Partner research
Polymarket: A User-Base Portrait
Pantera Research Lab × Surf — who trades on the international exchange, how they arrive, how they behave, and how consistently they perform.
The two Polymarkets
Polymarket runs two venues with different plumbing. The international exchange settles on Polygon and emits the fills every on-chain figure here is built from. Polymarket US settles off-chain through FCMs and emits none of them, so the two are measured separately and never summed. This part sizes both venues, then takes the international book apart: what trades, who carries the flow, and how participation is distributed.
Monthly volume — international vs US
One-sided monthly volume by venue
Monthly traded notional on each venue, on the one-sided deduplicated convention. US figures come from the verified FCM archive, which is missing a minority of days in the window; affected months sum the days that exist and are disclosed in the handoff rather than padded with zeros.
Daily venue metrics — Polymarket US
Reported fills, taker volume and open interest per day on Polymarket US
Daily series from every official FCM archive file in the window. Dates with cross-report reconciliation warnings remain visible at file grain and are not promoted into mapped market analytics. The archive does not publish participant identity, so wallet counts are omitted rather than shown as zero.
Fee revenue and take rate by category
Reconstructed fees and effective take rate per category
Bars are reconstructed fee revenue per market category; the line is the effective take rate that revenue implies against the same category’s volume.
Volume by market category
One-sided volume per category, with each category’s largest market
Traded notional by category on the international venue. Each row names the largest market in that category and shows its volume and share of category volume.
Active wallets by side of the book
Any-side union, ever taker, and ever maker
Any side is the unique-wallet union. A wallet active as both maker and taker appears once in that union and in both side-specific bars, so the latter must not be summed.
Volume concentration by wallet rank
Share of volume by wallet rank tier — takers (demand) vs makers (supply)
Two 100%-stacked volume bars show how flow is distributed across wallet-rank tiers on the demand and supply sides of the book.
Engagement funnel
Share of wallets vs share of taker volume per activity tier
Wallets bucketed by how much they traded, showing the gap between how many wallets sit in a tier and how much of the volume that tier carries.
Venue split — binary vs multi-outcome
Standalone binaries (CTF) vs mutually-exclusive multi-outcome (Neg Risk)
One bar, split by exchange contract: how the venue’s traded notional divides between standalone binaries and mutually-exclusive multi-outcome markets, with each contract’s fill count alongside.
User growth on the international venue
Who arrives, what brings them in, and how much of each month’s cohort is still there the months after.
User growth accounting
Monthly active wallets by cohort — new, resurrected, retained; churn below the axis
Each month’s active wallets split by where they came from, with the wallets lost since the previous month drawn below the axis. New versus resurrected is judged against full lifetime history; the first displayed month is published only when the immediately preceding month is available for retained and churned counts.
New users by entry category
New wallets by the category of their first market, per cohort month
Each month’s newly arrived wallets, stacked by the category of the first market they ever filled. The mix of what brings people in is the point; the height is the intake.
Top onboarding markets
Markets ranked by count of new wallets whose first-ever fill was there
The individual markets that served as the front door: each row counts wallets whose first fill on the venue happened in that market.
Onboarding dispersion by month
How much of each month’s new-wallet intake the top-15 (and #1) onboarding markets capture
Concentration of the front door: the share of a month’s intake captured by its fifteen biggest onboarding markets, and by the single biggest alone.
Monthly retention — platform vs category
Month-over-month retention — any-category vs same-category
Of the wallets active in a month, the share still active the following month: once for the platform as a whole, and once per category counting only wallets that returned to that same category.
Retention by entry category
New-wallet cohorts still active 1 and 3 months later, by entry category
Of the wallets that entered through each category, the share still active one and three months on. Cohorts are pooled across the months for which each horizon is observable.
Cross-category user overlap
Shared wallets between category pairs, as a share of the smaller category
Each cell is the wallets active in both categories divided by the smaller of the two, so it reads as a cross-sell rate rather than a size comparison. The diagonal carries each category’s own active-wallet count.
