Make probabilities an asset: A forward-looking perspective on prediction market intelligence

Author: 0xjacobzhao

In our previous Crypto AI series reports, we consistently emphasized the following point: the most practically valuable scenarios in the current crypto space are mainly stablecoin payments and DeFi, while Agents are the key interface between AI industry and users. Therefore, within the trend of integrating Crypto and AI, the two most valuable paths are: short-term AgentFi based on existing mature DeFi protocols (such as lending, liquidity mining, and advanced strategies like Swap, Pendle PT, and funding rate arbitrage), and mid- to long-term Agent Payment centered around stablecoin settlement, relying on protocols like ACP/AP2/x402/ERC-8004.

Forecast markets have become an industry trend by 2025, with annual total trading volume skyrocketing from about $9 billion in 2024 to over $40 billion in 2025, representing over 400% year-over-year growth. This significant increase is driven by multiple factors: macro-political events creating uncertainty demand, infrastructure and trading model maturity, and regulatory breakthroughs (Kalshi’s victory in court and Polymarket’s return to the US). Prediction Market Agents are expected to emerge in early 2026, potentially becoming a new product form in the AI field within the next year.

1. Prediction Markets: From Betting Tools to “Global Truth Layer”

Prediction markets are financial mechanisms that trade on the outcomes of future events, with contract prices essentially reflecting the market’s collective judgment of event probabilities. Their effectiveness stems from the combination of crowd wisdom and economic incentives: in environments of anonymous, real-money betting, dispersed information is rapidly integrated into price signals weighted by capital willingness, significantly reducing noise and false judgments.

Prediction Market Nominal Trading Volume Trend Chart

Data Source: Dune Analytics (Query ID: 5753743)

By the end of 2025, prediction markets have largely formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 is about $44 billion, with Polymarket contributing approximately $21.5 billion and Kalshi about $17.1 billion. Data from February 2026 shows Kalshi’s trading volume ($25.9B) has surpassed Polymarket’s ($18.3B), approaching 50% market share. Kalshi’s rapid expansion is attributed to its legal victory over election contracts, its early compliance advantage in US sports prediction markets, and clearer regulatory expectations. Currently, their development paths are clearly diverging:

• Polymarket employs a hybrid off-chain matching and on-chain settlement architecture with decentralized clearing, building a global, non-custodial, highly liquid market. After re-entering the US market legally, it operates a dual-track “onshore + offshore” structure.

• Kalshi integrates into traditional finance via API access to mainstream retail brokers, attracting deep participation from Wall Street market makers in macro and data-driven contracts. Its products are constrained by traditional regulatory processes, with long-tail demand and sudden events lagging behind.

Beyond Polymarket and Kalshi, other competitive participants in prediction markets mainly develop along two paths:

• Compliance distribution: embedding event contracts into broker or large platform accounts and clearing systems, leveraging channel coverage, compliance credentials, and institutional trust (e.g., Interactive Brokers × ForecastEx’s ForecastTrader, FanDuel × CME Group’s FanDuel Predicts). This path has significant compliance and resource advantages but remains early in product and user scale.

• Crypto-native on-chain path: represented by Opinion.trade, Limitless, Myriad, leveraging token mining, short-term contracts, and media distribution for rapid growth, emphasizing performance and capital efficiency. Long-term sustainability and risk control robustness remain to be validated.

The combination of traditional financial compliance entry points and crypto-native performance advantages forms a diverse competitive landscape in prediction markets.

On the surface, prediction markets resemble gambling, but fundamentally they are zero-sum games. The key difference lies in whether they generate positive externalities: by aggregating dispersed information through real-money trading to publicly price real-world events, forming valuable signals. The trend is shifting from mere betting to a “Global Truth Layer”—with institutions like CME and Bloomberg participating, event probabilities are becoming decision-making metadata that can be directly called by financial and corporate systems, providing more timely and quantifiable market-based truths.

From the global regulatory perspective, the compliance paths for prediction markets are highly fragmented. The US is the only major economy explicitly regulating prediction markets as financial derivatives. Europe, the UK, Australia, Singapore generally treat them as gambling and tighten regulations. China and India have outright bans. Future global expansion of prediction markets will still depend on each country’s regulatory framework.

