How to Scrape the Polymarket Top Wallets PnL Leaderboard in 2026

Knowing what Polymarket says about an outcome is one thing. Knowing who keeps being right is another — and arguably more valuable. Polymarket’s leaderboards rank the wallets that consistently make money across politics, sports, crypto and Trump-tagged markets, and following the smart money is a strategy in its own right. This guide is about extracting that trader-level data: a merged, deduplicated leaderboard of top performers with PnL, ROI, winrate and volume, plus optional per-wallet deep-dives. (If you want live market odds and event prices instead, that’s the companion events-and-odds scraper — a different feed entirely. This one is about people, not prices.)

What’s worth extracting

This scraper produces a ranked roster of top traders and, optionally, a detailed profile per wallet:

Leaderboard row (per wallet):

• Identity — pseudonym, profile image, and the wallet address.
• Performance — PnL, ROI, win rate, average bet size, total volume.
• Activity — transaction count and last-active timestamp.
• Tags — smart-money classification and per-category top-list membership (politics / sports / crypto / Trump, etc.).

Optional per-wallet 24h deep-dive:

• Account age — when the wallet started trading.
• Portfolio value — current total value held.
• Realized profit — lifetime and 24h realized PnL.
• Recent flow — recent buy/sell counts and cash inflows/outflows.

The leaderboard alone is the alpha-following dataset; the deep-dive turns a name into a full trader profile you can analyze or display.

Leaderboard data, not odds data

This is a fundamentally different job from scraping market prices. The events-and-odds scraper answers “what does the market think?”; this one answers “who is good at this market, and what are they doing?” Mechanically:

• It harvests leaderboards, then deep-dives. It queries future.news leaderboard endpoints across selectable time intervals × categories, then optionally hits a per-wallet overview endpoint for each trader.
• It merges and deduplicates across slices. Because it pulls multiple interval×category combinations, the same wallet appears in several lists. The actor merges those, dedups on the wallet, and applies tag classification (smart-money, per-category top lists).
• It’s API-based with backoff. Proxy-enabled requests and retry with exponential backoff handle rate/error responses. No browser.
• It scales to ~1,000 unique profiles per run. A single run can return roughly a thousand deduplicated top-trader profiles — the whole relevant roster, not a slice.

The interesting engineering is the merge: a wallet that’s #3 in 24h politics and #18 in all-time crypto is the same trader, and the actor reconciles those views into one ranked, tagged record.

How the interval × category sweep works

You select which time intervals (e.g. 24h, weekly, all-time) and which categories (politics, sports, crypto, Trump-tagged, all) to sweep. The actor calls the leaderboard endpoint for each combination, collects the wallets, merges them into a single deduplicated set, classifies tags, and — if you enable deep-dives — fetches a 24h overview for each wallet to enrich the profile. The result is one ranked roster you can sort by any performance metric.

▶ Run the Polymarket Top Wallets & PnL Leaderboard Scraper — top traders by PnL, ROI, winrate and volume across politics, crypto, sports and Trump categories, with smart-money tags and optional per-wallet 24h deep-dives.

Schema design for downstream use

When the roster lands in your analytics product, you want it shaped for ranking and per-wallet drill-down. A clean leaderboard row with an embedded deep-dive:

{
  "wallet": "0x7a3f...c21b",
  "pseudonym": "OracleOfElections",
  "profile_image": "https://...",
  "pnl_usd": 482300,
  "roi_pct": 38.4,
  "win_rate": 0.71,
  "avg_bet_usd": 1240,
  "volume_usd": 9820000,
  "tx_count": 7913,
  "last_active": "2026-05-28T09:42:00Z",
  "tags": ["smart_money", "top_politics", "top_crypto"],
  "deep_dive": {
    "account_age_days": 612,
    "portfolio_value_usd": 215400,
    "realized_profit_lifetime_usd": 482300,
    "realized_profit_24h_usd": 6120,
    "buys_24h": 14,
    "sells_24h": 9,
    "cash_in_24h_usd": 22000,
    "cash_out_24h_usd": 8400
  },
  "scraped_at": "2026-05-28T12:00:00Z"
}

