Analyze a backtest

Drop a CSV with a 'return' column (QuantConnect, MT4/5, pandas export). Full verdict in seconds.

Drop your returns CSV here, or click to choose one

Statistical Methodology: Why the Classical Sharpe Ratio Is Not Enough

A backtest displaying a high annualized Sharpe Ratio (> 2.0) is frequently the artifact of selection bias (multiple testing / data-snooping) or parameter overfitting on a finite historical sample. The TrueVerdikt auditing engine subjects your return series to three institutional econometric filters:

1. Deflated Sharpe Ratio (DSR — Bailey & López de Prado, 2014)

DSR = Φ( ((SR̂ − SR₀) · √(T − 1)) / √(1 − γ₃·SR̂ + ((γ₄ − 1)/4)·SR̂²) )

Adjusts the observed Sharpe Ratio (SR̂) for the number of independent trials tested (N), return skewness (γ₃), excess kurtosis / fat tails (γ₄), and sample length T.

2. Combinatorial Purged Cross-Validation (CPCV)

PBO = Prob(SR_OOS < Median | Optimal In-Sample)

Partitions the return series into training and Out-of-Sample (OOS) folds while purging overlapping serial observations to prevent look-ahead and serial correlation leakage.

3. Fractional Kelly Sizing & 95% Conditional Value-at-Risk (CVaR)

f* = ½ · (μ / σ²) | CVaR₉₅ = E[R | R ≤ VaR₉₅]

Computes a conservative half-Kelly theoretical capital allocation ceiling alongside Expected Shortfall (CVaR 95%) across the worst 5% of historical return observations.

Methodological Disclaimer (YMYL): This tool performs a purely statistical audit on the historical return series you upload. A 'GO' verdict indicates historical sample robustness against overfitting but never guarantees future live trading performance.