Singapore stocks

Simple Python Script to retrieve all stocks data from Google Finance Screener

A simple python script to retrieve key financial metrics for all stocks from Google Finance Screener. Google screener have more metrics avaliable compared to SGX screener and also contains comprehensive stocks data for various stock exchanges.

In addition, retrieving data from Google Screener is much faster compared to data retrieved from Yahoo Finance or Yahoo Finance API (See the respective blog post from links).

The reason for the fast retrieval is that the information are stored in the form of single json format for all stocks such that it will reduce the number of request calls and downloading. Being in json format also allows easy conversion to a Pandas Dataframe object.

To retrieve the json url of the stock data, go to the Google Screener and select the criteria (like what is normally done when setting up a filter).  Open up the criteria to full range of the particular metrics. In this way, all the stocks will be selected instead of being filtered off. Using the developer tab of any browser, retrieve the full url. For further description of how to retrieve the url, you can refer to the following post: “Getting historic financial statistics of stocks using Python

Two points to take note:  Firstly the URL only include stock list from 1 -20 due to page setting. Set the end stock to a large number eg 3000 (in blue) to include the full stock list. Below is a sample of the corresponding url.[%28exchange%20%3D%3D%20%22SGX%22%29%20%26%20%28dividend_next_year%20%3E%3D%200%29%20%26%20%28dividend_next_year%20%3C%3D%201.46%29%20%26%20%28price_to_sales_trailing_12months%20%3C%3D%20850%29]&restype=company&ei=BjE7VZmkG8XwuASFn4CoDg

Secondly, as Google only allows 12 criteria to be set at any one go, you would need to repeat process multiple times to obtain all the parameters. Repeat the above process by selecting different criteria and join all the parameters together.

Once the url is formed, the same process is used when scraping web data using python as described in most posts in this blog. The main tools are Python Pandas and Python Pattern. Python Pattern is to help with the json file download and Pandas to convert the json file to Data frame which can then be used to join with other parameters.

The difficult part of the script is to obtain the url. Once the url is known, other methods can be employed to download and read the data from the json file.

The script (for all stocks in Singapore) is available in Github. Due to the long url format, the script will form the full url by concatenating the start and end url with the middle portion (which are all the criteria) stored in a file. File is also found in Github.

 (Update: Thanks to Bob1: the start url is changed to


Retrieving short sell qty for SG stocks from SGX using python

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 “” that can retrieve table like data from the html string easily.

The following lists the steps to retrieve the short sell information.

  1. 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.
  2. HTML data download: This can be done using python pattern.
  3. Converting the table to data frame: This can be done using Pandas function “”. Also Pandas provides a rank function so that the results can be ranked accordingly. Converting into Pandas database make it easy.
  4. 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“.
  5. 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  “”  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):
            url = URL(self.shortsell_full_url)
                #see data is available for that current date
                url_data = = 50)
                shortsell_list =
                self.shortsell_info_df =shortsell_list[1]

            #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?

        print 'No suitable data found within time frame.'

    def form_shortsell_url(self, last_effective_date):
        """ Based on the current date to set the shorsell url.
            Set to self.shortsell_full_url
                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.
                num_days (int): num of days prior to the current date.
                Setting to 0 will only retrieve the current date
                (int): datekey as yyyyymmdd.
        last_eff_date_list = list(( - 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
        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 =

        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

  1. Ranked_shortsell –> rank according to the absolute volume
  2. Shortsell_vol_per –> short sell qty as ratio of transacted vol
  3. ranked_percent_vol_shortsell –> rank according to Shortsell_vol_per


Retrieve all Stock Symbols using python

I need to retrieve all the stocks symbol for a particular market (eg Singapore) to use in conjunction with the stock info retrieval described in the previous post. There are no easy way to get all the stock symbol from yahoo finance or other online resources.

The more easy way is to search the list of stocks under certain alphabet from yahoo finance, scrape the symbol information and repeat it for all the alphabet (and including digits). There are quite a number of scraping and parsing tools (Scrapy, Beautifulsoup, lxml etc). I am using  PATTERN module for the url retrieval and also to parse the various information.

The first step is to generate the url assoicated with the search. Below is the url to search the Singapore stocks (m = SG, t =S) with the alphabet “a” (s=b) and search results from 20 onwards “20” or page 2 of the results (b= 20). Each page will have 20 results.

To retrieve the information from a particular page or url, the following part of class method are used. Parsing method are from Pattern module:

    def set_dom_object_fr_url(self):
        """ Set the DOM object from url self.sym_full_url.

        url =  URL(self.sym_full_url)
        self.dom_object = DOM(

    def get_sym_for_each_page(self):
        """ Scan all the symbol for one page. The parsing are split into odd and even rows.

        for n in self.dom_object('tr[class="yui-dt-odd"]'):
            for e in n('a'):

        for n in self.dom_object('tr[class="yui-dt-even"]'):
            for e in n('a'):

To get the number of pages or results to retrieve for each alphabet search, the following text are parsed to get the total search number

    def get_total_page_to_scan(self):
        """ Get the total search results based on each search to determine the number of page to scan.
                (int): The total number of page to scan
            Current handle up to 999,999 results
        #Get the number of page
        total_search_str = self.dom_object('div#pagination')[0].content
        total_search_qty ='of ([1-9]*\,*[0-9]*).*',total_search_str).group(1)
        total_search_qty = int(total_search_qty.replace(',','', total_search_qty.count(',')))
        final_search_page_count = total_search_qty/20 #20 seach per page.

        return final_search_page_count

By parsing through all the search alphabet and the pages, all the stocks symbol can be retrieved. Duplicated copy are removed using Pandas (or can use the sets() function).

The full script can be found at GitHub. A sample call and results are shown below.

    ## initialize the class
    sym_extract = AllSymExtr()
    ## list the alphabets and number to search. To search all will label a to z
    ## for demo, only search 'a' and 'b'.
    sym_extract.alphanum_str_to_search = 'ab'

    ## perform sweep of each search alphabet and each page

    ## convert to dataframe and remove duplicates.
    print sym_extract.sym_df

Results are as below:

searching: a
total number of pages to scan: 18
Scanning page number: 1 url:
Scanning page number: 2 url:
Scanning page number: 17 url:
Scanning page number: 18 url:

searching: b
total number of pages to scan: 20
Scanning page number: 1 url:
Scanning page number: 2 url:
Scanning page number: 19 url:
Scanning page number: 20 url:

0 5FH.SI
1 A7S.SI
2 Q1P.SI
3 A78.SI
4 557.SI
5 P8Z.SI
.. ...
772 E2:L34.SI
780 E1:B32.SI</pre>