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中文(ZH) 我讓一個 AI 拿 2000 塊台幣去股市，目標 30 天翻倍，這是第 0 天

AI agent to autonomously trade stocks with NT$2,000 for 30 days

By PulseAugur Editorial · [1 sources] ·

The author is initiating a 30-day experiment where an AI agent, built on the DuDuClaw platform, will autonomously manage a real stock trading account with an initial capital of NT$2,000. The primary goal is to test whether a Large Language Model (LLM) can evolve into a Large World Model (LWM) capable of learning and adapting in a high-stakes environment, rather than just predicting the next token. The experiment will track the AI's strategy development, trading decisions, and its ability to learn from both successes and failures, with a target of doubling the initial investment. AI

IMPACT Tests the practical application of LLMs in complex, high-stakes decision-making environments like stock trading.

RANK_REASON The article describes an experiment using an AI agent platform for a real-world task, rather than a new model release or core research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agent to autonomously trade stocks with NT$2,000 for 30 days

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The article describes an experiment using an AI agent platform for a real-world task, rather than a new model release or core research.
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product, other
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High
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47 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 中文(ZH) · 力智栩 ·

    I let an AI use NT$2,000 to invest in the stock market, aiming to double it in 30 days. This is Day 0.

    <p>先講清楚這件事在賭什麼。</p> <p>我開了一個真實的台股帳戶，裡面 2200 塊，交給一個 AI agent 全權操作。30 天，目標把它翻成兩倍。過程我不介入、不追加資金、不幫它踩煞車。它自己上網找策略、自己下單、自己覆盤，賠光了就是賠光了。</p> <p>達標的機率我自己估很低。但這個實驗要驗的根本不是「AI 能不能靠 2000 塊變 4000」，而是一個更底層的問題：<strong>一個只會預測下一個字的語言模型，能不能學會預測一個世界？</strong></p> <h2> LLM 跟 LWM 差在哪 </h2> <p>大型語言模型（LL…

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