板块异动类

来源：https://uqer.io/community/share/54ccf06af9f06c276f651a5a

本代码主要实现以下功能

• 由DataAPI.EquIndustryGet获得每只个股的所属行业，这里采用的是申万二级分类；
• 根据个股行业获得所有行业的成分股
• 根据成分股的每天涨幅和市值，获得主题的加权涨幅，将其排序，即得到每日涨跌幅最大的行业前十
• 根据成分股出现的涨跌停次数，获得涨跌停比例最大的行业前十
• 根据成分股的换手率，获得换手率最大和最小的行业前十
• 将每个行业所包含的个股，储存到csv文件中，如果对某个行业感兴趣，可以进一步查看其成分股

此处定义了一些函数，使得代码功能更明确

def GetIndInfo(universe,field):    #获得行业数据
    num = 100
    count_num = len(universe)/num
    if count_num>0:
        indus_df = pd.DataFrame({})
        for i in range(count_num):
            sub_ind = DataAPI.EquIndustryGet(secID=universe[i*num:(i+1)*num],field=field)
            indus_df = pd.concat([indus_df,sub_ind])
        sub_ind = DataAPI.EquIndustryGet(secID=universe[(i+1)*num:],field=field)
        indus_df = pd.concat([indus_df,sub_ind])
    else:
        indus_df = DataAPI.EquIndustryGet(secID=universe,field=field)
    filed_new = ['secID']+field
    indus_df = indus_df[filed_new]
    return indus_df

def GetMktInfo(secID,beginDate,endDate,field):    #获得市场行情数据
    num = 50
    count_num = len(secID)/num
    if count_num>0:
        MktInfo_df = pd.DataFrame({})
        for i in range(count_num):
            sub_info = DataAPI.MktEqudGet(secID=secID[i*num:(i+1)*num],beginDate=beginDate,endDate=endDate,field=field)
            MktInfo_df = pd.concat([MktInfo_df,sub_info])
        sub_info = DataAPI.MktEqudGet(secID=secID[(i+1)*num:],beginDate=beginDate,endDate=endDate,field=field)
        MktInfo_df = pd.concat([MktInfo_df,sub_info])
    else:
        MktInfo_df = DataAPI.MktEqudGet(secID=secID,beginDate=beginDate,endDate=endDate,field=field)
    return MktInfo_df

def CountTime():
    today = datetime.today()
    cal_date = Date.fromDateTime(today)
    if cal.isBizDay(cal_date):    #如果是交易日，则判断当天是不是在15点前        
        today_str = today.strftime("%Y%m%d")
        time1=" 15:05:00"
        ben_time = datetime.strptime(today_str+time1,"%Y%m%d %H:%M:%S")
        if today>ben_time:
            date = today_str
    else:    #如果当天不是交易日，则获得前一个交易日
        cal_wd = cal.adjustDate(cal_date,BizDayConvention.Preceding)    #Date格式
        dtime_wd = cal_wd.toDateTime()    #datetime格式
        date = dtime_wd.strftime("%Y%m%d")
    return date

获得个股的行情数据，并以此来计算主题的：涨幅、涨跌停比例、换手率

from datetime import timedelta
cal = Calendar('China.SSE')

universe = set_universe('A')

indus_df = GetIndInfo(universe=universe,field =['secShortName','industryName2'])
cnt_date = CountTime()    #获得可用的时期
field_mkt = ['preClosePrice','openPrice','highestPrice','lowestPrice','closePrice','turnoverRate','marketValue']
MktInfo_df = GetMktInfo(secID=universe,beginDate=cnt_date,endDate=cnt_date,field=field_mkt)

ind_inc_dic = {}    #记录行业的涨幅
ind_gb_dic = {}    #记录行业的涨跌停数目
ind_turn_dic = {}    #记录行业的换手率
ind_tknm_dic = {}    #记录行业包含的个股

