A very high trade-count backtest with a 96.8% win rate and PF 2.54, but with repetitive same-timestamp multi-ticket clusters and large recurring stop-out losses that require out-of-sample and forward validation before any confidence.

Data transparency

Key metrics

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.

Detailed calculations

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 positives: 2413 closed trades, PF 2.54, return 317.4% on a 1000 base, max drawdown 34.0%, win rate 96.8%; profit is not concentrated (top 1% of trades = 6.3% of gross profit, top 5% = 21.3%; remaining PF stays 2.0-2.38).
• Repeatable multi-ticket clusters: many identical size 0.01 tickets open and close at the same minute and price, e.g. rows 1157-1162 all +3.19, rows 25021-25026 all -25.07/-50.14, rows 213123-213129 all about -50, indicating several positions taken as one basket at one timestamp.
• Clustered exits produce large simultaneous losses: -25.07 x5 plus -50.14 and -24.06 on 2024.05.09; about -24.85 x6 plus -23.84 on 2024.06.07; about -25.10 x5 plus -50.20 on 2025.03.05; about -50.30/-75.45 x3 plus -48.96 on 2026.02.19; the same-minute sequences are repeated multiple times across the record.
• Loss magnification: same direction, same time, and the 0.02 tickets have exactly double the loss of the 0.01 tickets (for example -50.20 versus -25.10; -50.30 versus -25.15), so losses scale additively across the cluster at a single event.
• Same-bar/same-minute opens and closes appear throughout (open equals close timestamp on almost every sample row), an execution assumption to verify, not by itself proof of invalid data.
• Equity swings are uneven: a run from about 1358 to about 1609 is followed by a drop to about 915, and later from about 3453 to about 2254, so gains are not monotonic and basket stop-outs materially reset the curve.
• Hard evidence of tester quality warning: 24 mismatched charts errors are reported for this run.
• No commission, swap, or fee fields are present in the supplied context; cost sensitivity is therefore an open question.
• Exit type labeling is mostly s/l while many of those exits show positive profit, characteristic of a trailing-stop/breakeven exit style rather than open losses.

Key risk flags

• Clustered same-timestamp basket entries with correlated losses: the 2024.05.09 sequence opens around 17:18 with about five -25.07 tickets plus -50.14 and -24.06, removing roughly 6-7% of account value in one minute before the next recovery.
• On 2024.06.07 a similar cluster of about six -24.85 tickets plus -23.84 takes the balance from about 1088 to about 916 in one minute, a drop of roughly 16%.
• On 2025.03.05 approximately four -25.09 tickets, one -50.18 ticket, and one -24.42 ticket drain the balance from about 2104 to about 1904, roughly 10% in one minute.
• On 2026.02.19 three large same-minute tickets of about -50.30 and -75.45 plus -48.96 remove roughly 9-10% of account value in one minute.
• If cluster direction is correlated, simultaneous stop-outs scale linearly with ticket count rather than being independent; the 0.02 ticket magnitude being exactly twice the 0.01 loss confirms additive exposure within the cluster.
• Test-quality flag of 24 mismatched charts errors means the reported metrics could differ on the intended symbol and data set.

What the data cannot tell

• Exact instrument, broker, and chart symbol are not provided.
• Commission, swap, spread, and fee treatment are absent, so net-of-cost behavior is unknown.
• Order logic, cluster sizing rules, stop distance, and whether the 0.02 tickets are a scaling grid or part of a basket cannot be confirmed without the full backtest settings.
• Minimum holding time, stop level, and tick model are not shown.
• Whether the mismatched charts errors materially changed the symbol, timeframe, or bar data is not stated.

Next actions

• Re-run the test with the exact intended symbol and a clean chart history to resolve the 24 mismatched charts errors, then compare summary metrics.
• Export the backtest settings and add commission, swap, and spread assumptions to test whether the clustered multiple-ticket losses remain survivable after costs.
• Analyze the full trade list for same-timestamp ticket clusters to measure the worst simultaneous loss and confirm whether cluster direction is correlated, which would clarify the exposure risk.
• Run a forward or demo sample over the same instrument and conditions to compare live vs backtest fill quality and cluster behavior.
• Stress-test position sizing and cluster rules under degraded conditions, such as wider spreads or slippage on the cluster exit timestamps.

Frequently asked questions