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Use Dune to turn saved SQL queries over onchain data into repeatable measures of tokenized-asset trading activity, then retrieve or publish those results through its APIs and integrations. The important first step is not an API call: define which assets and venues count, and decide exactly what “volume” and “liquidity” mean. Without those choices, a query can return numbers that look precise but are not comparable. Dune supports APIs, shared queries, dashboards, DuneSQL, and integrations for onchain analytics; its platform includes tokenized assets among the markets it covers (Dune APIs and connectors; Dune platform overview).
What should you decide before querying tokenized-asset activity?
Write down the scope and definitions before you build a dashboard or automate a request. Tokenized assets can trade across chains and venues, and different datasets may describe trades, swaps, transfers, pools, or order books. A result is useful only when its inputs and measurement rules are clear enough for someone else to reproduce.
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Define the asset universe
For every asset you intend to include, record its chain, token contract address, venue, quote asset, and token standard. A ticker or display name alone is not a reliable identifier for a cross-chain dataset: make the contract and chain part of the asset definition. Decide whether wrapped or bridged representations, related share classes, or multiple quote markets belong in the same reporting group, and document that choice.
For regulated tokens, inspect the relevant identity and compliance contracts as well as the token contract. The ERC-3643 standard describes transfers that require identity verification and compliance checks (ERC-3643). That means a transfer may be conditional rather than an unrestricted movement between any two addresses. Decide in advance how your reporting will treat failed, restricted, or otherwise non-settled transfer attempts; do not assume that every attempted movement represents a completed trade.
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Choose the event or market data that matches the question
Use the most appropriate available decoded trade, swap, transfer, pool, or order-book datasets for the chain and venue. A transfer table can help measure token movements, but a transfer by itself does not establish that a sale occurred or reveal the sale price. Likewise, a pool reserve snapshot is not a record of completed trading. Match each source to the metric it can support.
If a contract is not decoded in the available data, document the raw-event decoding assumptions before using its events. Keep separate queries when chains or venues have different schemas; forcing unlike records into one query without making the differences explicit can create silent omissions or misleading totals.
How should you define trading volume?
State whether volume means token quantity, quote-currency notional, or both. Also specify the time window, timestamp field, decimal handling, fee treatment, and filters. These decisions determine what the reported number means; there is no single volume definition that is correct for every token or venue.
Choose the unit and conversion rules
Token quantity measures the amount of the token traded. It is useful when the question concerns activity in units of the asset, but totals for different tokens are not directly comparable. Apply the token’s decimals consistently so raw integer quantities are not mistaken for human-readable units.
Quote-currency notional expresses trade size in the asset used to quote or settle the trade. This can make activity easier to compare across markets, but the query must specify which quote asset it uses and how any conversion is handled. Do not combine amounts denominated in different quote assets as though they were one currency without an explicit conversion method.
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When useful, publish both measures. Label them separately and retain the quote asset and conversion assumptions in the query notes. A number without its unit, market scope, and conversion policy is not a complete volume metric.
Set the time and trade filters
Choose whether to group activity by the event’s block timestamp or another timestamp available in the source, and use the same convention across the reporting period. Record the aggregation window—such as the interval your dashboard reports—alongside the query. Where the underlying data supports it, document how fees are treated and whether self-trades or obvious wash activity are filtered. If you cannot reliably identify such activity, say so rather than implying that the result is clean of it.
These definitions belong in the saved query’s description or adjacent documentation, not only in the chart title. When the metric changes, preserve a version note so readers can tell whether a historical series was recalculated under a new rule.
Which liquidity measure fits the market you are tracking?
