Real AI agent use cases, with sources and setup prompts

How to build AI trading and investing agents

What people actually built to research, test and watch trades with AI agents, and the checks that caught their mistakes. 10 matching use cases below, each with a public source.

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Get started in 3 steps

  1. Start with paper trading or a tiny account, and keep a human approval on every order or deposit.
  2. Have the agent validate strategies with out-of-sample tests and realistic costs, and reject anything that only works on one lucky split.
  3. Use a second model or person to review trading code; one builder below caught an agent that could sell a position it never bought.
MoneyX · @RRRangle

Turn Claude Code into a machine-learning quant lab that rejects overfit models

Following Marcos Lopez de Prado's ML trading workflow, you give Claude Code market data, labeling rules, features, costs and risk limits. It applies triple-barrier labels, builds XGBoost, LightGBM, random forest and neural network variants, runs purged K-fold and CPCV validation, stresses costs and slippage, and rejects models that only work because of leakage or lucky splits.

$17/moDaysAdvanced

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