2. Architecture Design of Prediction Market Agents

Currently, Prediction Market Agents are entering early practice stages. Their value is not about “more accurate AI predictions,” but about amplifying information processing and execution efficiency within prediction markets. Prediction markets are essentially information aggregation mechanisms, with prices reflecting collective probability judgments; inefficiencies in real markets stem from information asymmetry, liquidity, and attention constraints. The rational positioning of prediction market agents is as Executable Probabilistic Portfolio Management: converting news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies faster, more disciplined, and at lower costs, capturing structural opportunities through cross-platform arbitrage and portfolio risk management.

An ideal prediction market agent can be abstracted into four layers:

• Information Layer: aggregating news, social data, on-chain and official data;

• Analysis Layer: using LLM and ML to identify mispricings and compute edges;

• Strategy Layer: converting edges into positions via Kelly criterion, batch sizing, and risk controls;

• Execution Layer: completing multi-market order placement, slippage and gas optimization, and arbitrage execution, forming an efficient automated closed loop.

3. Strategy Framework of Prediction Market Agents

Unlike traditional trading, prediction markets differ significantly in settlement mechanisms, liquidity, and information distribution. Not all markets and strategies are suitable for automation. The core of prediction market agents is whether they are deployed in scenarios with clear, codable rules and structural advantages. The following analyzes from three levels: underlying asset selection, position management, and strategy structure.

Prediction Market Asset Selection

Not all prediction markets have tradable value. Participation depends on: clarity of settlement (rules and data sources), quality of liquidity (depth, spreads, volume), insider risk (information asymmetry), time structure (expiration and event rhythm), and traders’ informational advantage and expertise. Only when most dimensions meet basic requirements does the prediction market have a foundation for participation. Participants should match their strengths and market characteristics:

• Human core advantages: rely on expertise, judgment, and integrating fuzzy information, with relatively wide decision windows (days/weeks). Typical markets include political elections, macro trends, and corporate milestones.

• AI agent core advantages: rely on data processing, pattern recognition, and rapid execution, with extremely short decision windows (seconds/minutes). Typical markets include high-frequency crypto prices, cross-market arbitrage, and automated market making.

• Unsuitable areas: markets dominated by insider information or purely random/manipulable, offering no advantage to any participant.

Prediction Market Position Management

Kelly Criterion is a prominent capital management theory in repeated games, aiming not to maximize single-trade profit but to maximize long-term compound growth. It calculates the optimal betting proportion based on estimated win probability and odds, assuming positive expected value, widely used in quantitative investing, professional betting, poker, and asset management.

The classic formula:

f* = (b*p - q) / b

where f* is the optimal fraction to bet, b is net odds, p is estimated win probability, q=1−p.

In simplified form:

• p: subjective true probability

• market_price: implied market probability

The effectiveness of Kelly depends heavily on accurate estimation of true probability and odds. In practice, traders find it difficult to consistently estimate true probabilities, so they tend to adopt rule-based strategies with better executability and lower reliance on precise probability estimates:

• Unit System: dividing capital into fixed units (e.g., 1%), betting different units based on confidence, with automatic risk limits per bet.

• Flat Betting: fixed proportion per bet, emphasizing discipline and stability, suitable for risk-averse or low-confidence environments.

• Confidence Tiers: preset discrete position levels with absolute caps, reducing decision complexity and avoiding the pseudo-precision of Kelly.

• Inverted Risk Approach: starting from maximum tolerable loss to determine position size, focusing on risk constraints rather than expected returns.

For prediction market agents, strategy design should prioritize executability and stability over theoretical optimality. Clear rules, simple parameters, and fault tolerance to judgment errors are key. Under these constraints, the ladder confidence method combined with fixed position caps is most suitable, as it does not rely on precise probability estimates but divides opportunities into limited tiers with fixed positions, with explicit risk limits even in high-confidence scenarios.

