How to Scrape DexScreener Boosted & Promoted Tokens in 2026

On DexScreener, projects pay for visibility — “boosts,” token profiles, community takeovers and token ads push a coin up the rankings and slap badges on its pair. That paid-promotion layer is one of the earliest observable signals in the memecoin lifecycle: a token that suddenly starts spending on visibility is a token whose team is trying to manufacture attention, often right before a coordinated move. This guide covers the promotion endpoints, what you can reconstruct from them, and why a managed scraper that stitches them together is the right tool for pay-to-pump intelligence.

What’s worth extracting

The signal here is the combination of who is paying, how much, when and what the token’s market looks like right now. Per promoted token you want:

• Promotion discovery — whether the token appears in top-boosted, latest-boosted, or latest-profiles feeds.
• Boost spend — the boost amount/score, i.e. how much visibility the project bought.
• Order history — the full list of marketing orders (boosts, token profiles, community takeovers, token ads) with payment timestamps and status.
• Live top-pair metrics — auto-enriched: price USD, liquidity, market cap / FDV, multi-window volume, transaction counts and pair age for the token’s top-liquidity pair.
• Chain & identity — chain, token address, symbol, name, links.

The magic is the join: promotion spend on its own is noise; promotion spend correlated against subsequent price and volume is a tradeable (or at least researchable) pattern.

How the data is exposed

DexScreener publishes a few promotion-related endpoints, free and no-key:

GET https://api.dexscreener.com/token-boosts/top/v1
GET https://api.dexscreener.com/token-boosts/latest/v1
GET https://api.dexscreener.com/token-profiles/latest/v1
GET https://api.dexscreener.com/orders/v1/{chainId}/{tokenAddress}

The discovery feeds (top/latest boosts, latest profiles) tell you which tokens are being promoted right now. The orders endpoint, queried per token, returns that token’s full marketing-order history with timestamps. And to make the data actionable you then have to pull each token’s pairs to attach live price/liquidity. None of these are joined for you — you get three disconnected feeds plus two per-token lookups.

Rate limits and the fan-out problem

DexScreener rate-limits its public API (the boosts/profiles endpoints sit around 60 requests/minute; pair lookups around 300/minute). The trouble with this actor’s job specifically is fan-out: each discovered token needs an orders call and a pairs call. A few hundred boosted tokens becomes a thousand requests, and naive parallelism trips HTTP 429 immediately.

Doing it well means: pull the discovery feeds, merge and dedup tokens across them, then fetch orders + top pair in controlled parallel batches with exponential backoff. That orchestration is the whole point of using a managed scraper rather than three scripts and a prayer.

▶ Run the DexScreener Boosted Tokens Scraper — merges top-boosted, latest-boosted and latest-profiles, pulls each token’s full order history with payment timestamps, and auto-enriches with live top-pair price, liquidity and FDV across 30+ chains. No API key. Pay per row.

Build it yourself vs. use a managed scraper

• Building from scratch — wire up three discovery feeds, dedup across them, fan out to orders and pairs per token, rate-limit the fan-out, flatten boosts + orders + market metrics into one row, and keep it working as DexScreener tweaks the boost model. Easily a few days, and the rate-limit tuning is fiddly.
• Using a managed actor — run it, get a deduplicated, flattened, multi-chain “what’s being promoted right now, how much they’re spending, and what their market looks like” dataset.

For a one-time look at today’s boosted list, the raw top feed is enough. For a repeatable pay-to-pump monitor, the orchestration is the work you don’t want to own.

Schema design for downstream use

A flattened per-token row that fuses promotion and market data:

{
  "chain": "solana",
  "token_address": "7xKXtg2CW87d97TXJSDpbD5jBkheTqA83TZRuJosgAsU",
  "symbol": "WIF",
  "name": "dogwifhat",
  "boost_amount": 500,
  "boost_total": 1500,
  "in_top_boosted": true,
  "in_latest_profiles": false,
  "orders": [
    { "type": "tokenProfile", "status": "approved", "payment_ts": "2026-05-23T08:11:00Z" },
    { "type": "boost", "status": "approved", "payment_ts": "2026-05-23T08:40:00Z" }
  ],
  "price_usd": 2.41,
  "liquidity_usd": 8200000,
  "fdv_usd": 2410000000,
  "volume_24h_usd": 145000000,
  "pair_age_hours": 4120,
  "scraped_at": "2026-05-23T12:00:00Z"
}

Decisions worth making early:

• Keep orders as an array with timestamps. The pay-to-pump signal lives in the sequence and timing of orders versus price — flatten it and you lose the correlation.
• Snapshot price/liquidity at scrape time. To test “did spend precede a pump,” you need the market state at the moment you observed the spend.
• Track pair_age_hours. Fresh pools + paid promotion + thin organic liquidity is the classic rug/pump setup. Age is a core risk feature.
• Dedup across the three feeds on chain + token_address. A token can appear in all three discovery feeds at once.

Typical use cases

• Memecoin signal services & alpha alerts — detect tokens that just started spending on visibility and flag potential coordinated pumps.
• Pay-to-pump detection — correlate marketing-payment timestamps with subsequent price action to identify likely organized pumps.
• Marketing intelligence — track which projects buy visibility, the chain distribution of spend, and spend sizes for competitor analysis.
• Solana trend tracking — surface newly promoted Solana memecoins with significant spend.
• Anti-rug intelligence — flag fresh profiles or takeover orders on new pools with low organic liquidity as risk signals.
• Newsletter & directory feeds — “what’s being promoted now” lists and daily promotion leaderboards; power “hot on DEX” feeds.
• Research & academia — quantify the paid-promotion ecosystem, spend distributions, and promotion-to-outcome correlations.

Cost math for the managed approach

Each run yields one enriched row per promoted token. The discovery feeds typically surface a few hundred actively promoted tokens at any moment, so a scheduled run every 15–30 minutes is a few hundred rows per run, a fraction of a cent each. A frequent intraday pay-to-pump monitor lands in the low tens of dollars a month — far cheaper than the engineering time to build and babysit the three-feed fan-out yourself.

Common pitfalls

• Promotion is not endorsement. A boost means someone paid, nothing more. Treat it as an attention signal, never as a quality or safety signal.
• Boost decay. Boosts expire; a token in today’s top-boosted feed may vanish tomorrow. Capture timestamps so you can reconstruct the visibility window.
• Fan-out throttling. Per-token orders + pairs calls are where you hit 429. Controlled batching is mandatory.
• Top-pair selection. A token has many pairs; enriching with the top-liquidity pair per chain avoids picking a dead pool’s stale price.
• Self-promotion noise. Some projects boost continuously as a marketing baseline — distinguish a steady drip from a sudden spike before calling it a pump signal.

Wrapping up

DexScreener’s promotion endpoints hand you three disconnected feeds and a per-token order lookup; the value only appears once you merge them, attach live market data, and timestamp everything for correlation. If you’re building memecoin alpha, pay-to-pump detection or marketing intel, run a managed scraper that already stitches the feeds, paces the fan-out and flattens spend + orders + price into one analyzable row.

▶ Open the DexScreener Boosted Tokens Scraper on Apify — schedule it intraday for a live pay-to-pump and promotion-spend feed across 30+ chains. JSON/CSV, no API key, free monthly credit to start.