How to compare Binance, Coinbase and OKX market data correctly

Can I compare Binance, Coinbase and OKX prices directly?

Only after defining the question. A delayed market-context panel, a portfolio valuation, a historical predictive study and an arbitrage engine require different clocks, price types and tolerances. Start with exact spot base/quote markets; do not silently mix a perpetual mark price, a spot last price and a spot bid.

Our test asks whether the local archive can supply a recent observation from all three venues at predefined checkpoints. It does not ask whether each venue's last trade occurred simultaneously. The hypothesis is that headline venue coverage can overstate usable matched coverage for a tighter task; the published denominator makes that visible.

Verify: Full-day audit and assumptions

Which timestamps are source events, and which are local collection times?

For the archived Coinbase product-metadata feed, the mapped updated field is local collection time, not an exchange last-trade event time. This changes the interpretation of the original four-row example: its source_event_time label was incorrect. The corrected September 27 comparison uses observed_at for local availability at each checkpoint; it does not infer source-trade synchronization or network latency.

Binance's mapped value uses its rolling ticker closeTime; OKX uses ticker ts. Both normalizers can fall back to local time, and these older archived rows do not preserve a per-row fallback flag. A ticker window boundary or generation timestamp is also not necessarily the time of its last underlying trade.

The revised experiment uses observed_at, the local observation clock, for every venue. It can check when this archive had a value and how old that local capture was at a checkpoint. It cannot estimate network latency without compatible source clocks or certify that the quoted last price reflects a very recent trade. The original v1 file is retained for provenance, but its source_event_time label must be read with this correction.

Verify: Coinbase Advanced Trade public products · Binance Spot market-data fields · OKX public market-data reference · Frozen correction and protocol

How did we test a full day of cross-exchange availability?

We fixed the window as September 27, 2026 UTC, midnight inclusive to the next midnight exclusive. BTC and ETH each have 288 checkpoints, five minutes apart. At each checkpoint and for each exact venue/base/quote, choose the latest locally observed row at or before the grid time. A one-hour preceding buffer allows a prior observation at the first checkpoint; future observations are never selected.

The primary tolerance is 120 seconds, with 5-, 30- and 300-second sensitivity cases. A USDT comparison passes only if Binance, Coinbase and OKX all have a matching local observation within the tolerance. USD is a separate diagnostic quote category and is not silently substituted when USDT is old or missing.

The resulting public file contains 3,456 diagnostic rows: 288 checkpoints × two coins × three venues × two quote categories. These are evaluation slots, not 3,456 distinct trades. Missing pairs stay in the diagnostic output so failure is not hidden by an inner join.

Verify: Every checkpoint and source-selection diagnostic · Venue-level coverage summaries

How often were all three exchanges locally fresh at the same checkpoint?

At the primary 120-second threshold, 55/288 checkpoints pass for BTC and 55/288 for ETH: 19.1% each. At 300 seconds, 224/288 pass, or 77.8%. A five-second requirement passes none; 30 seconds passes six. Loosening the threshold changes the usable sample substantially.

BTC and ETH have identical counts because the collector observes products in shared batches. This is not independent replication across two markets. It would be misleading to pool the counts and claim 576 independent observations of exchange reliability.

These results describe our local collection and archive path on one day. They do not show that exchanges were unavailable for the failed checkpoints. Poll timing, local caching, archive cadence and the chosen grid phase can all affect the result. No continuous-time freshness rate or venue latency percentile is inferred.

Verify: Threshold sensitivity results

Is a failed comparison a missing pair, an old observation or an outage?

A failed cross-exchange comparison must distinguish no matching market, an observation older than the declared limit and a proven source outage. In the frozen September 27 USDT audit, the three streams have preceding observations, but many fail the local-age limit. At 120 seconds, each coin has 68 passing Binance slots, 59 Coinbase slots and 59 OKX slots; their intersection is 55, not the sum of those counts.

Binance USD has no row in this selected feed; Coinbase and OKX USD observations are a separate category with 59 fresh checkpoints per coin. That says what this feed contains, not every product a venue offers. A missing normalized field, a valid zero and an unsupported pair must not be collapsed into one value.

If a live panel requires three venues, show the comparison unavailable when one fails. If the application permits a two-venue comparison, state the changed venue universe and preserve the failed member. Do not carry yesterday's price forward and retain a 'live' label.

Verify: Venue and quote coverage evidence

How do I compare BTC-USD with BTC-USDT without inventing a premium?

Use the same base asset and a contemporaneous conversion for the quote currency. If one USDT is worth q USD, then a BTC price quoted in USD converts to USDT by dividing by q. This is a currency normalization step, not an executable arbitrage calculation. It inherits the conversion feed's own timestamp, spread and availability uncertainty.

Illustrative arithmetic: a 100 USD price divided by 0.998 USD per USDT becomes about 100.2004 USDT, not 100 USDT. Assuming parity would suppress roughly 0.2% of the converted difference in that example. The numbers are deliberately hypothetical; this audit publishes no measured premium.

