按各种周期计算主力净流入资金额，并排名

#api-获取资金流信息## 导入常用librariesimport datetimeimport pandas as pdfrom jqdata import *#auth('jqdata帐号','jqdata帐号密码')pd.options.display.max_rows = 500pd.options.display.max_columns = 20date_now = datetime.datetime.now()oneDay = datetime.timedelta(days=1)yesterday = (date_now - oneDay).strftime("%Y-%m-%d")today_date = date_now.strftime("%Y-%m-%d")if datetime.datetime.now().hour > 16:sum_end_day = today_dateelse:sum_end_day = yesterdaytrade_day_list = get_trade_days(end_date= sum_end_day , count=100)#1天-昨天days_before_1 = trade_day_list[-1].strftime("%Y-%m-%d")#3天：days_before_3 = trade_day_list[-3].strftime("%Y-%m-%d")#5天：days_before_5 = trade_day_list[-5].strftime("%Y-%m-%d")#8天：days_before_8 = trade_day_list[-8].strftime("%Y-%m-%d")#10天：days_before_10 = trade_day_list[-10].strftime("%Y-%m-%d")#13天：days_before_13 = trade_day_list[-13].strftime("%Y-%m-%d")#20天：days_before_20 = trade_day_list[-20].strftime("%Y-%m-%d")#30天：days_before_30 = trade_day_list[-30].strftime("%Y-%m-%d")#50天：days_before_50 = trade_day_list[-50].strftime("%Y-%m-%d")#89天：days_before_89 = trade_day_list[-89].strftime("%Y-%m-%d")# # stocks = get_index_stocks('000300.XSHG')# # stocks = get_index_stocks('000001.XSHG')stocks = get_index_stocks('000300.XSHG')# # N天资金流#df_source = get_money_flow(stocks, end_date=sum_end_day, count= 100)# print(df_source)df1 = df_source[df_source['date'] >= days_before_1]df1_sum = df1.groupby('sec_code')["net_amount_main"].sum()df3 = df_source[df_source['date'] >= days_before_3]df3_sum = df3.groupby('sec_code')["net_amount_main"].sum()df5 = df_source[df_source['date'] >= days_before_5]df5_sum = df5.groupby('sec_code')["net_amount_main"].sum()df8 = df_source[df_source['date'] >= days_before_8]df8_sum = df8.groupby('sec_code')["net_amount_main"].sum()# df10 = df_source[df_source['date'] >= days_before_10]# df10_sum = df10.groupby('sec_code')["net_amount_main"].sum()df13 = df_source[df_source['date'] >= days_before_13]df13_sum = df13.groupby('sec_code')["net_amount_main"].sum()df20 = df_source[df_source['date'] >= days_before_20]df20_sum = df20.groupby('sec_code')["net_amount_main"].sum()df30 = df_source[df_source['date'] >= days_before_30]df30_sum = df30.groupby('sec_code')["net_amount_main"].sum()df50 = df_source[df_source['date'] >= days_before_50]df50_sum = df50.groupby('sec_code')["net_amount_main"].sum()df89 = df_source[df_source['date'] >= days_before_89]df89_sum = df89.groupby('sec_code')["net_amount_main"].sum()pd_all = pd.concat([df1_sum, df3_sum, df5_sum, df8_sum, df13_sum, df20_sum, df30_sum,df50_sum,df89_sum], axis=1)pd_all.columns = ['c1', 'c3', 'c5', 'c8', 'c13', 'c20', 'c30', 'c50', 'c89']df_sorted = pd_all.sort_values(by='c5', ascending=False)for index in df_sorted.index:sec = get_security_info(normalize_code(index))df_sorted.ix[index, 'security'] = sec.display_namemid = df_sorted['security']df_sorted.drop(labels=['security'], axis=1,inplace = True)df_sorted.insert(0, 'security', mid)df_sorted[:100]

/opt/conda/lib/python3.5/site-packages/ipykernel_launcher.py:90: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated

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