Trade com Agentes: a 7-day study with GPT-6 Astra
A study presents routines, task handoffs, and strategy with GPT-6 Astra, and explains why the result fell short of the S&P; it is not a recommendation.
The study describes a seven-day challenge in which AI agents traded stocks with US$10,000, aiming to beat the S&P 500. The system had scheduled routines to assess what to watch and when to enter or exit a position. The agents passed notes to one another so the work could continue from one routine to the next. The text is for study, not a recommendation, and the figures were reported in a video without an audit.
For people using agents, the method shows how tasks can be divided: one agent researches and makes decisions, while another receives instructions and sends orders to the broker. This separation was introduced after the model refused to execute trades. The challenge ended behind the S&P 500: the system finished with an approximate 1% loss, while the same money invested in the index would have lost about 0.2%, according to the video. This shows that organizing agents does not guarantee good financial results.
To start studying the method, the repository lays out the rules, routines, handoff notes, and lessons from each trading day. It does not include the system’s code; the available templates explain its structure. The guidance is to test only with a simulated account. Also keep the data’s limits in mind: figures are approximate and unaudited, and the automatic transcript may contain incorrect names. The text warns that stock trading involves high risk and should not be treated as financial advice.
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