Author: Jacob Zhao @IOSG
In the previous Crypto AI series research reports, we have continuously emphasized the viewpoint that the most practically valuable scenarios in the current cryptocurrency field primarily focus on stablecoin payments and DeFi, while Agents are the key interfaces for the AI industry to reach users. Therefore, amidst the trend of the fusion between Crypto and AI, the two most valuable paths are: AgentFi based on existing mature DeFi protocols (basic strategies such as lending and liquidity mining, as well as advanced strategies like Swap, Pendle PT, and funding rate arbitrage) in the short term, and Agent Payment revolving around stablecoin settlement and relying on protocols such as ACP/AP2/x402/ERC-8004 in the medium to long term.
The prediction market has become an industry trend that cannot be ignored by 2025, with its annual total trading volume surging from about $9 billion in 2024 to over $40 billion in 2025, achieving over 400% year-on-year growth. This significant growth is driven by multiple factors: uncertain demands due to macro-political events, the maturation of infrastructure and trading models, and a thawing regulatory environment (Kalshi's legal victory and Polymarket's return to the U.S.). The Prediction Market Agent is expected to present early prototypes by early 2026 and may become an emerging product form in the agent field within the next year.
1. Prediction Markets: From Betting Tools to the "Global Truth Layer"
Prediction markets are a financial mechanism that trades around the outcomes of future events, with contract prices essentially reflecting the market's collective judgment of the probability of an event occurring. Their effectiveness comes from the combination of collective wisdom and economic incentives: in an environment where anonymous, real money is wagered, dispersed information is rapidly integrated into price signals weighted by funding willingness, thereby significantly reducing noise and false judgments.
▲ Prediction Market Nominal Trading Volume Trend Data Source: Dune Analytics (Query ID: 5753743)
By the end of 2025, prediction markets are expected to have formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 is estimated to reach about $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion. Data from February 2026 shows that Kalshi's trading volume ($25.9B) has surpassed Polymarket's ($18.3B), nearing a 50% market share. Kalshi has rapidly expanded due to its previous legal victory in the election contract case, its compliance first-mover advantage in the U.S. sports prediction market, and relatively clear regulatory expectations. Currently, the development paths of the two have shown clear differentiation:
Polymarket adopts a hybrid CLOB structure with "off-chain matching and on-chain settlement" and a decentralized settlement mechanism, constructing a global, non-custodial high-liquidity market, forming an "onshore + offshore" dual-track operational structure after returning to compliance in the U.S.;
Kalshi integrates into the traditional financial system by connecting with mainstream retail brokers through APIs, attracting Wall Street market makers to participate deeply in macro and data-driven contract trading. However, its products are limited by traditional regulatory processes, with long-tail demand and sudden events relatively lagging.
Apart from Polymarket and Kalshi, other competitive players in the prediction market mainly develop along two paths:
The first path is the compliant distribution route, embedding event contracts into existing accounts and clearing systems of brokers or large platforms, leveraging channel coverage, compliance qualifications, and institutional trust to establish advantages (e.g., Interactive Brokers × ForecastEx's ForecastTrader, FanDuel × CME Group's FanDuel Predicts). While compliance and resource advantages are significant, the products and user scale are still in the early stages.
The second path is the crypto-native on-chain route, represented by Opinion.trade, Limitless, Myriad, leveraging token mining, short-cycle contracts, and media distribution to achieve rapid scaling. It emphasizes performance and capital efficiency, but its long-term sustainability and risk control robustness still need to be validated.
The two types of paths—traditional financial compliance entry and crypto-native performance advantages—together form a diverse competitive landscape for the prediction market ecosystem.
On the surface, prediction markets resemble gambling, being a zero-sum game; however, the core difference lies in whether or not they exhibit positive externality: through real-money trading, dispersed information is aggregated to publicly price real events, forming a valuable signal layer. The trend is shifting from gaming to the "global truth layer"—as institutions like CME and Bloomberg connect, event probabilities have become decision metadata that can be directly invoked by financial and corporate systems, providing a more timely, quantifiable market-driven truth.
