2% of users contribute 90% of trading volume: The true profile of Polymarket

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Original author: sealaunch intelligence

Original compilation: Chopper, Foresight News

Most reports on Polymarket only scratch the surface: trading volume milestones, user growth, transaction counts, open positions, but never delve into who is actually trading behind these numbers. This article classifies all active wallets from the dimensions of trading frequency and trading volume, outlining the true user profile structure of Polymarket.

The vast majority of Polymarket’s trading volume is contributed by a small group of algorithmic traders and high-frequency trading entities; a large number of low-frequency retail investors have almost no interaction with this group of professional traders. Understanding the differences between these two groups directly determines the platform’s fee structure design, product prioritization planning, and market category strategic layout.

Note: All data in this article is sourced from the Dune data dashboard, with an analysis period covering nearly three months of wallet-level full behavior; user profiles are defined by cross-referencing trading frequency levels (T1–T7) and trading amount levels (V1–V7), with the amount expressed in US dollars.

User trading frequency and trading volume distribution

Trading frequency shows a typical log-normal distribution decay characteristic. The largest user group has a trading count between 2 and 10 during the entire study period, accounting for 32% of all users. Along with users trading between 11 and 50 times, they comprise nearly two-thirds of the total user base. These individuals typically engage in trading during elections, sports events, or significant macroeconomic events, wagering small amounts of money.

Trading frequency distribution chart

The trading volume distribution is entirely different. Although the transaction frequency sharply declines from the left, the trading volume histogram presents a bell shape in a logarithmic coordinate system, peaking at around $600 to $3,000 per user. This indicates that the typical active user has a trading amount in the four-digit range, but the number of users on the right tail starting from $25,000 is relatively small, yet they account for the vast majority of the platform’s trading volume.

Trading volume distribution chart

These two histograms together reveal a structural split: one part consists of low-frequency participants; the other part consists of high trading volume participants, whose footprints are almost invisible in the user chart, but their impact on the trading volume chart is dominant.

The matrix of user proportion & volume concentration is more intuitive: user dimension concentrates in the low-frequency, small amount range, while the volume dimension is completely reversed.

How to build a user profile system

Relying solely on frequency or volume to classify users overlooks the correlation logic between the two. Similarly, executing 500 transactions with a total amount of $50 is entirely different from executing 500 transactions with a total of $5 million. We classify each wallet based on these two dimensions.

We first assign each wallet to different trading frequency levels: from T1 (one transaction) to T7 (more than 10,000 transactions). Then we assign it to different trading volume levels: from V1 (total trading amount below $100) to V7 (over $2 million). The intersection of these two dimensions produces seven user profiles, each representing a distinctly different type of participant.

P1 Single Silent User: Only 1 transaction, total amount less than $100, a one-time trial experience on the platform.

P2 Low-Active Retail Investor: 2–10 transactions, total amount below $1,000, casual participants driven purely by hot events.

P3 Moderate Participant: 11–200 transactions, volume $1,000–$10,000, repeatedly entering but lacking a systematic trading logic.

P4 High-Depth Retail Investor: 201–1,000 transactions, volume $10,000–$100,000, actively and stably participating but not at the institutional level.

P5 Low-Frequency High-Net-Worth Individual: Fewer than 50 transactions, single large amounts exceeding $100,000, selectively taking opportunities with targeted heavy investments.

P6 High-Frequency Professional Major: More than 200 transactions, volume exceeding $100,000, algorithm strategies and institutional trader groups.

P7 High-Frequency Small Player: More than 200 transactions, total amount below $10,000, high turnover but limited capital participants.

2% of users account for nearly 90% of trading volume.

The scale of P2 low-active retail investors reaches 849,000, accounting for 69% of all users; P6 high-frequency high-investment users number only 27,000, making up about 2%.

However, within the statistical period, the P6 group generated a total trading volume of $39 billion. This is the most extreme manifestation of the Pareto principle: it is not the conventional 80/20, but rather 2% of users support nearly 90% of the trading volume.

User profile summary table: Seven major user types obtained by layering and cross-referencing trading frequency and trading scale.

The number of users, median transaction counts, and median transaction amounts for each user group: these three data sets show distinctly different user distribution characteristics.

The user growth chart and trading volume growth chart depict almost completely different user groups. Platforms targeting user growth and those targeting trading volume growth have entirely different product decisions.

