The stock pitching list updated by securities firm in Sep, according to the number of pitching times, the top 20 stocks rank as below:

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:

Top 20 stocks pitched for both in Sep and Aug :

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:

in_09_and_08.csv

in_09_not_08.csv

stock_picking_times_sectorid-2022-09-01.xlsx

sector_picking_times.xlsx