Market data is the one purchase every systematic trader makes, and the one where marketing pages are least informative. Vendors compete on symbol counts and API polish; backtests die from properties that never appear on a pricing page — survivorship handling, restatement history, timestamp semantics. This guide gives you the checklist that actually predicts research quality, then maps the 2026 landscape by tier.

Disclosure: this guide currently contains no affiliate links or sponsored placements. If that changes, links will be labeled inline.

The five questions that matter more than price

1. Does history include the dead?

Survivorship bias is the classic retail-backtest killer: a universe of currently listed symbols excludes every bankruptcy and delisting, inflating long-strategy returns by roughly 1–4% annually on US equities depending on period and universe (the effect is largest in small caps). Test any vendor before trusting them: pick five well-known delistings and query their full history. If the data isn’t there — or worse, is silently missing final months of trading — the vendor is for dashboards, not research.

2. Is fundamental data point-in-time?

Companies restate earnings. A point-in-time (PIT) feed gives you the numbers as investors saw them on each date, with the restatement trail; a non-PIT feed silently overwrites history with corrected figures. Backtesting on restated data is look-ahead bias with a lag of months — value and quality factors are especially distorted. PIT fundamentals are the sharpest quality line in the market: they’re what separates the institutional tier (and a handful of serious mid-tier vendors) from the commodity APIs. If a cheap vendor claims PIT, ask for the as-reported/restated field pair and spot-check a known restatement.

3. What do the timestamps mean?

For anything intraday, ask: exchange timestamp or vendor receipt time? Which timezone, and is it DST-consistent? Are daily bars stamped at the exchange close or midnight UTC? Consolidated tape or single venue? None of this is exotic — but every one of these has silently shifted a backtest’s signals by a bar. The tell of a good vendor is documentation that answers these without a support ticket.

4. Corporate actions: who does the adjusting?

Splits and dividends must be handled somewhere. The clean setup is unadjusted prices plus a separate corporate-actions table, letting you adjust reproducibly in code. Pre-adjusted-only feeds are acceptable for quick research but make cross-vendor validation and dividend-strategy work painful. Check dividend treatment specifically: price-only adjustment understates total returns by the dividend yield, compounded.

5. What does the license actually permit?

Personal-use plans commonly prohibit redistribution, publishing derived datasets, or commercial use — which can include managing outside money. Exchange fees for real-time data are a separate layer with their own professional/non-professional distinction; historical and delayed data is far less encumbered. Read the license before building a business on a feed, and budget time for this if you ever take outside capital.

The 2026 vendor landscape, by tier

Prices are order-of-magnitude as of September 2026 — verify current terms; this market reprices constantly.

Commodity API tier (~$0–100/month)

Alpha Vantage, Finnhub, Twelve Data, EODHD, Tiingo and similar. Fine for prototyping, dashboards, and learning. The common failure modes: thin or absent delisted history, non-PIT fundamentals, and rate limits that make universe-wide downloads slow. Tiingo and EODHD punch above their price on data hygiene; verify survivorship handling for your specific universe regardless.

Serious individual tier (~$50–500/month)

  • Polygon.io — the default US equities/options API for individual quants: full-market coverage, real tick history, websockets, sane licensing. Check delisted coverage depth against your backtest start date.
  • Databento — usage-based pricing, exchange-native feeds (including futures via CME), precise timestamping, and the best documentation-honesty in the tier; the pay-per-query model rewards the raw-zone archiving habit from our stack guide.
  • Norgate — daily-frequency specialist for US/AU equities with meticulous delisting and corporate-action handling; a quiet favorite for end-of-day systematic research.
  • Crypto: exchange APIs are free and canonical for their own venues; aggregators (Kaiko, CoinAPI, Amberdata) matter only when you need normalized multi-venue history.

Institutional tier (four figures/month and up)

CRSP (the academic gold standard for survivorship-clean US equities), Compustat/S&P for PIT fundamentals, LSEG/Refinitiv, Bloomberg, FactSet, and direct exchange feeds. You buy this tier when compliance, PIT rigor, or asset-class breadth demands it — not for API convenience. Academic affiliations get CRSP/Compustat access through WRDS at a fraction of commercial cost; it remains the cheapest legitimate path to research-grade equity history.

A decision framework in four steps

  1. Write down your universe and horizon first. “US equities, daily, 15 years, long/short” and “BTC perpetuals, 1-minute, 3 years” have almost disjoint vendor shortlists. Most vendor regret comes from buying breadth you never query.
  2. Run the survivorship test (five dead tickers) and the timestamp test (one known market event, checked bar by bar) on a trial plan before subscribing annually.
  3. Buy prices cheap, fundamentals carefully. The tiers above apply to prices; for fundamentals, the PIT question dominates everything else including price.
  4. Archive everything you pull (raw, append-only). Vendor churn is normal over a research career; your archive is what makes it survivable.

Red flags, condensed

  • “10,000+ tickers!” with no mention of delistings → dashboard data.
  • Fundamentals with no as-reported/restated distinction → look-ahead bias on tap.
  • Pricing page without a license summary → licensing surprise later.
  • No documented timestamp semantics for intraday data → debugging session prepaid.

Next: Which database should hold all this? TimescaleDB vs ClickHouse vs DuckDB vs kdb+ → · Budget it: tick data storage sizer →