Report analysis: Green Profit Gold EA · MT5 XAUUSD M5 Backtest

Data transparency

This review is parsed from a user-uploaded MT4/MT5 report. It does not verify account ownership, rerun the backtest on independent market data, or forecast 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: 352 closed rows over 644.3 days (2025-01-03 to 2026-10-09) on XAUUSD M5, net profit 4,811.52 on 10,000 capital, PF 1.24, win rate 46.59%.

• Verified: profit concentration is severe — top 1% of trades (4) supply 13.56% of gross profit and removing them drops remaining PF to 1.07; removing the top 5% (18 trades) leaves net -5,503.38 and PF 0.72.
• Verified: maximum drawdown 17.07% with recovery to a 48.12% net return; drawdown is moderate relative to return but the return depends on a thin tail of large winners (e.g. 874.30, 858.00, 861.40, 758.88).
• Observed: repeated clusters of losses in the -90 to -176 range between winners (e.g. several consecutive negatives around 2026-06 and 2026-08) show the strategy bleeds steadily and needs an outlier to stay positive.
• Test quality: History Quality 99%, 125,209 bars and 2,502,977 ticks, no tester warnings — backtest mechanics look clean.
• Cost treatment: commission/swap/fee are not present in the supplied evidence, so net-of-cost robustness cannot be confirmed from this report.

Key risk flags

Profit concentration: top 5% of trades (18 of 352) generate 41.73% of gross profit; removing them leaves -5,503.38 net and PF 0.72 — the system loses money outside a small set of outlier winners.

• Thin PF: 1.24 overall but only 1.07 after removing the top 4 trades, meaning a slightly different market or one missed big move flips the account to breakeven or loss.
• Loss asymmetry: the largest single losses run -145 to -176 while many wins are +1 to +60, so a run of bad trades bleeds capital faster than small wins replenish it (visible in the 2026-06 drawdown cluster).
• Outcome depends on catching a few large gold moves (winners of 700–874 appear roughly monthly); if those become rarer or smaller, the 17.07% max drawdown can deepen beyond what this test shows.

What the data cannot tell

Entry timestamps, trade durations, and paired entry tickets are not in the parsed rows, so exposure overlap and grid/scale-in structure cannot be confirmed.

• Commission, swap, and actual live slippage are absent, so net-of-cost performance is unverified.
• The exact lot-sizing rule (observed sizes range 0.01–0.18) is not documented.
• Position-level exposure at any single moment cannot be reconstructed from close-only rows.

Next actions

Run a forward Demo/Real account for at least 3–6 months and compare the outlier-winner frequency to this backtest.

• Re-run the backtest with realistic commission, swap, and slippage and check whether PF stays above 1.15.
• Test the strategy with the top 10–20 winners excluded to confirm whether it can survive without outlier moves.
• Obtain the full MT4/MT5 statement with paired entry tickets and holding times to verify exposure and confirm or rule out position clustering.

Frequently asked questions

The 0–100 score is an AI risk judgement based on the submitted evidence, not a win-rate forecast. Read it alongside the score rationale and blind spots.