Transaction Completion, Sample Selection, and Backtest Inference in Bitcoin Options on Deribit​

Abstract

Empirical option backtests observe strategy returns only when all required contracts can be mapped and qualifying transactions appear within the measurement window. This creates endogenous strategy-sample formation before performance is measured. We reconstruct that process for a predefined registry of daily inverse Bitcoin-option strategies on Deribit. Across both study layers, 69,020 nominal template-date mappings yield 49,558 primary actual strategy-date positions after geometry checks and deduplication, and 18,457 of those positions complete within 60 minutes. In the primary Layer A sample, the unweighted mean of date-level completion rates is 40.9% (stationary date-block 95% interval, 37.0%–45.0%). A construct-validity diagnostic based on 355 provider-ID event-strategy matches, corresponding to 352 exact-signature clusters, shows that the public-tape index captures a related activity margin but is neither atomic package execution nor an investor fill. Completion varies across strategy families and market states. Moreover, completed-observation, full-calendar, inverse-probability-weighted, and augmented conventions can change backtest rankings and signs. Conventional inference does not identify a rule superior to the at-the-money benchmark, and the evidence supports no deployable trading claim. The contribution is a financial-inference framework that makes strategy formation, missing outcomes, dependence, and temporal transport explicit.