Bloomberg Python API Guide for Data

The Bloomberg Python API is a powerful tool. It connects Python to the Bloomberg Terminal's vast data network. This guide explains how to use it.

You will learn to set up the API. You will also learn to pull market data. This is essential for financial analysis.

What is the Bloomberg Python API?

The Bloomberg Python API is officially called `blpapi`. It is a client library. It allows Python scripts to communicate with Bloomberg services.

You can access real-time data. You can also get historical pricing, company fundamentals, and news. It is the programmatic gateway to Bloomberg's data.

This is different from a standard web API. It requires a Bloomberg Terminal subscription and specific software. For a primer on general API concepts, see our Python API Tutorial for Beginners.

Prerequisites and Installation

You need a few things before you start. First, you must have an active Bloomberg Terminal subscription. The API will not work without it.

Second, install the Bloomberg API Software Development Kit (SDK). You download this from the Bloomberg Developer Portal. Follow their installation guide for your operating system.

Finally, install the Python package. Use pip for this.

pip install blpapi
    

Ensure your Bloomberg Terminal or Bloomberg Server API (SAPI) service is running. The Python library needs this connection.

Core Concepts: Sessions and Services

The API works with sessions and services. A Session manages the connection to Bloomberg. A Service provides access to specific data types.

You start by creating a Session object. Then, you open a service, like the market data service. All data requests flow through these objects.

Fetching Real-Time Market Data

Real-time data is crucial for trading desks. Use the Market Data service. You subscribe to specific securities and fields.

Here is a basic example. It gets the last price for Apple Inc.

import blpapi

# Define the securities and fields
securities = ["AAPL US Equity"]
fields = ["LAST_PRICE"]

# Create a Session and start it
session_options = blpapi.SessionOptions()
session_options.setServerHost('localhost')
session_options.setServerPort(8194)

session = blpapi.Session(session_options)

if not session.start():
    print("Failed to start session.")
    exit()

if not session.openService("//blp/mktdata"):
    print("Failed to open market data service.")
    session.stop()
    exit()

# Get the service and create a subscription list
service = session.getService("//blp/mktdata")
subscription_list = blpapi.SubscriptionList()
subscription_list.add(securities[0], fields, "", blpapi.CorrelationId(1))

# Subscribe to the data
session.subscribe(subscription_list)

# Simple event loop to process incoming data (simplified)
try:
    while True:
        event = session.nextEvent(500)  # Wait for 500ms
        if event.eventType() == blpapi.Event.SUBSCRIPTION_DATA:
            for msg in event:
                print(f"Received update for {msg.getElement('securityData').getElementAsString('security')}:")
                field_data = msg.getElement('securityData').getElement('fieldData')
                if field_data.hasElement('LAST_PRICE'):
                    print(f"  LAST_PRICE: {field_data.getElementAsFloat('LAST_PRICE')}")
except KeyboardInterrupt:
    print("\nStopping subscription...")
    session.unsubscribe(subscription_list)
    session.stop()
    

This code sets up a subscription. It prints the last price whenever Bloomberg sends an update. Managing these subscriptions is a key skill. For more on handling data from APIs, read our Python API Data Pulling Guide.

Requesting Historical Data

For backtesting and analysis, you need historical data. Use the Historical Data Request service. You specify a date range and fields.

The sendRequest() method is used here. It sends a single request and waits for a response.

import blpapi

session_options = blpapi.SessionOptions()
session_options.setServerHost('localhost')
session_options.setServerPort(8194)
session = blpapi.Session(session_options)

if not session.start():
    print("Failed to start session.")
    exit()

if not session.openService("//blp/refdata"):
    print("Failed to open refdata service.")
    session.stop()
    exit()

service = session.getService("//blp/refdata")
request = service.createRequest("HistoricalDataRequest")

# Set request parameters
request.getElement("securities").appendValue("AAPL US Equity")
request.getElement("fields").appendValue("PX_LAST")  # Last Price
request.getElement("fields").appendValue("VOLUME")   # Trading Volume
request.set("periodicityAdjustment", "ACTUAL")
request.set("periodicitySelection", "DAILY")
request.set("startDate", "20240101")
request.set("endDate", "20240105")
request.set("maxDataPoints", 100)

print("Sending Historical Data Request...")
session.sendRequest(request)

# Process the response
try:
    while True:
        event = session.nextEvent()
        if event.eventType() == blpapi.Event.RESPONSE:
            for msg in event:
                security_data = msg.getElement("securityData")
                security_name = security_data.getElementAsString("security")
                field_data_array = security_data.getElement("fieldData")

                print(f"\nHistorical data for {security_name}:")
                print("Date        | PX_LAST | VOLUME")
                print("-" * 30)
                for i in range(field_data_array.numValues()):
                    field_data = field_data_array.getValueAsElement(i)
                    date = field_data.getElementAsString("date")
                    price = field_data.getElementAsFloat("PX_LAST")
                    volume = field_data.getElementAsInteger("VOLUME")
                    print(f"{date} | {price:7.2f} | {volume:9,}")
            break  # Response is complete
except Exception as e:
    print(f"An error occurred: {e}")
finally:
    session.stop()
    

Sending Historical Data Request...

Historical data for AAPL US Equity:
Date        | PX_LAST | VOLUME
------------------------------
2024-01-02 |  185.64 | 58,273,000
2024-01-03 |  184.25 | 52,333,000
2024-01-04 |  181.91 | 60,198,000
2024-01-05 |  181.18 | 64,475,000
    

This output shows daily price and volume for Apple. The sendRequest() method is central to this operation. Handling the numeric data correctly is important. Our Python API Number Handling Guide can help.

Common Use Cases and Best Practices

The API is used for portfolio monitoring, risk analysis, and automated reporting. Always handle errors and session timeouts gracefully.

Use correlation IDs to match requests with responses. Manage your subscriptions carefully to avoid overwhelming the system. Cache data where possible to reduce repeated requests.

For building larger systems that use this data, understanding Python API Frameworks is beneficial.

Conclusion

The Bloomberg Python API is a gateway to professional financial data. It requires a Terminal subscription but offers unmatched data quality and speed.

You learned to set up sessions, subscribe to real-time data, and request historical information. Start with simple requests. Gradually build more complex data pipelines.

Mastering