A few schema choices worth making early:

• Use the wallet address as the primary key. Pseudonyms and images can change; the on-chain address is the stable identity that survives across runs and merges.
• Keep tags as an array. A single trader can be top-politics and smart-money and top-crypto. Booleans-per-tag lose the multi-membership; an array preserves it.
• Store the interval context if you sweep multiple. ROI over 24h and ROI all-time are different stories; if you flatten across intervals, record which interval a metric came from.
• Make deep_dive optional/nullable. Not every run enables per-wallet lookups, and not every wallet returns a full overview. Allow the field to be absent.

Typical use cases

What customers actually do with leaderboard data:

• Alpha-following / copy-trading dashboards — surface the top wallets and mirror or alert on their positions.
• Election forecasting — track the politics-category top wallets and watch where the proven forecasters are putting money.
• Prediction-market analytics SaaS — power leaderboard pages and public trader profiles for your users.
• Sports betting research — isolate the sports-category top performers and study their betting patterns.
• Crypto-only analysis — slice to the crypto category to find the sharpest crypto-outcome traders.
• Influencer verification — cross-check a public figure’s claimed trading record against the actual leaderboard.
• Trump / tag-specific tracking — monitor a single tag’s top wallets for a focused dashboard.
• AI training data — build labeled top-trader datasets for prediction-market ML.

The common thread is identifying and tracking skill. The value is in the ranked, deduplicated, tagged roster — who’s good, in what category, and what they’re doing now.

Cost math for the managed approach

This actor charges a small per-result fee on top of the per-run start cost. A profile is a “result,” so a run that pulls the full ~1,000-wallet roster with deep-dives enabled costs more than a lightweight top-50 leaderboard refresh. Tune the interval×category sweep and the deep-dive toggle to the granularity you actually need. Because it’s API-based with no browser, compute per run is light and there’s no page-rendering proxy bandwidth.

What you avoid by using a managed actor rather than building your own:

• Sweeping and merging many interval×category leaderboard calls into one deduplicated set
• Implementing tag classification (smart-money, per-category top lists)
• Per-wallet overview enrichment with rate-limit-aware backoff
• Reconciling the same wallet appearing across multiple lists

Common pitfalls

A few things to know before wiring leaderboard data into production, whether you build or buy:

• Past performance is not a position. A high all-time PnL doesn’t mean the wallet is right now. Pair leaderboard rank with current activity (last-active, recent flow) before following.
• Survivorship in the rankings. Leaderboards show the winners; the wallets that blew up aren’t there. Don’t mistake the visible roster for the full distribution of outcomes.
• Interval matters enormously. A 24h leaderboard rewards a lucky streak; all-time rewards consistency. Be explicit about which interval your “top traders” come from.
• Dedup on the address, not the name. Pseudonyms repeat and change. Merging on display name corrupts the roster.
• Deep-dives are heavier. Enabling per-wallet overviews multiplies requests and cost. Only deep-dive the wallets you’ll actually display or analyze.

Wrapping up

Following the smart money is a distinct strategy from reading the market, and it needs a distinct dataset: a merged, deduplicated, tagged leaderboard of proven Polymarket traders, optionally enriched with per-wallet profiles. The hard part is the interval×category sweep and the reconciliation — which is exactly what a managed actor handles, returning up to ~1,000 ranked profiles per run. If your question is about the markets themselves rather than the traders, use the events-and-odds scraper instead.

▶ Open the Polymarket Top Wallets Leaderboard Scraper on Apify — ranked top traders with PnL, ROI, winrate, smart-money tags and optional 24h deep-dives. API-based, ~1,000 profiles per run, pay per result. Start with Apify’s free monthly credit.