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

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解说

        所谓“三种图线交叉点，卖出股票需抢先”，是指均线交叉、MACD线交叉和VOL成交量交叉。在最近两天内连续出现这3种图形交叉，投资者需要尽早抛售手中的股票。

         出现“三种图线交叉点，卖出股票需抢先”的形态后，股票投资者需遵循以下操作原则。

        1）从上图可以看出，5日移动平均线交叉10日移动平均线，5日均量线（VOL:成交量指标）交叉10日均量线，DIF线交叉MACD线。该形态从三种不同角度客观地分析市场动向，为投资者判断行情恶化提供行之有效的脱身依据，属于大跌之前的最佳卖点。

        2）在这种股市形态的实战中，5日均线交叉10日均线，从趋势转化的角度分析上档承接无力，股价只有通过下跌方式寻找新的支撑；5日均量线交叉10日均量线，从上涨缺乏量能配合角度衬托下降的必然；而DIF线交叉MACD线，则从股价顶背离的角度显示弱势状态。

        3）当股价处于高位区域时，出现三种图线的同一天或间隔一两天的时间里交叉的走势，可断定趋势已经彻底转变，此时卖出股票虽然已错过相对高点，但可以躲过随之而来更为凶猛的下跌行情。

策略代码

1. 上文解说中MACD使用的是DIFF与MACD的交叉，本策略使用的是DIFF与DEA的交叉

2. 本策略检测的交叉均为死叉

def excute_strategy(base_data,data_dir):
    '''
    卖出口诀 - 三种图线交叉点，卖出股票需抢先
    解析：
    1. 两天内连续出现均线交叉、MACD交叉和VOL成交量交叉
    自定义：
    1. 交叉 =》短线下穿长线
    2. 卖出时点 =》 形态出现后下一交易日
    3. 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','MACD']
    ma_list = ['ma5','ma10']
    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['ma_point'] = 0
        df.loc[(df['ma5'].shift(1)>=df['ma10'].shift(1)) & (df['ma5']<df['ma10']),'ma_point'] = 1
        df['vol_point'] = 0
        df.loc[(df['vol5'].shift(1)>=df['vol10'].shift(1)) & (df['vol5']<df['vol10']),'vol_point'] = 1
        df['macd_point'] = 0
        df.loc[(df['DIFF'].shift(1)>=df['DEA'].shift(1)) & (df['DIFF']<df['DEA']),'macd_point'] = 1
        df['target_yeah'] = df['macd_point'] + df['ma_point'] + df['vol_point'] + df['macd_point'].shift(-1) + df['ma_point'].shift(-1) + df['vol_point'].shift(-1)

        df['three_chg'] = round(((df['close'].shift(-3) - df['close'])/df['close'])*100,4)
        df['three_after_close'] = df['close'].shift(-3)

        df_target = df.loc[df['target_yeah']==3].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 + 2
            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-3,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