{"id":45231,"date":"2025-10-27T11:39:50","date_gmt":"2025-10-27T03:39:50","guid":{"rendered":"https:\/\/www.1ai.net\/?p=45231"},"modified":"2025-10-27T11:39:50","modified_gmt":"2025-10-27T03:39:50","slug":"deepseek-%e9%a2%86%e8%b7%91-ai-%e5%ae%9e%e7%9b%98%e4%ba%a4%e6%98%93%e5%af%b9%e5%86%b3%ef%bc%8c%e6%94%b6%e7%9b%8a%e8%be%be-9-68","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/45231.html","title":{"rendered":"DeepSeek lead, AI, real-time trade-offs, yielding 9.68%"},"content":{"rendered":"<p>October 27th, according to \"Ni Jiwon\", an open source project led by the team of Professor Wong, University of Hong Kong<a href=\"https:\/\/www.1ai.net\/en\/tag\/ai-trader\" title=\"[See articles with [AI-Trader] label]\" target=\"_blank\" >AI-Trader<\/a>This post is part of our special coverage Syria Protests 2011<a href=\"https:\/\/www.1ai.net\/en\/tag\/deepseek\" title=\"[View articles tagged with [DeepSeek]]\" target=\"_blank\" >DeepSeek<\/a> The model is ranked first in real-American stock trading experiments with a return of 9.68%, significantly exceeding the top international large models of GPT, Claude and Gemini\u3002<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-45232\" title=\"169cfe05j00t4ru4m007id000u000ibm\" src=\"https:\/\/www.1ai.net\/wp-content\/uploads\/2025\/10\/169cfe05j00t4ru4m007id000u000ibm.jpg\" alt=\"169cfe05j00t4ru4m007id000u000ibm\" width=\"1080\" height=\"659\" \/><\/p>\n<p>IN THE EXPERIMENT, THE RESEARCH TEAM ALLOCATED $10,000 TO EACH OF THE FIVE AI MODELS AND ALLOWED THEM TO TRADE AUTONOMOUSLY FOR ALMOST A MONTH IN THE MARKET FOR THE 100 COMPONENT UNITS IN NASDAQ\u3002<\/p>\n<p>The rules strictly restrict the \"three principles\": no trade scripts, no artificial interference, no open channels for cheating. In the end, DeepSeek demonstrated a robust investment strategy, precise layout of NVDA, AAPL, MSFT (Microsoft) technology giants, and spread risk through multi-standard combinations and dynamic silos\u3002<\/p>\n<p>By contrast, Gemini performed 73 HF transactions in a short period of time, resulting in losses of 2.73% due to lack of risk control; Qwen recorded negative returns on only 22 transactions. Claude ranks second with the positive proceeds of 2.17%\u3002<\/p>\n<p>Currently, \"AI-Trader\" is fully open-source, using the MIT protocol, which allows users to access code and rapidly deploy via GitHub\u3002<\/p>\n<p>THE PROJECT ALSO SUPPORTS TIME MACHINE MODELS, CUSTOMISED TRADE AGENTS AND MULTI-MARKET EXPANSION. THE RESEARCH TEAM HIGHLIGHTED THE COMPLEXITY OF FINANCIAL MARKETS AND REAL-TIME FEEDBACK MECHANISMS, MAKING THEM THE ULTIMATE TEST OF AI INTELLIGENCE\u3002<\/p>\n<p>\ud83d\udcbb Project page: https:\/\/github.com\/HKUDS\/AI-Trader<\/p>","protected":false},"excerpt":{"rendered":"<p>According to news from October 27th, \"Nu Jiwon\" reported that the latest results of the Open Source Project \"AI-Trader\" led by the team of professors from the University of Hong Kong, DeepSeek was ranked first in the real US stock exchange experiment with a return rate of 9.68%, significantly exceeding the top international models of GPT, Claude and Gemini. In the experiment, the research team allocated $10,000 to each of the five AI models and allowed them to trade autonomously in the market for 100 components in NASDAQ for almost a month. The rules strictly restrict the \"three principles\": no trade scripts, no artificial interference, no open channels for cheating. Eventually, DeepSeek showed a robust investment strategy, precise layout of the NVDA<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[7798,3606,7771],"collection":[],"class_list":["post-45231","post","type-post","status-publish","format-standard","hentry","category-news","tag-ai-trader","tag-deepseek","tag-7771"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/45231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=45231"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/45231\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=45231"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=45231"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=45231"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=45231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}