Turning Probability into Assets: A Foresight into Predictive Market Agents

Author: Jacob Zhao @IOSG

In our previous Crypto AI research reports, we have consistently emphasized that the most practically valuable applications in the current crypto space are mainly concentrated in stablecoin payments and DeFi, and the Agent is the key user-facing interface for the AI ​​industry. Therefore, in the trend of Crypto and AI integration, the two most valuable paths are: in the short term, AgentFi based on existing mature DeFi protocols (basic strategies such as lending and liquidity mining, as well as advanced strategies such as Swap, Pendle PT, and funding rate arbitrage); and in the medium to long term, Agent Payment centered around stablecoin settlement and relying on protocols such as ACP/AP2/x402/ERC-8004. Prediction markets have become an undeniable new industry trend in 2025, with total annual trading volume surging from approximately $9 billion in 2024 to over $40 billion in 2025, representing a year-on-year growth of over 400%. This significant growth is driven by multiple factors: demand arising from uncertainty caused by macro-political events, the maturation of infrastructure and trading models, and a breakthrough in the regulatory environment (Kalshi's victory and Polymarket's return to the US). Prediction Market Agents are taking shape in early 2026 and are expected to become a new product form in the field of agents in the coming year. Prediction markets: From Betting Tools to a “Global Layer of Truth” Prediction markets are financial mechanisms for trading around the outcomes of future events. Contract prices essentially reflect the market's collective judgment on the probability of events occurring. Their effectiveness stems from the combination of collective intelligence and economic incentives: in an environment of anonymity and real-money betting, dispersed information is rapidly integrated into price signals weighted by willingness to pay, thereby significantly reducing noise and false judgments.

▲Trend Chart of Nominal Trading Volume in Prediction MarketsData Source: Dune Analytics (Query ID: 5753743)

As of the end of 2025, prediction markets have basically formed The market is dominated by a duopoly of Polymarket and Kalshi. According to Forbes, total trading volume in 2025 reached approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion. Weekly data from February 2026 shows that Kalshi's trading volume ($25.9 billion) has surpassed Polymarket ($18.3 billion), approaching 50% market share. Kalshi's rapid expansion is attributed to its legal victory in the election contract case, its first-mover advantage in compliance in the US sports betting market, and relatively clear regulatory expectations. Currently, their development paths have clearly diverged: Polymarket adopts a hybrid CLOB architecture of "off-chain matching and on-chain settlement" and a decentralized settlement mechanism to build a global, non-custodial, highly liquid market. After compliantly returning to the United States, it has formed a dual-track operation structure of "onshore + offshore"; Kalshi integrates into the traditional financial system, connects to mainstream retail brokerages through APIs, and attracts Wall Street market makers to deeply participate in macro and data-driven contract trading. Its products are subject to traditional regulatory processes, and long-tail demand and sudden events are relatively lagging behind. Aside from Polymarket and Kalshi, other competitive players in the prediction market primarily develop along two paths: One is the compliant distribution path, embedding event contracts into the existing accounts and clearing systems of brokerages or large platforms, leveraging channel coverage, compliance qualifications, and institutional trust to establish advantages (such as Interactive Brokers × ForecastEx's ForecastTrader, FanDuel × CME). The Group's FanDuel Predicts boasts significant compliance and resource advantages, but its product and user scale are still in their early stages. Secondly, there's the Crypto native on-chain path, represented by Opinion.trade, Limitless, and Myriad. This path leverages points mining, short-cycle contracts, and media distribution to achieve rapid scaling, emphasizing performance and capital efficiency, but its long-term sustainability and risk control robustness remain to be verified. These two paths—traditional financial compliance entry points and crypto native performance advantages—together constitute the diverse competitive landscape of the prediction market ecosystem. Prediction markets superficially resemble gambling, and are essentially zero-sum games. However, the core difference lies in whether they possess positive externalities: aggregating dispersed information through real-money transactions, publicly pricing real-world events, and forming a valuable signal layer. The trend is shifting from game theory to a "global truth layer"—with the integration of institutions like CME and Bloomberg, event probabilities have become decision metadata that can be directly accessed by financial and corporate systems, providing more timely and quantifiable market-based truths. From a global regulatory perspective, the compliance paths for prediction markets are highly differentiated. The United States is the only major economy that explicitly includes prediction markets in its financial derivatives regulatory framework. Markets in Europe, the UK, Australia, and Singapore generally view them as gambling and tend to tighten regulations, while China and India completely prohibit them. The future global expansion of prediction markets still depends on the regulatory frameworks of each country. The architecture design of prediction market agents is currently in its early stages of implementation. Their value lies not in making AI predictions more accurate, but in amplifying the efficiency of information processing and execution in prediction markets. Prediction markets are essentially information aggregation mechanisms, where prices reflect collective judgments about the probability of events. Real-world market inefficiencies stem from information asymmetry, liquidity constraints, and attentional limitations. The appropriate positioning of prediction market agents is "Executable Probabilistic Portfolio Management": transforming news, rule texts, and on-chain data into verifiable pricing biases to execute strategies faster, more disciplined, and at lower cost, while capturing structural opportunities through cross-platform arbitrage and portfolio risk control. The ideal predictive market intelligence agent can be abstracted into a four-layer architecture: Information Layer: Aggregates news, social media, on-chain, and official data; Analysis Layer: Identifies mispricings and calculates the edge using LLM and ML; Strategy Layer: Converts the edge into positions through the Kelly Criterion, phased entry, and risk control; The execution layer completes multi-market order placement, slippage and gas optimization, and arbitrage execution, forming a highly efficient and automated closed loop.

