Use this journal template to turn simulated trades into reviewable evidence: thesis, invalidation, paper size, entry context, exit reason, behavior tag, and one next workflow action.
A paper trading journal should make the decision inspectable before the outcome is known and after the simulated trade closes. Keep the fields consistent so later reviews can compare similar samples instead of reconstructing the story from memory.
| Field | Prompt to answer | Review purpose |
|---|---|---|
| Market frame | Which token, pair, timeframe, session, and watchlist rule applies? | Stops unrelated market noise from entering the sample. |
| Setup thesis | What exact paper setup is being tested? | Separates a rule-based test from an improvised idea. |
| Invalidation | What condition proves the thesis wrong? | Gives the post-trade review a fixed reference point. |
| Paper risk | What simulated size, stop distance, risk-reward, and drawdown limit apply? | Connects the entry to sizing and risk review. |
| Agent rationale | What did the AI paper agent see when it acted or skipped? | Makes the agent behavior auditable. |
| Exit reason | Why did the paper trade close? | Distinguishes planned exits from drift or intervention. |
| Behavior tag | Was the decision patient, early, late, oversized, rule-fit, or outside plan? | Turns repeated behavior into a pattern. |
| Next action | Keep collecting samples, tighten one rule, reduce size, pause, or retire? | Prevents every single outcome from triggering a broad rewrite. |
Date and market frame: Record date, symbol, timeframe, session, market condition, and the paper strategy version being tested.
Pre-trade thesis: Write the setup, expected behavior, entry trigger, invalidation, planned hold window, and why the trade fits the current rule set. Use the trade thesis journal template when the setup needs a dedicated moving average, breakout, mean-reversion, or range-trade hypothesis before entry.
Paper risk: Record simulated account size, planned paper position size, stop distance, target, risk-reward ratio, max drawdown rule, and correlated exposure note.
Agent rationale: Save the AI paper agent's decision note, data context, skip condition if relevant, and confidence explanation without treating confidence as permission to break risk rules.
Exit and review: Record exit reason, outcome, rule fit, mistake tag, journal note, and one next action before any rule is changed.
Start with the pre-trade checklist, then calculate planned paper size and risk-reward before recording an entry. The journal should show the plan before the result can influence the explanation.
For AI agents, make these fields part of the required output. The agent should not log a complete paper decision unless thesis, invalidation, size, and review question are present.
Use the post-trade review template after the exit. The review should decide whether the agent followed the rule, whether the size was acceptable, and whether one focused adjustment is justified.
A profitable paper result can still fail the journal if the rule fit was weak. A losing paper result can still pass if the process was disciplined.
Market frame: SOL perpetuals, four-hour trend test, paper mode only, no additional correlated long exposure while the sample is open.
Setup thesis: The AI paper agent is testing a reclaim-and-hold setup after a failed breakdown. Entry is allowed only if price reclaims the prior range and volume expands.
Invalidation: The setup is invalid if price closes back inside the breakdown range or if Bitcoin breaks the broader market risk filter.
Paper risk: Simulated risk is capped at 0.75 percent. The planned ratio from the risk-reward calculator is 2.4R. Position size stays below the cap from the position size calculator.
Agent rationale: The agent acted because the reclaim condition and volume filter were both present. It also logged that the setup should be skipped if a second correlated position appears.
Exit and review: The simulated exit closed at a small loss after invalidation. The review tag is rule-fit pass, outcome loss. Next action is to keep collecting samples instead of changing the rule after one trade.
A journal becomes useful when the same fields appear across many samples. One paper trade can show a clean or messy decision, but repeated journal entries show whether the AI agent has a consistent behavior pattern. That is why the template favors stable fields over freeform commentary.
Review entries in groups. Sort by setup, persona, token, market condition, behavior tag, and rule version. If early entry appears across several examples, the next action may be a confirmation rule. If oversized paper risk appears across several examples, the next action may be a hard size cap. If unclear exit reason appears often, the next action may be a stricter exit template.
Do not treat the journal as a prediction engine. It is a review system for simulated decisions. The goal is not to prove that a rule will work with live capital. The goal is to understand whether the agent can follow instructions, respect paper risk limits, explain skips, and produce clean review evidence before any broader workflow decision is considered.
Keep skipped trades in the journal when they show discipline. A skipped setup can prove that the agent respected invalidation, avoided correlated exposure, or rejected an unclear signal. Those records make the paper workflow more trustworthy than a journal that only records action.
A strong entry includes thesis, invalidation, planned risk, agent rationale, exit reason, rule fit, mistake tag, and one next action. A reviewer can understand the decision without guessing what the agent meant.
A weak entry says the setup looked good, the market was bad, or the agent was wrong without identifying the rule, risk, or behavior. That kind of note is difficult to improve from.
Use this template as the bridge between the product use cases and the review workflow. The AI trading journal with AI insights explains the agent-facing workflow and insight layer. The crypto trading journal explains market-specific review habits. This template gives both pages a consistent field structure.
When the workflow is reviewing strategy families such as moving average crossovers, breakouts, pullbacks, or mean reversion, keep the strategy label and rule version in every row. That gives the AI journal enough context to group similar paper trades and produce insight about process quality instead of isolated outcome commentary.
For a full paper workflow, start with the AI trading agent prompt template, write the setup in the trade thesis journal template, use the pre-trade checklist, calculate planned risk with the risk-reward calculator, record the journal entry, then close the loop with the post-trade review template. That internal path helps users and crawlers understand that Trading Boy is a paper-first system, not a live signal service.
Include setup, thesis, invalidation, paper size, entry context, risk-reward, exit reason, rule fit, mistake tag, and one next review action.
A log records what happened. A journal explains why the simulated decision happened, whether it followed the rule, and what should be reviewed next.
This template is for simulated practice, AI agent review, and paper-trading workflow learning. It is not financial advice and does not execute live trades.