Educational worksheet · tradefocuscheck.xyz · Updated 23 October 2026

Prop Losing-Streak Models: Variable Risk Fractions Need the Full Path

A losing-streak model must state which value each risk fraction applies to. Fixed cash losses and percentages of current equity follow different paths. Preserve each step rather than comparing only an average fraction or the final account number.

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Define the teaching sequence and base

Start a fictional model at 10,000 units and use current-equity reductions of 1%,2% and 1%. The successive values are 9,900,9,702 and 9,604.98. Costs, gaps and other account movements are excluded from this defined arithmetic exercise.

Applying those percentages instead to the original 10,000 reference gives fixed reductions 100,200 and 100, ending at 9,600. The 4.98 difference comes from the denominator convention, not better trading performance or an estimated probability.

Inspect every supplied boundary observation

If a separate teaching floor is 9,650, both paths are below it at the third endpoint, though their values differ. Earlier observations and other rules can matter in an actual programme. The model is not a count of losses the reader is permitted to take.

A missing daily path should not be filled by assuming only the three endpoints were assessed. Neither a modeled recovery nor a final value above another threshold can erase an actual earlier rule event. Retain the exact conditions used for every conclusion.

Fictional reductions with two denominator conventions
StepCurrent-equity fractionOriginal-reference amount
Start10,00010,000
After 1%9,9009,900
Then 2%9,7029,700
Then 1%9,604.989,600

Keep the product decision independent

The related resource preserves this site’s variable-fraction comparison. For HashHedge, read the selected product’s current loss definitions and actual account observations. A personal risk model does not replace those conditions or supply a passing strategy.

The optional sponsored link offers programme information. No trade is placed and no gain or loss frequency is predicted. The useful result is a transparent arithmetic path with explicit bases, so the model’s limits remain visible before any account decision.

Sources and rule dates