AI Journal Reviewer: Tool Limits and a Practical Review Method

An AI trading journal reviewer should help you examine recorded decisions and check repeated mistakes against the underlying trades. This page currently provides a single-trade analyzer with Quick and Detailed modes. It cannot read a batch of journal entries or calculate your historical strategy performance. Use the form for its stated setup inputs, and use the review method below for your own records. AI feedback can be wrong.

What the form above actually accepts

Quick mode takes an instrument, Long or Short, Entry, Target, Stop Loss, Lots/Qty, Capital and Strategy. It calculates price-distance risk and reward from those inputs and can request AI commentary. Detailed mode adds setup context, technical levels, a checklist and reasoning.

There is no active journal-entry box, trading-days field or psychology-focus selector in this shared form. The page does not provide a working route to paste and review a trading history. A screenshot or a manually entered setup also cannot establish how all your earlier trades performed.

What an AI journal review should look for

The form above looks at one setup. A review of many trades, done by you or by a paid journal feature on your own logged entries, usually checks the same few habits. Use this list as your own checklist, and verify each point against the rows.

• Broken rules: trades where the written entry condition was not met.
• Moved stops: a stop widened or removed after entry.
• Oversizing: quantity larger than your planned risk allows, especially after a loss.
• Revenge trades: a quick re-entry soon after a loss. See revenge trading and the revenge trading detector.
• Time of day: whether results differ between the open, mid-session and the close.
• Holding time: whether you cut winners earlier than losers.
• Costs: whether charges take most of a small gross profit.

Each item is a question about your records, not a verdict. A pattern across a few trades can be chance.

Worked example: check the calculation before the commentary

Illustrative Quick-mode inputs: Stock/Equity, Long, Entry ₹250, Stop Loss ₹245, Target ₹260, quantity 12 shares and Capital ₹60,000. Select a strategy category for context. The prices, quantity and capital are invented and are not a trade recommendation.

• Planned risk = (₹250 − ₹245) × 12 = ₹60.
• Planned reward = (₹260 − ₹250) × 12 = ₹120.
• Reward divided by risk = ₹120 ÷ ₹60 = 2, displayed as a risk-to-reward ratio of 1:2.
• Account risk percentage = ₹60 ÷ ₹60,000 × 100 = 0.10%.

These are setup calculations before charges and execution differences. They do not show that the target is likely or that the stop will cap the final loss. A score, verdict or probability-like label does not replace evidence about your trading history.

The Detailed mode also displays assumption-based quantities such as probability, expected value and position-sizing suggestions. They are not measured from a historical journal in this form. Do not treat them as a verified success rate, broker margin requirement or instruction to place an order.

Build the journal record an actual review needs

Keep the original plan, actual execution and later reflection distinguishable. CME Group’s trade-log lesson recommends saving trade reasons and daily conclusions for later reference. Your review is stronger when the reason was written before the result rather than reconstructed after it.

• Preserve the instrument and exact contract, executed prices and quantity.
• Record the planned entry condition, stop and target as they were before any change.
• Keep charges and net result beside the exit reason.
• Write observable deviations: added quantity, moved stop, chased entry or entered without the written condition.
• Mark missing information as missing and identify any note written retrospectively.

Use what to track in a journal for the full record. These fields describe a journal-review method; they are not additional controls hidden inside the single-trade form above.

Check each explanation against your trade records

A useful review question is narrow: “Which recorded trades changed the stop after entry, and what information is missing?” A weak question asks an AI to identify a permanent winning strategy from a small selection. Keep the review tied to rows you can inspect.

A generated explanation can confuse an observation with a cause. A loss following a frustrated note does not prove frustration caused the loss. Changes in the contract, quantity or market conditions may matter too. Treat the explanation as a question to investigate.

Sample review: one week of made-up trades

This is an invented week, not real results and not output from the tool. It shows how you would check a review against the rows.

