AI Agent Research And Trading Workflow Automation

Every week a trader blows up because they trusted an AI-generated trade idea without verifying a single assumption. The pattern never changes: paste a chart into a chatbot, get a confident-sounding call, take the trade, and watch the market do the opposite. This course builds the opposite habit. You wire language models into the one job they are actually good at — reading, summarizing, structuring, and attacking research — and you keep them permanently away from the decision. Nothing here predicts price, and nothing places an order.

You build the discipline in order: why AI is a research assistant and never an oracle, four separated agent roles with a risk officer who holds a veto, a prompt-to-hypothesis contract validated as JSON, citation verification that quarantines fabricated sources, a backtester whose signal is mechanical and whose model only reviews for bias, an adversarial critic that blocks on a single critical flaw, hallucination detection against ground truth, reproducible experiment logs, a two-key human-approval gate, a tutor that refuses signals, a tamper-evident audit trail, and a capstone that welds them into one pipeline whose verdict is always a research package. Every Python sample runs offline with the model mocked, so you can execute the whole file end to end.

This is position 35. It assumes AI For Trading Research And Review, the bias catalog from Backtesting, Replay, And Forward Testing, and the per-trade limits from Risk, Position Sizing, And Invalidation. It extends the review discipline from AI For Trading Research And Review into runnable, guardrailed automation rather than repeating it.