Introduction
In today’s fast moving financial environment it is crucial to keep abreast with manner in which data is processed. Trading, finance, analysts, quants, and developers are now spending more and more time on specific ways to extract and analyze big data from the Financial markets. An Example of this is Bloomberg, market data supplier; they offer an API through which their services can be incorporated in different applications; through this API, users can gain access to market data, historical prices, and reference data among other. Bloomberg API example Python notebook, we will demonstrate on how to use Python in pulling financial data.
The steps are as follows and I intend to create this blog in a step by step manner: Introduction of Bloomberg API and installation of Bloomberg API with Python and configurations Setting up of the Bloomberg API services of Real time and Historical data Quoting of Real time and Historical data Basic financial analysis with Python’s strong modules. From this Bloomberg API example Python notebook, you will learn how to consume data from an API, and do analysis in a single environment.
Prerequisites
To follow along with this Bloomberg API sample Python notebook, you must have the following:
- Bloomberg Terminal requires an active subscription to use the Bloomberg API.
- Ensure API access is enabled on your Bloomberg Terminal account.
- Install Bloomberg Python SDK (blpapi) to communicate with the API.
- This Bloomberg API example Python notebook requires Jupyter Notebook for code execution.
Install necessary libraries
You need to install additional libraries before you can start the use of the Bloomberg API sample Python notebook.
1. Bloomberg Python SDK Installation
To send message to the Bloomberg server you need to connect via the Bloomberg API, for which you have to install the official SDK blpapi. In this Bloomberg API sample Python notebook, there will be the installation which is done via pip.
pip install blpapi2. Setup of Jupyter Notebook
Jupyter can be installed using pip if it isn’t already installed: NOTE: notebook is a JavaScript library so you can download it using npm and include it in your HTML with the help of the HTML <script> tag pip install notebook
pip install notebookAs these installs are done you can move to our example .
Setting up the Bloomberg Python API
Done with the installation of the libraries let’s move to the creation of the connection. In the following sample Python notebook , we first establish a connection to Bloomberg’s API using the blpapi library.
Bloomberg’s API using the blpapi library.
import blpapi
from blpapi import SessionOptions, Session
# Set the session options
session_options = SessionOptions()
session_options.serverHost = 'localhost'
session_options.serverPort = 8194
# Start a session
session = Session(session_options)
if not session.start():
print("Failed to start session.")
exit()
if not session.openService("//blp/refdata"):
print("Failed to open //blp/refdata")
exit()
Saying it in large – this piece of code establishes a session in your Python notebook for the Bloomberg API example. Thanks to the session you have your Python code that is capable to communicate with the Bloomberg servers and extract the required data.
Analzing Financial Information on Bloomberg
In this part, we will discuss on how to request for financial data from the Bloomberg apparatus, which begins with price data for a firm like Apple’s (AAPL).
Getting Historical Information
To access historical data follow these steps:
# Create a request for historical data
service = session.getService("//blp/refdata")
request = service.createRequest("HistoricalDataRequest")
# Add security and fields to the request
request.getElement("securities").appendValue("AAPL US Equity")
request.getElement("fields").appendValue("PX_LAST")
request.set("startDate", "20210101")
request.set("endDate", "20211231")
request.set("periodicitySelection","DAILY")
# Send the request
session.sendRequest(request)
# Process the response
while True:
ev = session.nextEvent(500)
for msg in ev:
if ev.eventType() ==blpapi.Event.RESPONSE:
print(msg)
break
The code above is the example of getting the closing prices for Apple Inc. stock on daily basis from 1 January 2021 to 31 December 2021 using the Bloomberg API in API example notebook. For Ref etching
The data of different securities or time frames you can alter the code used.
Data in Real Time
After that, we Shall consider the use of this Bloomberg API example Python notebook to get real-time Market data. Thanks to real-time data, you can follow the real-time shifts of the market straight from your connection to Bloomberg.
# Request real-time data for a security
service = session.getService("//blp/mktdata")
request = service.createRequest("MarketDataRequest")
# Add securities and fields
request.getElement("securities").appendValue("AAPL US Equity")
request.getElement("fields").appendValue("LAST_PRICE")
# Send the request
session.sendRequest(request)
# Process the response
while True:
ev = session.nextEvent(500)
for msg in ev:
if ev.eventType() == blpapi.Event.RESPONSE:
print(msg)
breakBy the way, if you are interested in getting continued updates for Apple from the most recent traded rate that this Bloomberg API sample Python notebook, you will be able to keep abreast of market trends.