Liquidity is multidimensional. Reserves, order-book depth, bid-ask spread, turnover, and slippage answer different questions, so report the measure by name rather than presenting any one of them as “liquidity” without qualification.
| Measure | What it describes | What to specify |
|---|---|---|
| AMM reserves or TVL | Assets held in an automated market maker pool, or the reported value locked in it. | Pool and venue, snapshot time, included assets, and valuation basis. Reserves or TVL do not by themselves show how much can be traded at a given price impact. |
| Order-book depth | Available bids or asks within a stated price band. | Venue, side of the book, price band, quote unit, and observation time. |
| Bid-ask spread | The distance between the best available bid and ask at an observation time. | Venue, timestamp, spread convention, and whether the result is absolute or relative. |
| Turnover | Trading activity expressed relative to a stated base, such as reserves or another chosen quantity. | Numerator, denominator, reporting window, and treatment of quote assets. The chosen formula must be explicit. |
| Slippage for a standard trade size | The price impact or execution difference estimated for a trade of a specified size. | Trade direction, size, quote asset, venue, reference price, and calculation method. |
Use a measure that matches the venue. Pool reserves can help describe an AMM, while price-band depth is more natural for an order book. If you compare venues, keep the price band, trade size, time window, and valuation rules consistent where possible. A reserve total and an order-book depth figure are not interchangeable, even if both are displayed in currency terms.
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How do you build a repeatable Dune query workflow?
Build saved SQL queries around a documented asset universe and metric definition, then retain enough metadata to reproduce each run. Dune offers programmatic access to SQL-based onchain analytics and supports workflows involving queries, dashboards, BI integrations, monitoring, and alerts (Dune APIs and connectors).
- Inventory the assets and venues. Create a maintained list of chain, token contract, token standard, venue, and quote asset. Note which representations are included or excluded and why.
- Map each source to a metric. Identify the available decoded trade, swap, transfer, pool, or order-book datasets for each chain and venue. Check their fields and schema before writing an aggregation. If a contract requires raw-event decoding, record the decoding assumptions and keep them reviewable.
- Separate queries where schemas differ. Do not assume one SQL statement can safely cover every chain or venue. Use distinct saved queries when fields or event meanings differ, and make any normalization step visible.
- Write and save the SQL. Keep volume and liquidity definitions explicit. Parameterize dates, chains, venues, and contract addresses where supported. Store each query ID with version notes so an automated job can refer to a known query and definition.
- Validate the output. Check that the query covers the intended contracts and dates, that token decimals and quote units are handled consistently, and that aggregates correspond to the selected source records. Spot-checking sample records against the underlying events can expose incorrect assumptions before you publish totals.
- Execute or reference the saved query through the API. Authenticate with an API key and follow the current Dune REST API reference for the supported execution and result-retrieval flow. The API documentation is the authority for request details, accepted parameters, response structure, and current limits.
- Track execution and retrieve results. Poll execution status as documented, then retrieve the completed results. For large responses, use the documented pagination behavior rather than assuming a single response contains every row.
- Publish and operate. Send results to a Dune dashboard, BI integration, alert, or external data store according to the use case. Cache results when appropriate, apply retry backoff, and set refresh frequency to fit the current account limits and pricing.
How can you automate API retrieval without making the data hard to audit?
An automated pipeline should preserve the connection between a displayed figure and the query execution that produced it. Dune’s REST API documentation describes the API workflow; consult it for exact request fields, endpoint behavior, and response details, which can change over time (Dune REST API reference).
Keep a run record
For each refresh, retain the query ID, query version or notes, parameter values, execution time, and retrieval time. Also store the metric definition and relevant source-table choices. This lets you explain whether a changed chart reflects market activity, a changed query, a different parameter, or a new run.
Handle delayed or incomplete runs deliberately
Do not treat a request to execute a query as proof that results are ready. Follow the documented status flow, retrieve results after completion, and account for pagination when the response is large. If a run fails or a retrieval is incomplete, mark the output accordingly rather than silently replacing it with an older result.
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Should you use a Dune dashboard or an API-plus-database pipeline?
