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Use SQLAlchemy ORMs to Access OFX Data in Python

The CData Python Connector for OFX enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of OFX data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for OFX and the SQLAlchemy toolkit, you can build OFX-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to OFX data to query OFX data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live OFX data in Python. When you issue complex SQL queries from OFX, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to OFX and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to OFX Data

Connecting to OFX data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The OFXUser and OFXPassword properties, under the Authentication section, must be set to valid OFX user credentials. In addition to this, you will need to configure FIURL, FIOrganizationName, and FIID, which will be specific for the financial institution. You will also need to provide application-specific settings, including OFXVersion, ApplicationVersion, and ApplicationId.

To connect to some services, you will need to provide additional account information such as AccountId, AccountType, BankId, BrokerId, and CCNumber.

Follow the procedure below to install SQLAlchemy and start accessing OFX through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model OFX Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with OFX data.

engine = create_engine("ofx///?OFXUser=myUser&OFXPassword=myPassword&FIID=myFIID")

Declare a Mapping Class for OFX Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the InvBalances table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class InvBalances(base):
	__tablename__ = "InvBalances"
	Id = Column(String,primary_key=True)
	Amount = Column(String)

Query OFX Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("ofx///?OFXUser=myUser&OFXPassword=myPassword&FIID=myFIID")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(InvBalances).filter_by(ServiceType="CREDITCARD"):
	print("Id: ", instance.Id)
	print("Amount: ", instance.Amount)

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

InvBalances_table = InvBalances.metadata.tables["InvBalances"]
for instance in session.execute( == "CREDITCARD")):
	print("Id: ", instance.Id)
	print("Amount: ", instance.Amount)

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the OFX Python Connector to start building Python apps and scripts with connectivity to OFX data. Reach out to our Support Team if you have any questions.