Also match contract type, index composition and settlement currency. A perpetual's basis relative to spot is an economically meaningful difference, not a data-cleaning error. Do not normalize it away by assigning both the same generic BTC ticker.

Verify: Cross-market Coinbase premium proxy and limits · Perpetual basis definitions

Why do 24-hour crypto trading volumes disagree across exchanges?

Base volume, quote turnover and available order-book liquidity are different measures. A rolling 24-hour ticker also differs from a fixed UTC calendar-day total. Comparing equal-looking volume columns without their units, window endpoints and calculation methods can create a false market-share ranking.

This collector can use an approximate Coinbase quote-volume field or estimate quote turnover by multiplying base volume by the current price. That fallback is not the exact sum of trade price × quantity throughout the window. The archive does not provide enough provenance to treat every normalized value as an identically computed raw turnover measure.

For a robust volume comparison, reconstruct equal UTC buckets from sufficiently complete trades or compatible bars, retain native source totals, flag estimation, normalize quotes explicitly and disclose excluded venues. Use order-book depth and spreads for executable capacity; a large historical turnover alone does not establish low slippage now.

Verify: Spot-volume market-share scope

Can these snapshots identify a profitable cross-exchange arbitrage?

No. Last prices may refer to trades at different times and sizes. A basic executable gross edge uses the sell venue's bid minus the buy venue's ask for the intended quantity, after quote normalization. Then subtract both trading fees, book-walking slippage and applicable financing or transfer costs. Inventory already held on both venues changes the operational problem.

A hypothetical 20-basis-point last-price difference can disappear if combined taker fees and spread costs exceed it. Transfers can take longer than the opportunity, withdrawals can be constrained, and venue access differs by jurisdiction. These are conditions to model, not profits implied by a ticker spread.

For a production execution study, request sequence-preserving order-book updates or verified snapshots, trade timestamps, receipt clocks, missing-message recovery, fee tiers and contract history. This report's local ticker archive cannot answer those tick-level questions; a buyer needing them should choose a feed that can.

Verify: Cross-exchange price-spread observations · Choosing backtesting market-data types

How can a buyer reproduce the cross-exchange comparison audit?

The diagnostic CSV gives each checkpoint, exact venue/quote, selected observed_at and resulting age. Group USDT rows by coin and checkpoint; require three venues and nonnegative age within the threshold. Repeating that rule yields the published 0, 6, 55 and 224 counts per coin. Never select the next row simply because it is closer.

Download the three study JSON files, their diagnostic CSVs and reproduce.py into one directory and run python reproduce.py, or python3 reproduce.py if Python 3 is installed under that command. The standard-library verifier rebuilds the public aggregates; the manifest records checksums. Raw prices and actual source-event validity are outside this public diagnostic verification.

Repeat on multiple days and offset checkpoint phases before using these proportions for capacity planning. Batch-driven dependence means ordinary independent-binomial intervals would overstate precision; we publish a fixed-day census and no population reliability estimate. A stronger study should predefine additional days and phases before inspecting favorable results.

The acceptance output is four counts per coin—0, 6, 55 and 224 for 5-, 30-, 120- and 300-second limits—in the frozen September 27 checkpoint grid. If your run differs, verify quote category, backward selection, grid phase and observed_at precision first. Retain failed slots; silently removing them before taking the denominator would answer a different question.

Verify: Checkpoint diagnostic CSV · Independent diagnostic reproducer · Frozen protocol and limitations · Release checksums

What should I ask a cross-exchange crypto data provider before paying?

Describe the actual task in the request: market context, accounting valuation, research features or executable arbitrage. Ask for a venue × pair × date overlap report, native and normalized field definitions, genuine source and receipt timestamps, correction policy, missingness and a sample containing failures. Require a contractual distinction between a locally fresh ticker and a synchronized executable quote.

Accept slower local snapshots only when their labelled age fits your workflow. Defer a PIT claim when authentic availability is absent; reject an execution dataset that lacks the required book, clock or cost evidence. No supplier should satisfy a tighter requirement simply by relaxing the definition after seeing its results.

CoinNudge's single coverage inventory is the place to check each offered family's status, pack and fixed edition. Current one-time packs do not promise a comprehensive cross-exchange PIT or execution feed. If the required venues, authentic clocks or book fields are not explicitly included, request a separate confirmed scope or choose another feed; do not pay on the assumption that an internal collector or this public audit creates delivery rights.

Verify: Inspect data families and delivery status · View public product samples · Read API requests and file-access rules

Frequently asked questions

Sources and verification

• Frozen full-day cross-venue auditFixed-grid BTC/ETH results and correction.
• Audit protocol and clock dictionaryEligibility, fallback limitations and reproduction scope.
• Coinbase Advanced Trade public productsActual source family used by the collector.
• Binance Spot market-data documentationTicker, price and volume definitions.
• OKX API documentationPublic ticker field reference.

Define the venues, clocks and executable fields your research needs.

Use the full-day audit to distinguish usable local context from evidence that still requires a tighter data source.