Performance statistics

Performance answers how strong is the edge in the selected sample, and what must remain true for it to stay above break-even? It combines outcome magnitude, frequency, extremes, sequences, and Trading Mastery.

Performance is available with Pro. Start with Overview to confirm the population and read How statistics are calculated for shared return and availability rules.

Edge statistics

Trade expectancy, profit factor, and average win/loss ratio use numerical outcomes in the selected Profit, R, or Percentage unit:

trade expectancy = sum(outcomes) / observation count

profit factor = sum(positive outcomes) / abs(sum(negative outcomes))

Average win/loss = average(positive outcomes) / abs(average(negative outcomes))

They answer different questions:

• Trade expectancy — What did an observed trade produce on average?
• Profit factor — How much positive result was produced per unit of negative result?
• Average win/loss ratio — How large was a typical positive outcome compared with a typical negative outcome?

Percentage expectancy is an arithmetic per-trade average. It is intentionally different from compounded selected-trade return.

Interpreting the combination

A positive trade expectancy with a low win rate can be supported by large average wins. A high win rate can support an edge with a smaller payoff ratio. Profit factor reflects the combined effect of frequency and magnitude, but it can be dominated by a small number of outsized trades.

Always check trade count, extreme outcomes, and the selected date range. A strong number from a small or unusually concentrated sample should be treated as a hypothesis to monitor.

Largest winning and losing trades

Largest winning trade = maximum outcome greater than zero
Largest losing trade  = minimum outcome less than zero

The numerical sign in the selected return unit defines these observations. Largest losing trade remains negative. If no positive or negative observation exists, only the corresponding statistic is unavailable.

Compare each extreme with total return and expectancy. If removing one trade would materially change the conclusion, the edge may be concentrated rather than broadly demonstrated. The statistic does not automatically label concentration as good or bad. It makes that dependency visible.

Break-even analysis

Break-even analysis connects decisive outcome frequency with payoff. It uses only wins and losses because the conventional equations model two decisive outcomes.

decisive win rate = W / (W + L)

break-even win rate = 1 / (average win/loss ratio + 1) * 100

break-even reward:risk = (100 / decisive win rate percentage) - 1

Example: required win rate

If average wins are 1.5R and average losses are -1R, the payoff ratio is 1.5:

required win rate = 1 / (1.5 + 1) * 100 = 40%

A decisive win rate above 40% is above the break-even boundary for that observed payoff. Below 40%, that magnitude relationship would not support positive two-outcome expectancy.

Example: required payoff

If the decisive win rate is 45%:

required payoff = (100 / 45) - 1 = 1.22

The observed average positive outcome needs to be about 1.22 times the average negative outcome to break even before considering differences outside this simplified relationship.

Buffers

win-rate buffer = decisive win rate - break-even win rate
payoff buffer = observed payoff ratio - break-even payoff ratio

A positive buffer is above the corresponding boundary. A negative buffer is below it. The two buffers express the same edge from different directions, so use them diagnostically rather than adding them together.

Your configured descriptive win rate may include break-even trades as wins or losses. That preference does not change the decisive win rate used here.

Outcome streaks

Performance shows the longest consecutive sequence of saved Win, Loss, and Break-even statuses. Streaks follow close-date order and include supporting result and duration.

For a streak:

• Profit and R results are summed.
• Percentage returns are compounded.
• Duration runs across the sequence represented by the closed trades.

If multiple streaks have the same length, Trading Vault prefers the sequence with the greater summed Profit and then the greater summed R. This produces one stable representative sequence rather than switching unpredictably between ties.

Streaks are descriptive evidence, not forecasts of the next outcome. They are useful for planning psychological and financial tolerance. A historically observed losing streak can inform review expectations, but a future streak can always exceed the recorded maximum.

Trading Mastery

Trading Mastery combines Edge, Resilience, Downside Performance, Reliability, and Risk Control. It uses a regularized weighted geometric mean so a strong result in one dimension cannot completely hide a failed one.

Because its normalization and evidence rules are more involved than an ordinary ratio, read the dedicated Trading Mastery Score guide.

How to use Performance

1. Choose R first when you want a position-size-neutral view of edge.
2. Review trade expectancy, profit factor, payoff ratio, and decisive win rate together.
3. Inspect largest wins and losses for concentration.
4. Use break-even buffers to identify whether frequency or payoff is closest to the boundary.
5. Review streaks to understand the sequences already present in the sample.
6. Use Trading Mastery as a balanced summary, then open its components instead of treating the total as a verdict.

Performance statistics describe the selected history. They do not estimate statistical significance, predict future profitability, or replace strategy and market-context review.