A Deflated Sharpe Ratio is not an allocation decision. Here is how to turn it into one. A number is not an action The previous post in this series walked through the math and the honest implementation of the Deflated Sharpe Ratio. We ended with a number — a calibrated probability between 0 and 1 […]
The formula is twenty lines of Python. What kills you is the inputs. Why a raw Sharpe Ratio is a lie of omission Every quantitative trader has computed a Sharpe ratio. Most have published one to an audience — an allocator, a risk committee, a partner, a tweet thread. The Sharpe is intuitive, comparable across […]
Allocation under real constraints, multi-scenario evaluation, and the monitoring discipline that keeps the portfolio honest after launch. The gap between a score and a portfolio The composite score from the previous post tells you, for each strategy in your universe, the probability that its edge is real. It does not tell you how many contracts […]
Four statistical tests, one number. How to design a composite that defends against survival bias without throwing away signal. Why a composite rather than a gate In the previous post I described four diagnostic tools for survival-bias correction: the Deflated Sharpe Ratio, Haircut Sharpe, Probability of Backtest Overfitting, and bootstrap-based Stability selection. The natural […]
The most beautiful backtests are the most dangerous. Here’s why, and what to do about it. The selection pipeline that lies to itself Every developer of systematic strategies has roughly the same workflow. You design a strategy idea. You run it on historical data — in-sample. If it works, you split off a chunk you […]
In Part 1 I laid out why simulated incubation fails as a substitute for real incubation: it inverts the temporal asymmetry of knowledge that makes incubation informative. In Part 2 I quantified the bias with a Monte Carlo simulation. If simulated incubation is statistically broken, what should you do instead? This part covers five alternatives, […]