阴跌。说的就是类似于绵绵阴跌的走势。因为这种缓跌延长了下跌的时间，拓展了下跌的空间，预示着后市走势不容乐观，股价很可能就此长期走弱。交易者一旦发现绵绵阴跌走势的个股，必须保持高度警觉，空仓者不能买入，持仓者及时离场，也许不就就会迎来快速下跌，甚至是暴跌。
K线形态策略代码
def excute_strategy(daily_file_path):'''名称：绵绵阴跌识别：收出若干夹着一些小阳线、十字线的小阴线（一般不少于8根），整体走势略向下倾斜自定义：1. 略微向下倾斜 =》1）以实体中心点值计算涨跌幅，小于0的占比要大于三分之二2）最大跌幅不要超过第一根收盘价的5%3) 最后三个涨跌幅必须为负2. 小阴线占多数 =》小阴线占比大于三分之二前置条件：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['body_length'] = abs(df['closePrice'] - df['openPrice'])df['target_yeah'] = 0df.loc[df['body_length']/df['closePrice'].shift(1)<0.015,'target_yeah'] = 1# df['center_val'] = (df['closePrice']+df['openPrice'])/2df['center_val'] = df['closePrice']df['center_chg'] = df['center_val'] - df['center_val'].shift(1)df.reset_index(inplace=True)df['i_row'] = [i for i in range(len(df))]df['ext_0'] = df['target_yeah'] - df['target_yeah'].shift(1)df['ext_1'] = df['target_yeah'] - df['target_yeah'].shift(-1)df_s = df.loc[df['ext_0']==1].copy()df_e = df.loc[df['ext_1']==1].copy()s_row_list = df_s['i_row'].values.tolist()e_row_list = df_e['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 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'] = ''# 只标记出区间中起始开始有效的8根K线for s,e in zip(s_list,e_list):if e-s < 8:continuetemp_s = Nonetemp_e = Nonefor i in range(s,e-8):chg_num = 0n_num = 0max_chg = Nonechg_list = []for j in range(8):if max_chg is None or df.iloc[i+j]['center_chg'] < max_chg:max_chg = df.iloc[i+j]['center_chg']if df.iloc[i+j]['center_chg']<0:chg_list.append(-1)chg_num += 1else:chg_list.append(1)if df.iloc[i+j]['type']==-1:n_num += 1passif chg_num/8>0.66 and n_num/8>0.66:if abs(max_chg)/df.iloc[i]['closePrice'] > 0.05:continueif chg_list[-1]==1 or chg_list[-2]==1 or chg_list[-3]==1:continuetemp_s = itemp_e = i + 8breakpassif temp_s:df.loc[(df['i_row']>=temp_s) & (df['i_row']<=temp_e),'signal'] = 1df.loc[(df['i_row']>=temp_s) & (df['i_row']<=temp_e),'signal_name'] = str(e-temp_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
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