2% of Users Contribute 90% of Trading Volume: The True Portrait of Polymarket

marsbitPublicado em 2026-03-27Última atualização em 2026-03-27

Resumo

A deep analysis of Polymarket's user base reveals a stark concentration of trading activity: just 2% of users, classified as high-frequency, high-volume professional traders (P6), generate nearly 90% of the platform's total trading volume. This contrasts with the 69% of users who are low-activity, small-scale participants (P2), driven primarily by major events like elections or sports. The study, analyzing three months of on-chain data, segments users into seven profiles based on transaction frequency (T1-T7) and volume (V1-V7). While the user base is dominated by infrequent, small-scale bettors, the trading volume is overwhelmingly controlled by a small cohort of algorithmic and systematic traders. This structural split directly influences market dynamics. Cryptocurrency markets are dominated by P6 traders, indicating heavy algorithmic activity. Sports markets attract a more diverse mix, including seasoned human bettors. Political markets have the highest share of users but are filled with event-driven, one-time participants. The findings have critical implications for fee structures. Polymarket's current tiered fees—highest for crypto (1.80%), lower for sports (0.75%) and politics (1.00%), and zero for geopolitics—are strategically aligned with the tolerance of each user profile. A flat fee could severely damage liquidity by discouraging the vital P6 segment. The report concludes that platform growth strategies must be tailored to target specific user profiles: P6 for v...

Original Author: sealaunch intelligence

Original Compilation: Chopper, Foresight News

Most reports on Polymarket only scratch the surface with data: trading volume milestones, user growth, number of transactions, open interest, but never delve into who is actually trading behind these numbers. This article categorizes all active wallets based on transaction 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 and high-frequency traders; the massive number of low-frequency retail users have almost no intersection with these professional traders. Recognizing the differences between these two groups directly impacts platform fee design, product priority planning, and market category strategy.

Note: All data in this article is sourced from the Dune analytics dashboard, covering a three-month period of wallet-level behavior; user profiles are defined by cross-referencing transaction frequency tiers (T1–T7) and transaction amount tiers (V1–V7), with amounts denominated in USD.

User Transaction Frequency and Volume Distribution

Transaction frequency shows a typical log-normal distribution decay pattern. The largest user group made between 2 and 10 transactions during the study period, accounting for 32% of all users. Combined with users who made 11 to 50 transactions, they make up almost two-thirds of the total user base. These users typically participate during elections, sports events, or major macroeconomic events, placing small bets.

Transaction Frequency Distribution Chart

The trading volume distribution is entirely different. While transaction frequency drops sharply on the left, the volume histogram is bell-shaped on a logarithmic scale, peaking at around $600 to $3,000 per user. This means the typical active user trades in the four-figure range, but the right tail, starting from $25,000, consists of fewer users who dominate 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-volume participants, who are almost invisible on the user chart but dominate the trading volume chart.

User Proportion & Volume Concentration Matrix: User dimension is concentrated in the low-frequency, small-amount range, while the volume dimension is completely reversed.

How the User Profile System is Built

Relying solely on frequency or volume to classify users ignores the logical connection between the two. Making 500 transactions totaling $50 is completely different from making 500 transactions totaling $5 million. We classify each wallet by combining these two dimensions.

We first assign each wallet to a transaction frequency tier: from T1 (single transaction) to T7 (over 10,000 transactions). Then, we assign it to a transaction volume tier: from V1 (total volume under $100) to V7 (over $2 million). The intersection of these two dimensions produces seven user profiles, each representing a distinct participant type.

