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PrismStudio | by PRISM39

Financial data, ready for research.

PrismStudio is a financial data management and analytics platform for investment and enterprise teams. It brings data onboarding and ongoing operations into a shared workflow, with access through Python, SFTP and WebSocket.

Discuss your data needs See how it works
Python workspace
Example usage
Terminal
$ pip install prismstudio
research.py
import prismstudio as ps ps.login(username="username", password="password")  # SIGN INps.market.close().get_data("US Primary", "1990-01-01", "YYYY-MM-DD")  # GET DATA()

Supported data vendors

Additional local vendors supported.

  • LSEG logo
  • Bloomberg logo
  • ICE logo

Ready for research

One query, a complete data workflow behind it.

Discover data, retrieve it and continue with your research. PrismStudio coordinates the data collection, validation, standardization and ongoing operations behind that workflow.

  1. Discover

    Data Catalog and Security Master

  2. Query

    get_data()

  3. Analyze

    Python DataFrame and research tools

Shared operations layer

  • Data loading
  • Standardization
  • Identifier mapping
  • Universe management
  • Point-in-time history
  • Resampling
  • Monitoring
.py

Python

Work in your existing Python environment. Discover data through Python or the Web UI, retrieve it as a DataFrame and continue with your research tools and AI workflows.

feed

SFTP and WebSocket

Receive prepared financial data through SFTP or WebSocket for use in your systems and applications. Both channels use PrismStudio's shared onboarding and operations framework.

Getting started

From pip install to DataFrame

The lightweight Python client connects your workspace to PrismStudio services. Data management and compute run in the selected deployment environment, while results return to the tools you already use.

  1. 01

    Install

    pip install prismstudio

  2. 02

    Sign in

    Use your account credentials.

  3. 03

    Discover

    Explore securities, universes and data items through Python or the Web UI.

  4. 04

    Get data

    Return results to your Python environment as a DataFrame.

Data Catalog

Find the right data, faster.

Explore supported data packages through Python or the Web UI. Review item descriptions and metadata to find the data you need.

Data Catalog is part of PrismStudio and is also available as a standalone product.

Data CatalogIllustrative product view

Cash

Category
Financial
Component
Balance Sheet
Data module
Standard
Package
Fundamentals

Description (summary)

Cash held on hand or in bank accounts that is readily available to meet operating needs. Examples include petty cash, cash in transit and unrestricted demand deposits. Restricted cash and cash equivalents are classified separately in this illustrative example.

Descriptions are shortened for this preview. Full catalog entries include detailed definitions, calculation notes, and inclusion and exclusion criteria where applicable.

Data for equity research and investment workflows.

PrismStudio supports a broad range of financial data categories, including:

  • Market
  • Fundamentals
  • Estimate
  • Reference
  • Corporate Action
  • News & Events
  • Transcripts
  • Economics
  • Factors
  • Index
  • Ownership
  • ETFs
  • ESG
  • Relationship
  • Industry Classification
  • Alternative Data

Available data and permitted uses depend on your vendor subscriptions and permissions.

Security Master

Map and track securities.

Identify securities across company, security and exchange-listing levels. Map identifiers across supported sources, build reusable universes and use historical records that reflect the information applicable on each date.

Point-in-time history means you can follow identifiers, classifications and listing information as they change over time, and see what was true on any given date.

Security Master is part of PrismStudio and is also available as a standalone product.

Company

Issuer record

Security

e.g. common share

Listing · Exchange A
Listing · Exchange B
Listing · Exchange C

History

  1. Date 1

    Identifier assigned

  2. Date 2

    Classification change

  3. Date 3

    New exchange listing

Illustrative labels
Countries
107+
Countries
Exchanges
248+
Exchanges
Exchange listings
1M+
Exchange listings

These figures describe coverage across the platform. Each listing represents a security on a particular exchange.

Research at scale · Python

Retrieve, transform and analyze.

  1. 01

    Retrieve

    Retrieve supported data through get_data(), use parameters that fit the request and return results as a Python DataFrame.

  2. 02

    Transform

    Chain more than 100 financial functions, with support for resampling, currency conversion and point-in-time logic.

  3. 03

    Analyze

    Continue with factor, portfolio, risk, transaction-cost and ESG workflows, or return results to your own Python tools.

Run work in your active Python session, or submit background jobs and track their progress.

  • Concurrent retrieval
  • Arrow-based multithreading

Use cases

How teams use PrismStudio

Hedge fund

New data, ready for research

With a new financial data subscription in place, a hedge fund began using the data in Python on its first day of PrismStudio service. Automated data onboarding and ongoing operations helped the team focus on investment research and strategy development. The Python package also supported the fund's AI workflows.

Asset manager

Working with a new data source

An asset manager used PrismStudio to bring a new data source into its research workflow. The platform helped the team review data items, check coverage and validate datasets against its existing research requirements.

AI COMPANY

Financial data pipeline for AI teams

An AI company adopted PrismStudio as its financial data pipeline. The platform supported data onboarding and ongoing operations, helping the team prepare data for its AI workflows and focus on developing models and applications.

Deployment

Choose the deployment model that fits your environment.

Use the same Python interface across deployment models. The service location and responsibility for the underlying infrastructure depend on the selected model.

Managed SaaS

  • Runs in the PrismStudio cloud.
  • Users install the Python client.
  • Data and compute infrastructure are managed as part of the service.

Local / private deployment

  • Runs on customer-controlled servers.
  • Users install the Python client and PrismStudio server.
  • Data and compute infrastructure are managed by the customer.

Tell us about your financial data needs.

Share your data sources, current workflow and deployment requirements. We can discuss whether PrismStudio is a good fit for your team.

Discuss your data needs