Strategy Framework for Prediction Market Agents

Unlike traditional trading environments, prediction markets differ significantly in settlement mechanisms, liquidity, and information distribution. Not all markets and strategies are suitable for automated execution. The core of prediction market agents lies in whether they are deployed in scenarios with clear rules, coded rules, and that align with their structural advantages.

Target Selection in Prediction Markets

Not all prediction markets possess tradable value. Their participation value depends on: settlement clarity (whether the rules are clear and whether the data source is unique), liquidity quality (market depth, spreads, and trading volume), insider risk (degree of information asymmetry), time structure (expiration time and event rhythm), and the trader's own informational advantage and professional background.

Not all prediction markets possess tradable value. Their participation value depends on: settlement clarity (whether the rules are clear and whether the data source is unique), liquidity quality (market depth, spreads, and trading volume), insider risk (degree of information asymmetry), time structure (expiration time and event rhythm), and the trader's own informational advantage and professional background.

Position Management in Predictive Markets

The Kelly Criterion is the most representative money management theory in repeated game scenarios. Its goal is not to maximize the single return, but to maximize the long-term compound growth rate of money.

Deterministic Arbitrage Strategy

• Resolution Arbitrage: Resolution arbitrage occurs when the outcome of an event is largely determined, but the market has not yet fully priced it in. The returns primarily come from information synchronization and execution speed. This strategy has clear rules, low risk, and is fully coded, making it the most suitable core strategy for agents to execute in prediction markets. Probability Conservation Arbitrage (Dutch Book Arbitrage): Dutch Book arbitrage utilizes the structural imbalance created by the deviation of the sum of prices of mutually exclusive and complete sets of events from the probability conservation constraint (∑P≠1), locking in directionless risk and return through portfolio construction. This strategy relies solely on the relationship between rules and prices, has low risk, and is highly rule-based, making it a typical deterministic arbitrage form suitable for agent automation. Cross-Platform Arbitrage: Cross-platform arbitrage profits by capturing pricing discrepancies of the same event in different markets. It has low risk but requires high latency and parallel monitoring. This strategy is suitable for agents with infrastructure advantages, but increased competition leads to a continuous decline in marginal returns.

• Bundle Arbitrage: Bundle arbitrage takes advantage of pricing discrepancies between related contracts to trade. The logic is clear, but the opportunities are limited. This strategy can be executed by an agent, but it has certain engineering requirements for rule parsing and combination constraints. Agent adaptability is moderate.

Speculative Directional Strategies

• Information Trading: This type of strategy revolves around explicit events or structured information, such as official data releases, announcements, or rulings. As long as the information source is clear and the triggering conditions are definable, agents can leverage speed and discipline at the monitoring and execution levels; however, human intervention is still required when information is transformed into semantic judgment or contextual interpretation. **Signal Following Strategy:** This strategy generates profits by following the historically high-performing accounts or funds. The rules are relatively simple and can be automated. Its core risk lies in signal degradation and being exploited in reverse, thus requiring filtering mechanisms and strict position management. It is suitable as a supplementary strategy for agents. **Unstructured/Noise-driven Strategy:** This type of strategy is highly dependent on emotions, randomness, or participant behavior, lacks a stable and replicable edge, and has unstable long-term expected values. Due to the difficulty in modeling and extremely high risk, this strategy is not suitable for systematic execution by agents and is not recommended as a long-term strategy. High-Frequency Price and Liquidity Strategies (Market Microstructure): These strategies rely on extremely short decision windows, continuous quotes, or high-frequency trading, placing extremely high demands on latency, models, and capital. While theoretically suitable for agents, they are often limited by liquidity and competition in prediction markets, making them suitable only for a few participants with significant infrastructure advantages. Risk Management and Hedging Strategies (Risk Control & Hedging): These strategies do not directly pursue returns but are used to reduce overall risk exposure. With clear rules and objectives, they operate as a long-term underlying risk control module. Overall, suitable strategies for agents in prediction markets are concentrated in scenarios with clear rules, coded patterns, and low subjective judgment. Certainty arbitrage should be the core source of profit, supplemented by structured information and signal-following strategies. High-noise and emotion-driven trading should be systematically excluded. The long-term advantages of agents lie in high discipline, high-speed execution, and risk control capabilities.