Prediction Market Strategy Types

From a structural perspective, prediction market strategies mainly fall into two categories:

• Deterministic Arbitrage: rule-based, codable strategies such as

  • Resolution Arbitrage: occurs when event outcomes are nearly certain but not fully priced; profits come from information synchronization and execution speed. Clear rules, low risk, fully codable, ideal for agents.

  • Dutch Book Arbitrage: exploits imbalance when prices of mutually exclusive, complete events deviate from probability conservation (∑P≠1). By constructing a portfolio, locks in riskless profit. Fully rule-based, low risk, suitable for automation.

  • Cross-platform Arbitrage: capturing price discrepancies of the same event across different markets; low risk but requires infrastructure for latency and parallel monitoring. Marginal returns decline as competition intensifies.

  • Bundle Arbitrage: trading related contracts with pricing inconsistencies; logical but opportunities are limited. Suitable for agents but requires engineering for rule parsing and constraints.

• Speculative Strategies: based on information interpretation and directional judgment, such as

  • Structured Information Trading: around official data releases, announcements, or rulings. Speed and discipline are advantages; semantic or scenario interpretation still often requires human input.

  • Signal Following: copying the behavior of historically successful accounts or funds; simple rules, automatable, but risks include signal degradation and reverse exploitation. Good as auxiliary strategies.

  • Unstructured/Noisy Strategies: rely on sentiment, randomness, or participant behavior, lacking stable edge, with unstable long-term expectations. Not suitable for systematic agent execution.

  • Market Microstructure: high-frequency, continuous quoting strategies requiring ultra-low latency and significant infrastructure; theoretically suitable but limited by liquidity and competition in prediction markets.

Risk Management and Hedging Strategies

These are not aimed at profit but at reducing overall risk exposure. Clear rules, objectives, and long-term operation make them suitable as foundational risk controls.

Overall, suitable prediction market strategies for agents are those with clear, codable rules and weak subjective judgment dependence. Deterministic arbitrage should be the core revenue source, supplemented by structured information and signal-following strategies. High-noise and sentiment-driven trading should be systematically excluded. The long-term advantage of agents lies in disciplined, high-speed execution and risk management.

4. Business Models and Product Forms of Prediction Market Agents

The ideal commercial model for prediction market agents can be explored at different levels:

• Infrastructure Layer: providing multi-source real-time data aggregation, Smart Money address databases, unified prediction market execution engines, and backtesting tools, charging B2B, ensuring stable income regardless of prediction accuracy.

• Strategy Layer: incorporating community and third-party strategies, building a reusable, evaluable strategy ecosystem, capturing value via calls, weights, or performance sharing, reducing reliance on single alpha.

• Agent/Vault Layer: agents manage funds directly with entrusted participation, leveraging transparent on-chain records and strict risk controls, earning management and performance fees.

Corresponding product forms include:

• Entertainment / Gamification: intuitive interfaces like Tinder to lower participation barriers, with strong user growth and market education potential; ideal for breaking into new audiences but requiring transition to subscription or execution-based monetization.

• Strategy Subscription / Signal: no fund custody, regulation-friendly, clear responsibilities, stable SaaS revenue; currently the most feasible commercial path. Limitations include easy strategy copying, execution slippage, and capped long-term revenue; can be improved with semi-automated “signal + one-click execution” models.

• Vault Custody: scalable and efficient, similar to asset management products, but faces licensing, trust, and centralization risks. Business depends heavily on market environment and profitability; not suitable as the main path unless backed by long-term performance and institutional trust.

Overall, a “Infrastructure monetization + strategy ecosystem expansion + performance participation” diversified revenue approach reduces reliance on the single assumption of “AI continuously beating the market.” Even as alpha converges with market maturity, underlying capabilities in execution, risk control, and settlement retain long-term value, enabling a more sustainable business cycle.

5. Examples of Prediction Market Agent Projects

Currently, prediction market agents are still in early exploration. While various attempts from foundational frameworks to upper-layer tools have emerged, no mature, standardized product exists in strategy generation, execution efficiency, risk control, and business closure.

The ecosystem can be divided into three levels: Infrastructure, Autonomous Agents, and Prediction Market Tools.

