Trade A
The entry signal failed the rule, the trader chased anyway, and size was twice the limit. Price then kept rising.
A losing streak can mean two very different things: a process that isn't being followed, or ordinary variance inside a process that's working exactly as designed. Telling them apart is what a review is actually for.

Trade attribution decomposes P&L into research, rules, sizing, execution, discipline, and randomness so a lucky profit cannot automatically certify a bad process.
Grade the process before the P&L. A single outcome mixes strategy, sizing, execution, discipline, and randomness.
The entry signal failed the rule, the trader chased anyway, and size was twice the limit. Price then kept rising.
Signal, size, stop, and order all followed the plan. The trade then took a normal stop.
Four straight 5% losses might sound like a 20% drawdown, but each loss is taken from an already-smaller balance, so the real figure is larger than simple addition suggests. Reviewing drawdown correctly means accounting for this compounding effect, not just adding up the individual losses.
Adjust the streak length and the loss per trade, and compare the compounded result to simple multiplication.
Enter the number of straight losses. The longer the streak, the deeper the drawdown.
Enter the loss percentage per trade. Each loss is taken from an already-smaller balance.
Four straight losses of 5% each compound to a drawdown of 1 − (0.95)⁴ ≈ 18.5% — larger than simply multiplying 4 × 5%.
A review's central question is whether a string of losses shares an identifiable, fixable cause — a stop consistently placed narrower than the sizing formula called for, for instance — or whether every trade actually followed the rules and the losses are simply the ordinary variance any system has. The first is a process problem; the second usually isn't.
Pick a case and judge whether the described result points to a fixable process deviation or a small sample being overinterpreted.
A review shows a string of losing trades all shared one thing in common: the trader's stop distance was consistently narrower than what their own sizing formula called for. This points to a process deviation — execution didn't match the plan — rather than an unlucky stretch.
A review shows a string of losing trades where every entry, stop, and size matched the trader's rules exactly, and the setups were reasonably diverse. This looks more like ordinary variance within a sound process than a flaw in the process itself.
After one losing trade that followed the plan exactly, a trader concludes the entire strategy is broken and abandons it. A single trade that followed the plan is too small a sample to conclude the strategy itself is broken.
What does the combined expectancy math actually show over this stretch?
What is the compounded, not simply added, size of the current losing stretch?
Did entries, stops, and sizing actually match the written rules?
Is this stretch long enough to distinguish a process problem from ordinary variance?
A losing streak shrinks a balance faster than simple addition suggests.
An identifiable, repeated deviation is fixable; ordinary variance in a sound process usually isn't.
A review needs a pattern across enough trades to mean something.
Submit your answers to see detailed explanations.
Describe the trades in the stretch and whether execution matched your rules, and Mira can help you separate a process issue from ordinary variance — it won't tell you whether to keep trading the strategy.
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A non-quantitative attribution framework: a trade's outcome can be checked from four angles — strategy/setup quality, position sizing/portfolio context, execution quality, and normal variance. A loss can't be automatically attributed to 'the strategy is bad,' and a profit can't be automatically attributed to 'the strategy is good' either — both require checking each of the four categories, not just looking at the direction of the single result.
Understand that a review has to first distinguish an execution gap, normal variance, and a strategy/environment problem, without changing the rules right away because of a handful of wins or losses; changing a strategy needs evidence, and only a limited number of variables should change at once.