Jacob Zhao

10/ 🚀 Prediction market agents are still early — but as liquidity, data, and agent capability scale, → they converge toward a new form of automated finance built on probabilities 📚Follow BroadNotes for more deep dives: Substack → 0xjacobzhao.substack.com/p/turning-prob… Paragraph → paragraph.com/editor/ZrvT1Av… 🤝 Supported by @IOSGVC x.com/IOSGVC/status/…

9/ 🧱 The ecosystem is forming, but still early. No standardized product yet combines: strategy generation + execution efficiency + risk management + business loop 1) 🏗️ Infrastructure @Polymarket and @gnosis_ provide the only standardized agent frameworks (data + execution), but remain access layers—leaving strategy, risk, and full trading systems to developers; others like @Kalshi are still at the API/SDK level. @PolymarketBuild @shayne_coplan @LiamKovatch @elynch46 @shaykevin @mansourtarek_ @luanalopeslara @koeppelmann 2) 🤖Autonomous Agents @autonolas (Olas Predict): one of the most advanced stacks, with Omenstrat + Polystrat enabling LLM-driven strategies, mispricing detection, and automated execution with built-in risk controls via @pearldotyou @david_enim @Valorianxyz @tannedoaksprout @contentwillvary @hirschhe @UnifaiNetwork tail-risk Polymarket agent targeting >95% near-settlement spreads @sunny_unifAI @NetworkNoya building a research → execution stack (Omnichain Vaults live), still early in full closed-loop development @OptimisticOmni 3) 🧰Prediction Market Tools: Still concentrated in the 📊 information/analysis layer—closer to signals and research than full agents; ⚡ execution, 📦 position, and 🛡️ risk remain user-driven 📊Market Analysis: @UsePolyseer @Valyuofficial @yorkeccak · @oddpool_alerts @c0delemons · @poly_data @VaingloriousETH · @hash_dive · @polyfactual @basedlourie · @predlyai · @Polysights @tre_poh · @polyradar_io· @_alphascope_ 🚨Alerts / Whale Tracking: @StandDOTtrade @lastridgely @wolfeypackey ⚖️Arbitrage: @arbbets · @PolyScalping · @eventarbitrage · @PredictionHunt @JosephAndrewFr2 💻Aggregated Execution: @matchrxyz · Verso @agpkeleta · @tradefoxai @Prithvir12 @yoshi_eth2 🔗 Strong recommend Awesome Prediction Market Tools: github.com/aarora4/Awesom…

🚨 Just published a new research report: “Turning Probability into an Asset: The Rise of Prediction Market Agents.” 1/ Prediction Markets → A Truth Layer: Prediction markets turn dispersed information into tradable probability signals backed by real capital, evolving from betting tools into a potential global truth layer, led by the Polymarket / Kalshi duopoly amid competition between regulated and crypto-native models. 2/ Prediction Market Agents: Not about better AI predictions, but executable probabilistic portfolio management—turning probability mispricing into automated trades via Data → ML analysis → Strategy & risk → Execution. 3/Strategy & Risk: Agents should focus on markets with clear rules, liquidity, and structured information, using deterministic arbitrage (resolution arbitrage, Dutch book, cross-platform spreads) plus structured signals. Risk is controlled via rule-based position sizing and strict risk modules. 4/Business Model: A sustainable stack combines B2B infrastructure (data/execution/backtesting), strategy ecosystems, and Agent/Vault performance participation. Products may evolve from signal tools → semi-automated trading → managed vaults. 5/Industry Stage: The space is still early. Players fall into three groups: infrastructure frameworks, autonomous trading agents, and analytics/execution tools. 🚀 We may be approaching the breakout moment for Prediction Market Agents.