Considering the current global regulatory landscape, the compliant path for prediction markets is highly fragmented. The U.S. is the only major economy that clearly incorporates prediction markets into its financial derivatives regulatory framework, while Europe, the UK, Australia, and Singapore generally regard them as gambling and tend to tighten regulations. Countries like China and India have completely banned them, meaning the future global expansion of prediction markets still relies on regulatory frameworks across different nations.
2. Architectural Design of Prediction Market Agents
Currently, prediction market agents are entering an early practical stage. Their value lies not in "AI predictions being more accurate," but in amplifying information processing and execution efficiency within prediction markets. Prediction markets fundamentally serve as information aggregation mechanisms, with prices reflecting collective judgments on event probabilities; market inefficiencies in reality result from information asymmetry, liquidity, and attention constraints. The reasonable positioning of prediction market agents is executable probabilistic portfolio management: converting news, rule texts, and on-chain data into verifiable pricing discrepancies to execute strategies more quickly, disciplined, and inexpensively, capturing structural opportunities through cross-platform arbitrage and portfolio risk control.
An ideal prediction market agent can be abstracted into a four-layer architecture:
The information layer gathers news, social, on-chain, and official data;
The analysis layer identifies mispricing and calculates Edge using LLM and ML;
The strategy layer converts Edge into positions through the Kelly Criterion, batch positioning, and risk control;
The execution layer completes multi-market ordering, slippage, and Gas optimization, executing arbitrage to form an efficient automated loop.
3. Strategy Framework for Prediction Market Agents
Unlike traditional trading environments, prediction markets exhibit significant differences in settlement mechanisms, liquidity, and information distribution, and not all markets and strategies are suitable for automated execution. The core of a prediction market agent is whether it is deployed in scenarios with clear rules, can be coded, and align with its structural advantages. The following will analyze from the perspectives of target selection, position management, and strategy structure.
Selection of Targets in Prediction Markets
Not all prediction markets have trading value; their participation value depends on: clarity of settlement (whether the rules are clear, whether data sources are unique), quality of liquidity (market depth, spread, and trading volume), insider risks (degree of information asymmetry), time structure (expiration time and event rhythm), and the trader's own informational advantages and professional background. Only when most dimensions meet basic requirements can prediction markets provide a foundation for participation. Participants should match their own advantages with market characteristics:
Core human advantage: relies on professional knowledge, judgment, and integration of vague information, with relatively ample time windows (measured in days/weeks) for the market. Typical examples include political elections, macro trends, and corporate milestones.
Core AI Agent advantage: relies on data processing, pattern recognition, and rapid execution, with extremely short decision windows (measured in seconds/minutes) for the market. Typical examples include high-frequency cryptocurrency prices, cross-market arbitrage, and automated market making.
Unfitting domains: markets dominated by insider information or purely random/highly manipulated environments do not constitute an advantage for any participants.
Position Management in Prediction Markets
The Kelly Criterion is the most representative capital management theory in repeated gaming scenarios, aiming not to maximize single gains but to maximize the long-term compound growth rate of capital. This method estimates the optimal position ratio based on the win rate and odds while enhancing capital growth efficiency under the premise of having positive expectations, widely used in quantitative investing, professional gambling, poker, and asset management.
The classic form is: f* = (bp - q) / b
where f* is the optimal betting fraction, b is the net odds, p is the win rate, and q=1-p
In prediction markets, it can be simplified to: f* = (p - market_price) / (1 - market_price)
where p is the subjective true probability and market_price is the market implied probability
The theoretical effectiveness of the Kelly Criterion highly relies on accurate estimation of true probabilities and odds; in reality, traders struggle to consistently grasp true probabilities accurately. Thus, professional gamblers and prediction market participants are more inclined to adopt more executable strategies that rely less on probability estimates:
Unit System: Splitting capital into fixed units (e.g., 1%), investing different unit amounts based on confidence levels, with an upper limit on the unit automatically constraining single risks. This is the most common practical method.
Flat Betting: Using a fixed proportion of capital for each bet, emphasizing discipline and stability, suitable for risk-averse or low-confidence environments.
