The stock pitching list updated by securities firm in Sep, according to the number of pitching times, the top 20 stocks rank as below:
code
times
Sector_id
Sector_name
0
600519.SH
11
6230201020000000
Wine
1
600048.SH
9
6260102030000000
Real estate development
2
601888.SH
8
6225301020000000
Hotel & resort
3
601012.SH
7
6245301020000000
Semiconductor
4
000625.SZ
7
6225102010000000
Automobile manufacturing
5
000568.SZ
5
6230201020000000
Wine
6
600919.SH
5
6240101015000000
Regional bank
7
002714.SZ
5
6230202030000000
Food processing
8
002244.SZ
5
6260102030000000
Real estate development
9
603613.SH
4
6245101010000000
10
603019.SH
4
6245202010000000
11
002475.SZ
4
6245203010000000
12
300251.SZ
4
6225401030000000
13
600383.SH
4
6260102030000000
14
002705.SZ
4
6225201040000000
15
300979.SZ
4
6225203020000000
16
000301.SZ
4
6215101011000000
17
688777.SH
4
6245102010000000
18
300059.SZ
4
6240203020000000
19
000858.SZ
3
6230201020000000
And the corresponding top 8 most pitched stocks belong to sectors as below:
1,Wine, 2,Real estate development,3, Hotel & resort; 4,Automobile manufacturing, 5, Automobile manufacturing,5,Wine,6, Regional bank,7,Food processing,8,Real estate development.
Top 20 newly added stocks in Sep:
code |
times |
603613.SH |
4 |
300979.SZ |
4 |
000301.SZ |
4 |
002840.SZ |
3 |
000921.SZ |
3 |
002385.SZ |
3 |
300401.SZ |
3 |
600760.SH |
3 |
603712.SH |
3 |
600487.SH |
3 |
300776.SZ |
3 |
600875.SH |
3 |
603936.SH |
3 |
002675.SZ |
3 |
300014.SZ |
3 |
002738.SZ |
3 |
000541.SZ |
2 |
000739.SZ |
2 |
000933.SZ |
2 |
300910.SZ |
2 |
Top 20 stocks pitched for both in Sep and Aug :
code |
times |
600519.SH |
11 |
600048.SH |
9 |
601888.SH |
8 |
601012.SH |
7 |
000625.SZ |
7 |
000568.SZ |
5 |
600919.SH |
5 |
002714.SZ |
5 |
002244.SZ |
5 |
603019.SH |
4 |
002475.SZ |
4 |
300251.SZ |
4 |
600383.SH |
4 |
002705.SZ |
4 |
688777.SH |
4 |
300059.SZ |
4 |
000858.SZ |
3 |
300610.SZ |
3 |
The top 5 sectors ( ranked by the number of stocks pitched by securities firms) are:
industrial machinery
electrical equipment
electronic components
semiconducator
motor vehicles equipment
import pandas as pd from WindPy import * w.start() import datetime from collections import Counter date = datetime.datetime(2022,9,1) date_str = date.strftime("%Y-%m-%d") indexes = ["8841453.WI","8841454.WI","8841455.WI","8841456.WI","8841457.WI", "8841458.WI","8841459.WI","8841460.WI","8841461.WI","8841462.WI", "8841463.WI","8841464.WI","8841465.WI","8841466.WI","8841467.WI", "8841468.WI","8841469.WI","8841470.WI","8841471.WI","8841472.WI", "8841473.WI","8841474.WI","8841475.WI","8841476.WI","8841477.WI", "8841478.WI","8841479.WI","8841481.WI","8841482.WI","8841485.WI", "8841486.WI","8841487.WI","8841488.WI","8841489.WI","8841494.WI", "8841551.WI","8841552.WI","8841553.WI","8841554.WI","8841555.WI", "8841556.WI","8841565.WI","8841566.WI","8841590.WI","8841591.WI", "8841602.WI","8841603.WI","8841604.WI","8841605.WI","8841606.WI", "8841607.WI","8841608.WI","8841609.WI","8841610.WI","8841611.WI", "8841612.WI","8841613.WI","8841614.WI","8841615.WI","8841616.WI", "8841617.WI","8841618.WI","8841619.WI","8841620.WI","8841621.WI", "8841622.WI","8841623.WI","8841624.WI","8841625.WI","8841626.WI", "8841627.WI","8841628.WI"] counter = Counter() out = pd.ExcelWriter('stock_picking_%s.xlsx'%date_str) for index in indexes: data = w.wset("sectorconstituent","date=%s;windcode=%s"%(date_str,index)) if len(data.Data) == 0: continue df = pd.DataFrame(data.Data).T df.columns = data.Fields df.to_excel(out,index) for code in df["wind_code"]: counter[code]+=1 out.save() out2 = pd.ExcelWriter('stock_picking_times_%s.xlsx'%date_str) df2 = pd.DataFrame(counter.most_common(len(counter))) df2.columns =['code', 'times'] df2.to_excel(out2,date_str) out2.save() import pandas as pd from WindPy import * w.start() import datetime date = datetime.datetime(2022,9,1) date_str = date.strftime("%Y-%m-%d") sectorids = ("6210101010000000","6210101020000000","6210102010000000","6210102020000000", "6210102030000000","6210102040000000","6210102050000000","6215101010000000", "6215101011000000","6215101020000000","6215101030000000","6215101040000000", "6215101050000000","6215102010000000","6215103010000000","6215103020000000", "6215104010000000","6215104020000000","6215104030000000","6215104040000000", "6215104050000000","6215104045000000","6215105010000000", "6215105020000000","6220101010000000","6220102010000000","6220103010000000", "6220104010000000","6220104020000000","6220105010000000","6220106010000000", "6220106020000000","6220106015000000","6220107010000000","6220201010000000", "6220201050000000","6220201060000000","6220201070000000","6220201080000000", "6220202010000000","6220202020000000","6220301010000000","6220302010000000", "6220303010000000","6220304010000000","6220304020000000","6220305010000000", "6220305020000000","6220305030000000","6225101010000000","6225101020000000", "6225102010000000","6225102020000000","6225201010000000","6225201020000000", "6225201030000000","6225201040000000","6225201050000000","6225202010000000", "6225202020000000","6225203010000000","6225203020000000","6225203030000000", "6225301010000000","6225301020000000","6225301030000000","6225301040000000", "6225302010000000","6225302020000000","6225401010000000","6225401020000000", "6225401025000000","6225401030000000","6225401040000000","6225501010000000", "6225502010000000","6225502020000000","6225503010000000","6225503020000000", "6225504010000000","6225504020000000","6225504030000000","6225504040000000", "6225504050000000","6225504060000000","6230101010000000","6230101020000000", "6230101030000000","6230101040000000","6230201010000000","6230201020000000", "6230201030000000","6230202010000000","6230202030000000","6230203010000000", "6230301010000000","6230302010000000","6235101010000000","6235101020000000", "6235102010000000","6235102015000000","6235102020000000","6235102030000000", "6235103010000000","6235201010000000","6235202010000000","6235202011000000", "6235203010000000","6240101010000000","6240101015000000","6240102010000000", "6240201020000000","6240201030000000","6240201040000000","6240202010000000", "6240203010000000","6240203020000000","6240203030000000","6240301010000000", "6240301020000000","6240301030000000","6240301040000000","6240301050000000", "6245101010000000","6245102010000000","6245102020000000","6245103010000000", "6245103020000000","6245103030000000","6245201020000000","6245202010000000", "6245202020000000","6245203010000000","6245203015000000","6245203020000000", "6245203030000000","6245204010000000","6245301010000000","6245301020000000", "6250101010000000","6250101020000000","6250102010000000","6255101010000000", "6255102010000000","6255103010000000","6255104010000000","6255105010000000", "6255105020000000","6260102010000000","6260102020000000","6260102030000000", "6260102040000000") out1 = pd.ExcelWriter('sector_stock_list.xlsx') dfs = [] for sectorid in sectorids: data1=w.wset("sectorconstituent","date=%s;sectorid=%s"%(date_str,sectorid)) if len(data1.Data) == 0: continue df1=pd.DataFrame(data1.Data).T df1.columns=data1.Fields df1.insert(1,"sectorid",sectorid) dfs.append(df1) df1 =pd.concat(dfs) df1.to_excel(out1) out1.save()
Files:
stock_picking_times_sectorid-2022-09-01.xlsx