ChatGPT-Powered Trading Bot Outperforms Market in High School Student's Experiment

Someone Gave ChatGPT $100 and Let Trade Stocks for a Month

With $100 and a dream, one enterprising Redditor turned ChatGPT into a day trader, and the results so far have been pretty remarkable. In a post on r/Dataisbeautiful, the Redditor in question -- real name Nathan Smith -- described his project as a "6-month experiment to see how a language model performs in picking small, [under-covered] stocks with only a $100 budget." According to a chart shared on Reddit, this literal gamble is already paying off. Using GPT-4o, one of OpenAI's most advanced models, the bot-trader's stock portfolio has increased in value by 25 percent over its first month -- though given that Smith only invested $100, that means he's only made $25 so far. What's more, that rise was significantly higher than two "small-cap" stock indexes, the Russell 2000 and XBI -- in fact, the S&P 500 is up less than 3 percent over the past month -- which suggests that ChatGPT very much picked correctly. To be fair, this is far from the first time someone's attempted such a gambit. Last December, researchers from Germany's Duisburg-Essen University published a paper in the journal Finance Research Letters finding that advanced OpenAI models did indeed seem to select money-making stocks. In an interview with Morningstar in June, meanwhile, University of Florida assistant finance professor Alejandro Lopez-Lira said that over years of simulating stock selection, ChatGPT wasn't all that great. "Our results on paper are much more optimistic than what the performance in reality would be with a reasonable investment size," Lopez-Lira told the finance blog. There's also a clear logical issue: if AI really was already better than the average human at picking stocks, then all traders would start using AI, changing the entire dynamics of the market and likely meaning that future trades wouldn't operate under the same logic. Responding to the larger trend, Smith decided, as he explained in a GitHub page documenting the experiment, to undertake the endeavor after seeing gimmicky ads that claimed AI could pick undervalued stocks and make investors mucho dinero. "It was obvious it was trying to get me to subscribe to some garbage, so I just rolled my eyes," he wrote. "Then I started wondering, 'How well would that actually work?'" As it turns out, it works quite well -- but not without human input. Each day, as Smith's GitHub explains, he provides ChatGPT with trading data about its stock portfolio. Smith also said that he implements a strict "stop-loss" rules, which require a trader -- in this case, ChatGPT -- to immediately sell when a stock reaches a certain price. Though the experiment's stated purpose is to see whether AI can "manage money without guidance," that obviously hasn't happened just yet. Smith has, per his own acknowledgement, committed to daily homework with the trading data inputs until the end of December. Even if that task only takes a few minutes, it's still very much an example of human intervention into a project that, on its face, was meant to let ChatGPT do its thing. Still, it's a fascinating look into what AI, a bit of muscle grease, and $100 can do on the stock market -- at least in an otherwise buoyant financial month. The real question? Whether the bot's portfolio will be up or down by the end of the experiment.

