Why do small samples overstate a strategy’s edge?
A short trade log can post a high win rate and a clean equity curve by luck alone. With 20 trades, one or two outliers move the mean enough to fake an edge that vanishes over the next 50. Bailey and López de Prado document this directly: testing many strategy variants on short histories inflates the apparent Sharpe ratio, and their Deflated Sharpe Ratio adjusts for sample length and the number of trials.
Quantprove treats a short record as provisional because a handful of trades can dominate its statistics. The reported assessment becomes more representative as the result holds across more trades and market conditions. The exact adjustment remains part of the private scoring model.
A short backtest is evidence to investigate, not a verdict. Quantprove keeps the internal sample adjustment private.
How does Quantprove catch an overfit backtest?
Overfitting happens when you tune entries, exits, and filters until the backtest curve looks perfect on the data you fit it to. The Edge Score reads high. Live, the same rules meet trades they never saw, and the edge collapses.
Validation pairs a backtest log against a live log and scores how closely they match. EV Retention and Sortino Retention divide live performance by backtest performance, so a 0.50R backtest EV that drops to 0.10R live reports as 20% retention. A two-sample Anderson-Darling test (Anderson and Darling, 1954, which replaced the earlier KS check) then verifies whether the live return distribution plausibly came from the same process as the backtest. A wide gap is the overfitting signature. See the Stability Score glossary entry for how the buckets combine.
Why does mixing R-multiples and dollars break a comparison?
Quantprove reads one unit per log. The smart parser flags R-multiples when more than 10% of values equal exactly -1.0, or when the mean sits near 0 with a standard deviation of 1 to 2; high-variance values read as dollar P&L. A backtest in R and a live log in dollars are not on the same scale, so Validation rejects a mixed-unit pair rather than score a meaningless comparison.
Changing account size or adding deposits mid-stream distorts a dollar log the same way: a $200 trade at a $10k balance and a $200 trade at a $50k balance carry different risk, but the raw numbers look identical. Keep one unit across both logs. If account size changed, rescale to R so each trade reports as a multiple of risk, not an absolute amount.
Why should you never annualize R-multiples?
R-multiples measure profit as a function of risk per trade, not a time-series percentage return. Multiplying them by sqrt(252), the convention for daily percentage returns, produces numbers with no meaning. The same error turns a strategy’s total result into a headline like "36R per year" that no underlying math supports.
Quantprove uses per-trade statistics throughout and never applies sqrt(252) to R data. The Sortino and EV figures behind the Edge Score are computed per trade. For aggregate results, read Total R or EV per trade. The metrics and tools guide covers the forward projection, which also runs on per-trade metrics rather than annualizing a return figure. If a yearly R figure shows up in a sales page, walk.
R-multiples scale with risk, not time. There is no valid sqrt(252) annualization for them; use Total R or EV per trade.
What is the fastest way to check your own log against these four?
Run the trade log through Backtest first and read the evidence-depth warning alongside the score. When you have live results, run Validation to see whether the behavior still resembles the backtest.
Before either run, confirm the unit. A log that mixes R and dollars, or spans a mid-stream account-size change, gives both modes a distorted baseline. The Best Practices guides cover sample size, validation, and unit handling in more depth, and the glossary defines each metric the modes report.