AI Agent Trade Thesis Prompt

Paper-first boundary

Trading Boy does not execute live trades, hold funds, or provide financial advice. This page is for simulated paper-trading review, prompt design, journal structure, and human review. It is not a signal feed, broker instruction, or promise of live trading results.

When to use this page

Use it when the agent must explain why a simulated entry is being considered before the result is known.

The search intent behind how to write an AI agent trade thesis prompt is usually practical: the user wants a reusable asset, an example, and a checklist. This page treats those variants as one owner page rather than splitting guide, example, checklist, and template into thin sibling pages.

Use the asset only after the paper-trading workflow, prompt version, permission boundary, and journal output format are clear. If the user cannot identify the setup, invalidation, risk field, or review question, the next action should be a skip, caveat, or human review rather than a confident simulated entry.

Trade thesis prompt block

Review field	Pass condition	Paper-mode boundary
Setup name	Names the exact written setup being tested.	The thesis is not allowed to invent a new setup mid-sample.
Market context	Records timeframe, trend state, volatility, and data freshness.	Stale or missing context must create a caveat.
Invalidation	Defines the price, condition, or time rule that proves the idea wrong.	No invalidation means no simulated entry.
Paper risk	States simulated size, stop logic, and drawdown context.	The thesis cannot override the risk rule.
Review question	Leaves one question for post-trade review.	The output should be reviewable even if the paper result is a loss.

Reusable prompt or worksheet text

Role: You are helping organize a simulated paper-trading review. You may summarize context, apply written rules, identify missing fields, and prepare journal evidence. You may not route live trades, request secrets, or provide financial advice.

Required output: Decision type, setup name, rule version, thesis, invalidation, paper risk, data caveat, behavior tag, and one human review question.

Skip rule: If setup, invalidation, paper risk, or data quality is missing, produce a skip or excluded row. Do not invent the missing field to make the record look complete.

Review handoff: Send the output to the paper journal, evaluation checklist, or human review checklist. Choose one next paper-mode action: collect more samples, tighten one field, pause, or revert a prompt change.

Example paper workflow

Scenario: The agent reviews a large-cap crypto pullback. The thesis prompt forces it to name the setup, explain why the pullback still fits the written rule, list invalidation, and state why the idea should be skipped if market data is stale.

Good output: The thesis is specific enough that another reviewer can later decide whether the paper entry followed the rule.

Weak output: The thesis says momentum is strong but never explains what would make the idea wrong.

Decision: The reviewer keeps the evidence in paper mode, checks the output with the AI paper trading agent evaluation checklist, and records any prompt change in AI trading agent prompt versioning.

Use it with these controls

Prompt version: every sample should name the version that produced the output.
Output format: the agent should use consistent fields for entries, exits, skips, and missed setups.
Risk gate: size, exposure, drawdown, stop distance, and pause rules should be checked before judging the idea.
Human review: a person should approve prompt, risk, or workflow changes before the next sample.
Data privacy: private exchange credentials, account identifiers, and secret values should stay out of prompts and screenshots.

How it supports ranking

This owner page consolidates the guide, example, template, checklist, and for-paper-trading variants into one durable page. That gives searchers a complete answer without flooding the sitemap with near duplicates.

It also links into the surrounding Trading Boy system: paper-trading hub, AI paper agent, prompt template, output format, risk controls, permission boundaries, and paper-trading limitations.

Related AI paper-agent pages

Use these links to move between setup, output, risk, journal, and review pages without leaving the paper-first cluster.

AI Agent Trade Thesis Prompt FAQ

Why use a thesis prompt for paper trading?

A thesis prompt records the reason for a simulated decision before outcome bias changes the story.

Should the thesis prompt predict price?

No. It should define the setup, invalidation, and review evidence, not make return claims.

Where should thesis output be stored?

Store it in the paper journal or output format template with rule version, risk fields, and review tags.