Dune, best known for the SQL dashboards crypto analysts use to dissect blockchain activity, now wants a seat on Wall Street’s trading desks.
The company has launched real-time market data feeds covering trades from more than 200 onchain venues across 12 blockchain networks. The target audience is institutions, not hobbyists.
What Dune is actually selling
The new product delivers both spot and perpetual futures trade data from decentralized venues. Supported networks include Ethereum, Solana and BNB Chain.
Perpetual futures, for the uninitiated, are derivatives contracts with no expiration date. Traders can hold leveraged positions on an asset’s price indefinitely, which has made perps one of the most heavily traded products in crypto.
Dune is aiming the feeds at hedge funds, asset managers, market makers and brokers. The pitch is that these firms can pipe onchain price discovery directly into the market data systems they already run.
The coverage claims are ambitious. Dune says the feeds capture around 90% of onchain spot trading volume.
On the derivatives side, perpetual trading volume across the covered venues is estimated at approximately $9 billion per day.
Speed is where things get interesting for professional traders. Dune reports spot trade latency of 1.34 seconds at the 95th percentile, and 590 milliseconds for perpetuals at the same measure.
That “p95” label matters. It means 95% of data deliveries arrive at or faster than those times, so it describes near-worst-case performance rather than a flattering average.
Why onchain data suddenly matters to TradFi
Dune co-founder and CEO Fredrik Haga has said onchain trading accounts for roughly 25% of spot trading volume. Haga has framed the expanded data offering as a key step in bringing onchain activity into traditional finance.
Regulation is also shifting the ground underneath this market. The SEC granted a five-year exemption in September 2026 allowing certain venues to trade tokenized versions of US stocks without full exchange registration.
Tokenized stocks are blockchain-based representations of traditional equities. They can trade around the clock, unlike shares on conventional exchanges that close at 4 pm Eastern and take weekends off.
That creates a practical headache for banks, brokerages and funds. If a tokenized version of a stock trades on a Saturday afternoon, someone needs reliable pricing data for it, and the usual exchange feeds are asleep.
From dashboards to data pipes
Dune’s original product was built around SQL-based analytics. Users write database queries against indexed blockchain data and turn the results into charts and dashboards spanning multiple networks.
Real-time feeds are a different business entirely. Dashboards serve researchers who can tolerate a lag. Market data feeds serve trading systems that make decisions automatically and can’t afford gaps or delays.
The feeds are designed to slot into existing market data workflows. That integration point is arguably as important as the data itself, since institutions rarely rebuild their infrastructure to accommodate a new vendor.
What this means for institutions and the market
The clearest beneficiaries are firms that already want onchain exposure but lack the engineering resources to aggregate it themselves. A market maker quoting tokenized assets needs a consolidated view of where trades are actually happening across chains.
Without that view, pricing is guesswork. With fragmented liquidity spread across more than 200 venues, even a well-funded desk could miss meaningful activity on a smaller chain.
Risk management is the less glamorous but possibly more important use case. Compliance and risk teams at banks need defensible pricing for any position they hold, and an aggregated feed with documented coverage and latency makes that conversation with auditors easier.
There are open questions worth watching. The 90% coverage figure is Dune’s own claim, and institutions will likely want to test how the feeds perform during volatile periods, when latency tends to spike and accurate data matters most.
Disclosure: This article was edited by Vivian Nguyen. For more information on how we create and review content, see our Editorial Policy.