策略验证_卖出口诀_长箭射天股价落地

写在前面：
1. 本文中提到的“股票策略校验工具”的具体使用操作请查看该博文；
2. 文中知识内容来自书籍《同花顺炒股软件从入门到精通》
3. 本系列文章是用来学习技法，文中所得内容都仅仅只是作为演示功能使用

目录

解说

策略代码

结果

解说

        所谓“长剑射天，股价落地”，是指股价进入高位后，出现一条长上影小实体K线，该形态具备如下特征。

        1）应是上影较长的星形小阳或小阴线，而且长影长度是实体的2倍以上。

        2）当天成交量有较大幅度的提升。

        3）必须是处于股价高位或者波段顶部。

         出现“长剑射天，股价落地”的形态后，股票投资者需遵循以下操作原则。

        1）此形态出现在上升行情的价格顶部是强烈卖出信号。

        2）本形态出现频率相当高，能在任何部位形成，但只有在高位出现时，方为可信卖出信号。

策略代码

def excute_strategy(base_data,data_dir):
    '''
    卖出口诀 - 长箭射天，股价落地
    解析：
    1. 出现一条长上影小实体K线
    2. 长影长度是实体的2倍以上
    3. 成交量有较大幅度的提升
    4. 上影线要高于前一日高点
    自定义：
    1. 较大幅度的提升 =》 前一日的两倍
    2. 小实体 =》 K线实体是前一日收盘价的1.5%以下0.5%以上
    3. 卖出时点 =》 形态出现后下一交易日
    4. 胜 =》 卖出后第三个交易日收盘价下跌，为胜
    只计算最近两年的数据
    :param base_data:股票代码与股票简称 键值对
    :param data_dir:股票日数据文件所在目录
    :return:
    '''
    import pandas as pd
    import numpy as np
    import talib,os
    from datetime import datetime
    from dateutil.relativedelta import relativedelta
    from tools import stock_factor_caculate
 
    def res_pre_two_year_first_day():
        pre_year_day = (datetime.now() - relativedelta(years=2)).strftime('%Y-%m-%d')
        return pre_year_day
    caculate_start_date_str = res_pre_two_year_first_day()
 
    dailydata_file_list = os.listdir(data_dir)
 
    total_count = 0
    total_win = 0
    check_count = 0
    list_list = []
    detail_map = {}
    factor_list = ['VOL']
    ma_list = []
    for item in dailydata_file_list:
        item_arr = item.split('.')
        ticker = item_arr[0]
        secName = base_data[ticker]
        file_path = data_dir + item
        df = pd.read_csv(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'] >= caculate_start_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']
 
        if len(df)<=0:
            continue
 
        # 开始计算
        for item in factor_list:
            df = stock_factor_caculate.caculate_factor(df,item)
        for item in ma_list:
            df = stock_factor_caculate.caculate_factor(df,item)
        df.reset_index(inplace=True)
        df['i_row'] = [i for i in range(len(df))]
        df['three_chg'] = round(((df['close'].shift(-3) - df['close']) / df['close']) * 100, 4)
        df['three_after_close'] = df['close'].shift(-3)
 
 
        df['body_length'] = abs(df['closePrice']-df['openPrice'])
        df['up_shadow'] = 0
        df.loc[df['closePrice']>df['openPrice'],'up_shadow'] = df['highestPrice'] - df['closePrice']
        df.loc[df['closePrice']'openPrice'],'up_shadow'] = df['highestPrice'] - df['openPrice']
        df['target_yeah'] = 0
        df.loc[(df['body_length']/df['closePrice'].shift(1)>0.005) & (df['body_length']/df['closePrice'].shift(1)<0.015) & (df['highestPrice']>df['highestPrice'].shift(1)) & (df['up_shadow']>2*df['body_length']) & (df['turnoverVol']>=2*df['turnoverVol'].shift(1)),'target_yeah'] = 1
 
        df_target = df.loc[df['target_yeah']==1].copy()
 
        # 临时 start
        # df.to_csv('D:/temp006/'+ticker + '.csv',encoding='utf-8')
        # 临时 end
 
        node_count = 0
        node_win = 0
        duration_list = []
        table_list = []
        i_row_list = df_target['i_row'].values.tolist()
        for i,row0 in enumerate(i_row_list):
            row = row0 + 1
            if row >= len(df):
                continue
            date_str = df.iloc[row]['tradeDate']
            cur_close = df.iloc[row]['close']
            three_after_close = df.iloc[row]['three_after_close']
            three_chg = df.iloc[row]['three_chg']
 
            table_list.append([
                i,date_str,cur_close,three_after_close,three_chg
            ])
            duration_list.append([row-2,row+3])
            node_count += 1
            if three_chg<0:
                node_win +=1
            pass
 
        list_list.append({
            'ticker':ticker,
            'secName':secName,
            'count':node_count,
            'win':0 if node_count<=0 else round((node_win/node_count)*100,2)
        })
        detail_map[ticker] = {
            'table_list': table_list,
            'duration_list': duration_list
        }
 
        total_count += node_count
        total_win += node_win
        check_count += 1
        pass
    df = pd.DataFrame(list_list)
 
    results_data = {
        'check_count':check_count,
        'total_count':total_count,
        'total_win':0 if total_count<=0 else round((total_win/total_count)*100,2),
        'start_date_str':caculate_start_date_str,
        'df':df,
        'detail_map':detail_map,
        'factor_list':factor_list,
        'ma_list':ma_list
    }
    return results_data

结果

 本文校验的数据是随机抽取的81个股票