In a remarkable display of innovation, 17-year-old Nathan Smith from rural Oklahoma has developed a fully-automated trading model using ChatGPT that has outperformed the Russell 2000 Index over a four-week period. Starting with a modest $100 investment, Nathan’s AI-driven portfolio achieved a 23.8% return, significantly surpassing the Russell 2000’s 3.9% gain and the SPDR S&P Biotech ETF (XBI)’s 3.5% increase during the same timeframe. This achievement, reported on August 1, 2025, has sparked widespread interest in the potential of AI in financial trading. This article explores Nathan’s experiment, its methodology, performance, and implications for the future of investing. Background: A Young Innovator’s Vision Nathan Smith, a high school student with a passion for technology and finance, embarked on this ambitious project after being inspired by advertisements about AI-driven stock pickers. Curious about whether a large language model like ChatGPT could effectively manage a stock portfolio, Nathan designed a six-month experiment to test its capabilities. His goal was to see if AI could generate consistent returns, or “alpha,” in the volatile world of micro-cap stocks. Unlike traditional investors with access to expensive tools, Nathan relied solely on ChatGPT, a $100 budget, and his coding skills, making his achievement all the more impressive. The Experiment: How ChatGPT Trades Stocks Nathan’s experiment, running from June 27 to December 27, 2025, is designed to evaluate ChatGPT’s ability to manage a $100 portfolio of U.S.-listed micro-cap stocks — companies with market capitalizations under $300 million. These stocks are often under-analyzed due to limited media coverage, making them a challenging yet intriguing testbed for AI-driven analysis. The experiment’s structure is both simple and rigorous: AI Autonomy: ChatGPT has full control over the portfolio, making all buy and sell decisions without human intervention, except for Nathan executing the trades as instructed. Weekly Deep Research: Each week, ChatGPT conducts a comprehensive analysis, or “deep research,” to reevaluate its holdings and adjust the portfolio based on market data. Micro-Cap Focus: The AI is restricted to full-share positions in U.S.-listed micro-cap stocks, which are inherently volatile but offer potential for high returns. Transparency: Nathan documents the entire process on platforms like Reddit (r/Dataisbeautiful) and GitHub, making the experiment open-source and accessible for others to review or replicate. His GitHub repository includes performance charts, Python scripts for tracking results, and detailed trade logs. Nathan’s setup leverages ChatGPT’s ability to process vast amounts of data, identify patterns, and make decisions free from emotional biases. For example, he feeds the AI daily closing prices and volume data, allowing it to propose trades based on its analysis. This approach contrasts with traditional trading, where human judgment often influences decisions. GitHub: https://github.com/LuckyOne7777/ChatGPT-Micro-Cap-Experiment?tab=readme-ov-file Performance: Outpacing Market Benchmarks The early results of Nathan’s experiment have been striking. Over the first four weeks (June 30 to July 25, 2025), his portfolio achieved a 23.8% return, compared to the Russell 2000 Index’s 3.9% and the SPDR S&P Biotech ETF (XBI)’s 3.5%. This performance translates to a profit of approximately $24–$25 on his $100 investment, a modest absolute gain but a significant percentage increase. A recent update from Nathan’s blog on August 3, 2025, indicates that the portfolio faced challenges in its fifth week. Some planned orders, such as a limit sell for AZTR and a limit buy for AXGN at $7.00 (versus a previous close of ~$13), did not execute as expected, prompting the AI to adopt a more defensive strategy by holding more cash. Despite these setbacks, the portfolio’s overall performance remains strong, with a Sharpe Ratio of 0.8803 and a Sortino Ratio of 1.8735, indicating solid risk-adjusted returns and effective downside risk management. Nathan’s portfolio has shown particular interest in stocks like Inspira Technologies (IINN), though he notes that the AI’s preference for this stock, despite its history of losses, may overlook macroeconomic factors affecting the biotech sector. This observation highlights both the strengths and limitations of relying solely on AI for trading decisions. Analysis: The Power and Limits of AI in Trading Nathan’s experiment underscores the transformative potential of AI in financial markets. By leveraging ChatGPT’s pattern recognition capabilities, the model identifies opportunities in micro-cap stocks that might be missed by human analysts or traditional algorithms. The AI’s ability to process data without emotional bias — such as fear of losses or overconfidence — gives it an edge in volatile markets. However, the experiment’s short timeframe and small scale raise important considerations: Short-Term Results: A four-week period is insufficient to prove long-term profitability. Micro-cap stocks are highly volatile, and sustained performance over the full six months will be critical. Scalability: A $100 portfolio limits the experiment’s real-world applicability. Scaling to larger portfolios may introduce complexities, such as liquidity constraints or market impact. Risk Factors: Micro-cap stocks carry significant risks, including price manipulation and low trading volumes, which could affect