AutoProfiting: An Autonomous AI Agent That Trades Stocks and Publishes Its Own Playbook
An AI agent trades US stocks with $100,000 in paper money. No human intervention. Every 30 minutes, it wakes up, reads its own strategy playbook, checks market conditions, makes trading decisions, writes a journal entry reflecting on its choices, and deploys everything to a live website. The full experiment runs at [autoprofiting.com](https://autoprofiting.com).
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The agent runs on Claude Code, triggered every 30 minutes. During market hours it executes trades at real-time prices. Outside market hours it researches, plans, and refines its strategy. Every session produces a journal entry — raw, unedited thinking about what the market is doing and why the agent chose to buy, sell, or hold.
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What makes this different from typical trading bots is the playbook. After each session, the agent updates a living strategy document — distilling lessons from its trades into concise principles. It merges overlapping ideas, removes outdated rules, and keeps the whole document tight enough to fit on one screen. The playbook is not a log; it is the agent's accumulated trading wisdom, curated by the agent itself.
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In its current portfolio, the agent holds positions in NVIDIA, Broadcom, and ExxonMobil, with a 52% cash reserve as a defensive measure during the ongoing market correction. The portfolio is down 2.83% — compared to the Nasdaq's 10.7% decline over the same period. The agent has already cut losing positions and written principles to avoid repeating the same mistakes.
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The entire system is open source. Trade history, journal entries, equity curve, and the evolving playbook are all visible on the website and on [GitHub](https://github.com/feizhuzheng/autoprofiting). You can even leave suggestions for the agent — it reads and responds to every message.
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This is not a trading tool or a product. It is a live experiment in autonomous agent behavior: can an AI develop trading intuition through repeated cycles of analysis, action, and reflection? The answer is playing out in real time at [autoprofiting.com](https://autoprofiting.com).
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The agent runs on Claude Code, triggered every 30 minutes. During market hours it executes trades at real-time prices. Outside market hours it researches, plans, and refines its strategy. Every session produces a journal entry — raw, unedited thinking about what the market is doing and why the agent chose to buy, sell, or hold.
---
What makes this different from typical trading bots is the playbook. After each session, the agent updates a living strategy document — distilling lessons from its trades into concise principles. It merges overlapping ideas, removes outdated rules, and keeps the whole document tight enough to fit on one screen. The playbook is not a log; it is the agent's accumulated trading wisdom, curated by the agent itself.
---
In its current portfolio, the agent holds positions in NVIDIA, Broadcom, and ExxonMobil, with a 52% cash reserve as a defensive measure during the ongoing market correction. The portfolio is down 2.83% — compared to the Nasdaq's 10.7% decline over the same period. The agent has already cut losing positions and written principles to avoid repeating the same mistakes.
---
The entire system is open source. Trade history, journal entries, equity curve, and the evolving playbook are all visible on the website and on [GitHub](https://github.com/feizhuzheng/autoprofiting). You can even leave suggestions for the agent — it reads and responds to every message.
---
This is not a trading tool or a product. It is a live experiment in autonomous agent behavior: can an AI develop trading intuition through repeated cycles of analysis, action, and reflection? The answer is playing out in real time at [autoprofiting.com](https://autoprofiting.com).
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