A dashboard is a practical destination for analysis and sharing; an API-plus-database pipeline is useful when you need a separate store, downstream processing, or a controlled reporting history. Choose based on refresh needs, limits, retention, alerting, schema control, and auditability rather than assuming one option is universally better.
| Decision factor | Dune-only dashboard | API plus external database |
|---|---|---|
| Refresh cadence | Use the refresh options available to the account and dashboard workflow; exact cadence is not stated in the platform overview. | Can be scheduled by the pipeline, subject to Dune’s current API limits and account pricing; exact limits are not stated here. |
| Query cost and limits | Check current account pricing and limits before choosing a refresh rate; specific figures are not stated here. | Check the same Dune account constraints and account for the additional infrastructure and operations of the external store; specific figures are not stated here. |
| Historical retention | Use the retention behavior and options documented for the account; a universal retention period is not stated here. | You control what your pipeline stores and for how long, subject to your database design and operating costs. |
| Alerting | Dune supports monitoring and alerts as part of its analytics and integration workflows; exact alert options depend on current product capabilities. | You can build downstream alerting around stored outputs, but must maintain that processing yourself. |
| Schema control | Query and dashboard definitions remain within the Dune workflow. | You can define the destination schema, but must own transformations and compatibility when source queries change. |
| Auditability | Keep query IDs, parameters, execution details, and metric notes with the dashboard workflow. | Keep those same records alongside stored results so a database row can be traced back to its query run. |
The platform supports APIs, shared queries, dashboards, and integrations, but specific limits, retention periods, and pricing can depend on the current account and product terms. Verify those details in the current Dune documentation before designing a production schedule; do not infer a guaranteed refresh interval or historical retention from the fact that an API is available.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What checks make a volume or liquidity series more trustworthy?
Run quality checks that test both the data and the meaning of the metric. A stable query can still produce an unsuitable measure if its asset list, unit, or source does not match the question.
- Scope check: confirm chain and contract addresses against the maintained asset inventory; make exclusions and cross-chain representations visible.
- Unit check: verify decimals, token quantity, quote asset, and any conversion assumptions before comparing or summing values.
- Source check: confirm that the selected dataset represents the activity being measured. Transfers alone do not prove a trade; pool balances alone do not establish executable depth.
- Time check: confirm the timestamp convention, aggregation interval, and observation time for snapshot measures.
- Definition check: ensure fees, self-trade or wash-activity filters, price bands, and standard trade sizes are documented and consistent with the stated metric.
- Execution check: record query identifiers, parameters, completion status, execution times, and whether all result pages were retrieved.
- Change check: retain version notes when source tables, SQL logic, asset coverage, or metric definitions change.
For regulated tokens, include the transfer restrictions in interpretation. Because ERC-3643 uses identity verification and compliance checks for transfers, a movement or failed attempt should not automatically be treated as an ordinary open-market fill. Define the event treatment in the query notes and report what the available data can establish.
What should a published chart tell the reader?
A chart should make the metric understandable without requiring the reader to reverse-engineer the SQL. Put the unit, time window, chain and venue scope, and liquidity method in the title, axis labels, or accompanying notes.
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- For volume, label token quantity and quote notional separately, and identify the quote asset and conversion approach.
- For depth or spread, state the price band or spread convention and the observation time basis.
- For slippage, state the trade size, direction, venue, and reference price used.
- For pool measures, identify which pool assets are counted and how any value denomination is derived.
- Link the displayed result to its saved query ID or dashboard context, and keep the query’s parameter and version notes available.
A dashboard is easier to interpret when it shows whether data is a completed query result or a stale cached value. Where the output cannot establish a claim—such as whether every transfer was a bona fide trade—say so in the description instead of letting a volume label imply more than the query measures.
Frequently Asked Questions
Can Dune track tokenized assets on more than one blockchain?
Dune describes its platform as multi-chain and includes tokenized assets among the markets it covers. Whether a particular token contract or venue has suitable decoded data must be checked against the datasets available for that chain.
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Not reliably as a general measure of completed trades. A transfer can occur for reasons other than a sale, so use trade or swap records when available, or clearly label a transfer-based measure as token movement rather than trading volume.
Does ERC-3643 mean every token transfer needs an identity check?
The standard specifies identity verification and compliance checks as part of its transfer model, but the relevant implementation and rules are contract-specific. Inspect the token’s contracts and actual data before applying the standard as a blanket assumption about a particular asset.
Where do I find Dune API request parameters and response formats?
Use the current Dune REST API reference at getdune.org/docs/reference/api/. It is the place to verify the exact request flow and behavior rather than relying on examples that may describe an older API version.
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