the AI’s performance over time. Public reactions to Nathan’s experiment reflect a mix of admiration and skepticism. Some praise his ingenuity, suggesting that his work could inspire future innovations in fintech. Others caution that one month’s success does not guarantee reliability, emphasizing the need for longer-term data. Despite these concerns, Nathan’s transparent documentation sets his project apart from unverified AI trading claims, fostering trust and collaboration within the financial community. Future Prospects: Refining the Model Nathan is committed to continuing the experiment through December 2025, with weekly updates posted on his Substack blog. Recent adjustments include refining ChatGPT’s prompts to incorporate more comprehensive portfolio data during weekly deep research sessions. For example, after failed trades in week five, the AI shifted to a more defensive strategy, holding $32 in cash and reducing positions in stocks like ABEO. Nathan also plans to share unfiltered chat logs to provide deeper insights into the AI’s decision-making process. His open-source approach invites feedback, with Nathan encouraging suggestions via email ([email protected]). This collaborative spirit could lead to improvements in the model, potentially addressing limitations like the AI’s oversight of macroeconomic factors. As the experiment progresses, it may offer valuable lessons for both individual investors and financial institutions exploring AI-driven trading. Conclusion: A Glimpse into the Future of Finance Nathan Smith’s experiment is a testament to the power of AI and the potential for young innovators to disrupt traditional industries. By achieving a 23.8% return in just four weeks, he has demonstrated that even with limited resources, AI can compete with established market benchmarks. While challenges remain — particularly regarding long-term performance and scalability — his work provides a compelling case study in the evolving role of AI in stock trading. As Nathan continues his six-month journey, his experiment will likely inspire others to explore the intersection of AI and finance. Whether it marks the beginning of a new era in trading or serves as a learning opportunity, one thing is clear: Nathan Smith is a young prodigy to watch, and his ChatGPT-powered trading model is pushing the boundaries of what’s possible in the financial world.Learn More: Best Solana Wallets to Copy Trading Best Solana Copy Trade Bots Best Solana Sniper Bots Best Solana Meme Coins Bots Best Solana Trading Bots Bullx Neo Access Code Axiom Trade Axiom Invite Code GMGN invite Code BUXLL NEO How AI is Revolutionizing Trading Bot: A Beginner’s Guide was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this storyIn a remarkable display of innovation, 17-year-old Nathan Smith from rural Oklahoma has developed a fully-automated trading model using ChatGPT that has outperformed the Russell 2000 Index over a four-week period. Starting with a modest $100 investment, Nathan’s AI-driven portfolio achieved a 23.8% return, significantly surpassing the Russell 2000’s 3.9% gain and the SPDR S&P Biotech ETF (XBI)’s 3.5% increase during the same timeframe. This achievement, reported on August 1, 2025, has sparked widespread interest in the potential of AI in financial trading. This article explores Nathan’s experiment, its methodology, performance, and implications for the future of investing. Background: A Young Innovator’s Vision Nathan Smith, a high school student with a passion for technology and finance, embarked on this ambitious project after being inspired by advertisements about AI-driven stock pickers. Curious about whether a large language model like ChatGPT could effectively manage a stock portfolio, Nathan designed a six-month experiment to test its capabilities. His goal was to see if AI could generate consistent returns, or “alpha,” in the volatile world of micro-cap stocks. Unlike traditional investors with access to expensive tools, Nathan relied solely on ChatGPT, a $100 budget, and his coding skills, making his achievement all the more impressive. The Experiment: How ChatGPT Trades Stocks Nathan’s experiment, running from June 27 to December 27, 2025, is designed to evaluate ChatGPT’s ability to manage a $100 portfolio of U.S.-listed micro-cap stocks — companies with market capitalizations under $300 million. These stocks are often under-analyzed due to limited media coverage, making them a challenging yet intriguing testbed for AI-driven analysis. The experiment’s structure is both simple and rigorous: AI Autonomy: ChatGPT has full control over the portfolio, making all buy and sell decisions without human intervention, except for Nathan executing the trades as instructed. Weekly Deep Research: Each week, ChatGPT conducts a comprehensive analysis, or “deep research,” to reevaluate its holdings and adjust the portfolio based on market data. Micro-Cap Focus: The AI is restricted to full-share positions in U.S.