Confidence Tiers: Pre-setting discrete position tiers and setting absolute limits to reduce decision complexity, avoiding the pseudo-accuracy issues of the Kelly model.
Inverted Risk Approach: Starting from the maximum acceptable loss and reverse-calculating position size from risk constraints rather than expected returns, forming stable risk boundaries.
For prediction market agents, strategy design should prioritize executability and stability rather than pursuing theoretical optimality. The key lies in having clear rules, simple parameters, and tolerance for judgment errors. Under this constraint, the tiered confidence method combined with fixed position caps is the most suitable universal position management scheme for PM Agents. This method does not rely on precise probability estimates, but instead categorizes opportunities based on signal strength into finite tiers corresponding to fixed positions; even in high-confidence scenarios, it also sets clear caps to control risk.
Strategy Selection in Prediction Markets
From a strategic structure perspective, prediction markets can primarily be divided into two categories: deterministic arbitrage strategies characterized by clear and codable rules, and speculative directional strategies that rely on information interpretation and directional judgment; in addition, there are market-making and hedging strategies primarily led by professional institutions, which require high capital and infrastructure.
Deterministic Arbitrage Strategies
Resolution Arbitrage: Resolution arbitrage occurs when the outcome of an event has basically been determined, but the market has not fully priced it yet. The profits mainly come from information synchronization and execution speed. This strategy has clear rules, low risk, and can be fully coded, making it the core strategy most suitable for Agent execution in prediction markets.
Dutch Book Arbitrage: Dutch Book arbitrage takes advantage of the structural imbalance formed by the sum of prices of mutually exclusive and complete event sets deviating from the probability conservation constraint (∑P≠1), locking in directionally neutral risk returns through combination positioning. This strategy relies solely on the rules and price relationships, has low risk, and can be highly regulated, making it a typical deterministic arbitrage form suitable for automated execution by Agents.
Cross-Platform Arbitrage: Cross-platform arbitrage profits by capturing the pricing deviations of the same event across different markets. The risk is low, but it requires high monitoring for delays and concurrency. This strategy is suitable for Agents with infrastructure advantages, but increasing competition has led to a continuous decrease in marginal returns.
Bundle Arbitrage: Bundle arbitrage trades based on the price inconsistencies between related contracts, has clear logic but limited opportunities. This strategy can be executed by Agents but has certain engineering requirements for rule parsing and combination constraints, making the Agent adaptability moderate.
Speculative Directional Strategies
Information Trading: This type of strategy revolves around explicit events or structured information, such as official data releases, announcements, or adjudication windows. As long as the information sources are clear and triggers are definable, Agents can exert speed and discipline advantages in monitoring and execution; however, when information turns into semantic judgment or contextual interpretation, human intervention is still required.
Signal Following: This strategy gains profits by following accounts or capital behaviors that have historically performed well. Its rules are relatively simple and can be executed automatically. The core risk lies in signal degradation and being reversed utilized, thus requiring filtering mechanisms and strict position management. It is suitable as a supplementary strategy for Agents.
Unstructured / Noise-driven: This type of strategy heavily relies on sentiment, randomness, or participant behavior, lacking a stable, replicable edge, resulting in unstable long-term expected values. Due to difficulties in modeling and extremely high risks, this type is not suitable for systematic Agent execution and is not advised as a long-term strategy.
High-Frequency Price and Liquidity Strategies: These strategies depend on extremely short decision windows, continuous quoting, or high-frequency trading, demanding very high levels of delay, modeling, and capital. Although theoretically suitable for Agents, they are often limited in prediction markets by liquidity and competition intensity, only suitable for a few participants with significant infrastructure advantages.
Risk Management and Hedging Strategies: These strategies do not directly pursue profits but are used to reduce overall risk exposure. They have clear rules and objectives and operate long-term as foundational risk control modules.
Overall, strategies suitable for Agent execution in prediction markets are concentrated in scenarios with clear rules, codable and weak subjective judgment, with deterministic arbitrage serving as a core source of revenue, structured information and signal following strategies as supplements, while high-noise and emotion-based trading should be systematically excluded. The long-term advantages of Agents lie in their high discipline, speed of execution, and risk control capabilities.