  • P1 Single Inactive User: Only 1 transaction, total under $100, a one-time trial of the platform.
  • P2 Low-Activity Retail User: 2–10 transactions, total volume under $1,000, casual participants driven purely by hot events.
  • P3 Moderate Participant: 11–200 transactions, volume $1,000–$10,000, repeatedly enter the market but lack systematic trading logic.
  • P4 High-Engagement Retail User: 201–1,000 transactions, volume $10k–$100k, actively and steadily participate but not at an institutional level.
  • P5 Low-Frequency High-Net-Worth User: Fewer than 50 transactions, single large bets over $100k, selective opportunities, targeted heavy positions.
  • P6 High-Frequency Professional Core: Over 200 transactions, volume exceeding $100k, algorithmic strategies and institutional trader groups.
  • P7 High-Frequency Small-Volume Player: Over 200 transactions, total volume under $10k, high frequency but capital-limited participants.

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

P2 low-activity retail users number 849,000, accounting for 69% of total users; P6 high-frequency, high-investment users number only 27,000, about 2%.

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

User Profile Summary Table: Seven user types derived from cross-referencing transaction frequency and volume tiers.

Number of users, median transaction count, and median transaction volume for each user group: The three datasets show distinctly different user distribution characteristics.

The user growth chart and the trading volume growth chart describe almost entirely different user groups. Product decisions for a platform targeting user growth versus one targeting trading volume growth are completely different.

Category Preferences of Different User Profiles

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

Percentage of trading volume by user profile and transaction category

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

Percentage of transaction count by user profile and category

Although sports betting is also dominated by high-frequency, high-capital (P6) trading volume, the proportion of moderate (P3) and high (P4) engagement participants is higher than in the cryptocurrency category. Sports betting has both institutional algorithmic funds and a large number of experienced manual研判 players who place orders based on subjective judgment rather than machine high-frequency iteration.

User percentage by user profile and category: User distribution is completely opposite to trading volume and transaction count.

Politics has the highest user percentage at 19%, but user numbers are relatively evenly distributed across user groups. Low-engagement users (P2) have the highest proportion among political users; compared to other categories, these users are typically event-driven, one-time retail users who register accounts to bet on elections.

The economy and finance categories attract a disproportionate number of low-frequency, high-capital (P5) participants, meaning they trade infrequently but with large single bets, deploying significant capital into macroeconomic outcomes with relatively few transactions.

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

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

Implications for the Fee Model

User stratification directly determines the fee design for prediction markets.

A fixed per-transaction fee model would excessively suppress the P6 (high-frequency, high-capital) and P7 (high-frequency, small-volume) groups; yet these are precisely the people who support the liquidity foundation crucial for the platform's survival.

The value of category-differentiated fees lies here. Polymarket's current fee system implements this logic:

  • Crypto category has the highest effective fee: 1.80%
  • Sports category: 0.75%
  • Politics & Finance category: 1.00%
  • Geopolitics category: Zero fees throughout

These standards are not arbitrarily set but precisely match the user structure and trading habits of each category. The crypto track is filled with P6 algorithmic professional capital, which can tolerate high fees without disrupting liquidity; the political track is dominated by low-threshold retail users, requiring lower friction costs to maintain retention. Fee design detached from user profiles is essentially blind trial and error.

Key Conclusions

  • The P6 high-frequency, high-capital group constitutes only 2% of users but creates 88% of the platform's trading volume;
  • Fee policies that harm P6 interests will severely damage the platform's foundation;
  • 69% of users are low-frequency, small-volume retail users, driven purely by hot events;
  • Cryptocurrency trading is highly concentrated among algorithmic high-frequency capital, while the sports track has a more diverse participant structure;
  • The average retail user makes only 12 transactions in 90 days, with a median total investment of $224;
  • Expanding into new categories must target specific user profiles, not simply chase topic热度 (hotness).

Conclusion

If trading volume is concentrated in a small, high-frequency core, why does Polymarket position itself as a retail product? Professional algorithmic funds support the vast majority of the flow, but the product experience, marketing approach, and category layout always cater to ordinary retail users.

Part of the answer may lie in structural factors. The proliferation of agent frameworks, Telegram bots, and no-code tools allows retail users to easily start automated trading. If retail users are now beginning algorithmic trading, the next natural evolution is AI agents operating autonomously at a large scale and high frequency.