Infrastructure Layer

• Polymarket Agents Framework:

  Official developer framework aimed at standardizing “connection and interaction.” Encapsulates market data retrieval, order construction, and basic LLM calls. Solves “how to place orders via code” but leaves core trading capabilities—strategy generation, probability calibration, dynamic position management, backtesting—largely unaddressed. More a “connection standard” than a profit-generating product. Commercial agents require building complete research and risk control cores on top.

• Gnosis Prediction Market Tools:

  Provides full read/write support for Omen/AIOmen and Manifold, but only read access for Polymarket, with clear ecosystem barriers. Suitable as a development foundation within Gnosis; less practical for developers focusing on Polymarket.

Polymarket and Gnosis are currently the only prediction market ecosystems with official “agent development” productization. Others like Kalshi mainly provide APIs and Python SDKs, requiring developers to build their own strategy, risk, operation, and monitoring systems.

Autonomous Trading Agents

Most current “prediction market AI Agents” are still early-stage, with capabilities far from fully automated, independent, and systematic trading loops. They lack comprehensive risk management, position control, stop-loss, hedging, and expectation constraints, resulting in low product maturity and no stable, long-term operational system.

• Olas Predict: the most developed prediction market agent ecosystem. Its core product Omenstrat, based on Gnosis’s Omen, uses FPMM and decentralized arbitration, supporting small, high-frequency interactions but limited by low liquidity in Omen markets. Relies mainly on general LLMs, lacking real-time data and systematic risk controls. Historical win rates vary across categories. In Feb 2026, Olas launched Polystrat, extending agent capabilities to Polymarket—users can set strategies via natural language, with agents automatically identifying probability deviations within 4 days and executing trades. Uses Pearl local operation, self-hosted Safe accounts, and hardcoded risk limits, making it the first consumer-level autonomous trading agent targeting Polymarket.

• UnifAI Network Polymarket Strategy: offers automated trading agents that scan for implied probabilities >95% near settlement and buy, aiming for 3–5% price differences. On-chain data shows ~95% win rate, but returns vary across categories; highly dependent on execution frequency and category choice.

• Noya.ai aims to integrate “research—judgment—execution—monitoring” into a closed loop, covering intelligence, abstraction, and execution layers. Delivered Omnichain Vaults; Prediction Market Agent is in development, not yet a complete mainnet loop, still in proof-of-concept.

Prediction Market Tools

Current tools are insufficient to form a complete “prediction market agent,” mainly providing information and analysis layers. Trading execution, position management, and risk control are still user responsibilities. Product-wise, more aligned with “strategy subscription / signal assistance / research enhancement,” representing early prototypes.

By systematically reviewing and empirically screening projects in Awesome-Prediction-Market-Tools, this report highlights representative projects with initial product forms and use cases, focusing on four directions: analysis and signals, whale alert systems, arbitrage detection tools, and trading terminals with aggregation execution.

Market Analysis Tools

• Polyseer: research-oriented prediction tool using multi-agent division (Planner / Researcher / Critic / Analyst / Reporter) for evidence collection and Bayesian probability aggregation, producing structured reports. Transparent methodology, engineering process, fully open-source.

• Oddpool: positioned as “Bloomberg terminal for prediction markets,” offers cross-platform aggregation, arbitrage scanning, and real-time dashboards for Polymarket, Kalshi, CME, etc.

• Polymarket Analytics: global Polymarket data analysis platform, systematically displaying trader, market, position, and transaction data; clear, intuitive, suitable for research.

• Hashdive: trader-oriented data tool using Smart Score and multi-dimensional screener for identifying smart money and copy trading, practical for real-time decision-making.

• Polyfactual: focuses on AI market intelligence and sentiment/risk analysis, embedding insights into trading interfaces via Chrome extensions, targeting B2B and institutional users.

• Predly: AI-based mispricing detection platform comparing market prices with AI-calculated probabilities, claims 89% alert accuracy, for signal discovery and opportunity filtering.

• Polysights: covers 30+ markets and on-chain indicators, tracks anomalies like new wallets and large bets, suitable for daily monitoring and signals.

