Here is a weekly performance review for stock pitched by securities firms on August since 1st Aug to 5th Aug, 2022.

There are total 174 stocks performed positive for the past week. Some of them are as below: (PCT_CHG_PER means the change of price in last week, the unit is %, for 300260.sz, the number of 34.38983051 means the price of 300260.sz increased 34.38% in last week)

And we countered the numbers of the stocks which performed positive categorized by their sectors.

We can see that the numbers of the stocks in the sectorid of 6245301020000000, 6245203010000000 and 6215104020000000 ranked the top 3.

6245301020000000 stands for the semiconductor products industry.

6245203010000000 stands for the sector of electronic component industry.

6215104020000000 stands for the sector of non-metallic industry.

No review for other monthly data yet as the wind data quota is limited.

stock_picking_factor file can be downloaded there.

# -*- coding: utf-8 -*-
"""
Created on Sun Aug  7 21:21:00 2022

@author : fang
"""

import pandas as pd
from WindPy import *
w.start()

import datetime

date = datetime.datetime(2022,8,1)
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()



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"]

out2 = pd.ExcelWriter('stock_picking_list.xlsx')
dfs = []
for index in indexes:
    data2=w.wset("sectorconstituent","date=%s;windcode=%s"%(date_str,index))
    df2=pd.DataFrame(data2.Data).T
    df2.columns=data2.Fields
    dfs.append(df2)
df2 = pd.concat(dfs)
df3=df2.drop_duplicates(["wind_code"])
df3.to_excel(out2)
out2.save()


startDate = datetime.datetime(2022,8,1)
startDate_str = startDate.strftime("%Y%m%d")
endDate = datetime.datetime(2022,8,5)
endDate_str = endDate.strftime("%Y%m%d")


out3 = pd.ExcelWriter('stock_picking_factor.xlsx')
df4 = pd.merge(df1,df3,on="wind_code")
dfs = []
for _, row in df4.iterrows():
    code = row['wind_code']
    sectorid = row['sectorid']
    #data = w.wss(code, "pct_chg_per","startDate=%s;endDate=%s"%(startDate,endDate))
    data = w.wss(code, "pct_chg_per,avg_amt_per,avg_MV_per,fa_roe_avg,west_netprofit_fy1_6m",
          "startDate=%s;endDate=%s;unit=1;currencyType=;tradeDate=%s"%(startDate,endDate,date_str))
    df=pd.DataFrame(data.Data).T
    df.columns=data.Fields
    df.insert(0,'code',code)
    df.insert(2,'sectorid',sectorid)
    dfs.append(df)
df = pd.concat(dfs)
df=df.sort_values(by=["PCT_CHG_PER"],ascending=False)
df.to_excel(out3)
out3.save()

df[df["PCT_CHG_PER"]>0].groupby("sectorid").count()["code"]

Without the criteria of positive return, the top 3 sectors ( ranked by the number of stocks pitched by securities firms) are:

industrial machinery;
semiconductor products ;
Electrical components and equipment.
The factor of sector is effective.