风险因子(离散类)

最后更新于:2022-04-01 21:57:15

# 风险因子(离散类) > 来源:https://uqer.io/community/share/54d2cee9f9f06c276f651a67 本代码用于计算风险因子 + 先根据`DataAPI.ThemeTickersGet`得到每个主题相关的个股 + 计算个股在前7天的每天涨跌幅,从而计算主题的每天涨跌幅(市值加权) + 计算个股前7天的涨跌停次数,计算主题涨跌停比例 + 对每个股票,按照股票市值占主题总市值的比例,计算涨跌幅和涨跌停比例(均为7日),将两个指标进行排名,个股有两个排名得分 + 再取两个排名得分的平均,对个股再次排名 排名越高,波动越大,风险越大 ```py datetime.today() datetime.datetime(2015, 2, 4, 22, 18, 57, 402881) ``` 此处定义了几个函数,方便调用 ```py def GetThemeInfo(thm_id_list): #由于ThemeTickersGet对于数据量有限制,一次调用1000个主题数据 num = 1000 #每一次调取多少个主题的信息 cnt_num = len(thm_id_list)/num #一次调取num个主题,要调用num次 beginDate = '20140601' #开始时间 endDate = '20150123' #结束时间 if cnt_num>0: thm_tk_pd = pd.DataFrame({}) for i in range(cnt_num): info_sub = DataAPI.ThemeTickersGet(beginDate=beginDate,endDate=endDate,themeID=thm_id_list[i*num:(i+1)*num]) #获取主题相关的个股 thm_tk_pd = pd.concat([thm_tk_pd,info_sub]) #将数据连接 info_sub = DataAPI.ThemeTickersGet(beginDate=beginDate,endDate=endDate,themeID=thm_id_list[(i+1)*num:]) thm_tk_pd = pd.concat([thm_tk_pd,info_sub]) else: thm_tk_pd = DataAPI.ThemeTickersGet(beginDate=beginDate,endDate=endDate,themeID=thm_id_list) return thm_tk_pd def GetMktInfo(tk_list,beginDate,endDate,field_mkt): #获得个股的日线行情数据 num = 50 cnt_num = len(tk_list)/num if cnt_num>0: tk_mkt_info = pd.DataFrame({}) for i in range(cnt_num): sub_info = DataAPI.MktEqudGet(ticker=tk_list[i*num:(i+1)*num],beginDate=beginDate,endDate=endDate,field=field_mkt) tk_mkt_info = pd.concat([tk_mkt_info,sub_info]) sub_info = DataAPI.MktEqudGet(ticker=tk_list[(i+1)*num:],beginDate=beginDate,endDate=endDate,field=field_mkt) tk_mkt_info = pd.concat([tk_mkt_info,sub_info]) else: tk_mkt_info = DataAPI.MktEqudGet(ticker=tk_list,beginDate=beginDate,endDate=endDate,field=field_mkt) return tk_mkt_info def GetDate(n): #获得最近7个交易日的日期 cal = Calendar("China.SSE") today_cal = Date.todaysDate() today_dtime = datetime.today() if cal.isBizDay(today_cal): #如果今天是交易日 today_ymd = today_dtime.strftime("%Y%m%d") hms = " 15:05:00" ben_time = datetime.strptime(today_ymd+hms,"%Y%m%d %H:%M:%S") if today_dtime>ben_time: #如果当前时间晚于15:05分,则可以获取到今日行情数据 end_date = today_ymd else: cal_wd = cal.advanceDate(today_cal, '-1B', BizDayConvention.Preceding) #获得前一个工作日Date格式 end_date = cal_wd.toISO().replace('-','') #转换成字符串格式‘20140102’ else: cal_wd = cal.advanceDate(today_cal, '-1B', BizDayConvention.Preceding) #获得前一个工作日Date格式 end_date = cal_wd.toISO().replace('-','') #转换成字符串格式‘20140102’ end_date_cal = Date.parseISO('-'.join([end_date[0:4],end_date[4:6],end_date[6:8]])) #更改日期格式为“2014-03-02” prd = '-'+str(n-1)+'B' #起始日期和终止日期间隔的天数 begin_date_cal = cal.advanceDate(end_date_cal, prd , BizDayConvention.Preceding) #获得6天前的工作日 begin_date = begin_date_cal.toISO().replace('-','') return begin_date,end_date ``` 读取主题id文件,先对个股和主题进行筛选,然后获得个股的行情数据 ```py #Main import pandas as pd f1 = read('20140601_20150203theme_list.txt') #从这个文档中读取所有的主题id thm_id_list = f1.split(',') thm_tk_pd = GetThemeInfo(thm_id_list=thm_id_list) #获得主题对应个股的信息 thm_tk_pd = thm_tk_pd[(thm_tk_pd['ticker'].str.len()==6) & (thm_tk_pd['ticker'].apply(lambda x:x[0]=='0' or x[0]=='6'))] #过滤港股和新三板,因为拿不到行情数据 grouped_thmid = thm_tk_pd.groupby('themeID') #根据主题id分类,得到每个主题对应的个股 ###对主题进行过滤如果该主题所包含的个股《5,则舍弃 fld_thmid_list = [] for name,group in grouped_thmid: if len(group)>=5: fld_thmid_list.append(name) thm_tk_pd = thm_tk_pd[thm_tk_pd['themeID'].isin(fld_thmid_list)] ThmId_Nm_dic = dict(zip(thm_tk_pd['themeID'],thm_tk_pd['themeName'])) #获得主题id与主题名称的对应 TkId_Nm_dic = dict(zip(thm_tk_pd['ticker'],thm_tk_pd['secShortName'])) #获得个股id与个股名称的对应 thm_tk_pd = thm_tk_pd[['themeID','ticker']] tk_list = list(set(thm_tk_pd['ticker'])) #获得所有的个股 n_prd =7 beginDate,endDate = GetDate(n_prd) #获取n_prd个交易日的具体日期 field_mkt = ['ticker','openPrice','closePrice','highestPrice','lowestPrice','marketValue','preClosePrice '] tk_mktinfo_pd = GetMktInfo(tk_list,beginDate,endDate,field_mkt) #获得所有个股的行情数据 tk_mktinfo_pd['return'] = (tk_mktinfo_pd['closePrice']-tk_mktinfo_pd['preClosePrice'])/tk_mktinfo_pd['preClosePrice'] #计算所有个股每天的涨跌幅 ``` 计算主题的涨跌幅(绝对值)和涨跌停比例 ```py grouped_thmid = thm_tk_pd.groupby('themeID') #根据主题id分类,得到每个主题对应的个股 grouped_tkid = thm_tk_pd.groupby('ticker') #根据ticker分类,得到每个个股对应的主题 thm_rtn_dic, thm_gb_dic, thm_mkv_dic = {},{},{} #主题的日涨幅,主题的日涨跌停比例,主题的市值 #获得主题的日收益的绝对值的平均 for thm,group_thm in grouped_thmid: sub_tk_list = list(group_thm['ticker']) sub_tk_mkt_pd = tk_mktinfo_pd[tk_mktinfo_pd['ticker'].isin(sub_tk_list)] #获得该主题下个股的行情数据 thm_rtn = (sub_tk_mkt_pd['marketValue']*abs(sub_tk_mkt_pd['return'])).sum()/sub_tk_mkt_pd['marketValue'].sum() #计算主题在这7天的平均每天绝对收益 thm_rtn_dic[thm] = thm_rtn thm_mkv_dic[thm] = sub_tk_mkt_pd['marketValue'].sum() #记录每个主题的市值(7天的和) num_gb = len(sub_tk_mkt_pd[(abs((sub_tk_mkt_pd['closePrice']-sub_tk_mkt_pd['preClosePrice']))/sub_tk_mkt_pd['preClosePrice']).round(2)==0.1]) #涨跌停的个股数目 thm_gb_dic[thm] = num_gb/n_prd #主题涨跌停比例7日均值 ``` 由主题涨跌幅和涨跌停比例,计算个股的涨跌幅和涨跌停比例 ```py tk_inc_gb_dic = {} #由主题计算的个股的涨幅和涨跌停比例 for tk,group_tk in grouped_tkid: tk_mkv = tk_mktinfo_pd['marketValue'][tk_mktinfo_pd['ticker']==tk].sum() #得到个股市值(7天的和) thm_list = group_tk['themeID'] inc,gb_ratio = 0,0 for thm in thm_list: pro = tk_mkv/thm_mkv_dic[thm] #个股占该主题的比例 inc += thm_rtn_dic[thm]*pro gb_ratio += thm_gb_dic[thm]*pro tk_inc_gb_dic[tk] = (inc,gb_ratio) #记录个股的涨幅和涨跌停比例 ``` 根据个股的涨跌幅和涨跌停比例进行排名,再将这两个排名进行平均,再排名 ```py sort1 = sorted(tk_inc_gb_dic.keys(), key = lambda x:tk_inc_gb_dic[x][0], reverse=True) #根据个股的涨幅排名,涨幅大的排名在前 sort2 = sorted(tk_inc_gb_dic.keys(), key = lambda x:tk_inc_gb_dic[x][1], reverse=True) #根据个股的涨跌停比例排名,涨跌停比例高的排名在前 rank = lambda x:(sort1.index(x)+sort2.index(x))*1.0/2+1 id2name = lambda x:TkId_Nm_dic[x] df = pd.DataFrame({'ticker':tk_list}) df['name'] = pd.Series(map(id2name,tk_list)) df['ranking_score'] = pd.Series(map(rank,tk_list)) df_sort = df.sort(columns=['ranking_score'],ascending = True) df_sort.reset_index(inplace=True,drop=True) print "最近个股风险因子排名:" df_sort ``` ```py datetime.today() datetime.datetime(2015, 2, 4, 22, 19, 15, 638752) ```
';