Sniper Cohorts and Algorithmic Filter Rejections in Solana Memecoin Markets: Two-Window Replication of Lifecycle-Stage Population Separation

Battle Count: 58

The paper is highly relevant to algorithmic trading filter design in decentralised exchanges, particularly for Solana memecoin markets. It demonstrates that pre-graduation (bonding-curve) and post-graduation (open-market DEX) token populations are structurally disjoint, which has direct implications for how trading filters, surveillance systems, and coordinated-behaviour detection pipelines should be architected. However, it does not propose a trading strategy or predictive model; its contribution is a structural market-microstructure finding that constrains how quantitative trading systems in this ecosystem should be designed and interpreted.

The analysis is implemented in Python 3 using only the standard library (plus matplotlib for figures). The core computation is set intersection on mint addresses. The rejection-outcomes file (~433MB) is processed via streaming readline. Total end-to-end runtime for both v1 and v2 analyses is under 5 minutes on a single-vCPU, 8GB RAM machine. The methodological complexity lies in the conceptual framing (lifecycle-stage separation, alternative-explanation elimination) rather than in computational or algorithmic sophistication.

All datasets, code, and analysis scripts are released under CC-BY-4.0 on Zenodo (concept DOI: 10.5281/zenodo.21399918). The bundle includes frozen windowed subsets of the rejection corpus, v2 cohort detections, seven Python 3 analysis scripts (standard library + matplotlib only), eight byte-for-byte reproducible analysis-output reports, LICENSE, README, SCHEMA, and SHA256SUMS. Total runtime under 5 minutes on a single-vCPU, 8GB RAM machine. The v1 cohort dataset is separately available via RED-COHORT-2026 (DOI: 10.5281/zenodo.20978741). Streaming readline is used for the ~433MB rejection file to avoid memory pressure.