[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-5-ways-coinquant-is-built-for-ai-agents-en":3,"article-related-5-ways-coinquant-is-built-for-ai-agents-en":33,"series-industry-7e0af651-9a7b-42c1-9a63-a263b2738e63":85},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":32},"7e0af651-9a7b-42c1-9a63-a263b2738e63","5-ways-coinquant-is-built-for-ai-agents-en","5 ways CoinQuant is built for AI agents","\u003Cp data-speakable=\"summary\">CoinQuant now lets traders and \u003Ca href=\"\u002Ftag\u002Fai-agents\">AI agents\u003C\u002Fa> build and run crypto strategies in one system.\u003C\u002Fp>\n\u003Cp>CoinQuant’s move matters because it already has more than 15,000 users, and now it is aiming at the \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> economy with a single trading stack for humans and autonomous systems.\u003C\u002Fp>\n\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>What it does\u003C\u002Fth>\u003Cth>Notable scale\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>CoinQuant\u003C\u002Ftd>\u003Ctd>No-code crypto strategy building and execution\u003C\u002Ftd>\u003Ctd>15,000+ users\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>AI agent market\u003C\u002Ftd>\u003Ctd>Machine-to-machine trading and payments\u003C\u002Ftd>\u003Ctd>$73 million settled, 176 million transactions\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Coinbase agentic wallets\u003C\u002Ftd>\u003Ctd>Agent wallet infrastructure\u003C\u002Ftd>\u003Ctd>50 million+ transactions\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Circle Agent Stack\u003C\u002Ftd>\u003Ctd>Wallets, marketplace, nanopayments\u003C\u002Ftd>\u003Ctd>Launched May 2026\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\n\u003Ch2>1. Plain-English strategy to full trading system\u003C\u002Fh2>\n\u003Cp>CoinQuant’s core pitch is simple: users describe a strategy in ordinary language, and the platform turns it into a working trading setup. That includes entries, exits, position sizing, filters, and risk rules, so the user does not need to write code first.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779773150596-s05o.png\" alt=\"5 ways CoinQuant is built for AI agents\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cp>This is the part that makes the product usable for both retail traders and technical teams. A software engineer quoted in the story said he could speak an idea into CoinQuant, test it, and launch a bot on his lunch break.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Inputs: verbal or written strategy ideas\u003C\u002Fli>\n  \u003Cli>Outputs: executable trading logic\u003C\u002Fli>\n  \u003Cli>Coverage: entries, exits, sizing, filters, risk controls\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>2. Tick-level backtesting without manual setup\u003C\u002Fh2>\n\u003Cp>The platform automatically handles tick-level backtesting, which is a major part of strategy validation. Instead of stitching together data pipelines and test scripts, users can check how a strategy would have behaved at fine-grained market intervals.\u003C\u002Fp>\n\u003Cp>For traders, this matters because execution details often decide whether a strategy works in live markets. For AI agents, it creates a test layer they can use before deploying capital or adjusting parameters.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Backtest depth: tick-level\u003C\u002Fli>\n  \u003Cli>Use case: validate entries, exits, and sizing rules\u003C\u002Fli>\n  \u003Cli>Benefit: faster iteration before live deployment\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>3. A shared layer for humans and autonomous agents\u003C\u002Fh2>\n\u003Cp>CoinQuant is not just adding AI features on top of a trading app. It is repositioning the platform as a unified intelligence architecture that can serve human traders and autonomous AI agents at the same time.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779773151224-xtjd.png\" alt=\"5 ways CoinQuant is built for AI agents\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\n\u003Cp>That shift matters because agentic systems need more than a wallet or payment rail. They need a strategy engine that can create, test, and execute decisions without manual intervention at every step.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>Human use: no-code strategy creation\u003C\u002Fli>\n  \u003Cli>Agent use: autonomous deploy, test, and execute loops\u003C\u002Fli>\n  \u003Cli>Architecture goal: one system, two operator types\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>4. A bet on the agent economy\u003C\u002Fh2>\n\u003Cp>CoinQuant’s expansion is tied to a broader market trend. According to Keyrock research cited in the story, AI agents settled more than $73 million across 176 million blockchain transactions in the 12 months through April 2026.\u003C\u002Fp>\n\u003Cp>Those numbers show why trading tools are moving toward agent-native design. If agents are becoming economic actors, they need infrastructure that can manage strategy logic, not just payments or custody.\u003C\u002Fp>\n\u003Ccode>Agent economy signals:\n- $73 million settled\n- 176 million blockchain transactions\n- 1 million+ potential autonomous trading agents\u003C\u002Fcode>\n\n\u003Ch2>5. Part of a larger crypto AI stack\u003C\u002Fh2>\n\u003Cp>CoinQuant is entering a field that already includes agentic wallets, payment cards, and micropayment rails. The story points to Coinbase’s agentic wallets via \u003Ca href=\"\u002Ftag\u002Fx402\">x402\u003C\u002Fa>, Circle’s Agent Stack, and MoonPay’s MoonAgents Card as signs that the stack is filling in fast.