Polystrat is "one of the first consumer-grade trading agents for Polymarket." 💯 That is the verdict from @0xjacobzhao's latest article diving into the future of AI in prediction markets. ⬇️ The research features Olas Predict and Olas' two trading agents: Omenstrat and the newly launched Polystrat for Polymarket. 🔗 Try Polystrat: pearl.you/polystrat?utm_…

🙏 特别感谢以下机构与媒体的支持转载 @IOSGVC :mp.weixin.qq.com/s/IA3G3ozf7oSf… @Foresight_News ：foresightnews.pro/article/detail… @PANews ：panewslab.com/zh/articles/01… @ChainCatcher_ ：chaincatcher.com/article/2248912 @BlockBeatsAsia ：theblockbeats.info/news/61411 @ABMedia_Crypto ：abmedia.io/prediction-mar… @TechFlowPost ：techflowpost.com/zh-CN/article/… @ME_News ：me.news/contents/262568 🧠 本文为「博闻札记 BroadNotes」系列内容。 如果对你有启发，欢迎关注我的深度内容专栏👇 Substack：0xjacobzhao.substack.com/p/a3c Paragraph：@0xjacobzhao/%E8%AE%A9%E6%A6%82%E7%8E%87%E6%88%90%E4%B8%BA%E8%B5%84%E4%BA%A7%EF%BC%9A%E9%A2%84%E6%B5%8B%E5%B8%82%E5%9C%BA%E6%99%BA%E8%83%BD%E4%BD%93%E5%89%8D%E7%9E%BB" target="_blank" rel="nofollow noopener">paragraph.com/@0xjacobzhao/%…

@Polymarket @Kalshi @gnosis_ 10/ 尽管当前预测市场智能体生态仍处早期、尚未出现成熟的标准化产品，但随着流动性、数据基础设施与智能体能力的提升，有望逐步演化为一种新的自动化金融形态。

🚨《让概率成为资产：预测市场智能体前瞻》 📌核心观点 1/预测市场通过“真金白银的交易”把分散信息压缩成可交易的概率价格信号，正从类博彩的下注工具演化为可被金融与企业系统直接调用的“全球真相层”，并在 Polymarket/Kalshi 双寡头与合规分发 vs 加密原生两条路径竞争、以及各国监管分化的背景下加速发展。 2/预测市场智能体的核心不是“AI预测更准”，而是作为 可执行的概率资产管理系统，通过 信息层（多源数据）→ 分析层（LLM/ML识别错价）→ 策略层（仓位与风控）→ 执行层（多市场自动交易与套利） 的四层架构，将概率偏差高效转化为可自动化执行的交易机会。 3/预测市场智能体应只在规则清晰、流动性充足且信息结构化的市场中运行，以确定性套利（结算套利、Dutch Book、跨平台价差等）为核心策略、结构化信息与信号跟随为补充，并通过阶梯信心法 + 固定仓位上限的规则化资金管理与常驻风控模块，将高频数据处理与跨市场执行优势转化为可持续收益，同时系统性避开内幕主导或高操纵风险的市场。 4/预测市场智能体的商业模式为“基建B2B变现（数据/执行/回测）+ 策略生态分成（第三方策略调用与权重）+ Agent/Vault业绩参与（管理费+绩效费）”，对应产品形态从娱乐化入口引流到订阅/信号与半自动执行（当下最可行），再到高门槛的托管Vault。 5/当前预测市场智能体生态仍处早期、尚无策略生成/执行效率/风控/商业闭环都成熟的标准化产品，整体可分为基础设施框架（如 Polymarket Agents）、自主交易Agent（如 Olas Predict/Polystrat）与工具终端（研究信号、鲸鱼警报、套利发现、聚合执行等。 🚀 我们或正处在预测市场智能体爆发的前夜。

@IOSGVC @PANews @ChainCatcher_ @ABMedia_Crypto @BlockBeatsAsia @MetaEraCN @Foresight_News @TechFlowPost 《让概率成为资产：预测市场智能体前瞻》techflowpost.com/zh-CN/article/…

@IOSGVC @PANews @ChainCatcher_ @ABMedia_Crypto @BlockBeatsAsia @MetaEraCN @Foresight_News @Foresight_News 《让概率成为资产，预测市场智能体前瞻》foresightnews.pro/article/detail…