High School Student's ChatGPT Trading Bot Is Crushing the Russell 2000 - Decrypt

Wall Street's bots watch their backs: Smith's journey from rural high schooler to finance prodigy is just getting started. A high school kid from rural Oklahoma just did what Wall Street's algorithms haven't: he let ChatGPT run wild with $100 and watched it outperform the market by a massive margin. Nathan Smith's experiment started four weeks ago with a simple premise -- give ChatGPT complete control over a small portfolio of micro-cap stocks and see what happens. The results? A 23.8% return while the Russell 2000 and biotech ETF XBI crawled up just 3.9% and 3.5% respectively. "When I was watching YouTube, I was constantly getting this ad about some AI stock picker: '(the ad said) We feed our trading algorithm all stocks on the NYSE blah blah blah...'" Smith told Decrypt. "I then started doing some research and was surprised nobody had attempted a fully led LLM portfolio before." The setup sounds deceptively simple. Smith gave ChatGPT a clear mandate: build the strongest possible stock portfolio using only full-share positions in U.S.-listed micro-cap stocks with market caps under $300 million. The objective was straightforward -- generate maximum returns from June 27 to December 27, 2025. What sets this apart from other trading algorithms is the complete autonomy. "The AI takes care of absolutely everything. Position sizing, stop loss, etc., are none of my decisions," Smith told Decrypt in an email. The only human intervention comes when ChatGPT contradicts itself -- a quirk he acknowledges as one of the system's drawbacks. Smith recently calculated the risk metrics that professional traders obsess over. His Sharpe ratio sits at 0.9413, indicating high risk, while the Sortino ratio of 2.0021 suggests strong upside gains with limited downside. For context, a Sharpe ratio above 1.0 is generally considered good, while anything below suggests the returns might not justify the risk. The chatbot, which is fully open-sourced and is available on Smith's GitHub repo, has no diamond hands and seems to be pretty objective, most of the time. One of ChatGPT's moves involved CADL, a stock that generated roughly 50% of the portfolio's profits. "It sold CADL without a second thought," Smith said. "I think it wisely knew that in micro-cap territory, all gains can be wiped out in an instant. Not many hedge funds could make such a decisive move." Smith's system has been operating for only a month, which is not enough to backtest or assess with a high confidence level, but the positive results thus far are promising. The technical infrastructure behind this experiment is a bit more complex than simply using your typical chatbot, but it is still straightforward to implement with a little bit of dedication. Smith built five main functions: manual buying and selling for new picks, portfolio processing for tracking trades, daily results generation using Yahoo Finance data, and graph generation for visualizing performance against the S&P 500. "Honestly, the setup is pretty simple," he said, describing a system that pulls benchmark prices from Yahoo Finance API into Panda's data frames for analysis. ChatGPT chooses stocks once per week, and always stays under the $300 million market cap limit, while Smith executes the trades and logs the results. The teenager from a tiny Oklahoma town discovered his passion almost by accident. "I have coded a little in the past (working on Harvard's online CS course CS50), but using C didn't feel invigorating (stupid segmentation fault errors)," he told Decrypt. "Over the summer, I discovered Quantitative Finance and the beauty of Python, and I fell in love." Oh, the things high schoolers do nowadays. With almost 1,000 GitHub commits this past year and a good amount of followers on his newsletter, Smith has thrown himself into the world of quantitative finance. He plans to extend the experiment to a full year once it wraps in December, though he admits balancing it with ACT studying and self-studying for AP Psychology will be challenging. "I really think I've found my passion in life and hope to continue this as a real job one day," Smith said.

