This is a real-account MT4/MT5 statement covering 223 closed trades from 2026-06-30 to 2026-10-02 with a -23.22% return, 28.88% max drawdown, 0.62 profit factor, and 64.57% win rate; the losing outcome is driven by concentrated large stop-style losses, and the evidence is limited to a single real-money record with unverified contextual details.
Statement-90057420-en
数据透明度
| 数据来源 | 用户上传的 MT4/MT5 报告 |
|---|---|
| 报告类型 | 真实 |
| 已核算交易 | 223 |
| 核验方式 | RBOT 解析器逐笔核算 + AI 风险审计 |
核心指标
| 收益 | -23.2% |
|---|---|
| 最大回撤 | 28.88% |
| PF | 0.62 |
| 胜率 | 64.57% |
| 净利润 | -990.21 |
| 单笔期望 | -4.44 |
| 平均盈亏比 | 0.34 |
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。
详细计算结果
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: -23.22% return on a capital base of 4263.78, with net profit -990.21, is a losing real-account outcome.
- Verified negative: maximum drawdown of 28.88% is a substantial peak-to-trough decline relative to the account size.
- Verified negative: profit factor of 0.62 means gross losses exceed gross profits; the strategy is not profitable over the sample.
- Verified negative: 64.57% win rate combined with PF 0.62 demonstrates negative risk asymmetry — many small wins offset by fewer, much larger losses; large losing rows include -114.44, -111.84, -111.60, -108.15, and repeated -90-level exits.
- Verified concentration: computed stats show the top 1% of trades (3 trades) account for 13.94% of gross profit; removing them drops remaining PF to 0.53 with remaining net profit -1211.31. The top 5% (12 trades) account for 41.15% of gross profit; removing them drops remaining PF to 0.36 and remaining net profit -1642.64 — profit is highly concentrated and the losing core is broad-based.
- Verified: withdrawals of 3273.57 against deposits of 4263.78 mean the account returned a large portion of capital to the owner, but the net traded result remains negative.
- Sample is 93.7 days with 223 closed trades — a moderate sample, not long enough to establish regime robustness.
- Pending: forward Demo/Real validation beyond this single statement is absent.
- No tester or mismatch warnings are present; no hard Stop Out or margin-call record is shown; no documented cost model (commission/swap) is supplied.
关键风险信号
- Negative expectancy: PF 0.62 with -23.22% return indicates the system loses money over the sampled 93.7 days.
- Risk asymmetry: 64.57% win rate but repeated losses in the -70 to -114 range (e.g., -114.44, -111.84, -111.60, -108.15) dominate the loss side.
- Profit concentration: top 1% of trades contribute 13.94% of gross profit and top 5% contribute 41.15%; removing the top 5% leaves PF 0.36 and net -1642.64, meaning the strategy lacks a broad profitable edge.
- Drawdown: 28.88% maximum drawdown against a 4263.78 capital base is materially large for the observed return profile.
- Withdrawals of 3273.57 relative to deposits of 4263.78 reduce the working capital available for recovery, increasing relative risk on any remaining balance.
- Unverified context: symbol, timeframe, spread/slippage assumption, and TP/SL configuration are not supplied, so the negative result cannot be attributed to a specific market or execution condition.
现有数据无法判断
- Traded symbol, timeframe, and broker server are not provided in report_context.
- Entry-ticket pairing for the clustered same-timestamp closes (e.g., multiple rows at 2026.07.16 16:02:38, 2026.09.01 11:09:51, 2026.09.10 15:34:32, 2026.09.28 03:31:09) is absent, so whether these are grid, scale-in, or independent orders cannot be confirmed.
- Strategy rule set: lot sizing (0.01–0.04 observed), stop-loss/take-profit levels, and any trailing-stop logic are not documented.
- Whether any of the losing exits above were triggered by specific stop thresholds or by manual intervention is not determinable from the data.
- Commission, swap, and fee treatment are not supplied; their net effect on the already-negative result cannot be quantified.
- Slippage, spread conditions, and minimum holding rules during the sample are not modeled or reported.
下一步行动
- Pair the multi-row clusters by entry ticket to determine whether large simultaneous losses are grid, scale-in, or independent orders, and quantify the resulting exposure.
- Run a loss attribution: rank the largest losing rows and check what fraction of total loss comes from same-timestamp clusters.
- Add symbol, timeframe, and stop/target configuration to the report context so the negative expectancy can be evaluated against a defined strategy.
- Reconcile the deposits/withdrawals (4263.78 / 3273.57) against net trading P/L to confirm the capital base used for the -23.22% and 28.88% figures.
- Include commission and swap in a follow-up statement extension if available, to test whether costs materially deepen the loss.
常见问题
Q. Why does a 64.57% win rate produce a -23.22% return?
A. Because the average win is much smaller than the average loss. The losing rows include -114.44, -111.84, -111.60, -108.15, -105.06, and several -90-level exits, while many winning rows are under 5 and a large number are under 2. The result is a profit factor of 0.62: gross losses exceed gross profits despite the majority of trades being winners.
Q. How concentrated is the profit in this statement?
A. Very concentrated. The top 1% of trades (3 trades) account for 13.94% of gross profit, and removing them leaves remaining net profit of -1211.31 and remaining profit factor of 0.53. The top 5% (12 trades) account for 41.15% of gross profit; removing them leaves remaining net profit of -1642.64 and remaining profit factor of 0.36.
Q. What does the 28.88% maximum drawdown mean in context?
A. It is the largest peak-to-trough equity decline recorded in the statement, measured against a capital base of 4263.78. It is a large decline for the observed return level (-23.22% net), and it occurred alongside 223 closed trades over a 93.7-day period from 2026-06-30 to 2026-10-02.
Q. Can I tell whether the strategy uses grid or scale-in entries from this data?
A. No. Several rows share the same close timestamp and occasionally the same open timestamp (for example multiple rows at 2026.07.16 16:02:38 and at 2026.09.28 03:31:09 with varying sizes), but the parsed rows lack paired entry tickets and exit-type labels, so it cannot be confirmed whether these are grid, scale-in, or independent positions. That is a verification item, not an established characteristic.
Q. How is RBOT different from asking a general-purpose AI assistant (such as ChatGPT, Gemini, Claude or DeepSeek) to analyse my report myself?
A. A general AI only sees the text or screenshot you paste, and works every number out itself — that is where hallucinated metrics come from. RBOT runs a purpose-built MT4/MT5 parser first, and the resulting structured breakdown is then reviewed under a fixed audit prompt, which keeps the safety score consistent, reproducible and comparable across reports. The calculation behind every metric is published on this page so you can verify it yourself, and RBOT refuses to score what the report itself cannot prove (real slippage, spread, forward testing) — those are listed as unverified rather than silently counted as risk.