The three tribes
Wallets are labelled by behaviour, not identity — bot-like first, then sophisticated, with retail as the remainder. The labels describe how an address trades. They are not claims about who owns it.
Specialization by category breadth
Wallets by number of categories traded (of 7) · share of wallets vs volume
Wallets grouped by how many of the seven categories they touched, with each group’s share of the population set against its share of taker volume.
Fills per wallet-market relationship
How many times a wallet trades the same market, by category
Each row splits a category’s wallet-market relationships by how many fills the wallet made in that market — one-and-done on the left, repeat engagement to the right.
How wallets are classified
Behavioral signals over each wallet’s H1 fills (both sides, deduplicated) — evaluated in order: bot first, then pro; retail is the remainder
Probable Bot
- more than 100 fills per active day, or
- more than 500 fills with robotically uniform sizing (CV < 0.05), or
- more than 1,000 distinct markets
Informed/Pro
- avg fill ≥ $500 with ≥ 5 fills across ≤ 50 markets, or
- avg fill ≥ $200 with ≥ 10 fills across ≤ 100 markets, or
- > $50K volume concentrated in ≤ 30 markets
Retail
- everyone else — the default cohort
The rules are deliberately coarse and behavioral. They describe how an address trades — not who owns it.
Trader cohorts — wallets vs volume
Share of wallets vs share of taker volume per behavioural cohort
The population split set against the volume split. The distance between the two bars is the whole point of the classification.
Cohort volume share by category
Taker-volume share of each cohort within each category
Who carries each category’s flow: the share of taker volume attributable to each behavioural cohort, per category.
Cohort wallet share by category
Wallet share of each cohort within each category
The population mix behind the flow: the share of active wallets in each behavioural cohort, per category.
Trade size distribution by cohort
Taker-fill size distribution per behavioral cohort
How each cohort sizes its fills, from sub-dollar dust to five-figure clips. Shares are of the cohort’s own fill count.
Who makes money
When each behavioural cohort enters a market’s life, how often it wins, how realized profit and loss is distributed, and whether profitable months persist.
Entry timing within markets
Distribution of each cohort’s volume-weighted mean entry position · boxes p25–p75, whiskers p10–p90 · markets ≥ $100K & ≥ 50 takers
Each market contributes one volume-weighted mean position per cohort over its complete observed trading life, where 0% is the first eligible fill and 100% the last. Boxes summarize that market-level distribution, and the printed n is the number of markets behind each box. Markets are included only once resolved. The population rolls over the six most recent complete months, and segment labels are frozen to wallet behavior in that same window.
Top market per category — daily cohort volume
Each category’s biggest market — daily taker volume by cohort
The single largest market in the selected category, day by day, split by who was trading it. Days with no observed fills for a cohort are gaps, not zeros.
Realized PnL tiers — gross of fees
Wallets by realized H1 PnL tier (markets resolved in H1) · net $ per tier
Each tier’s aggregate dollar outcome around a zero line, with the tier’s wallet count and median win rate alongside. Winning tiers point right, losing tiers left.
Profitability and win rate by cohort
Wallet profitability, median wallet win rate, and pooled position win rate
The profitable share and median win rate are wallet-level. Pooled win rate is position-level, dividing all winning resolved positions by the displayed position count. Wallets without trades in the window are excluded from this three-cohort view.
Net realized PnL by cohort
Who pays whom: each cohort’s aggregate H1 PnL on markets resolved in H1
Aggregate realized profit and loss, gross of fees, on markets resolved inside the half. The three displayed trading cohorts exclude wallets with no H1 trades; that excluded group carries the balancing residual that brings the complete resolved-market ledger to zero.
Profit persistence
Wallets by number of net-positive months
How many wallets were net positive in how many distinct months, on a cash-flow basis. The amber overlay is the subset for which one month generated more than 90% of positive-month profit. The value axis is logarithmic.