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

Original Author: sealaunch intelligence

Original Compilation: Chopper, Foresight News

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

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

Note: All data in this article comes from the Dune data dashboard, covering nearly three months of complete wallet-level behavior; user profiles are cross-defined based on trading frequency levels (T1–T7) and trading volume levels (V1–V7), with monetary statistics in US dollars.

User Trading Frequency and Trading Volume Distribution

Trading frequency exhibits a typical log-normal distribution decay characteristic. The largest user group traded between 2 and 10 times during the entire study period, accounting for 32% of all users. Together with the user group that traded between 11 and 50 times, they almost make up two-thirds of the total user base. These individuals usually engage in trading during elections, sports events, or significant macroeconomic events, betting a small amount of money.

Trading Frequency Distribution Chart

The trading volume distribution, however, is entirely different. Although the transaction frequency sharply declines from the left side, the trading volume histogram presents a bell shape on a logarithmic scale, peaking at around $600 to $3000 per user. This means that a typical active user has a trading amount in the four-digit range, but there are fewer users on the right tail starting from $25,000, who account for the vast majority of the platform’s trading volume.

Trading Volume Distribution Chart

These two histograms collectively reveal a structural split: one part consists of low-frequency participants; the other part consists of high-volume participants, whose footprints are nearly invisible in the user chart, yet they dominate the trading volume chart.

User Proportion & Volume Concentration Matrix 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 categorize users ignores the relational logic between the two. Completing 500 transactions with a total amount of $50 is entirely different from completing 500 transactions with a total amount of $5 million. We classify each wallet based on these two dimensions.

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

• P1 One-time Silent User: Only 1 transaction, total amount less than $100, a one-off experience to test the platform
• P2 Low Active Retail Investor: 2–10 transactions, total volume below $1000, casual participants driven purely by hot events
• P3 Moderate Participant: 11–200 transactions, volume $1000–$10,000, repeated entries but without systematic trading logic
• P4 Highly Active Retail Investor: 201–1000 transactions, volume $10,000–$100,000, actively stable participation but not at the institutional level
• P5 Low-Frequency High Net Worth Investor: Less than 50 transactions, single large amounts over $100,000, selective opportunities, targeted heavy investment
• P6 High-Frequency Professional Player: Over 200 transactions, volume over $100,000, algorithmic strategies and institutional trader group
• P7 High-Frequency Small Player: Over 200 transactions, total amount under $10,000, highly active but with limited capital participants

2% of Users Account for Nearly 90% of Trading Volume

The number of P2 low active retail investors reaches as high as 849,000, accounting for 69% of the overall user base; while the P6 high-frequency high-capital users number only 27,000, representing about 2%.

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

User Profile Summary Table: Seven main user types derived from the intersection of trading frequency and trading volume layers

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

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

Category Preferences of Different User Profiles

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

Transaction Volume Proportion of Different User Profiles and Trading Categories

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

Proportion of Transaction Counts of Different User Profiles and Categories

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

User Distribution of Different Profiles and Categories: User distribution is starkly opposite to trading volume and transaction counts

Political users account for the highest proportion, reaching 19%, but the user count is relatively evenly distributed among various user groups. Low participation users (P2) have the highest proportion among political users, and compared to other categories, these users are often one-time retail investors driven by events, registering accounts to participate in election betting.

The economic and financial fields attract a disproportionately high number of low-frequency high-capital (P5) participants, meaning that these participants trade infrequently but with large single transaction amounts, investing significant capital into macroeconomic outcomes while trading relatively less often.

The categories 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 ends. More specialized market forms, such as binary options or structured outcome markets, may further attract the high-frequency high-capital (P6) user group, who have already dominated the cryptocurrency market. If the goal is trading volume, then build towards the P6 user group. If the goal is user growth and brand influence, then build towards the P2 user group. These two objectives require entirely different category selections.

Implications for the Fee Model

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

A fixed per-transaction fee model will excessively suppress the P6 high-frequency high-capital and P7 high-frequency small groups; yet it is precisely these individuals who support the liquidity foundation upon which the platform relies.

The value of differentiated fee rates by category lies here; Polymarket’s current fee rate system is precisely the implementation of this logic:

• Cryptocurrency segment effective fee rate: 1.80%
• Sports segment: 0.75%
• Political & Financial segments: 1.00%
• Geopolitical segment: Zero fees throughout

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

Core Conclusions

• The P6 high-frequency high-capital group accounts for only 2% of users, generating 88% of the platform’s trading volume;
• Fee policies that harm P6 interests will severely undermine 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;
• The average ordinary user completes only 12 transactions within 90 days, with a median total investment of $224;
• Expanding new categories requires anchoring target user profiles, rather than simply chasing topical popularity.

Conclusion

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

Part of the answer may lie in structural factors. The proliferation of agent frameworks, Telegram bots, and no-code tools enables retail investors to easily engage in automated trading. If retail investors are now beginning to conduct algorithmic trading, the natural next step would be the autonomous large-scale high-frequency operations of AI agents.

This is also why Polymarket may nurture the first killer application at the intersection of cryptocurrency and artificial intelligence. In a market characterized by strong liquidity, driven by events, and with binary outcomes, autonomous agents can operate precisely, absorbing world events, social sentiments, and real-time reasoning information, identifying pricing errors in trading results, and executing trades without human intervention. When this application achieves breakthrough progress, it will no longer be just a cryptocurrency product. This will be the moment when agent trading goes mainstream.