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name: matlab-access-datafeed description: > Guide for accessing financial and economic data in MATLAB using the Datafeed Toolbox. Covers Bloomberg (market data via bloomberg/blp/bloombergHypermedia), FRED (Federal Reserve economic data via fredrs), Haver Analytics (economic data via haver/haverdirect/haverview), and LSEG Datastream (historical data via datastreamws). Use when connecting to any of these data providers from MATLAB. license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md metadata: author: MathWorks version: "1.2"

Datafeed Toolbox — Bloomberg, FRED, Haver Analytics, and LSEG Datastream

Access financial and economic data from four major providers through the MATLAB Datafeed Toolbox.

When to Use

• User wants to connect to Bloomberg, FRED, Haver Analytics, or LSEG Datastream from MATLAB
• User needs to retrieve market data (prices, history, real-time, reference data) via Bloomberg
• User needs to retrieve economic time series from the St. Louis Fed (FRED)
• User needs to fetch economic/financial time series from Haver Analytics databases
• User needs to retrieve historical financial data from LSEG Datastream
• User needs help choosing between interfaces within a provider (e.g., bloomberg vs blp vs bloombergHypermedia, haver vs haverview)
• User is writing MATLAB code involving securities, economic indicators, or data provider connectivity

When NOT to Use

• General financial modeling or portfolio optimization that doesn't involve data provider connectivity
• Bloomberg Terminal keyboard shortcuts or Excel add-in questions (not MATLAB)
• Datastream Excel add-in (not MATLAB)
• Real-time streaming from LSEG (datastreamws is historical only)
• Data sources not covered here (e.g., Yahoo Finance)

Provider Overview

| Provider | Data Type | Interfaces | Reference | |----------|-----------|------------|-----------| | Bloomberg | Market data (equities, fixed income, derivatives, indices) | bloomberg, blp, bloombergHypermedia, bloombergEMSX | references/bloomberg.md | | FRED | U.S. economic data (GDP, unemployment, CPI, interest rates) | fredrs (replaces obsolete fred) | references/fred.md | | Haver Analytics | Global economic & financial data (macro, industry, surveys) | haver, haverdirect, haverview | references/haver.md | | LSEG Datastream | Historical financial data (equities, indices, economics, expressions) | datastreamws | references/datastreamws.md |

Quick Interface Selection

Bloomberg — see references/bloomberg.md for full API patterns

• Windows Desktop/Terminal user → bloomberg (C++)
• Need platform independence (Data License) → bloombergHypermedia
• Server/cloud, no Terminal (BPIPE) → bloombergBPIPE
• Existing blp users → migrate to bloomberg (same API, no Java needed)

FRED — see references/fred.md for full API patterns

• Any platform → fredrs (requires R2026a+, API key)
• fred is obsolete — do NOT use it

Haver Analytics — see references/haver.md for full API patterns

• Windows with local data files → haver
• Windows needing remote access → haverdirect
• Non-Windows / server / cloud → haverview (REST API)

LSEG Datastream — see references/datastreamws.md for full API patterns

• Any platform → datastreamws (REST API, requires Datastream credentials)

How to Use References

Before generating code for a specific provider, read the corresponding reference file for detailed API patterns, function signatures, gotchas, and examples:

• Working with Bloomberg data → read references/bloomberg.md
• Working with FRED economic data → read references/fred.md
• Working with Haver Analytics data → read references/haver.md
• Working with LSEG Datastream data → read references/datastreamws.md

Common Rules

• Always use getSecret() for credentials and API keys — never hardcode secrets
• All four providers require the Datafeed Toolbox
• fredrs additionally requires R2026a or later
• datastreamws available since R2018b
• Bloomberg and Haver local interfaces (bloomberg, blp, haver, haverdirect) are Windows-only; cloud/REST variants (bloombergHypermedia, bpipe, haverview, fredrs, datastreamws) are platform-independent

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