-listed micro-cap stocks, which are inherently volatile but offer potential for high returns. Transparency: Nathan documents the entire process on platforms like Reddit (r/Dataisbeautiful) and GitHub, making the experiment open-source and accessible for others to review or replicate. His GitHub repository includes performance charts, Python scripts for tracking results, and detailed trade logs. Nathan’s setup leverages ChatGPT’s ability to process vast amounts of data, identify patterns, and make decisions free from emotional biases. For example, he feeds the AI daily closing prices and volume data, allowing it to propose trades based on its analysis. This approach contrasts with traditional trading, where human judgment often influences decisions. GitHub: https://github.com/LuckyOne7777/ChatGPT-Micro-Cap-Experiment?tab=readme-ov-file Performance: Outpacing Market Benchmarks The early results of Nathan’s experiment have been striking. Over the first four weeks (June 30 to July 25, 2025), his portfolio achieved a 23.8% return, compared to the Russell 2000 Index’s 3.9% and the SPDR S&P Biotech ETF (XBI)’s 3.5%. This performance translates to a profit of approximately $24–$25 on his $100 investment, a modest absolute gain but a significant percentage increase. A recent update from Nathan’s blog on August 3, 2025, indicates that the portfolio faced challenges in its fifth week. Some planned orders, such as a limit sell for AZTR and a limit buy for AXGN at $7.00 (versus a previous close of ~$13), did not execute as expected, prompting the AI to adopt a more defensive strategy by holding more cash. Despite these setbacks, the portfolio’s overall performance remains strong, with a Sharpe Ratio of 0.8803 and a Sortino Ratio of 1.8735, indicating solid risk-adjusted returns and effective downside risk management. Nathan’s portfolio has shown particular interest in stocks like Inspira Technologies (IINN), though he notes that the AI’s preference for this stock, despite its history of losses, may overlook macroeconomic factors affecting the biotech sector. This observation highlights both the strengths and limitations of relying solely on AI for trading decisions. Analysis: The Power and Limits of AI in Trading Nathan’s experiment underscores the transformative potential of AI in financial markets. By leveraging ChatGPT’s pattern recognition capabilities, the model identifies opportunities in micro-cap stocks that might be missed by human analysts or traditional algorithms. The AI’s ability to process data without emotional bias — such as fear of losses or overconfidence — gives it an edge in volatile markets. However, the experiment’s short timeframe and small scale raise important considerations: Short-Term Results: A four-week period is insufficient to prove long-term profitability. Micro-cap stocks are highly volatile, and sustained performance over the full six months will be critical. Scalability: A $100 portfolio limits the experiment’s real-world applicability. Scaling to larger portfolios may introduce complexities, such as liquidity constraints or market impact. Risk Factors: Micro-cap stocks carry significant risks, including price manipulation and low trading volumes, which could affect the AI’s performance over time. Public reactions to Nathan’s experiment reflect a mix of admiration and skepticism. Some praise his ingenuity, suggesting that his work could inspire future innovations in fintech. Others caution that one month’s success does not guarantee reliability, emphasizing the need for longer-term data. Despite these concerns, Nathan’s transparent documentation sets his project apart from unverified AI trading claims, fostering trust and collaboration within the financial community. Future Prospects: Refining the Model Nathan is committed to continuing the experiment through December 2025, with weekly updates posted on his Substack blog. Recent adjustments include refining ChatGPT’s prompts to incorporate more comprehensive portfolio data during weekly deep research sessions. For example, after failed trades in week five, the AI shifted to a more defensive strategy, holding $32 in cash and reducing positions in stocks like ABEO. Nathan also plans to share unfiltered chat logs to provide deeper insights into the AI’s decision-making process. His open-source approach invites feedback, with Nathan encouraging suggestions via email ([email protected]). This collaborative spirit could lead to improvements in the model, potentially addressing limitations like the AI’s oversight of macroeconomic factors. As the experiment progresses, it may offer valuable lessons for both individual investors and financial institutions exploring AI-driven trading. Conclusion: A Glimpse into the Future of Finance Nathan Smith’s experiment is a testament to the power of AI and the potential for young innovators to disrupt traditional industries. By achieving a 23.8% return in just four weeks, he has demonstrated that even with limited resources, AI can compete with established market benchmarks. While challenges remain — particularly regarding long-term performance and scalability — his work provides a compelling case study in the evolving role of AI in stock trading. As Nathan continues his six-month journey, his experiment will likely inspire others to explore the intersection of AI and finance. Whether it marks the beginning of a new era in trading or serves as a learning opportunity, one thing is clear: Nathan Smith is a young prodigy to watch, and his ChatGPT-powered trading model is pushing the boundaries of what’s possible in the financial world.Learn More: Best Solana Wallets to Copy Trading Best Solana Copy Trade Bots Best Solana Sniper Bots Best Solana Meme Coins Bots Best Solana Trading Bots Bullx Neo Access Code Axiom Trade Axiom Invite Code GMGN invite Code BUXLL NEO How AI is Revolutionizing Trading Bot: A Beginner’s Guide was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story