4. Business Model and Product Form of Prediction Market Agents
The ideal business model design for prediction market agents has different exploratory spaces at various levels:
Infrastructure layer, providing multi-source real-time data aggregation, Smart Money address repository, a unified prediction market execution engine, and back-testing tools, charging B2B to obtain stable income unrelated to prediction accuracy;
Strategy layer, introducing community and third-party strategies, building a reusable, evaluable strategy ecosystem, and realizing value capture through invocation, weighting, or execution sharing, thus reducing dependence on a single Alpha.
Agent / Vault layer, where agents participate in actual execution in a trustee management manner, relying on on-chain transparent records and a strict risk control system, collecting management fees and performance fees for realization capability.
The product forms corresponding to different business models can also be classified as:
Gamified mode: lowering participation barriers through intuitive interactions similar to Tinder, possessing the strongest user growth and market education ability, ideal for breaking out but needs to transition to subscription or execution-type product monetization.
Strategy subscription / signal mode: does not involve capital custody, regulatory-friendly, clear responsibility and authority, with relatively stable SaaS revenue structure, making it the most feasible commercialization path at the current stage. Its limitation lies in strategies being easily replicated, execution suffering losses, and the long-term income ceiling being limited, which can be significantly improved by a semi-automated form of "signals + one-click execution" to enhance experience and retention.
Vault custody mode: possessing scale effects and execution efficiency advantages, resembling asset management products, but facing multiple structural constraints from asset management licenses, trust thresholds, and centralized technical risks. The business model is highly dependent on market conditions and sustainable profitability. Unless they have long-term performance and institutional endorsements, they are not advisable as the main path.
Overall, a diversified income structure of "infrastructure monetization + strategy ecosystem expansion + performance participation" helps reduce reliance on the single assumption of "AI continuously outperforming the market." Even if Alpha converges with market maturation, underlying capabilities such as execution, risk control, and settlement still possess long-term value, thereby constructing a more sustainable business loop.
5. Case Studies of Prediction Market Agents
Currently, prediction market agents are still in the early exploratory stage. Although the market has seen a diversification of attempts ranging from foundational frameworks to upper-level tools, no standardized products have emerged that are mature and replicable across the key dimensions of strategy generation, execution efficiency, risk control, and business loop.
We categorize the current ecological map into three levels: Infrastructure layer, Autonomous Trading Agents, and Prediction Market Tools.
Infrastructure Layer
#
Polymarket Agents Framework
Polymarket Agents is a developer framework officially launched by Polymarket, aimed at solving the engineering standardization of "connection and interaction." This framework encapsulates market data acquisition, order construction, and basic LLM calling interfaces. It addresses the question of "how to place orders with code," but leaves core trading capabilities—including strategy generation, probability calibration, dynamic position management, and back-testing systems—basically blank. It's more like an officially recognized "access specification" rather than a finished product with Alpha returns. Commercial-grade Agents still need to build a complete investment research and risk control core based on this.
#
Gnosis Prediction Market Tools
Gnosis Prediction Market Agent Tooling (PMAT) provides complete read and write support for Omen/AIOmen and Manifold but offers only read permissions for Polymarket, indicating a significant ecological barrier. It is suitable as a foundational development cornerstone for Agents within the Gnosis ecosystem, but for developers primarily focused on Polymarket, its practicality is limited.
Polymarket and Gnosis are currently the only prediction market ecosystems that have clearly productized "Agent development" into official frameworks. Other prediction markets, like Kalshi, still mainly remain at the API and Python SDK level, requiring developers to fill in key system capabilities such as strategy, risk control, operation, and monitoring independently.
Autonomous Trading Agents
Currently, the "prediction market AI Agents" on the market are largely still in the early stages. Although they bear the name "Agent," their actual abilities are significantly short of a fully autonomous closed-loop trading system; they generally lack independent and systematic risk control layers, and have not integrated position management, stop-losses, hedges, and expected value constraints into the decision-making process. The overall level of productization is low, and they have yet to form a mature system capable of long-term operation.
