Complete Guide: Analyzing the Top 10 Ethereum NFTs Using the Dune API
By Jane Doe – March 4 2026
Executive summary – A new tutorial released this week walks analysts through a step‑by‑step process for extracting, visualising, and interpreting on‑chain data on the ten highest‑volume Ethereum NFT collections. Leveraging the Dune Analytics API, the guide showcases how to surface key performance indicators (KPIs) such as total sales volume, floor‑price dynamics, active wallet participation, and secondary‑market velocity. The resulting analysis uncovers shifting market leadership, emerging usage patterns, and risk signals that can inform both investors and creators.
1. Why Dune Analytics Matters for NFT Research
Dune has become the de‑facto platform for on‑chain data querying across DeFi and NFT ecosystems. Its public SQL‑based query engine and programmable API allow developers to pull granular event‑level data straight from the Ethereum ledger without relying on third‑party aggregators. For NFTs, this means access to the raw Transfer events of ERC‑721 and ERC‑1155 contracts, enabling precise calculations of:
- Cumulative sales volume (ETH and USD equivalents)
- Floor‑price evolution across hourly snapshots
- Unique buyer and seller counts (a proxy for community breadth)
- Holding period distributions (to gauge speculative vs. long‑term holding)
The tutorial demonstrates how to combine these metrics into a single, repeatable pipeline that can be refreshed on a daily or weekly cadence.
2. Methodology Overview
| Step | Description |
|---|---|
| 1. Identify the target contracts | Export the contract addresses of the ten highest‑volume collections from a reputable source (e.g., OpenSea’s “Top Collections by Volume” leaderboard). |
| 2. Write the SQL query | Use Dune’s ethereum.events table to filter Transfer events for each contract, then aggregate by day. The query also joins price oracle tables (such as price.usd) to normalise volumes into USD. |
| 3. Deploy the query | Save the query on Dune, assign a descriptive name, and enable the “Public API” toggle. |
| 4. Pull the data via the API | Request the query results in JSON format using a simple GET call (https://api.dune.com/api/v1/query/{query_id}/results). |
| 5. Post‑process & visualise | Load the JSON into a data‑science environment (Python, R, or Excel) to compute derived metrics (average price, daily active wallets, sell‑through rate) and plot time‑series charts. |
| 6. Automate | Schedule the API call with a cron job or cloud function to keep the dataset up‑to‑date. |
The guide includes a ready‑to‑run code snippet in Python that handles pagination, error handling, and conversion of timestamps to UTC.
3. Results: The Current Top‑10 Ethereum NFT Collections (as of 03‑04‑2026)
| Rank | Collection | Approx. 30‑day Volume (ETH) | Approx. 30‑day Volume (USD) | Avg. Floor Price (ETH) | Active Wallets (30 d) |
|---|---|---|---|---|---|
| 1 | CryptoPunks | 1,020 K | $3.2 B | 28.7 | 19,800 |
| 2 | Bored Ape Yacht Club | 870 K | $2.8 B | 45.3 | 22,400 |
| 3 | Azuki | 620 K | $2.0 B | 7.9 | 15,600 |
| 4 | Moonbirds | 530 K | $1.7 B | 6.1 | 13,200 |
| 5 | CloneX | 470 K | $1.5 B | 4.8 | 12,900 |
| 6 | Doodles | 430 K | $1.4 B | 5.2 | 11,700 |
| 7 | Pudgy Penguins | 380 K | $1.2 B | 2.3 | 9,500 |
| 8 | World of Women | 350 K | $1.1 B | 3.1 | 8,800 |
| 9 | Invisible Friends | 310 K | $970 M | 3.9 | 8,200 |
| 10 | Cool Cats | 285 K | $900 M | 2.8 | 7,600 |
Numbers are rounded to the nearest ten‑thousand ETH/USD and reflect data collected directly from the blockchain via Dune. They exclude secondary‑market fees and marketplace‑specific royalties.
4. Key Insights
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Blue‑chip dominance persists – The five “original” collections (CryptoPunks, Bored Ape Yacht Club, Azuki, Moonbirds, CloneX) together account for ~73 % of total volume, underscoring the continued brand‑value premium in the NFT market.
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Floor‑price compression on legacy assets – While CryptoPunks and BAYC maintain relatively high floor prices, both have shown a ~15 % dip over the six‑month window, suggesting price‑realisation pressure as institutional interest moderates.
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Rising participation from mid‑tier projects – Collections such as World of Women and Invisible Friends have posted the highest growth rate in active wallets (+34 % and +29 % YoY, respectively). This indicates expanding collector bases beyond the traditional “Blue‑chip‑only” cohort.
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Turnover velocity differences – By dividing 30‑day volume by the total supply of each collection, the analysis reveals that Azuki and Moonbirds exhibit the fastest secondary‑market turnover (≈0.28 ETH per token per day), hinting at higher speculative trading activity. In contrast, CryptoPunks’ turnover is an order of magnitude lower, reflecting a “hold‑and‑sell‑when‑high” strategy among long‑term holders.
- Correlation with broader market cycles – Overlaying the NFT volume chart with the Ethereum price index shows a strong positive correlation (ρ ≈ 0.71) during the last quarter, yet the recent dip in floor prices occurred even as ETH rallied 12 % in the same period, suggesting collection‑specific dynamics outweigh macro trends for the top tier.
5. Practical Takeaways for Market Participants
| Audience | Actionable Insight |
|---|---|
| Investors | Prioritise collections with a healthy balance of volume and active wallet growth (e.g., Azuki, Moonbirds) to capture both price appreciation and liquidity. |
| Creators | Monitor floor‑price compression metrics; a falling floor may signal market fatigue that can be mitigated through community incentives or utility upgrades. |
| Data Analysts | Replicate the Dune‑API pipeline to build real‑time dashboards; the modular query structure can be extended to include royalty‑flow analysis or cross‑chain NFT bridges. |
| Marketplace Operators | Leverage the turnover‑velocity indicator to flag high‑frequency trading pairs, enabling better fee‑structure optimisation and fraud detection. |
6. Looking Ahead
The tutorial’s modular approach allows analysts to swap the “top‑by‑volume” filter for alternative criteria—such as “top‑by‑unique‑owners” or “fastest‑growing floor price”—without rewriting the core query. As Ethereum’s scaling roadmap (e.g., danksharding and proto‑danksharding) matures, transaction costs for NFT transfers are expected to drop, potentially flattening the concentration of volume among legacy collections and opening the field to newer entrants.
Future work could integrate L2‑specific NFT bridges (Arbitrum, zkSync) into the Dune query, offering a more holistic view of the Ethereum‑wide NFT ecosystem.
Conclusion
By harnessing the Dune API for on‑chain NFT analytics, the guide demonstrates a repeatable, transparent method for dissecting the performance of the ten most‑traded Ethereum collections. The findings confirm the staying power of blue‑chip assets while also highlighting emerging opportunities in mid‑tier projects. As data pipelines become more automated, market participants will be better equipped to navigate the volatile NFT landscape with evidence‑based strategies.
For a full walkthrough of the SQL query, Python script, and visualisation templates, visit the original tutorial on Dune’s community hub.
Source: https://dune.com/blog/complete-guide-analyzing-the-top-10-ethereum-nfts-using-dune-api-6d883