grouped = indus_df.groupby('industryName2')
for name,group in grouped:
    ind_tknm_dic[name] = list(group['secShortName'])

    stk_list = list(group['secID'])
    sub_mkt_info = MktInfo_df[MktInfo_df.secID.isin(stk_list)]
    #计算行业涨跌幅
    sub_mkt_info['inc_rate'] = (sub_mkt_info['closePrice']-sub_mkt_info['preClosePrice'])/sub_mkt_info['preClosePrice']    #获得每个个股的涨跌幅
    ind_inc = (sub_mkt_info['inc_rate']*sub_mkt_info['marketValue']).sum()/sub_mkt_info['marketValue'].sum()    #获得行业的涨跌幅，利用市值加权平均值计算
    ind_inc_dic[name] = ind_inc

    num_good = len(sub_mkt_info[((sub_mkt_info['closePrice']-sub_mkt_info['preClosePrice'])/sub_mkt_info['preClosePrice']).round(2)==0.1])    #涨停的个股    
    num_bad  = len(sub_mkt_info[((sub_mkt_info['preClosePrice']-sub_mkt_info['closePrice'])/sub_mkt_info['preClosePrice']).round(2)==0.1])    #跌停的个股
    ind_gb_dic[name] = (num_good-num_bad)*1.0/len(group)

    turnover = sub_mkt_info['turnoverRate'].mean()    #计算行业的平均换手率
    ind_turn_dic[name] = turnover

以下是将结果进行展示

ind_turn_pd = pd.DataFrame.from_dict(ind_turn_dic,orient='index')
ind_turn_pd.rename(columns={0:u'换手率'},inplace=True)
ind_turn_pd = ind_turn_pd.sort(columns=u'换手率',ascending=False)
ind_turn_pd1 = ind_turn_pd.sort(columns=u'换手率',ascending=True)
print cnt_date+'换手率最大的行业前十：'
ind_turn_pd[0:10]

20150130换手率最大的行业前十：

print cnt_date+'换手率最小的行业前十：'
ind_turn_pd1[0:10]

20150130换手率最小的行业前十：

ind_gb_pd = pd.DataFrame.from_dict(ind_gb_dic,orient='index')
ind_gb_pd.rename(columns={0:u'涨跌停比例'},inplace=True)
ind_gb_pd = ind_gb_pd.sort(columns=u'涨跌停比例',ascending=False)
ind_gb_pd1 = ind_gb_pd.sort(columns=u'涨跌停比例',ascending=True)
print cnt_date+'涨停比例最大的行业前十：'
ind_gb_pd[0:10]

20150130涨停比例最大的行业前十：

print cnt_date+'跌停比例最大的行业前十：'
ind_gb_pd1[0:10]

20150130跌停比例最大的行业前十：

ind_inc_pd = pd.DataFrame.from_dict(ind_inc_dic,orient='index')
ind_inc_pd = ind_inc_pd.sort(columns=0,ascending=False)
ind_inc_pd.rename(columns={0:u'涨跌幅'},inplace=True)
ind_inc_pd1 = ind_inc_pd.sort(columns=u'涨跌幅')
print cnt_date+'涨幅最大的行业前十：'
ind_inc_pd[0:10]

print cnt_date+'跌幅最大的行业前十：'
ind_inc_pd1[:10]

20150130跌幅最大的行业前十：

将行业包含的个股信息储存到csv文件中，可以进行更细致的查看行业信息

ind_tk_pd = pd.DataFrame({})
for ind_nm,tk_list in ind_tknm_dic.items():
    sub_pd = pd.DataFrame(tk_list)
    sub_pd[u'行业名称'] = ind_nm
    ind_tk_pd = pd.concat([ind_tk_pd,sub_pd])
ind_tk_pd.rename(columns={0:u'成分股'},inplace=True)
ind_tk_pd = ind_tk_pd.loc[:,[u'行业名称',u'成分股']]
ind_tk_pd.to_csv('ind_tk.csv',encoding='GBK',index=False)