• PolyRadar: multi-model analysis platform providing real-time interpretation, timeline evolution, confidence scoring, and source transparency, emphasizing cross-AI validation, positioned as an analysis tool.

• Alphascope: AI-driven prediction market intelligence engine providing real-time signals, research summaries, and probability change monitoring; still early-stage, focused on research and signals.

Alert / Whale Tracking

• Stand: clearly targets whale follow and high-confidence action alerts.

• Whale Tracker Livid: productized whale position changes.

Arbitrage Detection Tools

• ArbBets: AI-driven arbitrage detection focusing on Polymarket, Kalshi, and sports betting markets, identifying cross-platform arbitrage and +EV opportunities, targeting high-frequency scanning.

• PolyScalping: real-time arbitrage and scalp analysis for Polymarket, supporting scans every 60 seconds, ROI calculation, Telegram alerts, with filters for liquidity, spreads, and volume, aimed at active traders.

• Eventarb: lightweight cross-platform arbitrage calculator and alert tool covering Polymarket, Kalshi, Robinhood; simple, free, suitable as a basic arbitrage assistant.

• Prediction Hunt: cross-exchange prediction market aggregation and comparison, providing real-time price comparisons and arbitrage detection (about every 5 minutes), aimed at uncovering information inefficiencies.

Trading Terminals / Aggregated Execution

• Verso: YC Fall 2024-backed institutional prediction market trading terminal, with Bloomberg-style interface, tracking 15,000+ contracts in real-time, deep data analysis, and AI news intelligence; aimed at professional and institutional traders.

• Matchr: cross-platform prediction market aggregation and execution tool, covering 1,500+ markets, with smart routing for best prices, planning automated strategies based on high-probability events, cross-market arbitrage, and event-driven yield; targeting execution and capital efficiency.

• TradeFox: supported by Alliance DAO and CMT Digital, a professional prediction market aggregation and prime brokerage platform, offering advanced order types (limit, stop-loss, TWAP), self-custody trading, and multi-platform routing; aimed at institutional traders, planning to expand to Kalshi, Limitless, SxBet, etc.

6. Summary and Outlook

Currently, prediction market agents are in early exploratory stages.

Market foundation and evolution: Polymarket and Kalshi have formed a duopoly, providing sufficient liquidity and scenario basis for building agents. The core difference between prediction markets and gambling is the positive externality—by aggregating real transactions and dispersing information, they 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 main task is to convert news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies with higher discipline, lower costs, and cross-market capabilities. The ideal architecture can be abstracted into four layers: information, analysis, strategy, and execution, but actual tradability heavily depends on settlement clarity, liquidity quality, and information structuring.

Strategy selection and risk logic: deterministic arbitrage (including settlement arbitrage, probability conservation arbitrage, and cross-platform price differences) are most suitable for automation, while directional speculation can only serve as a supplement. In position management, priority should be given to executability and fault tolerance, with ladder methods combined with fixed caps being most appropriate.

Business models and prospects: commercial deployment mainly involves three layers—Infrastructure for data and execution, Strategy ecosystem via third-party calls or revenue sharing, and On-chain participation with management and performance fees. Forms include entertainment/gamification, strategy subscription/signals (most feasible now), and high-threshold vault custody. A “Infrastructure + Strategy Ecosystem + Performance Participation” diversified revenue approach reduces reliance on the single assumption of “AI continuously beating the market.” Even as alpha converges with market maturity, underlying capabilities in execution, risk control, and settlement retain long-term value, enabling a more sustainable business cycle.

Despite the emergence of diverse foundational and tooling attempts, mature, replicable standard products in strategy generation, execution, risk management, and business closure are still lacking. We look forward to future iterations and evolution of prediction market agents.

Disclaimer: This report was assisted by AI tools such as ChatGPT-5.2, Gemini 3, and Claude Opus 4.5 during creation. The author has endeavored to verify and ensure accuracy but may have omissions. Please understand that in crypto markets, project fundamentals often diverge from secondary market prices. This content is for informational and academic/research purposes only and does not constitute investment advice or token buy/sell recommendations.