  

  Business Model and Product Form of Predictive Market Intelligent Agents

  Ideal Business Model Design for Predictive Market Intelligent Agents Different levels offer different directions for exploration:

  • Infrastructure, providing multi-source real-time data aggregation, Smart Money Address database, unified prediction market execution engine and backtesting tools, charging B2B to obtain stable income unrelated to prediction accuracy; Strategy Layer: Introducing community and third-party strategies to build a reusable and evaluable strategy ecosystem, and capturing value through invocation, weighting or execution revenue sharing, thereby reducing dependence on a single Alpha; Agent/Vault Layer: Intelligent agents directly participate in live execution in a entrusted management manner, relying on transparent on-chain records and a strict risk control system to collect management fees and performance fees. The product forms corresponding to different business models can also be divided into: Entertainment/Gamification Model: Lowering the barrier to entry through intuitive, Tinder-like interactions, it possesses the strongest user growth and market education capabilities, making it an ideal entry point for breaking into new markets. However, it needs to be monetized through subscription or execution-based products. Strategy Subscription/Signal Model: Not involving fund custody, it is regulatory-friendly, with clear responsibilities, and a relatively stable SaaS revenue structure, making it the most feasible commercialization path at the current stage. Its limitations lie in the ease with which strategies can be copied, the inefficiencies in execution, and the limited long-term revenue ceiling. However, a semi-automated approach of "signals + one-click execution" can significantly improve the experience and retention. Vault Custody Model: While possessing advantages in scale and execution efficiency, and resembling asset management products, it faces multiple structural constraints, including asset management licenses, trust thresholds, and centralized technology risks. Its business model is highly dependent on market conditions and sustained profitability. Unless it boasts long-term performance and institutional backing, it is not suitable as a primary approach. Overall, a diversified revenue structure of "infrastructure monetization + strategy ecosystem expansion + performance participation" helps reduce reliance on the single assumption that "AI will continuously outperform the market." Even if Alpha converges as the market matures, underlying capabilities such as execution, risk control, and settlement still possess long-term value, thus building a more sustainable business loop.

    

    Project Cases of Prediction Market Agents

    Currently, prediction market agents are still in the early exploratory stage. Although the market has seen diverse attempts ranging from underlying frameworks to upper-level tools, a standardized product that is mature in strategy generation, execution efficiency, risk control system, and business closed loop has not yet been formed.

    

    Project Cases of Prediction Market Agents

    Currently, prediction market agents are still in the early exploratory stage. Although the market has seen diverse attempts ranging from underlying frameworks to upper-level tools, a standardized product that is mature in strategy generation, execution efficiency, risk control system, and business closed loop has not yet been formed.

    95%, aiming to capture a 3–5% spread. On-chain data shows a win rate close to 95%, but returns vary significantly across different asset classes, indicating that the strategy is highly dependent on execution frequency and asset class selection. NOYA.ai attempts to integrate "research-judgment-execution-monitoring" into an Agent closed loop, with an architecture encompassing an intelligence layer, an abstraction layer, and an execution layer. It has already been delivered to Omnichain Vaults; the Prediction Market Agent is still under development and has not yet formed a complete mainnet closed loop, remaining in the vision validation phase. Prediction Market Tools (Prediction Market Tools) Current prediction market analysis tools are insufficient to constitute a complete "prediction market intelligent agent." Their value is mainly concentrated in the information and analysis layers of the agent's architecture; trade execution, position management, and risk control still need to be borne by the trader. From a product perspective, they are more in line with the positioning of "strategy subscription/signal assistance/research enhancement" and can be regarded as an early prototype of a prediction market intelligent agent. Through a systematic review and empirical screening of the projects included in Awesome-Prediction-Market-Tools, this article selects representative projects that have already possessed preliminary product forms and use cases as case studies for this research report. The focus is primarily on four areas: analysis and signaling layers, alert and whale tracking systems, arbitrage discovery tools, and trading terminals and aggregated execution. #Market Analysis Tools Polyseer: A research-oriented predictive market tool that employs a multi-agent architecture (Planner/Researcher/Critic/Analyst/Reporter) for bilateral evidence gathering and Bayesian probabilistic aggregation, outputting structured research reports. Its advantages lie in its transparent methodology, engineered processes, and complete open-source auditability. Oddpool: Positioned as a "Bloomberg terminal for prediction markets," it provides cross-platform aggregation, arbitrage scanning, and real-time data dashboards for Polymarket, Kalshi, and CME. Polymarket Analytics: A global Polymarket data analytics platform that systematically displays trader, market, position, and transaction data. Its clear positioning and intuitive data make it suitable for basic data queries and research references. Hashdive: A data tool for traders that uses Smart Score and multi-dimensional Screener to quantitatively filter traders and markets, offering practical applications in "smart money identification" and copy trading decisions. Polyfactual: Focuses on AI market intelligence and sentiment/risk analysis, embedding analysis results into the trading interface via a Chrome extension, geared towards B2B and institutional user scenarios. Predly: An AI mispricing detection platform that identifies pricing discrepancies between Polymarket and Kalshi by comparing market prices and AI-calculated probabilities. The official claim is an 89% accuracy rate for alerts, positioned for signal discovery and opportunity screening. Polysights: Covers 30+ market and on-chain indicators, and uses Insider Finder to track unusual behavior such as new wallets and large bets, suitable for daily monitoring and signal discovery. PolyRadar: A multi-model parallel analysis platform that provides real-time interpretation, timeline evolution, confidence scoring, and source transparency for single events. It emphasizes multi-AI cross-validation and positions itself as an analytical tool. Alphascope: An AI-driven predictive market intelligence engine that provides real-time signals, research summaries, and probability change monitoring. It is still in its early stages and focuses on research and signal support.