Different user profile category preferences

Sports and cryptocurrency are the two largest trading segments on Polymarket, accounting for 42% and 31% of total trading volume, respectively, with significant differences in the underlying demographic structures.

Trading volume share of different user profiles and trading categories

The proportion of high-frequency high-capital (P6) traders in the cryptocurrency market is significantly higher than that of the overall user base, which aligns with algorithmic trading models. These participants are not random bettors but utilize systematic strategies for cryptocurrency trading. The high trading volume and frequency indicate that trade execution is automated rather than based on subjective judgment.

Transaction counts share of different user profiles and categories

While sports betting is also dominated by high-frequency, high-capital (P6) trading volume, the proportion of moderate participation (P3) and high participation (P4) participants is higher than in the cryptocurrency category. Sports betting involves both institutional algorithmic funding and a large number of seasoned players making decisions based on subjective judgment rather than machine-driven high-frequency iterations.

User proportion by category of different user profiles: User distribution contrasts sharply with trading volume and transaction counts.

Political users have the highest proportion at 19%, but the number of users is relatively evenly distributed among various user groups. The proportion of low-participation users (P2) among political users is the highest; compared to other categories, these users are typically event-driven one-time retail investors who register accounts to participate in election betting.

The economic and financial fields attract a disproportionately high number of low-frequency high-capital (P5) participants, indicating that these participants trade infrequently but invest large amounts of money, placing significant capital into macroeconomic outcomes while trading relatively few times.

The categories available on the platform directly determine the user groups attracted and influence liquidity depth, user retention, and fee tolerance.

A new cryptocurrency market will attract algorithmic traders and high-frequency traders; a new political market will attract event-driven participants, who may never return after the event concludes. More specialized market forms like binary options or structured outcome markets may further attract high-frequency high-capital (P6) user groups, and these systematic traders have already dominated the cryptocurrency market. If the goal is trading volume, then build for the P6 user group. If the goal is user growth and brand influence, then build for the P2 user group. These two goals necessitate entirely different category choices.

Insights for the fee model

User stratification profiles directly determine the fee design for prediction markets.

A fixed fee per transaction model would excessively suppress P6 high-frequency high-capital and P7 high-frequency small groups; yet it is precisely these individuals who underpin the liquidity foundation that the platform relies on to survive.

The value of differentiated category rates lies in this; Polymarket’s current rate system reflects this logic:

The effective rate in the cryptocurrency segment is the highest: 1.80%

Sports segment: 0.75%

Political & financial segments: 1.00%

Geopolitical segment: zero fees throughout

This standard is not arbitrarily set but is precisely matched to the demographic structure and trading habits of each category. The cryptocurrency segment is filled with P6 algorithmic professional funds, which can bear high fees without disrupting liquidity; the political segment primarily consists of low-barrier retail investors who must lower friction costs to maintain retention. Designing rates without considering user profiles is essentially blind trial and error.

Core conclusions

The P6 high-frequency high-capital group comprises only 2% of users, generating 88% of the platform’s trading volume;

Fee policies that harm P6 interests will severely impact the platform’s foundation;

69% of users are low-frequency small retail investors, driven purely by hot events;

Cryptocurrency trading is highly concentrated among algorithmic high-frequency funds, while the sports segment has a more diverse participant structure;

Ordinary users complete an average of only 12 transactions within 90 days, with a median total investment of $224;

Expanding into new categories requires anchoring target user profiles rather than merely chasing topic popularity.

Conclusion

If trading volume is concentrated in a small high-frequency core area, why does Polymarket position itself as a retail product? Professional algorithmic funds underpin the vast majority of transaction volume, yet product experience, marketing strategies, and category layouts continue to cater to ordinary retail investors.

Some answers may lie in structural factors. The proliferation of agent frameworks, Telegram bots, and no-code tools allows retail investors to easily engage in automated trading. If retail investors have begun to engage in algorithmic trading, the next natural evolution would be autonomous large-scale high-frequency operations by AI agents.

This is also why Polymarket may give rise to the first killer application at the intersection of cryptocurrency and artificial intelligence. In a market characterized by strong liquidity, event-driven dynamics, and binary outcomes, autonomous agents can operate precisely, absorbing global events, social sentiment, and real-time reasoning information to identify mispricing in trading outcomes and execute trades without human intervention. When this application reaches a breakthrough, it will no longer be just a cryptocurrency product. It will mark the moment when agency trading enters the mainstream market.