12 pages. The math, code, and intuition behind why 87% of published Sharpe ratios above 2.0
are statistical artifacts — and the deflation procedure that filters them out. Drawn from López de Prado, Bailey, Harvey-Liu-Zhu.
Inside
▸ The selection-bias problem in plain English (and in code)
▸ Deflated Sharpe — full derivation + Python implementation
▸ Probabilistic Sharpe vs. point-estimate Sharpe
▸ Worked example: a "3.4 Sharpe" strategy collapses to 0.6
▸ Reading list — exactly which papers, in what order
► WHO IT'S FOR
Coaches who want to upgrade their material before applying to the cohort.
Not for: discretionary day-traders looking for a "system".