交易报告分析：XAUUSD Wolfe SMC Pro ea · MT5 XAUUSD H1 Backtest

数据透明度

本页基于用户上传的 MT4/MT5 报告解析，不认证账户所有权，不在独立行情下重跑回测，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: 435 parsed closed rows over 644 days, PF 1.31 and win rate 41.61% — a thin edge where the top 5% of trades (22 trades) account for 34.55% of gross profit; excluding them, the system is net negative (-7,927.80) with PF 0.86.

• Verified: Excluding only the top 1% of trades (5 trades, 11.51% of gross profit) still leaves PF 1.16 after removing them, meaning the strategy would still be marginally profitable without its biggest winners — the core edge is not entirely one trade, but it is heavily concentrated.
• Verified: Fixed 0.1-lot sizing throughout the parsed rows — no compounding, no martingale, no position escalation despite balance growth from 10,000 to ~26,861. This limits account-blowup risk from sizing.
• Verified: Largest single losses are -2,911.30 (2026-01-30) and -1,668.80 (2026-02-02), on a single 0.1-lot XAUUSD trade — these loss magnitudes on one trade are large relative to 0.1 lot and suggest the strategy is exposed to gap/event moves that bypass normal stops.
• Verified: Max drawdown 21.1% against a 168.6% return gives a return/drawdown ratio of ~8.0, which is attractive on its face but is a one-off maximum statistic on a backtest that ends 2026-10-09.
• Pending: No demo or real forward record exists; the report is a single MT5 Strategy Tester backtest with no out-of-sample validation.
• Verified: Strategy Tester history quality 99% and no explicit tester warnings — test quality is strong on the supplied evidence.

关键风险信号

Profit concentration: the top 5% of trades (22 of 435) generate 34.55% of gross profit; removing them leaves net profit of -7,927.80 and PF 0.86 — the strategy is loss-making without a small cluster of outlier winners, so live underperformance in just those trades would flip the account negative.

• Fat-tail loss exposure on fixed 0.1 lot: single-trade losses of -2,911.30 and -1,668.80 on 0.1 lot XAUUSD occurred within three days (2026-01-30 and 2026-02-02), showing the strategy can absorb very large single-trade hits that a 21.1% reported max drawdown may understate in a worse regime.
• Lower win rate (41.61%) with PF 1.31 means the strategy depends on winners being large; if the largest winners are clipped (as in the top-5% removal test), the edge disappears — a classic fragility to regime change in XAUUSD volatility.
• Backtest range (2025-01-03 to 2026-10-09) covers a specific period; no evidence of the strategy being stress-tested against different market regimes, so the 21.1% max drawdown is one observation, not a through-cycle estimate.

现有数据无法判断

Entry/exit logic: open prices, entry timestamps, and paired entry tickets are not present in the parsed rows (close-only events); the exact entry rule, TP, SL, and whether exits were trailing stops cannot be determined from the supplied evidence.

• Whether any of the large single-trade losses (-2,911.30, -1,668.80) were stop-loss exits, gap-through-stops, or manual closes — the parsed rows do not include exit reason.
• Real slippage, spread, commission, and swap for XAUUSD on this broker are not in the report_context, so the net-of-cost edge is unknown.
• Whether the 21.1% max drawdown would be worse under a different volatility regime or with live execution frictions.
• The maximum number of simultaneously open positions and total exposure at any point cannot be determined from the close-only parsed rows.

下一步行动

Run a demo forward test for at least 60–90 days on the same broker with real spreads to measure slippage and execution quality on XAUUSD.

• Recompute performance net of the broker's actual XAUUSD commission and swap, and evaluate how PF and net profit change after costs.
• Identify and stress-test the 22 trades that form the top 5% of gross profit; assess what market conditions produced them and whether those conditions are repeatable.
• Perform a walk-forward or out-of-sample test on a period not used in this 2025-2026 run, to check whether the 21.1% drawdown and 1.31 PF hold outside the tested window.
• Check whether the -2,911.30 and -1,668.80 single-trade losses are consistent with the EA's stop-loss logic or indicate stop-bypass/gap risk, and adjust position sizing or add a max-loss filter if the latter.

常见问题

0–100 分是 AI 基于报告证据给出的风险判断，不是盈利概率，请结合评分依据与数据盲区综合判断。