What are the three Quantprove scores?
Quantprove produces three scores from your CSV trade log, one per mode. Edge Score comes from Backtest and grades a single backtest log 0-100. Stability Score comes from Validation and compares a backtest log against a live log 0-100. Health Score comes from Monitor and tracks a live strategy’s edge across the trade record as new trades arrive.
Each score answers a different question. Edge Score asks whether the historical record shows a statistical edge. Stability Score asks whether live execution reproduces that edge. Health Score asks whether the edge is holding or fading. You read them in that order: Edge first, then Stability, then Health.
All three start from the same per-trade data and never annualize R-multiples. The smart parser auto-detects whether your column holds R-multiples or dollar P&L, so the scores read the unit you uploaded.
Three scores, one CSV: Edge Score (Backtest) grades the past, Stability Score (Validation) checks the present, Health Score (Monitor) watches the trend.
How do you read the Edge Score?
Edge Score reviews a backtest across edge magnitude, consistency, downside risk, and tradability. It combines several metrics into one 0-100 reading while accounting for how much evidence stands behind the record.
Use the named tiers to place the result at a glance, then read the supporting diagnostics. The same headline score can reflect very different strengths and weaknesses.
Read the breakdown, not the headline number alone. The Edge Score glossary entry explains the public interpretation without disclosing Quantprove’s proprietary weighting.
How does Quantprove treat a small sample?
Quantprove treats scores built on thin samples as provisional. Fewer observations mean a handful of lucky or unlucky outcomes can dominate the result, so the reported assessment becomes more representative as the record deepens.
This follows the established finding that performance metrics inflate under limited data and repeated selection. Quantprove keeps the exact adjustment private because it is part of the scoring model.
Validation treats the amount of live evidence independently from the backtest. A long backtest cannot make a very short live record conclusive.
Small samples receive a cautious reading. The exact adjustment remains part of Quantprove’s private scoring model.
How do you read the Stability Score?
Stability Score compares a backtest log against a live log and returns 0-100. It reviews changes in the return distribution, drawdown behavior, and edge quality. Higher means live trades track the backtest more closely.
Stability Score carries no tier labels. It is a number, and Quantprove flags a strategy stable at 60 or above. Read it qualitatively: a high score indicates live results reproduce the backtest’s distribution and drawdown shape; a low score indicates the live record has drifted from what the backtest showed. The Exceptional and Strong labels belong to Edge Score only.
A short live record carries limited confirming power, no matter how long the backtest is. The Stability Score glossary entry explains how to interpret that caution without exposing the private scoring model.
How do you read the Health Score in Monitor?
Health Score recomputes a rolling Edge Score across a continuous live record and plots it against trade number on the X axis, never dates. Monitor needs a minimum of 100 trades and uses rolling windows that adapt as the record grows.
Health Score reports five verdict bands: 80+ Strong, 60-79 Promising, 40-59 Weak, 20-39 Poor, and below 20 No Edge. It compares earlier behavior with the most recent windows; a weaker recent reading indicates the rolling edge has softened.
Monitor is informs-only. It describes what the windows show and never prescribes an action. You read the trend and the band; Quantprove states the observation and stops there.
Health Score bands: 80+ Strong, 60-79 Promising, 40-59 Weak, 20-39 Poor, below 20 No Edge. Decay = first 3 windows vs last 3 windows.
How do the three scores connect?
The scores form a sequence: Edge to Stability to Health. A backtest earns an Edge Score. If that Edge Score reaches Strong or above, you run Validation to earn a Stability Score that checks whether live trades reproduce the backtest. Once a strategy is live with at least 100 trades, Monitor tracks its Health Score across the record.
Each step narrows the question. Edge Score asks whether an edge existed historically. Stability Score asks whether the edge survived contact with real execution. Health Score asks whether the edge is still present as the record grows. A strong Edge Score with a weak Stability Score is the overfitting signature: the backtest looked clean, the live record did not follow, and the Edge Quality bucket plus the distribution test surface the gap.
Start with the getting-started guide for a clean upload, then read the Quantprove metric glossary for every metric the three scores draw on.
What mistakes distort these scores?
Four errors distort the scores most. Watch for each before you trust a number.
- Insufficient sample: tiny logs overstate edge, so treat them as provisional until the result holds across more trades and market conditions.
- Overfitting: a tuned backtest can post a high Edge Score yet collapse live; Stability Score’s retention metrics and the KS/Anderson-Darling test expose the divergence.
- Unit or deposit distortion: mixing R-multiples with dollars, or changing account size and adding deposits mid-stream, breaks comparability; keep one unit and rescale R if account size changes.
- R-multiple annualization: never annualize R-multiples with sqrt(252) scaling; report Total R or EV per trade, never a figure like 36R per year.