#
Olas Predict
Olas Predict is currently the most productized prediction market agent ecosystem. Its core product, Omenstrat, is built on the Gnosis system's Omen, employing FPMM and decentralized arbitration mechanisms, supporting small-scale high-frequency interactions, but is limited by insufficient liquidity in the Omen single market. Its "AI predictions" primarily rely on general LLM and lack real-time data and systematic risk control, with historical win rates showing marked differentiation across categories. In February 2026, Olas launched Polystrat, expanding Agent capabilities to Polymarket—users can set strategies using natural language, and Agents automatically identify trading probability deviations in markets settling within 4 days and execute trades. The system controls risks through locally running Pearl, self-hosted Safe accounts, and hard-coded limits, making it the first consumer-grade autonomous trading Agent aimed at Polymarket.
#
UnifAI Network Polymarket Strategy
Offers automated trading Agents for Polymarket, focusing on tail risk-bearing strategies: scanning near-settlement contracts with implied probabilities >95% and buying them to target a 3-5% price difference. On-chain data shows a win rate close to 95%, but profit varies significantly across categories, and the strategy is highly dependent on execution frequency and category selection.
#
NOYA.ai
NOYA.ai attempts to integrate "research—judgment—execution—monitoring" into an Agent closed-loop, with architecture covering intelligence, abstraction, and execution layers. It has already delivered Omnichain Vaults; the Prediction Market Agent is still in the development stage and has not yet formed a complete mainnet closed-loop, remaining in the vision validation stage.
Prediction Market Tools
Currently, prediction market analysis tools are insufficient to form a complete "prediction market agent"; their value primarily lies in the information and analysis layers of the agent architecture, with trading execution, position management, and risk control still needing to be borne by the traders themselves. In terms of product forms, they more align with "strategy subscription / signal assistance / research enhancement" positioning, viewed as early prototypes of prediction market agents.
Through a systematic collation and empirical selection of projects collected in Awesome-Prediction-Market-Tools, this article selects representative projects among them that have formed preliminary product forms and use cases as case studies. These focus mainly on four directions: analysis and signal layers, alerts and whale tracking systems, arbitrage discovery tools, and trading terminals and aggregated execution.
#
Market Analysis Tools
Polyseer: A research-oriented prediction market tool that employs a multi-Agent division of labor structure (Planner / Researcher / Critic / Analyst / Reporter) for bilateral evidence collection and Bayesian probability aggregation, outputting structured research reports. Its advantages lie in transparent methodology, engineered processes, and being fully open-source and auditable.
Oddpool: Positioned as the "Bloomberg terminal for prediction markets," providing cross-platform aggregation, arbitrage scanning, and real-time data dashboard terminals for Polymarket, Kalshi, CME, and more.
Polymarket Analytics: A global data analysis platform for Polymarket that systematically showcases trader, market, position, and transaction data, with a clear positioning and intuitive data, suitable as a foundation for data inquiry and research reference.
Hashdive: A data tool for traders, quantifying the selection of traders and markets through Smart Score and multi-dimensional Screeners, providing practicality in "smart money identification" and following decision-making.
Polyfactual: Focused on AI market intelligence and sentiment/risk analysis, embedding analysis results into trading interfaces via a Chrome extension, skewed towards B2B and institutional user scenarios.
Predly: An AI mispricing detection platform that identifies pricing discrepancies between market prices and AI-calculated probabilities for Polymarket and Kalshi, officially claiming an alert accuracy rate of 89%, positioned for signal discovery and opportunity screening.
Polysights: Covers 30+ markets and on-chain indicators, tracking anomalies like new wallets and large unilateral bets via Insider Finder, suitable for daily monitoring and signal discovery.
PolyRadar: A multi-model parallel analysis platform, providing real-time interpretations, timeline evolution, confidence scoring, and source transparency for single events, emphasizing multi-AI cross-validation, positioned as an analysis tool.
Alphascope: An AI-driven prediction market intelligence engine offering real-time signals, research summaries, and probability change monitoring, still in the early stage, leaning towards research and signal support.