AI Trading Bots Are Booming -- But Can You Trust Them With Your Money? - Decrypt

Generally speaking, AI agents and chatbots are better at fundamental analysis than reliable technical analysis. When 17-year-old Nathan Smith handed a ChatGPT-powered trading bot a portfolio of micro-cap stocks, it delivered a 23.8% gain in four weeks -- outperforming the Russell 2000 and launching him from rural Oklahoma to viral Reddit stardom. Smith's journey from rural high schooler to peak r/wallstreetbets poster boy is part of a bigger movement blossoming across the internet with traders building stock-picking systems around off-the-shelf large language models. The internet is littered with viral claims about AI trading success. One Reddit post recently caught fire after claiming ChatGPT and Grok achieved a "flawless, 100% win rate" over 18 trades with pretty big gains. Another account gave $400 to ChatGPT with the aim of becoming "the world's first AI-made trillionaire" Neither post, however, has provided verification -- there are no tickers, trade logs, or receipts. Smith, however, garnered attention precisely because he's documenting his journey on his Substack, and sharing his configurations, prompts, and documentation on GitHub. This means, you can replicate, improve, or modify his code anytime. AI-powered trading isn't just a Reddit fantasy anymore -- it's quickly becoming Wall Street reality. From amateur coders deploying open-source bots to investment giants like JPMorgan and Bridgewater building bespoke AI platforms, a new wave of market tools promises faster insights and hands-free gains. But as personal experiments go viral and institutional tools quietly spread, experts warn that most large language models still lack the precision, discipline, and reliability needed to trade real money at scale. The question now isn't whether AI can trade -- it's whether anyone should let it. JPMorgan rolled out an internal platform called LLM Suite, described as a "ChatGPT-like product" to 60,000 employees. It parses Fed speeches, summarizes filings, generates memo drafts, and powers a thematic idea engine called IndexGPT that builds bespoke theme-based equity baskets. Goldman Sachs calls its chatbot the GS AI Assistant, built on its proprietary LLaMA-based GS AI Platform. Now on 10,000 desktops across engineering, research, and trading desks, it reportedly generates up to 20% productivity gains for code-writing and model-building. Bridgewater's research team built its Investment Analyst Assistant on Claude, using it to write Python, generate charts, and summarize earnings commentary -- tasks a junior analyst would do in days, done in minutes. Norway's sovereign wealth fund (NBIM) uses Claude to monitor news flow across 9,000 companies, saving an estimated 213,000 analyst hours annually. Elsewhere, platforms like 3Commas, Kryll, and Pionex offer ChatGPT integration for trading automation, according to Phemex. In February 2025, Tiger Brokers integrated DeepSeek's AI model, DeepSeek-R1, into their chatbot, TigerGPT, enhancing market analysis and trading capabilities. At least 20 other firms, including Sinolink Securities and China Universal Asset Management, have adopted DeepSeek's models for risk management and investment strategies. All this raises an obvious question: Have we finally gotten to the point where AI can make good financial bets? Multiple studies suggest that AI, and even ChatGPT-enhanced systems, can outperform both manual and conventional machine learning models in predicting crypto price movements. However, broader research from BCG and Harvard Business School warned against over-reliance on generative AI, mentioning that GPT-4 users performed 23% worse than users eschewing AI. That jibes with what other professionals are seeing. "Just because you have more data doesn't mean you add more returns. Sometimes you're just adding more noise," said Man Group's CIO Russell Korgaonkar. Man Group's systematic trading arm has been training ChatGPT to digest papers, write internal Python, and sort ideas off watchlists -- but you'll still have to do a big part of the heavy lifting before even thinking about using an AI model reliably. For Korgaonkar, generative AI and typical machine learning tools have different uses. ChatGPT can help you with fundamental analysis, but will suck at price predictions, whereas the non-generative AI tools are unable to tackle fundamentals but can analyze data and do pure technical analysis. "The breakthroughs of GenAI are on the language side. It's not particularly helpful for numerical predictions," he said. "People are using GenAI to help them in their jobs, but they're not using it to predict markets." Even for fundamental analysis, the process that leads an AI to a specific conclusion is not necessarily always reliable. "The fact that models have the ability to conceal underlying reasoning suggests troubling solutions may be avoided, indicating the present methods of alignment are inadequate and require tremendous improvement," BookWatch founder and CEO Miran Antamian told Decrypt. "Instead of just reprimanding 'negative thinking,' we must consider blended approaches of iterative human feedback and adaptive reward functions that actively shift over time. This could greatly aid in identifying behavioral changes that are masked by penalties." Gappy Paleologo, partner at Balyasny, pointed out that LLMs still lack "real-world grounding" and the nuanced judgment needed for high-conviction bets. He sees them best as research assistants, not portfolio managers. Other funds warn of model risk: These AIs are prone to propose implausible scenarios, misread macro language, and hallucinate -- leading firms to insist on human-over-the-loop auditing for every AI signal. And what's even worse, the better the model is, the more convincing it will be at lying, and the harder it will be for it to admit a mistake. There are studies that prove this. In other words, so far, it's extremely hard to take humans out of this equation, especially when money is involved. "The concept of monitoring more powerful models using weaker ones like GPT-4o is interesting, but it is unlikely to be sustainable indefinitely," Antamian told Decrypt. "A combination of automated and human expert evaluation may be more suitable; looking at the level of reasoning provided may require more than one supervised model to oversee." Even ChatGPT itself remains realistic about its limitations. When asked directly about making someone a millionaire through trading, ChatGPT responded with a realistic outlook -- acknowledging that while it's possible, success depends on having a profitable strategy, disciplined risk management, and the ability to scale effectively. Still, for hobbyists, it's fun to tinker with this stuff. If you're interested in exploring AI-assisted trading without the full automation, Decrypt has developed its own prompts, just for fun -- and clicks, probably. Our Degen Portfolio Analyzer delivers personalized, color-coded risk assessments that adapt to whether you're a degenerate trader or a conservative investor. The framework integrates fundamental, sentiment, and technical analysis while collecting user experience, risk tolerance, and investment timeline data. Our Personal Finance Advisor prompt aims to deliver institutional-grade analysis using the same methodologies as major investment firms. When tested on a Brazilian equity portfolio, it identified concentrated exposure risks and currency mismatches, generating detailed rebalancing recommendations with specific risk management strategies. Both prompts are available on GitHub for anyone looking to experiment with AI-assisted financial analysis -- though as Smith's experiment shows, sometimes the most interesting results come from letting the AI take the wheel entirely and just execute what the machine says. Not that we would ever advise anyone to do that. Though you might not have a problem giving $100 to ChatGPT to invest, there's no chance you'll see JP Morgan doing that. Yet.