• Total gains: ₹300 + ₹150 + ₹200 = ₹650.
• Total losses: ₹450 + ₹600 = ₹1,050.
• Net for the week: ₹650 − ₹1,050 = −₹400.
• 3 wins in 5 trades is a 60% win rate, yet the week lost money.
• Profit factor (total gains ÷ total losses) = ₹650 ÷ ₹1,050 = 0.62.

What the rows show: both losing days had a moved stop, and every trade where the plan was followed made money. That is a question for next week (what happens if I never move a stop?), not proof. Five trades is far too few to conclude anything. The profit factor calculator does the last step for you.

Compare a completed trade with the original setup

Continue the same fictional share example: suppose the actual exit is ₹255 and total charges are ₹18. Gross profit is (₹255 − ₹250) × 12 = ₹60, and net profit is ₹60 − ₹18 = ₹42. This is a manual journal calculation for the example, not a promised output of the analyzer.

The planned reward was ₹120, but the completed net result is ₹42. The record should explain the actual exit rather than replace the plan with the profitable outcome. A result below the planned reward does not establish a mistake without the exit context.

Use the profit factor tool only after you have consistent totals from a defined set of closed trades. The journal review workflow explains how to compare records without treating a tiny sample as proof.

How many trades make a useful review

There is no magic number. Results from 5 or 10 trades swing wildly with one big loss or win, so treat them as notes, not conclusions. The more you split your trades into groups (by strategy, time of day, market), the fewer trades land in each group, so the less each group can tell you.

• Review process questions (did I write a reason? did I move the stop?) even on a small sample, because these are counts, not predictions.
• Be careful with profit questions (which strategy earns more?) until each group has many trades.
• If a tool shows a confidence or probability figure, ask what it is based on. In the Detailed form here, such figures come from your own assumptions.

The expectancy guide explains why a small sample can mislead.

Your data and privacy

The public form on this page needs only the setup details you type in. You do not connect a broker or give a login. OneTradeJournal's privacy policy says online AI features send the content needed for your request to an AI service provider (it names OpenAI, OpenRouter and Groq) for processing, and that it does not sell personal data. Read the privacy policy for the full wording, and avoid typing anything you would not want processed, such as account numbers.

In the paid journal, trades are entered by hand and kept in your account. You can export them to CSV, JSON or TXT.

How the paid journal features differ

OneTradeJournal’s paid AI includes Ask Your Journal, AI analysis and AI Trading Mentor. Ask Your Journal works with your own logged records. The quality and scope of that feedback depend on what you entered. The public form on this page is a separate single-setup tool.

Trades in OneTradeJournal are logged by hand. Strategy, emotion, mistake and plan-deviation notes provide review context. No automatic broker-history collection is available. See the AI journal guide for the product’s wider review features.

AI feedback is not a trading signal, a compliance certificate or an order. OneTradeJournal is not a broker or a SEBI-registered adviser. Review any numerical claim against the underlying entries and arithmetic before relying on it.

A weekly AI review routine

1. Finish the week's entries first. Fill reasons, stops and exits while they are fresh.
2. Pick one question, such as moved stops or trades after a loss.
3. Ask your question. Then open the rows the answer refers to and confirm each one.
4. Write one rule for next week in a single sentence.
5. Next week, check only that rule. Do not chase five new ones.

Weekly is a suggestion. Some traders prefer a short daily check and a longer monthly one. See the guide to using a trading journal effectively and the discipline scorecard.

Single-trade review versus period review

A single-trade review, like the form above, asks whether this one setup is sensibly sized and whether the reward justifies the risk. A period review asks about behaviour across many trades. They answer different questions, and the second needs your complete record.

AI journal review questions

Sources

• OneTradeJournal: AI journal reviewer page and active form (checked 15 October 2026).
• OneTradeJournal: Manual journal and paid AI features (checked 15 October 2026).
• CME Group: Keep a Trade Log (checked 15 October 2026).