Extract Mass Data Via Bloomberg API

Motivation

Bloomberg has integrated massive data from various of data vendors. However, as a typical finance terminal designed for traders, it’s technically hard to use as a database for scholars. This blog will introduce how to prepare your Bloomberg Terminal for massive data extracting and how to access data via Bloomberg API.

Deploy Operation Enviornment

Bloomberg Access

• Get a Bloomberg Terminal and of course a valid Bloomberg Account

• Make sure the Bloomberg Add-in in Excel in this terminal works

  
  
  Figure 1: Bloomberg Add-in in Excel

Install Blommberg C++ SDK

• Visit Bloomberg API Library and downlaod C++ Supported Release

  
  
  Figure 2: Bloomberg API Library

• Copy blpapi3_32.dll and blpapi3_64.dll from the lib folder of downloaded zip file (typically named blpapi_cpp_3.16.6.1-windows.zip) to Bloomberg BLPAPI_ROOT folder in the terminal (usually C:/blp/DAPI). If any note saying the files would be replaced appears, confirm the replacement.

  
  
  Figure 3: Replace Files in BlPAPI_Root

• Please make sure the Bloomberg App is closed before replacing those two root files.

Install Python/Anaconda

As Anaconda has integrated the majority of most frequently used Python packages, I would recommend installing Anaconda as a shortcut. If you choose to purely install Python, remember to click the option" Add Python into Enviornment Path" when installing to make sure your later procedures easier.

Suppose you’ve already installed Anaconda, launch the cmd from the navigator panel (or directly from the start menu).

Figure 4: Install Anaconda

Install Necessary Packages

Type the following orders in the cmd window you launched from Anaconda Navigator.

• Install Bloomberg official Python API

  1	pip install blpapi --index-url=https://bcms.bloomberg.com/pip/simple/

• Install numpy, pandas, ruamel.yaml and payarrow. As Anaconda has integrated numpy and pandas, we only need to install the last two packages.

  1 2	conda install ruamel.yaml conda install pyarrow

• Install a third-party package that enables better data extracting experience xbbg

  1	pip install xbbg

Login in Bloomberg Terminal

This steps activates your access to Bloomberg data and enables the following data extracting.

Test API

Type python in the cmd window, you will enter the Python enviornment. If you enter the following code and get the same result as mine, that means you’ve deployed the operation enviornment for Bloomberg API successfully. All of the following examples are obtained from the Github Page of xbbg.

1

In [1]: from xbbg import blp

BDP example:

1

In [2]: blp.bdp(tickers='NVDA US Equity', flds=['Security_Name', 'GICS_Sector_Name'])

1
2
3

Out[2]:
               security_name        gics_sector_name
NVDA US Equity   NVIDIA Corp  Information Technology

BDP with overrides:

1

In [3]: blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')

1
2
3

Out[3]:
                eqy_weighted_avg_px
AAPL US Equity               148.75

BDH example:

1
2
3
4

In [4]: blp.bdh(
   ...:     tickers='SPX Index', flds=['high', 'low', 'last_price'],
   ...:     start_date='2018-10-10', end_date='2018-10-20',
   ...: )

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11

Out[4]:
           SPX Index
                high      low last_price
2018-10-10  2,874.02 2,784.86   2,785.68
2018-10-11  2,795.14 2,710.51   2,728.37
2018-10-12  2,775.77 2,729.44   2,767.13
2018-10-15  2,775.99 2,749.03   2,750.79
2018-10-16  2,813.46 2,766.91   2,809.92
2018-10-17  2,816.94 2,781.81   2,809.21
2018-10-18  2,806.04 2,755.18   2,768.78
2018-10-19  2,797.77 2,760.27   2,767.78

BDH example with Excel compatible inputs:

1
2
3
4
5

In [5]: blp.bdh(
   ...:     tickers='SHCOMP Index', flds=['high', 'low', 'last_price'],
   ...:     start_date='2018-09-26', end_date='2018-10-20',
   ...:     Per='W', Fill='P', Days='A',
   ...: )

1
2
3
4
5
6
7

Out[5]:
           SHCOMP Index
                   high      low last_price
2018-09-28     2,827.34 2,771.16   2,821.35
2018-10-05     2,827.34 2,771.16   2,821.35
2018-10-12     2,771.94 2,536.66   2,606.91
2018-10-19     2,611.97 2,449.20   2,550.47

An Example : Extract ESG Disclosure Scores

Suppose we want to extract the ESG Disclosure Score for a list of 8000 securities via API. A very first obstacle is that we have no idea what does this variable is named in API and which function we should use to request the data. Thus, the first step is to obtain all those information you demand by building an example spreadsheet through Bloomberg Excel Add-in.