Table of Common Bloomberg Data Fields
| Field Name | Description | Example Data Type | Use Case |
|---|
| PX_LAST | Last Price/Closing Price | float | Used for historical price data |
| PX_OPEN | Opening Price | float | Analyzing market opening trends |
| PX_HIGH | High Price | float | Identifying the highest price in a day |
| PX_LOW | Low Price | float | Analyzing market volatility |
| DIVIDEND_YIELD | Dividend Yield | float | Measuring return on dividends |
| MARKET_CAP | Market Capitalization | float | Evaluating company size |
| VOLUME | Trading Volume | integer | Analyzing liquidity |
| PE_RATIO | Price to Earnings Ratio | float | Valuation metrics |
| SECURITY_NAME | Full Security Name | string | Company identification |
| COUNTRY_ISO | ISO Country Code of the Security | string | Geographic data analysis |
Analyzing Data in Python
In cases where it is possible to connect to the API and data has been acquired, this Bloomberg API sample Python notebook can be employed for the analysis of the data. In this part, we will demonstrate to you practices of data manipulation and visualization using Pandas and Matplotlib.
1. Converting Data to Pandas DataFrame
You can better manage this format in your Bloomberg API example Python notebook by converting the Bloomberg data into a Pandas DataFrame.
import pandas as pd
# Placeholder for API data
data_list = [{'date': '2021-01-01', 'PX_LAST': 132.0},
{'date': '2021-01-02', 'PX_LAST': 134.0}] # Example data
# Convert the list of dictionaries to a DataFrame
df = pd.DataFrame(data_list)
# Display the DataFrame
print(df)This basic Python notebook example of the Bloomberg API takes the API data and presents it in a way that’s easily manipulatable and analyzable.
2. Matplotlib for Data Visualisation
As soon as your data is in a Pandas DataFrame, you can use Matplotlib to see it immediately in your Bloomberg API sample Python notebook: Once you have the data in a Pandas DataFrame use Matplotlib to visualize it instantly in this Bloomberg API sample Python notebook:
import matplotlib.pyplot as plt
# Plot the closing prices over time
df.plot(x='date', y='PX_LAST', kind='line', title='Apple Stock Prices in 2021')
plt.xlabel('Date')
plt.ylabel('Closing Price (USD)')
plt.show()
Use the following code to get a Line plot similar to this of Apple — Open Prices for 2021. Quickly create visualized reports with this Bloomberg API example Jupyter notebook in Python.
Error Handling in the Bloomberg API Example Python Notebook
It’s important to include error handling in your python notebook example using the various parts of bloomberg. This will let your code to fail gracefully for problems that you can expect, like network failures or invalid requests.
try:
# Code to send request and process data
session.sendRequest(request)
# Event loop to handle responses
while True:
ev = session.nextEvent(500)
for msg in ev:
if ev.eventType() == blpapi.Event.RESPONSE:
print(msg)
break
except Exception as e:
print(f"An error occurred: {str(e)}")What this snippet of code does is add error-controlled handling, meaning that instead of crashing your Python notebook for the Bloomberg Api example in case an error with a log message.
Advanced Use Cases in Bloomberg API Example Python Notebook
1. Obtaining Several Securities
In this example Python notebook for the Bloomberg API, you can retrieve data for many securities by appending additional values to the request under securities.
# Add multiple securities to the request
request.getElement("securities").appendValue("AAPL US Equity")
request.getElement("securities").appendValue("GOOG US Equity")
2. Getting the Reference Data
The Bloomberg API also provides a way to access reference data. This is an example on how to do in the case of your Bloomberg API sample; Notebook for Python
request = service.createRequest("ReferenceDataRequest")
request.getElement("securities").appendValue("AAPL US Equity")
request.getElement("fields").appendValue("SECURITY_NAME")
Conclusion
By following this end-to-end workbook for the Bloomberg API, you have now learned how to use Python to programmatically get and analyze financial data. In this notebook you learn how to set-up a session with the Bloomberg API, query historical and current data as well use Python’s powerful tools for analysing that same information.
Researching, Reporting or trading algorithmically there are endless opportunities to which you could apply the versatility offered by this Bloomberg API example Python notebook designed to automate your data activities & extract value from financial Data.
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