A trading journal connects the original plan, executed trades and later review. AI can help organise a statement or suggest questions about recurring patterns, but its interpretation is not independent evidence. It can misread spreadsheet sections, confuse deposits with profit or assign a motive that the supplied history cannot establish.
Use the broker's original records as the reference and keep your written trade reasons alongside them. This guide explains an educational review workflow, not a way to predict the next winning trade or delegate account decisions. Any numerical example below is hypothetical and must not be mistaken for a Quantora user result.
Prepare the record before asking for conclusions
Choose a defined date range and preserve an unchanged copy of the statement. Identify its currency, time zone and representation: orders, deals or completed positions. A partially closed position may have several closing deals; counting every row as a separate trade can inflate activity and distort win rate.
Check that commission, swap, deposits, withdrawals and open positions are clearly labelled. Keep identifiers only where needed for reconciliation and avoid sharing credentials or unnecessary personal information. Review the tool's published data-handling terms before uploading sensitive records; an educational label is not a promise of any particular retention policy.
| Field | Why preserve it | What a statement may omit |
|---|---|---|
| Entry and final fills | Reconcile cash results and execution | Original intended entry |
| Initial planned loss | Calculate realised R consistently | Later changes to the stop |
| Commission and financing | Compare net outcomes | Charges on another statement section |
| Written setup reason | Review whether rules were followed | Motive or emotional state |
Recalculate a small sample before trusting the summary
Suppose an invented sample contains 20 completed trades: 12 wins of $40 and eight losses of $60 before a separate $40 total charge. Win rate is 12 divided by 20 = 60%. Gross winning profit and gross losing magnitude are both $480, so the gross result is zero and the net result is −$40.
A summary that calls the sample profitable because most trades won is wrong. Check net profit, average win, average loss and the cost basis yourself. Profit factor in this exercise is gross winning amount divided by gross losing amount, or 1 before the separate charges. Specify whether charges have been assigned to individual outcomes before comparing another calculation.
Ask for observations with traceable evidence
A useful question is which completed trades had the largest net losses and whether they share a documented symbol, session or position size. Request the supporting records and the count behind a pattern. A claim such as trades held longer than two days lost more should identify the relevant sample and explain missing or open positions.
A statement alone may not reveal that a trader chased price, moved a stop, revenge-traded or ignored a written plan. Those interpretations need original orders, modification history or contemporaneous notes. Treat unsupported motives as hypotheses to investigate, not facts to accept because they sound plausible.
Distinguish planned R from a changing denominator
Suppose a fictional trade originally planned a $30 price-distance loss but closes at a $90 loss after the stop was widened. Relative to the original budget, the result is −3R before any additional costs. Replacing the original budget with $90 afterward would disguise the plan change as an ordinary −1R outcome.
Likewise, if half a position exits at +1R and half at +2R, the weighted gross result is +1.5R for equal halves. The AI review should not add these as +3R or count them as two independent full trades. Preserve position grouping and costs so that the performance units describe the actual account experience.
Turn one finding into a testable process rule
Imagine the evidence shows that orders placed after a particular checklist was skipped had larger average losses in a small sample. A useful next step is to require a timestamped checklist entry before each new demo trade, then compare compliance and outcomes over a separately defined period. The first sample does not prove that the checklist caused the difference.
Choose one operational behavior to review rather than changing entry rules, stops and size simultaneously. Record exceptions and all subsequent trades, including losing ones. Evaluate whether the process was followed and whether the new sample is large and varied enough to support a conclusion. A higher AI discipline score alone is not that conclusion.
Understand Quantora's journal within its actual scope
Quantora's journal accepts supported MT4/MT5 spreadsheet reports and broker CSV statements within its upload limit and account usage allowance. It produces an educational AI review of the supplied history, with statistics and discussion of possible strengths, mistakes and improvements. Confirm the returned trade count and period against the intended statement.
The review does not automatically place or change broker orders. Missing fields, unusual layouts and incomplete history can affect the interpretation, and AI-read numbers may be imprecise. A discipline score is the model's assessment of the supplied record, not a measured probability of profit, proof of account safety or personalised allocation advice.
A repeatable review checklist
- Keep the original statement and a dated, redacted working copy with a clearly defined period.
- Verify trade grouping, account currency and treatment of fees, transfers and open positions.
- Recalculate several wins, losses and partial exits before relying on summary statistics.
- Require supporting records for claimed patterns, and separate evidence from guesses about motives.
- Select one process improvement, state how it will be observed and preserve the next evaluation period.
- Use a manual spreadsheet if AI is unavailable; continuity of the record matters more than the wording of one report.
Common questions
- Does an AI score tell me how much to risk? No. It is an interpretation of uploaded history, not an account-specific exposure limit.
- Can a high win rate hide losses? Yes, as the invented 60% example shows; costs and loss magnitude matter.
- Should I upload a password or broker login? No. A statement review needs records, not trading credentials.
- Can the journal infer every trading mistake? No. Missing plans, modifications and context constrain what the history can establish.