This is also why Polymarket might孕育 (give birth to) the first killer application at the intersection of cryptocurrency and artificial intelligence. In a market with strong liquidity, event-driven, binary outcomes, autonomous agents can operate precisely, absorbing world events, social sentiment, and real-time reasoning information, identifying mispriced trading outcomes, and executing trades without human intervention. When this application achieves a breakthrough, it will be more than just a cryptocurrency product. It will be the moment agent trading goes mainstream.

Perguntas relacionadas

QWhat percentage of users contribute nearly 90% of the trading volume on Polymarket?

A2% of users contribute nearly 90% of the trading volume.

QWhich two categories are the largest trading volume sectors on Polymarket?

ASports and Cryptocurrency are the two largest sectors, accounting for 42% and 31% of total trading volume, respectively.

QHow is the user base categorized in the article's analysis?

AUsers are categorized into seven profiles (P1 to P7) based on a cross-analysis of their transaction frequency (T1-T7) and transaction volume (V1-V7).

QWhat is the key implication of the user profile analysis for Polymarket's fee structure?

AThe fee structure must be carefully designed to avoid harming the P6 (high-frequency, high-capital) user group, as they are responsible for the vast majority of the platform's trading volume and liquidity.

QWhat potential future application does the article suggest Polymarket could pioneer at the intersection of cryptocurrency and AI?

AThe article suggests Polymarket could pioneer a killer application' where autonomous AI agents automatically identify mispriced outcomes and execute trades based on world events and real-time data, bringing agent-based trading to the mass market.

Leituras Relacionadas

Three Scenarios for BTC's Future Direction and a Duel Between Two Strong Forces | Special Invited Analysis

**Title: Three Scenarios for BTC's Future Trajectory and a Key Duel | Invited Analysis** The market remains at a critical juncture. Over the past week, Bitcoin (BTC) consolidated broadly between $79,500 and $80,600, validating previous technical analysis. The current focus is on whether this marks the start of a new uptrend or a pause within a larger correction. **BTC Multi-Cycle Analysis & Three Possible Scenarios** BTC's daily chart structure, following its peak at $126,200 in October 2025, presents three primary technical scenarios based on Elliott Wave theory: 1. **Bullish Scenario (End of Correction):** The corrective A-B-C wave from $126,200 ended at the $60,000 low in February 2026. The current price action is the start of a major Wave I uptrend. A subsequent Wave II pullback would not break below $60,000. 2. **Bearish Scenario 1 (Complex Correction):** The correction is unfolding as an A-B-C-D-E pattern. The current move from $60,000 is a D-wave rally. After its completion, a final E-wave decline could potentially breach the $60,000 level. 3. **Bearish Scenario 2 (Larger Correction):** The entire move down from $126,200 to $60,000 was a large A-wave. The current rally is a B-wave correction within a larger A-B-C structure, to be followed by a C-wave decline below $60,000. *Analysis suggests Scenario 2 is less probable due to time disproportions between waves. The battle is effectively between the Bullish Scenario (1) and Bearish Scenario (3).* **Key BTC Levels & Weekly Strategy** On the 4-hour chart, BTC trades above a crucial consolidation zone ("Central Pivot C"). * **Key Resistance:** $83,500-$84,500; $89,000-$90,500. * **Key Support:** $78,500-$79,500 (pivot upper bound); $73,500-$75,000; $69,500-$70,500. **Weekly Outlook:** The market direction hinges on BTC's ability to hold above or break below the $78,500-$79,500 support zone. * **Mid-term Strategy:** Neutral/Wait-and-see stance due to unclear direction. * **Short-term Tactics:** Two contingency plans using 30% max capital: * **Plan A (Bullish):** Look for long entries if price holds above $78,500-$79,500 with confirming signals. Initial stop-loss below $78,500. * **Plan B (Bearish):** Consider short positions if price breaks below $73,500-$75,000 with confirming signals. Initial stop-loss above $76,500. **HYPE Analysis & Strategy** HYPE's daily chart shows a seven-segment structure from its January low of $20.46, forming a "rising pivot" zone. * **Key Level to Watch:** $45.76 (previous high). A break above would confirm the bullish structure remains intact. * **Short-term Strategy:** Focus on pivot zone boundaries ($38.41 upper, $34.44 lower). * **Long:** Consider on support near $38.41 with bullish confirmation signals. * **Short:** Consider on a break below $34.44 with bearish confirmation signals. * Position size must be below 30% with strict stop-loss discipline. **Risk Management Reminder:** Always set an initial stop-loss upon entry. Move stop-loss to breakeven at +1% profit, then trail it upwards to lock in profits dynamically. All views are based on technical analysis for informational purposes only and do not constitute investment advice. The market is inherently risky.

