feat(quantitative): deflated Sharpe ratio command - #7633
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Adds obb.quantitative.performance.deflated_sharpe_ratio (Bailey & Lopez de Prado, 2014): the probability that a strategy's true Sharpe exceeds the expected maximum Sharpe of N zero-skill trials, answering whether a result selected as the best of N tested variants reflects skill or selection luck. Adjusts for sample length and return skewness/kurtosis. - helpers.deflated_sharpe_stats(): pure computation, parity-tested at 1e-7 against the numguard reference implementation - DeflatedSharpeModel + POST command in the performance router, following the omega_ratio pattern - trials_sr_std defaults to the null 1/sqrt(n-1); callers who track their optimizer's actual cross-trial Sharpe dispersion can pass it
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Why
obb.quantitative.performanceships Sharpe, Sortino and Omega — but no way to answer the question that decides whether any of them mean anything after optimization: was this result selected as the best of N attempts, and does it survive that selection? Under pure noise, the expected maximum Sharpe of N zero-skill strategies grows with N; a raw Sharpe from a parameter sweep is a best-of-N number.What
obb.quantitative.performance.deflated_sharpe_ratio(data=returns, target="close", trials=100)— the Deflated Sharpe Ratio (Bailey & López de Prado, 2014): the probability that the true Sharpe exceeds the expected maximum oftrialszero-skill attempts, adjusting for sample length and return skewness/kurtosis. Returns Sharpe, the expected-max bar, the DSR probability, observations and trials.helpers.deflated_sharpe_stats()holds the pure computation; the router command follows the existingomega_ratiopattern (POST,DeflatedSharpeModel, lazy imports, PythonEx/APIEx examples).trials_sr_std(cross-trial Sharpe dispersion) defaults to the null1/sqrt(n-1); callers who track their optimizer's actual trial spread can pass it through the helper.Tests
extensions/quantitative/tests/test_deflated_sharpe.py: parity at 1e-7 against the numguard reference implementation on a deterministic series, single-trial-reduces-to-PSR, monotonicity in trial count, input guards. 5/5 passing locally alongside the existing helper tests.The same math was merged into ffn last week (pmorissette/ffn#311), and equivalents are under review in backtesting.py (kernc/backtesting.py#1388) and jesse (jesse-ai/jesse#611).
Note: I haven't regenerated the static package reference in this PR — happy to run the build step and push it if you'd like it included, or leave it to your release tooling, whichever matches your process.