SGX usually releases short sell information for each stock at the end of each trading day. This information are found in their website. The daily short sell of all stocks are compiled into a report classified by day. We are interested in getting the short qty ranked by stocks per day.
If we examine the link, each report is in the form of a table format. To extract the information, we can use python pattern for web content download and Pandas for table extraction. Pandas has a function “pandas.io.html.read_html” that can retrieve table like data from the html string easily.
The following lists the steps to retrieve the short sell information.
- URL formation: As the link are joined by the date, need to retrieve the date str to join to the fixed url string. However, not all the date will be present, eg , during weekends. A better way is keep looping the the date back from current to get the latest date avaliable.
- HTML data download: This can be done using python pattern.
- Converting the table to data frame: This can be done using Pandas function “pandas.io.html.read_html”. Also Pandas provides a rank function so that the results can be ranked accordingly. Converting into Pandas database make it easy.
- Ranking by absolute qty may tend to mislead as it will also depends on the shares relative volume. Combining with the actual shares traded will give a more representative data. For this case, the data frame retrieved can be joined to the current price df created from the previous post “Retrieving stock news and Ex-date from SGX using python“.
- The last will be to set the alerts which can be done easily using PushBullet as describe as the following post “Sending alerts to iphone or Android phone using python“. You can customize to send the alert at the end of each trading day to determine the top 10 short sell stocks.
Below show the short sell info retrieval portion of the code found in the “SGX_stock_announcement_extract.py” for retrieving the short sell qty for each stocks. The updated code is found in Github.
def retrieve_shortsell_info(self):
""" Retrieve the shortsell information.
will form the url and retrieved the information using pandas to make into table.
The function will set to self_shortsell_info_df.
make it iterat over the days to get the latest data
"""
for last_effective_date in range(7):
self.form_shortsell_url(last_effective_date)
url = URL(self.shortsell_full_url)
try:
#see data is available for that current date
url_data = url.download(timeout = 50)
shortsell_list = pandas.io.html.read_html(url_data)
self.shortsell_info_df =shortsell_list[1]
except:
continue
#continue if there is no data
if len(self.shortsell_info_df) == 0: continue
self.shortsell_info_df.rename(columns={0:'Security',1:'Short Sale Volume',
2:'Currency',3:'Short Sale Value',
},inplace =True)
self.shortsell_info_df = self.shortsell_info_df[1:-3]
#change type of the columns
self.shortsell_info_df[['Short Sale Volume', 'Short Sale Value']] = self.shortsell_info_df[['Short Sale Volume', 'Short Sale Value']].astype(float)
#need a rank on the short sell
self.shortsell_info_df['ranked_shortsell'] = self.shortsell_info_df['Short Sale Volume'].rank(method='min',ascending=False)
self.shortsell_info_df['shortsell_lastdate'] = self.set_last_desired_date(last_effective_date)
#need percentage as well
# have a sorting of data?
return
print 'No suitable data found within time frame.'
return
def form_shortsell_url(self, last_effective_date):
""" Based on the current date to set the shorsell url.
Set to self.shortsell_full_url
Args:
last_effective_date (int): last desired date in yyyymmdd.
"""
#retrieve the current date in yyyymmdd format
self.shortsell_date_url = self.set_last_desired_date(num_days = last_effective_date)
self.shortsell_full_url = self.shortsell_info_start_url + self.shortsell_date_url + self.shortsell_end_url
def set_last_desired_date(self, num_days = 0):
""" Return the last date in which the results will be displayed.
It is set to be the current date - num of days as set by users.
Affect only self.print_feeds function.
Kwargs:
num_days (int): num of days prior to the current date.
Setting to 0 will only retrieve the current date
Returns:
(int): datekey as yyyyymmdd.
"""
last_eff_date_list = list((datetime.date.today() - datetime.timedelta(num_days)).timetuple()[0:3])
if len(str(last_eff_date_list[1])) == 1:
last_eff_date_list[1] = '0' + str(last_eff_date_list[1])
if len(str(last_eff_date_list[2])) == 1:
last_eff_date_list[2] = '0' + str(last_eff_date_list[2])
return str(last_eff_date_list[0]) + str(last_eff_date_list[1]) + str(last_eff_date_list[2])
def shortsell_notification(self):
""" Use for alerts on shortsell information.
Identify top ten short sell plus target stock short sell information.
"""
## get the current price df so can combined with the shortsell info
self.process_all_data()
merged_shortsell_df = pandas.merge(self.shortsell_info_df,self.sgx_curr_price_df,left_on = 'Security', right_on = 'CompanyName' )
## add in additional columns
merged_shortsell_df['shortsell_vol_per'] = merged_shortsell_df['Short Sale Volume']/merged_shortsell_df['DailyVolume']
merged_shortsell_df['ranked_percent_vol_shortsell'] = merged_shortsell_df['shortsell_vol_per'].rank(method='min',ascending=False)
top_shortsell_df = merged_shortsell_df[merged_shortsell_df['ranked_shortsell'].isin(range(1,16))]
top_shortsell_df = top_shortsell_df.sort(columns = 'ranked_shortsell', ascending =True)
top_shortsell_df = top_shortsell_df[['Security','Short Sale Volume','shortsell_lastdate']]
shortsell_top15_shtver = top_shortsell_df.to_string()
api_key_path = r'C:\Users\356039\Desktop\running bat\pushbullet_api\key.txt'
with open(api_key_path,'r') as f:
apiKey = f.read()
p = PushBullet(apiKey)
if shortsell_top15_shtver:
p.pushNote('all', 'Shortsell top10', shortsell_top15_shtver,recipient_type="random1")
## display for target watchlist
tar_watchlist_shortsell_df = merged_shortsell_df[merged_shortsell_df['Security'].isin(self.companyname_watchlist)]
tar_watchlist_shortsell_df = tar_watchlist_shortsell_df[['Security','Short Sale Volume','ranked_shortsell','shortsell_vol_per','ranked_percent_vol_shortsell']]
tar_watchlist_shortsell_df =tar_watchlist_shortsell_df[tar_watchlist_shortsell_df['ranked_shortsell'].isin(range(1,100))]
tar_watchlist_shortsell_df = tar_watchlist_shortsell_df.sort(columns = 'ranked_shortsell', ascending =True)
tar_watchlist_shortsell_shtver = tar_watchlist_shortsell_df.to_string()
if tar_watchlist_shortsell_shtver:
p.pushNote('all', 'Shortsell targetwatchlist', tar_watchlist_shortsell_shtver,recipient_type="random1")
Sample output as followed:
Security | Short Sale Volume| ranked_shortsell | shortsell_vol_per | ranked_percent_vol_shortsell
Sembcorp Ind | 3529600 | 6 | 0.437422 | 4
CapitaLand | 3313300 | 7 | 0.354216| 7
SingTel | 2809000 | 8 | 0.276471 | 16
Lippo Malls Tr | 2073800 | 11 | 0.492531 | 2
- Ranked_shortsell –> rank according to the absolute volume
- Shortsell_vol_per –> short sell qty as ratio of transacted vol
- ranked_percent_vol_shortsell –> rank according to Shortsell_vol_per
Simply Python 