Odaily星球日报Há 3m

Three Scenarios for BTC's Future Direction and a Duel Between Two Strong Forces | Special Invited Analysis

Odaily星球日报Há 3m

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up Entirely New Scientific Branches

Demis Hassabis, co-founder and CEO of Google DeepMind and Nobel laureate, discusses the path to AGI and its profound implications in a Sequoia Capital interview. He outlines his lifelong dedication to AI, tracing his journey from game development (e.g., *Theme Park*)—a perfect AI testing ground—to neuroscience and finally founding DeepMind in 2009. He emphasizes the critical lesson of being "5 years, not 50 years, ahead of time" for successful entrepreneurship. Hassabis reiterates DeepMind's two-step mission: first, solve intelligence by building AGI; second, use AGI to tackle other complex problems. He highlights the transformative potential of "AI for Science," particularly in biology where tools like AlphaFold have revolutionized protein folding. He envisions AI-powered simulations drastically shortening drug discovery from years to weeks and enabling personalized medicine. Furthermore, he predicts AI will spawn new scientific disciplines, such as an engineering science for understanding complex AI systems (mechanistic interpretability) and novel fields enabled by high-fidelity simulators for complex systems like economics. He posits a fundamental worldview where information, not just matter or energy, is the essence of the universe, making AI's information-processing core uniquely suited to understanding reality. He defends classical Turing machines as potentially sufficient for modeling complex phenomena, including quantum systems, as demonstrated by AlphaFold. On consciousness, Hassabis suggests first building AGI as a powerful tool, then using it to explore deep philosophical questions. He believes components like self-awareness and temporal continuity are necessary for consciousness but that defining it fully remains an open challenge. He predicts AGI could arrive around 2030 and, once achieved, would be used to probe the deepest questions of science and reality, much as envisioned in David Deutsch's *The Fabric of Reality*.

链捕手Há 21m

Sequoia Interview with Hassabis: Information is the Essence of the Universe, AI Will Open Up Entirely New Scientific Branches

链捕手Há 21m

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy Chinese Chips; Avoid Traditional Segments. The core theme is the shift in AI compute supply from NVIDIA dominance to a three-track system of GPU + ASIC + China-local chips. The key opportunity is capturing share in this expansion, while non-AI semiconductors face marginalization due to resource reallocation to AI. Key investment conclusions, in order of priority: 1. **Advanced Packaging (CoWoS/SoIC) - Highest Conviction**: TSMC is the primary beneficiary of explosive demand, driven by massive cloud capex. Its pricing power and AI revenue share are rising significantly. 2. **Test Equipment - Undervalued & High-Growth Certainty**: Chip complexity is causing test times to double generationally, structurally driving handler/socket/probe card demand. Companies like Hon Hai Precision (Foxconn), WinWay, and MPI offer compelling value. 3. **China AI Chips (GPU/ASIC) - Long-Term Irreversible Trend**: Export controls are accelerating domestic substitution. Companies like Cambricon, with firm customer orders and SMIC's 7nm capacity support, are positioned to benefit from lower TCO (30-60% vs NVIDIA) and growing local cloud demand. 4. **Avoid Non-AI Semiconductors (Consumer/Auto/Industrial)**: These segments face a weak, structurally hindered recovery due to AI's resource "crowding-out" effect on capacity and supply chains. 5. **Memory - Severe Internal Divergence**: Strongly favor HBM (Hynix primary beneficiary) and NOR Flash (Macronix). Be cautious on interpreting price rises in DDR4/NAND as true demand recovery. The report emphasizes a 2026-2027 time window, stating the AI capital expenditure cycle is far from over. Key macro variables include persistent export controls and AI's systemic "crowding-out" effect on traditional semiconductor supply chains.

