pythonbloombergblpapi

How to use the BQL Bloomberg excel formula for python API (xbbg or blpapi)?

Is there a way to use the BQL-formula in Python in the BLPAPI or XBBG API's instead of looping through a bunch of tickers to retrieve data on i.e. all of the stocks of the S&P500 using a BDP or BDS formula? (This will quickly reach the data limit for the day, I suspect, since I want to check a bunch of different indicies).

I found a post from 2019, where BQNT was suggested, but I would prefere to avoid using BQNT, link here: How to implement BQL Bloomberg excel formula to python API (blpapi)?.

Thanks in advance!

Solution

  • Further to the comments, I played around with a proof-of-concept for driving Excel from Python. This quick'n'dirty script opens Excel in the background, puts a BQL formula in a cell, polls for a return value, and fills a DataFrame:

    import pandas as pd
import time

import win32com.client as wc

#Get a dispatch interface for the Excel app
_xl = wc.Dispatch("Excel.Application")
#Ensure the Bloomberg addin is loaded
_xl.Workbooks.Open('c:\\blp\\API\\Office Tools\\BloombergUI.xla')

#Create a new workbook
wb = _xl.Workbooks.Add()
ws = wb.Sheets(1)
cl = ws.Cells(1,1) #Cell A1 on Sheet 1

#Define BQL query, and set cell formula
qry ='=@BQL.Query("get(YIELD) for(filter(bonds([\'IBM US Equity\']),CPN_TYP==Fixed and CRNCY==USD))")' 
cl.Formula=qry
_xl.Calculate()

#Check the cell's value: it will likely be #N/A ...
res = cl.Value
nLoop = 0
nTimeout = 100 #ie 10 seconds

#Loop until either get a non-# return or timeout
while res[0]=='#' and nLoop<=nTimeout:
    time.sleep(0.1) #100 ms
    res = cl.Value
    nLoop += 1

if res[0] == '#':
    print('Timed out')
    return

print('Results after {0:} secs'.format(nLoop/10.0))

#The Bloomberg addin will have changed the original BQL formula
#and added a 'cols=x,rows=y' parameter at the end
#This tells us the size of the data
#as BQL doesn't seem to have the option to return a dynamic array
f = cl.Formula
rc = f.split(',')[-1].split(';')
cols  = int(rc[0].split('=')[1])
s = rc[1].split('=')[1]
rows = int(s[0:len(s)-2])

#Retrieve the values from this new range
data = ws.Range(cl,ws.Cells(rows,cols)).Value

#Convert to DataFrame
df=pd.DataFrame(data[1:],columns=data[0])
print(df)

#Tidy up
_xl.DisplayAlerts = False
wb.Close()  
_xl.Quit()

    Output:

    Results after 1.4 secs
               ID     YIELD
0   DD103619 Corp  1.012017
1   BJ226366 Corp  1.921489
2   DD103620 Corp  3.695580
3   ZS542668 Corp  2.945504
4   BJ226369 Corp  2.899166
5   ZS542664 Corp  1.109456
6   BJ226365 Corp  1.350594
7   ZS542666 Corp  2.732168
8   ZS542661 Corp  0.147570
9   ZS542663 Corp  0.621825
10  EJ772545 Corp  0.391708
11  EJ222340 Corp  2.846866
12  ZS542665 Corp  1.842695
13  EJ299219 Corp  0.224708
14  DD108917 Corp  3.733077
15  AM269440 Corp  0.189621
16  QJ633474 Corp  0.295588
17  BJ226367 Corp  2.727445
18  EC767655 Corp  2.241108
19  EI062653 Corp  2.728811
20  JK138051 Corp  1.077776
21  DD115180 Corp  1.604258
22  DD112334 Corp  1.527195
23  EK063561 Corp  0.570778
24  AM269866 Corp  1.329918
25  JK138053 Corp  2.915085
26  EH589075 Corp  3.110513

    If I were to do this in production, I'd wrap the whole thing in a class to avoid stopping and starting Excel each time I wanted to perform a Query. Also, I haven't tested what happens in the user is already running Excel for something else!