Report analysis: The Gold Prop Guardian_XAUU · MT4/MT5 XAUUSD Real

Data transparency

This page analyzes a user-submitted MT4/MT5 report. It does not authenticate ownership, rerun an independent backtest or predict future profit.

Key metrics

These metrics come from imported records and supported calculations. Check capital, costs, cash flows and period before comparing reports.

Growth

Normalised to 100 at the starting capital of the imported report. The chart samples the saved per-trade results and is not a live broker equity curve.

Normalised to 100 at the starting capital of the imported report. The chart samples the saved per-trade results and is not a live broker equity curve.

Detailed calculations

These metrics come from imported records and supported calculations. Check capital, costs, cash flows and period before comparing reports.

Return = net profit ÷ initial capital × 100%; PF = gross profit ÷ absolute gross loss; expectancy = net profit ÷ number of closed trades; payoff ratio = average win ÷ absolute average loss. Max drawdown uses the value reported by the source report. If the report contains deposits or withdrawals, the return should be interpreted together with the cash flow.

Why this safety score

Verified negative: 223 closed rows over 2026-06-30 to 2026-10-02 (93.7 days) produced -23.22% return and -990.21 net profit on a 4,263.78 capital base, with profit factor 0.62.

• Verified negative: max drawdown 28.88% against a 64.57% win rate shows losses are much larger than wins (negative payoff asymmetry).
• Verified negative: top 1% of rows (3 rows) contribute 13.94% of gross profit; excluding them the remainder is -1,211.31 net at PF 0.53; top 5% (12 rows) contribute 41.15%, leaving -1,642.64 net at PF 0.36. Profit depends on a very small set of rows.
• Verified negative: repeated large clusters closed at identical timestamps (e.g. 2026-07-16 16:02:38 with 6 rows from -111.60 to +20.61, 2026-10-02 04:09:29 with 3 rows of -94.77/-111.84/-85.86, 2026-09-28 03:31:09 with 5 rows) — these coincide with the account's worst loss days and are a documented loss-concentration pattern.
• Verified positive: 64.57% of closed rows are winners, and 4,263.78 deposits were partly returned via 3,273.57 withdrawals during the period.
• Pending: gain/loss attribution by entry ticket is not possible because the parsed rows are close-event rows; the full report fields are not the limit of the source data.
• Pending: no independent Demo or Real forward comparison period exists to confirm the profile outside this statement window.

Key risk flags

Account lost 23.22% with a 28.88% maximum drawdown; the loss profile is confirmed by the statement totals, not by sampling.

• Profit factor 0.62 with a 64.57% win rate: the average loss must materially exceed the average win for this combination, i.e. negative payoff asymmetry on the same account.
• Concentration: without the 3 best rows net profit becomes -1,211.31 (PF 0.53); without the 12 best it becomes -1,642.64 (PF 0.36).
• Repeated same-timestamp closing clusters (up to 6 rows at one timestamp, 2026-07-16 16:02:38) sit on the largest loss days, indicating that the worst outcomes arrive as grouped exposures rather than isolated trades.
• The equity base was reduced by 3,273.57 of withdrawals during a losing run, which mechanically shrinks the cushion available for recovery.

What the data cannot tell

Entry-ticket pairing: cluster rows cannot be confirmed as a grid, scale-in, basket, or correlated direction because pairing is absent from the parsed rows.

• Actual commission, swap, and slippage per trade, and how much of the loss is cost versus price movement.
• Exact lot-sizing and risk-per-trade rules; observed lot changes (0.01–0.04) are consistent with several rules and are not attributable to one.
• Position-level stop-loss/take-profit/trailing settings behind the exit style seen in the rows.
• Maximum adverse excursion and recovery path between the largest drawdown and the statement end.
• Whether the 28.88% drawdown figure is the only material drawdown episode; episode-by-episode drawdown cannot be computed from the supplied fields.

Next actions

Obtain the raw MT4/MT5 report with entry tickets and opening times to pair entries and exits, then reclassify the same-timestamp closing clusters accurately.

• Compute per-row cost (commission, swap, spread) or rerun the strategy with broker cost fields included to separate execution cost from directional loss.
• Re-derive the drawdown as an episode series from the full equity column to see how many distinct drawdown periods exist and their depths.
• Ask the operator for the documented lot-sizing and risk rules, then verify them against the observed 0.01–0.04 lot sequence.
• Collect a longer real-account period (beyond 93.7 days) and, if available, a comparable backtest of the same EA settings to check whether the loss profile is regime-specific.

Frequently asked questions

The 0–100 AI score is an evidence-based assessment, not a probability of profit. Read it with the rationale and unknowns.