marsbitHá 1h

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

marsbitHá 1h

Circle：Sluggish Market? The Top Stablecoin Stock Continues to Expand

Circle, the issuer of the stablecoin USDC, reported its Q1 2026 earnings on May 11th, Eastern Time. Against a backdrop of weak crypto market sentiment, USDC's average circulation in Q1 was $752 billion, with a modest 2% sequential increase to $770 billion by quarter-end. New minting volumes declined due to the poor crypto market, but remained high, indicating demand expansion beyond crypto trading. USDC's market share remained stable at 28% of the total stablecoin market, while competition from Tether's USDT persists. A key highlight was "Other Revenue," which reached $42 million, more than doubling year-over-year, though sequential growth slowed to 13%. This revenue stream, including fees from services like Web3 software, the Cipher payment network (CPN), and the Arc blockchain, is critical for diversifying away from interest income. Circle's internally held USDC share increased to 18%, helping to improve gross margin by 130 basis points to 41.4% by reducing external sharing costs. However, profitability was pressured as total revenue growth slowed, primarily due to the significant weight of interest income, which is tied to USDC规模 and Treasury rates. Adjusted EBITDA was $133 million with a 19.2% margin. Management maintained its full-year 2026 guidance for adjusted operating expenses ($570-$585 million) and other revenue ($150-$170 million). The long-term target for USDC's CAGR remains 40%, though near-term volatility is expected. The article concludes that while Circle's current valuation of $28 billion appears reasonable after a recent recovery, further upside depends on the pace of stable币 adoption and potential positive sentiment from the advancement of regulatory clarity acts like CLARITY.

链捕手Há 1h

Circle：Sluggish Market? The Top Stablecoin Stock Continues to Expand

链捕手Há 1h

Tech Stocks' Narrative Is Increasingly Relying on Anthropic

The narrative of tech stocks is increasingly relying on Anthropic. Anthropic, the AI company behind Claude, has become central to the financial stories of major tech giants. Elon Musk dissolved xAI, merging it into SpaceX as SpaceXAI, and secured an exclusive deal to rent the massive "Colossus 1" supercomputing cluster to Anthropic. In return, Anthropic expressed interest in future space-based compute collaborations. Google and Amazon are also deeply invested. Google plans to invest up to $40 billion and provide significant compute power, while Amazon holds a 15-16% stake. Both companies reported massive quarterly profit surges largely due to valuation gains from their Anthropic holdings. Crucially, Anthropic has committed to multi-billion dollar cloud compute contracts with both Google Cloud and AWS. This creates a clear divide: the "A Camp" (Anthropic-Google-Musk) versus the "O Camp" (OpenAI-Microsoft). The A Camp's strategy intertwines equity, compute orders, and profits, making Anthropic a "systemic financial node." Its performance directly impacts its partners' financials and stock prices. In contrast, OpenAI, while leading in user traffic, faces commercialization challenges, lower per-user revenue, and a recently restructured relationship with Microsoft. The AI industry is shifting from a race for raw compute (symbolized by Nvidia) to a focus on monetizable applications, where Anthropic currently excels. However, this concentration of market hope on one company amplifies systemic risk. The rise of powerful open-source models like DeepSeek-V4 poses a significant threat, as they could undermine the value proposition of closed-source models like Claude. The article suggests ongoing geopolitical efforts to suppress such competitors will be a long-term strategic focus for Anthropic's allies.

marsbitHá 1h

Tech Stocks' Narrative Is Increasingly Relying on Anthropic

marsbitHá 1h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow