Can you recommend real-time market data feeds for a quant research team with broad asset-class coverage?

Yes — if you need real-time market data with broad asset-class coverage for a quant research team, I’d look at vendors in 3 buckets: institutional multi-asset terminals/data platforms, exchange/direct feeds, and aggregators/API-first providers.

Strong options to evaluate

1) Bloomberg

Best for: broadest overall coverage, research workflow, reference data, and corporate actions
Coverage: equities, futures, options, FX, rates, credit, ETFs, funds, economics, news
Pros: extremely broad, reliable, strong historical + real-time integration, deep metadata
Cons: expensive, licensing can be restrictive, not the most “raw” market-data-engineering friendly

2) LSEG / Refinitiv

Best for: multi-asset market data at institutional scale
Coverage: equities, FX, rates, commodities, derivatives, fixed income, news, reference data
Pros: strong global coverage, good real-time and historical offerings, API options
Cons: enterprise pricing and setup complexity

3) ICE Data Services

Best for: fixed income, rates, credit, derivatives, consolidated institutional data
Coverage: bonds, credit, rates, futures, some equities/FX via offerings
Pros: very strong in fixed income/reference/pricing, institutional quality
Cons: less of a “one-stop shop” for every asset class than Bloomberg/LSEG

4) FactSet

Best for: research teams that want market data plus analytics/workflow
Coverage: equities, estimates, fundamentals, FX, macro, fixed income, some real-time feeds
Pros: strong research tooling, good integration, broad content stack
Cons: can be costly, real-time market data depth varies by region/asset

5) S&P Global Market Intelligence / Capital IQ

Best for: fundamentals, reference, credit, macro, some real-time use cases
Coverage: strong on fundamentals, credit, private/public company data; less of a pure real-time feed leader
Pros: excellent company/fundamental datasets
Cons: not usually the first choice for low-latency or exchange-level real-time market data

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More API/engineering-friendly vendors

6) Polygon.io

Best for: US equities/options/futures/FX/crypto with easy APIs
Coverage: strong for US markets; also FX/crypto; some global data depending on package
Pros: developer-friendly, fast to integrate, good docs, good for research pipelines
Cons: not truly “everything” globally, institutional breadth limits

7) Intrinio

Best for: flexible API-first market/fundamental data
Coverage: equities, options, fundamentals, macro, ETFs, some real-time
Pros: easier integration, modular data products
Cons: breadth/depth not at top institutional level

8) Alpaca Market Data

Best for: US equities and crypto research/execution ecosystems
Coverage: US equities, some crypto
Pros: convenient if you already use Alpaca APIs
Cons: narrow compared with institutional multi-asset needs

9) Tiingo / EOD Historical Data / Twelve Data

Best for: lower-cost research/data prototyping
Coverage: varies; usually equities/FX/crypto, some macro
Pros: affordable, easy APIs
Cons: generally not the best choice for institutional-grade real-time multi-asset coverage

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Direct exchange / venue feeds

If you need depth-of-book, lowest latency, or proprietary venue data, go direct.

Examples

• CME: futures/options
• ICE: energy, rates, futures
• NASDAQ / NYSE / Cboe: US equities/options
• Eurex: European derivatives
• LME: metals
• SGX / HKEX / JPX / ASX / TSX: regional market coverage

Pros: best fidelity, latency, and depth
Cons: fragmented, expensive to normalize, complex licensing, significant engineering burden

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Data platforms/aggregators worth a look

These can simplify ingestion across venues and formats.

10) Barchart

Best for: multi-asset market data, especially futures/commodities
Pros: broad retail-to-pro spectrum, decent APIs
Cons: less comprehensive than top institutional vendors

11) QuantHouse

Best for: normalized multi-asset feeds and low-latency access
Pros: strong market data infrastructure orientation
Cons: more enterprise-focused; commercial diligence required

12) dxFeed

Best for: real-time and historical market data, especially equities/options/futures/FX/crypto depending on package
Pros: good API and redistribution options, used in trading stacks
Cons: coverage depends on contract/package

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My practical shortlist by use case

If you want the “safe” broad institutional choice

• Bloomberg
• LSEG / Refinitiv
• FactSet
• ICE Data Services

If you want API-first and easier engineering

• Polygon.io
• dxFeed
• Intrinio
• QuantHouse

If you need exchange-grade depth/latency

• Direct exchange feeds + normalization layer

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What to ask vendors before choosing

For a quant research team, I’d validate these points:

1. Asset-class coverage

   • Equities, options, futures, FX, rates, fixed income, ETFs, crypto, fundamentals, macro, corporate actions

2. Data type

   • Trades/quotes, NBBO, depth-of-book, real-time bars, reference data, corporate actions, fundamentals, estimates

3. Historical depth

   • Tick history? Intraday? End-of-day? Survivorship-bias-free? Point-in-time?

4. Latency and delivery

   • Websocket, multicast, FIX, SFTP, REST, cloud delivery, co-lo availability

5. Licensing

   • Research-only vs redistribution, internal use, derived data, storage rights

6. Normalization

   • Symbology mapping, timezone handling, corporate action adjustments, vendor consistency

7. Coverage quality

   • Missing ticks/quotes, venue consolidation, holiday calendars, corrections

8. Operational support

   • SLA, support responsiveness, schema stability, change management

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Suggested starting combination

A common setup for a quant research team is:

• Core institutional vendor: Bloomberg or LSEG
• Engineering-friendly supplement: Polygon.io or dxFeed
• Direct feeds only for critical markets: CME/Nasdaq/Cboe/etc.
• Reference/fundamental backup: FactSet or Capital IQ

That gives you:

• broad coverage,
• usable APIs,
• robust research data,
• and exchange-grade depth where needed.

If you want, I can also give you:

1. a vendor comparison matrix,
2. a recommended stack by budget level, or
3. a shortlist specifically for US vs global multi-asset coverage.