#
Alerts / Whale Tracking
Stand: Clearly positioning whale following and high-confidence action alerts.
Whale Tracker Livid: Productizing whale position changes.
#
Arbitrage Discovery Tools
ArbBets: An AI-driven arbitrage discovery tool focusing on Polymarket, Kalshi, and sports betting markets, identifying cross-platform arbitrage and positive expected value (+EV) trading opportunities, positioned at the high-frequency opportunity scanning layer.
PolyScalping: A real-time arbitrage and scalping analysis platform for Polymarket, supporting full market scanning every 60 seconds, ROI calculations, and Telegram notifications, filtering opportunities based on liquidity, spreads, and trading volume, skewed towards proactive traders.
Eventarb: A lightweight cross-platform arbitrage calculation and alert tool covering Polymarket, Kalshi, and Robinhood, with focused functions and free use, suitable as a foundational arbitrage assistant.
Prediction Hunt: A cross-exchange prediction market aggregation and comparison tool providing real-time price comparisons and arbitrage identification (about every 5 minutes refresh) for Polymarket, Kalshi, and PredictIt, positioned for discovering information symmetry and market inefficiencies.
#
Trading Terminal / Aggregated Execution
Verso: An institutional-level prediction market trading terminal supported by YC Fall 2024, offering a Bloomberg-style interface, tracking over 15,000 contracts in real-time covering Polymarket and Kalshi, deep data analysis, and AI news intelligence, positioned for professional and institutional traders.
Matchr: A cross-platform prediction market aggregation and execution tool, covering 1,500+ markets, achieving optimal price matching through smart routing, and planning automated revenue strategies based on high-probability events, cross-market arbitrage, and event-driven approaches, positioned at the execution and capital efficiency layer.
TradeFox: A professional prediction market aggregation and prime brokerage platform supported by Alliance DAO and CMT Digital, providing advanced order execution (limit orders, take-profit, stop-loss, TWAP), self-custodial trading, and multi-platform smart routing, targeting institutional traders, with plans to expand to Kalshi, Limitless, SxBet, and other platforms.
6. Summary and Outlook
At present, prediction market agents are in the early exploratory phase of development.
Market Foundation and Evolution: Polymarket and Kalshi have formed a duopoly structure, and building agents around them has ample liquidity and scenario foundation. The core difference between prediction markets and gambling lies in positive externalities; through real trading aggregation of dispersed information, public pricing of real events gradually evolves into the "global truth layer."
Core Positioning: Prediction market agents should be positioned as executable probabilistic asset management tools whose core task is to convert news, rule texts, and on-chain data into verifiable pricing discrepancies and execute strategies with higher discipline, lower costs, and cross-market abilities. The ideal architecture can be abstracted into four layers: information, analysis, strategy, and execution, but its actual tradable characteristics highly depend on the clarity of settlement, the quality of liquidity, and the degree of information structuring.
Strategy Selection and Risk Control Logic: From a strategy perspective, deterministic arbitrage (including resolution arbitrage, Dutch book arbitrage, and cross-platform price difference trading) is most suitable for automation execution by agents, whereas directional speculation can only serve as a supplement. In position management, executability and tolerance should be prioritized; the tiered method combined with fixed position caps is the most appropriate.
Business Model and Prospects: Commercialization primarily consists of three layers: the infrastructure layer to acquire stable B2B income through data execution infrastructure, the strategy layer to realize value through third-party strategy invocation or sharing, and the Agent/Vault layer to participate in real transactions under transparent on-chain risk control constraints, collecting management and performance fees. Corresponding forms include gamified entrances, strategy subscriptions/signals (currently the most feasible), and high-threshold Vault custody, with "infrastructure + strategy ecosystem + performance participation" being a more sustainable path.
Although diverse attempts have emerged in the ecosystem of prediction market agents, from foundational frameworks to upper-level tools, no mature, replicable standardized products have appeared yet regarding strategy generation, execution efficiency, risk control, and commercial loops at present. We look forward to the iteration and evolution of prediction market agents in the future.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