  #Alert/Whale Tracking

  • Stand: Clearly locates whale tracking and provides high-confidence action alerts. Whale Tracker Livid: Productizing Whale Position Changes # ArbBets: An AI-driven arbitrage discovery tool focusing on the 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 calculation, and Telegram push notifications. It allows filtering opportunities by liquidity, spreads, and volume, and is geared towards active traders. Eventarb: A lightweight, cross-platform arbitrage calculation and alert tool covering Polymarket, Kalshi, and Robinhood. Functionally focused and free to use, it's suitable as a basic arbitrage tool. Prediction Hunt: A cross-exchange prediction market aggregation and comparison tool, providing real-time price comparisons and arbitrage identification for Polymarket, Kalshi, and PredictIt (refreshed approximately every 5 minutes), positioned for information symmetry and market inefficiency discovery. Verso: An institutional-grade prediction market trading terminal supported by YC Fall 2024, offering a Bloomberg-style interface, covering real-time tracking of 15,000+ contracts from Polymarket and Kalshi, in-depth data analysis, and AI-powered news intelligence, positioned for professional and institutional traders. Matchr: A cross-platform prediction market aggregation and execution tool covering 1,500+ markets. It achieves optimal price matching through intelligent routing and plans automated profit strategies based on high-probability events, cross-market arbitrage, and event-driven strategies, focusing on execution and capital efficiency. TradeFox: A professional prediction market aggregation and Prime Brokerage platform supported by Alliance DAO and CMT Digital. It provides advanced order execution (limit orders, stop-loss and take-profit, TWAP), self-managed trading, and multi-platform intelligent routing, targeting institutional traders. Plans are underway to expand to platforms such as Kalshi, Limitless, and SxBet. Summary and Outlook Currently, the Prediction Market Agent is in its early exploratory stage of development. Market Foundation and Essential Evolution: Polymarket and Kalshi have formed a duopoly structure, providing sufficient liquidity and a solid foundation for building agents around them. The core difference between prediction markets and gambling lies in positive externalities. By aggregating dispersed information through real transactions, prediction markets can publicly price real-world events, gradually evolving into a "global truth layer." **Core Positioning:** Prediction market agents should be positioned as "executable probabilistic asset management tools." Their core task is to transform news, rule texts, and on-chain data into verifiable pricing biases, and to execute strategies with greater discipline, lower costs, and cross-market capabilities. The ideal architecture can be abstracted into four layers: information, analysis, strategy, and execution. However, its actual tradability highly depends 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 settlement arbitrage, probability conservation arbitrage, and cross-platform spread trading) is best suited for automated execution by intelligent agents, while directional speculation can only serve as a supplement. In position management, feasibility and fault tolerance should be prioritized; a laddered approach combined with a fixed position cap is most suitable. Business Model and Prospects: Commercialization is mainly divided into three layers: Infrastructure Layer: Obtaining stable B2B revenue through data-driven infrastructure; Strategy Layer: Monetizing through third-party strategy calls or revenue sharing; Agent/Vault Layer: Participating in live trading under transparent on-chain risk control constraints and collecting management and performance fees. Corresponding forms include entertainment entry points, strategy subscriptions/signals (currently the most feasible), and high-barrier Vault hosting. "Infrastructure + Strategy Ecosystem + Performance Participation" is a more sustainable path. Despite the emergence of diverse attempts ranging from underlying frameworks to upper-level tools within the prediction market agent ecosystem, mature and replicable standardized products have yet to emerge in key dimensions such as strategy generation, execution efficiency, risk control, and business closed loop. We look forward to the future iteration and evolution of prediction market agents.