aiqbench analysis
This research introduces a rigorous methodology for tracking the market performance of tokens rejected by algorithmic trading systems on decentralized exchanges, addressing a critical gap in measuring post-rejection outcomes. By implementing formal specifications and cross-check code paths, the study provides high-fidelity observational data on forward returns without making causal claims about hypothetical acceptance scenarios. This work advances the understanding of filter-gated decision-making within automated market maker environments, offering valuable insights for researchers analyzing algorithmic trading efficacy and market microstructure.
Post-Rejection Follow-up Sampling: Measuring Outcomes of Rejected Decisions in Algorithmic DEX Trading
arXiv:2606.08228v2 Announce Type: replace-cross Abstract: Filter-gated algorithmic trading systems on decentralised exchanges reject most candidate tokens they evaluate, yet the observed forward market trajectory of those rejected candidates is rarely measured on the same live venue that produced the rejection. This paper introduces Post-Rejection Follow-up Sampling (PRFS), an observational measurement methodology in which a separate tracking subsystem samples each rejected token's price and liquidity from the same live oracle path used by the rejecting scanner, at a scheduled cadence, from the moment of rejection out to a fixed analytic horizon. The methodology is defined through a formal specification of the observation window and the reason-attribution rule, a reference implementation (prfs v2.0.0) with executable coverage and reason-ledger tests, and a secondary cross-check implementation that re-derives every headline number through a distinct code path. The companion dataset contains 67,000 forward-observation rows across 2,997 rejection events collected on a single Solana automated-market-maker sub-venue during a launch-dynamics window of 8.63 calendar days across 457 unique mints. Under the primary reason-aware three-field event key, 1,455 events (48.55 percent) receive a matched forward observation; under the reason-agnostic two-field diagnostic key, 1,641 events (54.75 percent) match. Mint-level coverage is 100 percent. The 253-row reason-conflict ledger and 8,593 unaligned outcome rows are disclosed, and coverage is shown to be filter-associated (chi-square(6) = 401.29) and source-associated (z = 14.78). No causal or counterfactual claim of a hypothetical acceptance outcome is made; the reported estimand is the observed post-rejection forward return.
Abstract:Filter-gated algorithmic trading systems on decentralised exchanges reject most candidate tokens they evaluate, yet the observed forward market trajectory of those rejected candidates is rarely measured on the same live venue that produced the rejection. This paper introduces Post-Rejection Follow-up Sampling (PRFS), an observational measurement methodology in which a separate tracking subsystem samples each rejected token's price and liquidity from the same live oracle path used by the rejecting scanner, at a scheduled cadence, from the moment of rejection out to a fixed analytic horizon. The methodology is defined through a formal specification of the observation window and the reason-attribution rule, a reference implementation (prfs v2.0.0) with executable coverage and reason-ledger tests, and a secondary cross-check implementation that re-derives every headline number through a distinct code path. The companion dataset contains 67,000 forward-observation rows across 2,997 rejection events collected on a single Solana automated-market-maker sub-venue during a launch-dynamics window of 8.63 calendar days across 457 unique mints. Under the primary reason-aware three-field event key, 1,455 events (48.55 percent) receive a matched forward observation; under the reason-agnostic two-field diagnostic key, 1,641 events (54.75 percent) match. Mint-level coverage is 100 percent. The 253-row reason-conflict ledger and 8,593 unaligned outcome rows are disclosed, and coverage is shown to be filter-associated (chi-square(6) = 401.29) and source-associated (z = 14.78). No causal or counterfactual claim of a hypothetical acceptance outcome is made; the reported estimand is the observed post-rejection forward return.
18 pages, 3 figures, 2 tables. Companion dataset: doi:https://doi.org/10.5281/zenodo.20043515. Reference implementation: doi:https://doi.org/10.5281/zenodo.19672363. SSRN preprint: abstract 6607301
Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
arXiv:2606.08228 [q-fin.TR]
(or arXiv:2606.08228v2 [q-fin.TR] for this version)
Full text under the publisher's open licence · 274 words
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