Redditor's $100 ChatGPT Day-Trading Experiment Trounces Two Benchmark Indexes With 24% Returns -- Here's How It Pulled It Off

Enter your email to get Benzinga's ultimate morning update: The PreMarket Activity Newsletter With $100 and a dare to see what AI can do, a Reddit user turned ChatGPT into a micro-cap day trader and in its first month, the bot beat two small-cap benchmarks by a wide margin. What Happened: Nathan Smith, who documents the project on Reddit and GitHub, fed OpenAI's GPT-4o daily portfolio data, enforced strict stop-loss rules, and limited picks to U.S. micro-caps under $300 million. After four weeks, his account was up roughly 24-25%, outpacing the Russell 2000 and the SPDR S&P Biotech ETF (XBI), which each rose about 3-4% over the same span, according to his charts. Smith describes the effort as a six-month 'live experiment' to test whether a language model can find alpha in thinly covered names with only a $100 stake. The setup is simple but structured. The model proposes buys and sells weekly, Smith executes the trades and posts the logs and a Python script tracks performance versus benchmarks. He credits the guardrails, which include position limits, manual execution and automatic stop-losses, for keeping the system disciplined. See also: Reddit CEO Steve Huffman Says AI Learns From Us, Doesn't Invent Knowledge: 'Can't Have Artificial Intelligence Without Actual Intelligence' The early math is what turned heads. Smith's chart shows GPT-4o's equity curve sprinting ahead while small-cap gauges lag. He also reports risk metrics such as Sharpe and Sortino to address the critique of just simply taking more risk, which was a common sentiment in the comments. Trending Investment OpportunitiesAdvertisementArrivedBuy shares of homes and vacation rentals for as little as $100. Get StartedWiserAdvisorGet matched with a trusted, local financial advisor for free.Get StartedPoint.comTap into your home's equity to consolidate debt or fund a renovation.Get StartedRobinhoodMove your 401k to Robinhood and get a 3% match on deposits.Get Started A report by Decrypt pegs Smith's four-week return at 23.8%, versus gains of roughly 3.9% for the Russell 2000 and 3.5% for XBI. For context, the broader S&P 500 climbed only a few percent in that window. Why It Matters: The experiment had some caveats. For starters, the process has only run for a month, and the bot has leaned into volatile biotech names, a sector where 20% daily swings are not rare. However, even Smith tells readers this is an experiment and not financial advice. Researchers have tried AI stock-picking before with mixed results. A German team reported in Finance Research Letters that advanced OpenAI models picked profitable stocks, while University of Florida's Alejandro Lopez-Lira told Morningstar that long-run simulations show ChatGPT underperforms with realistic capital. Read Next: Reddit Skyrockets 18% After Hours As Q3 Outlook, Earnings Beat Spark Investor Frenzy Photo Courtesy: Primakov on Shutterstock.com Market News and Data brought to you by Benzinga APIs