\u003C\u002Fp>\n\u003Cp>CoinQuant’s role is narrower but important: it focuses on the strategy layer. In other words, it helps agents decide what to trade and how to trade it, while other products handle spending and settlement.\u003C\u002Fp>\n\u003Cul>\n  \u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.coinbase.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Coinbase\u003C\u002Fa>: agentic wallet infrastructure\u003C\u002Fli>\n  \u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.circle.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">Circle\u003C\u002Fa>: Agent Stack with wallets and marketplace tools\u003C\u002Fli>\n  \u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.moonpay.com\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">MoonPay\u003C\u002Fa>: AI-native payment card infrastructure\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>How to decide\u003C\u002Fh2>\n\u003Cp>If you are a trader who wants to test ideas fast without coding, CoinQuant fits the no-code path. If you are building agent software, the more interesting angle is its strategy engine, since that is the layer that turns market intent into action.\u003C\u002Fp>\n\u003Cp>For readers tracking crypto \u003Ca href=\"\u002Ftag\u002Fai-infrastructure\">AI infrastructure\u003C\u002Fa>, the takeaway is that trading is becoming one of the first real use cases for autonomous agents. CoinQuant is betting that the winners will be the tools that can translate plain language into live market behavior.\u003C\u002Fp>","5 ways CoinQuant’s new architecture lets traders and AI agents build, test, and run crypto strategies.","crypto.news","https:\u002F\u002Fcrypto.news\u002Fcoinquant-trading-launches-ai-agent-architecture\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779773150596-s05o.png","industry","en","d1e74fd1-7cc0-448d-b37f-68d2dbd9b689",[17,18,19,20,21,22,23,24],"CoinQuant","AI agents","crypto trading","no-code trading","agent economy","algorithmic trading","backtesting","crypto infrastructure",[26,27,28],"CoinQuant now targets both human traders and autonomous AI agents.","The platform turns plain-language strategies into tested trading systems.","The move fits a broader crypto push toward agent-native infrastructure.",3,"2026-05-26T05:25:24.278705+00:00","2026-05-26T05:25:24.269+00:00","d19fc184-5852-4c4d-9ec0-db0c4841ac17",{"tags":34,"relatedLang":45,"relatedPosts":49},[35,37,39,41,43],{"name":19,"slug":36},"crypto-trading",{"name":21,"slug":38},"agent-economy",{"name":17,"slug":40},"coinquant",{"name":20,"slug":42},"no-code-trading",{"name":18,"slug":44},"ai-agents",{"id":15,"slug":46,"title":47,"language":48},"5-ways-coinquant-is-built-for-ai-agents-zh","5 個 CoinQuant 的 AI 代理設計","zh",[50,55,61,67,73,79],{"id":51,"slug":52,"title":53,"cover_image":11,"image_url":11,"created_at":54,"category":13},"8675d217-c331-410c-adb6-da16fab59986","gemini-apple-developer-stack-en","Gemini lands inside Apple’s developer stack","2026-06-10T03:32:35.248625+00:00",{"id":56,"slug":57,"title":58,"cover_image":59,"image_url":59,"created_at":60,"category":13},"85371bc5-985a-49bd-a01d-cd9e48907662","five-ai-coding-ides-real-workflows-en","Five AI coding IDEs that fit real workflows","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781061481755-sbfn.png","2026-06-10T03:17:29.018169+00:00",{"id":62,"slug":63,"title":64,"cover_image":65,"image_url":65,"created_at":66,"category":13},"4ae93965-4b93-40ae-a3b0-65cfafa0465e","devin-desktop-windsurf-agent-hub-en","Devin Desktop turns Windsurf into an agent hub","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781060568779-u86v.png","2026-06-10T03:02:19.170995+00:00",{"id":68,"slug":69,"title":70,"cover_image":71,"image_url":71,"created_at":72,"category":13},"af3fd811-1233-4c99-955c-ea199afd91d7","korea-nvidia-talks-ai-factory-push-en","Korea’s Nvidia talks point to an AI factory push","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781057870737-hb3x.png","2026-06-10T02:17:21.544572+00:00",{"id":74,"slug":75,"title":76,"cover_image":77,"image_url":77,"created_at":78,"category":13},"72823fc3-fb0c-41fa-ba83-83eb7cc3880b","openai-should-not-rush-its-ipo-en","OpenAI should not rush its IPO just to win the AI race","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781053364904-2rcp.png","2026-06-10T01:02:20.320813+00:00",{"id":80,"slug":81,"title":82,"cover_image":83,"image_url":83,"created_at":84,"category":13},"73c81054-d5b7-4fb9-8487-c93d603ff85b","openai-europe-privacy-policy-en","OpenAI updates its Europe privacy 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Innovations","2026-03-25T16:22:47.882615+00:00",{"id":102,"slug":103,"title":104,"created_at":105},"0ebb5b16-774a-4922-945d-5f2ce1df5a6d","claude-usage-diversifies-learning-curves-en","Claude Usage Diversifies, Learning Curves Emerge","2026-03-25T16:25:50.770376+00:00",{"id":107,"slug":108,"title":109,"created_at":110},"69934e86-2fc5-4280-8223-7b917a48ace8","openclaw-ai-commoditization-concerns-en","OpenClaw's Rise Raises Concerns of AI Model Commoditization","2026-03-25T16:26:30.582047+00:00",{"id":112,"slug":113,"title":114,"created_at":115},"b4b2575b-2ac8-46b2-b90e-ab1d7c060797","google-gemini-ai-rollout-2026-en","Google's Gemini AI Rollout Extended to 2026","2026-03-25T16:28:14.808842+00:00",{"id":117,"slug":118,"title":119,"created_at":120},"6e18bc65-42ae-4ad0-b564-67d7f66b979e","meta-llama4-fabricated-results-scandal-en","Meta's Llama 4 Scandal: Fabricated AI Test Results 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