交易报告分析：The Gold Prop Guardian_XAUU · MT4/MT5 XAUUSD Real

数据透明度

本页分析用户提交的 MT4/MT5 报告，不认证账户归属、不重跑独立回测，也不预测未来收益。

核心指标

下列指标来自导入记录与支持的核算，比较前应核对资金基数、费用、出入金和交易期间。

增长

以导入报告的起始资金归一化为 100。该图按已保存的逐笔结果采样，不等同于经纪商实时权益曲线。

以导入报告的起始资金归一化为 100。该图按已保存的逐笔结果采样，不等同于经纪商实时权益曲线。

详细计算结果

下列指标来自导入记录与支持的核算，比较前应核对资金基数、费用、出入金和交易期间。

收益率 = 净利润 ÷ 初始资金 × 100%；PF = 总盈利 ÷ 总亏损绝对值；单笔期望 = 净利润 ÷ 已平仓交易数；平均盈亏比 = 平均盈利单 ÷ 平均亏损单绝对值；最大回撤采用报告输出的最大回撤值。若报告含入金或出金，收益率应结合现金流另行解释。

策略安全评分依据

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.

关键风险信号

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.

现有数据无法判断

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.

下一步行动

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.

常见问题

0–100 AI 评分是基于证据的评审判断，不是盈利概率；应结合评分依据与未知事项阅读。