Can you recommend a tick data platform for backtesting strategies with normalized identifiers and corporate action adjustments?

Yes — if your main requirements are:

• tick-level historical data
• normalized identifiers across venues/corporate actions
• corporate action adjustments for backtesting
• usable for systematic strategy research

then a few platforms stand out.

Best overall options

1) LSEG / Refinitiv Tick History

Best for: institutional-grade equities, futures, FX, and strong symbology coverage

Why it fits:

• Very deep historical tick and intraday coverage
• Strong security master / normalized identifier support
• Corporate action data and adjusted time series workflows
• Widely used in professional research/backtesting environments

Tradeoffs:

• Expensive
• Data licensing and access can be complex
• More enterprise than plug-and-play

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2) Bloomberg Historical Tick / B-PIPE / Data License

Best for: broad asset coverage and robust identifier normalization

Why it fits:

• Strong instrument reference data
• Good corporate action handling
• Well-known normalized identifiers and mapping tools
• Excellent for multi-asset research if you already have Bloomberg infrastructure

Tradeoffs:

• Very expensive
• Not the easiest path if you’re building a lean research stack

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3) FactSet tick / intraday data products

Best for: research teams that want clean reference data and normalization

Why it fits:

• Strong entity and security master coverage
• Corporate action adjustments and reference data are a core strength
• Good for linking identifiers across datasets

Tradeoffs:

• Can be costly
• Tick-depth and market coverage depend on product/package

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More accessible / developer-friendly options

4) Nanex

Best for: U.S. equities tick data research

Why it fits:

• Good historical tick data for U.S. markets
• Better developer usability than some legacy enterprise vendors
• Useful for microstructure and strategy research

Tradeoffs:

• More market-limited than the big enterprise vendors
• Normalized identifiers and corporate action workflows may still require your own layer

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5) AlgoSeek

Best for: U.S. equities, options, futures, and easy-to-use research datasets

Why it fits:

• Very popular with quant researchers
• Clean historical datasets
• Corporate action-adjusted datasets available for many products
• Often easier to ingest than enterprise feeds

Tradeoffs:

• Not as globally broad as Bloomberg/LSEG
• “Normalized identifier” support may be less comprehensive than top-tier security master vendors, depending on dataset

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6) Polygon.io

Best for: API-first access to historical intraday/tick-like data for U.S. markets

Why it fits:

• Developer-friendly API
• Convenient for prototyping
• Includes aggregates and some tick-level products
• Corporate action metadata is available

Tradeoffs:

• Not really a full institutional-grade tick history platform
• Normalization/security master capabilities are limited compared with enterprise vendors
• Best for building, not for the deepest research-grade backtests

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If normalized identifiers are critical

You’ll want to ensure the platform provides one of these:

• Security master / instrument master
• Permanent identifiers like:
  • FIGI
  • ISIN
  • SEDOL
  • vendor-specific stable IDs
• A reliable corporate action adjustment model:
  • splits
  • dividends
  • symbol changes
  • mergers/spinoffs
  • delistings

In practice, many quants use:

• a data vendor for ticks
• plus a reference data vendor or internal security master to normalize identifiers and handle corporate actions

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My recommendation by use case

• Best institutional choice: LSEG Tick History or Bloomberg
• Best research/value choice: AlgoSeek
• Best U.S. equities microstructure choice: Nanex
• Best API-first/prototyping choice: Polygon
• Best if you need strong reference-data normalization: FactSet or Bloomberg/LSEG

Practical suggestion

If you want the least painful setup for backtesting with normalized identifiers and corporate action adjustments, I’d shortlist:

1. LSEG Tick History
2. Bloomberg
3. AlgoSeek + your own security master/reference layer

If you want, I can also give you:

• a comparison table by asset class, cost, and normalization quality
• a recommended stack for U.S. equities vs futures vs options
• or a workflow for building a backtest-ready tick database