Get The Function Name and Key Parameters

Click the Spreadsheet Builder in Bloomberg Excel Add-in. Click Historical Data Table.

Figure 5: Open Spreadsheet Builder

You can randomly pick one (or more) security. I would choose Apple (AAPL) here as an example. Then there appears a window where you can select fields you want.

Figure 6: Select Fields

Then you can select the data range and periodicity. For variables like ESG Disclosure Score, we typically choose “Yearly”.

Figure 7: Select Data Range and Periodicity

Finally, you will obtain the results. Click the cell exactly below Dates, you will get

• Function bdh
• Variable name ESG_DISCLOSURE_SCORE
• Equity Name APPL US Equity
• Start Date 1/01/2010
• End Date 8/07/2021
• Periodicity Per=Y

Figure 8: Find Out the Variable Names in API

With the above information, you can construct the function you need for extracting data via API.

Code

Purely Extract Single Variable

In this case, you only need to

• Prepare a cusip list cusiplist.xlsx
• Customize the key parameters as you need
  • Searching start date date_from
  • Searching end date date_until
  • Searching variable name target

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

import pandas as pd
from xbbg import blp
from tqdm import tqdm

df = pd.read_excel('cusiplist.xlsx')

date_from = '20090101'
date_until = '20210630'
target = ['ESG_DISCLOSURE_SCORE']

for cusip in tqdm(df_index['cusip']):
    df=blp.bdh(tickers=cusip,flds=target,start_date=date_from,end_date=date_until,\
    Per = 'Y')
    df.to_csv('ESG_Score_Single.csv', mode='a')

Extract Multiple Variables

As the institutional quota of request is monthly limited, it would be more efficient to request multiple variables together. In this case, you need to deal with a tricky situation. Suppose you request 7 variables for each firm, but some firm only has 4 variables valid while others have 5 variables valid. The distribution is random. API only returns the valid columns, which means the size of the returned dataframe and the order of the variable names could be random.

To deal with this situation, I write a function prepare() to pre-specify an order for all the requested variables to make sure the data returned for each variable should be written to the pre-specified column

• For example, the data returned for SOCIAL_SCORE is always written to the 3rd column while the data returned for ENVIRON_DISCLOSURE_SCORE is always written to the 6th column

• If there is no data returned for a variable during one request, then write a blank stirng "" to the respect column

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54

import pandas as pd
from xbbg import blp
from tqdm import tqdm
import csv

df = pd.read_excel('cusiplist.xlsx')

date_from = '20090101'
date_until = '20210630'
target = ['ESG_DISCLOSURE_SCORE', 'SOCIAL_SCORE', 'ENVIRONMENTAL_SCORE',\
          'SOCIAL_DISCLOSURE_SCORE', 'ENVIRON_DISCLOSURE_SCORE', 
        'GOVNCE_DISCLOSURE_SCORE', 'ESG_RISK_SCR_MOMENTUM']

def prepare(temp):
    cols = [i[1] for i in temp.columns]
    diff = set(target) - set(cols)
    diffindex = [target.index(i) for i in diff]
    leftindex = set(range(len(target))) - set(diffindex)
    dictt = list(zip(range(len(cols)), leftindex))
    return([cols, dictt])

# Iterate each cusip in the cusip list
for i in tqdm(df.iterrows()):
  	# Obtain cusip
    cusip = i[1][2]
    # Request data from Bloomberg API
    temp = blp.bdh(tickers=cusip,flds=target, start_date=date_from,\
                   end_date=date_until, Per = 'Y')
    [cols, dictt] = prepare(temp)

    with open('Esg_Score_Multiple','a') as f:
      	# Open a csv file with mode 'a', which allows adding new rows
        # without covering existed rows
        g = csv.writer(f)
        # Create a list with length equal to the number of requested 
        # variables plus 3
        headline =['DATE']+target+['CUSIP', 'FIELDS']
        res = [""]*(len(target)+3)
        
        # Iterate each row of returned dataframe
        for row in temp.iterrows():
            for j,k  in dictt:
              	# Put date to the first cell
                res[0] = row[0]
                # Put variables returned by API following the
                # pre-specified order
                res[k+1] = row[1][j]
                # Put the identifier of security into the list
                res[len(target)+1] = cusip
                # Put names of valid variables returned for the security
                # into the last cell of the list for cross-check
                res[len(target)+2] = cols
            # Write the revised list to the opened csv file
            g.writerow(res)

Sample Outcome

Figure 6: Sample Outcome - Multi Variables

Main References

• https://github.com/alpha-xone/xbbg
• https://www.bloomberg.com/professional/support/api-library/

• ← Previous Post
• Next Post →