交易报告分析：GoldX9 strategy 9 · MT5 XAUUSD H1 Backtest

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

Reported return is +174.69% on a 10,000 base (net 17,468.72) across 1,279 parsed closed rows, with profit factor 1.38 and win rate 86.55%.

• Maximum drawdown is 46.98%, which is large relative to a 1.38 profit factor and sits just under the 50% hard-loss band; balance ranged from a 10,000 start to a peak above 28,000 and down to roughly 8,000 in the visible rows.
• Profit concentration is material: computed stats show the top 1% of trades (13) produce 8.97% of gross profit and removing them drops profit factor to 1.25; the top 5% (64 trades) produce 30.64% of gross profit and removing them leaves net profit of -2,016.25 and profit factor 0.96.
• Many losses appear as same-close-timestamp groups of identical P/L and identical lot size (e.g., three -448.89 rows on 2022.01.12, four -1,135.40 rows on 2025.10.09, eight multi-row clusters in 2024.09), and the panel carries no entry tickets; pairing cannot be confirmed from the supplied rows.
• Trailing-stop style exits are visible: several stop-loss-type exits still show positive profit, which is a normal profit-lock mechanism rather than a data defect.
• Sample period is long (2022-01-03 to 2026-01-29, ~1,487 days) and History Quality is reported at 99%, but this measures the modelled history only.
• No commission, swap, fee, or slippage values are present in report_context; cost impact cannot be assessed from the supplied data.

关键风险信号

46.98% maximum drawdown against a 1.38 profit factor: a small edge is carrying very large equity swings.

• Profit is concentrated: removing the top 5% of trades turns the strategy net loss-making (remaining net -2,016.25, PF 0.96).
• Multiple identical-loss clusters close on the same timestamp with identical lot sizes (e.g., three -448.89 rows, four -891.47 rows, three -1,135.40 rows), which points to correlated exposure whose structure cannot be verified from the supplied rows.
• Individual losing exits reach roughly -1,342 (2026-01-29), -1,135 (2025-10-09 and 2026-01-16), and -973 (2025-06-17), dwarfing the typical small winning exit near +5 to +15, evidencing an unfavourable payoff asymmetry.
• Historical drawdown episodes in the visible rows repeatedly cut the balance by roughly 40-45% (e.g., from about 17,600 to about 8,000-10,000 in 2024.09 and from about 28,000 to about 19,500 in 2025.10).

现有数据无法判断

Entry timestamps, entry tickets, and trade direction on entry are not in the supplied rows, so simultaneous openings, grid/scale-in structure, and basket correlation cannot be confirmed.

• Commission, swap, spread, and slippage settings are absent, so the cost-adjusted edge is unknown.
• Whether the repeated equal-P/L, same-timestamp exits are independent trades or components of a multi-position structure is unresolved.
• Behaviour in live or forward accounts, including real fills, slippage, and broker minimum-holding rules, is not observable from this report.

下一步行动

Re-export the trade list with entry tickets and open timestamps so same-timestamp clusters can be paired and classified.

• Recompute performance after removing the top 5% and top 1% of trade P/L to quantify dependence on large winners.
• Run the same model with explicit commission, swap, and slippage values representative of the intended broker to test cost sensitivity.
• Reconcile the gap between the 2026-10-05 report range end and the 2026-01-29 last parsed close, and confirm no trades are missing.
• Forward-test on a Demo account for a defined period and compare fill quality and drawdown against the backtest.

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。