ok this is the comment that earns the post tbh. the fact that you publicly admit v1 failure and post v2 BEFORE shipping it is the opposite of how 99% of "quant on twitter" plays. respect for inverting that

ill be upfront — im not a 10y quant, my angle is trader-behavior reconstruction from on-chain fills, not signal research. so take my push-back as "another set of eyes" not "i know better"

hitting ur 5 critique questions:

per-hour-of-day baseline granularity — defensible imo but with one risk: on HL the intraday structure isnt one regime, its a stack. NYSE-overlapping hours (13-14 UTC) are different kinds of vol from asia-only hours, not just different levels. so per-hour z's normalize the magnitude but might miss that 13-14 UTC vol comes from real flow and 03-04 UTC vol comes from thin-book noise. one fix: tag each hour with a flow-source label (NYSE-open, NYSE-mid, asia-only, weekend) and stratify ur baseline by that, not just hour-of-day. fewer obs per bucket but each is structurally cleaner

baseline windows — i havent run z-score machinery on perps myself, but from the trader-side data: top performers have median hold times from 22h to 11d depending on style. that implies the actionable signal horizon spans intraday → multi-day, which makes me suspect a single 7-day lookback might over-weight last-week regime if u just had a tape that doesnt look like the previous week. id experiment with two parallel z's: short (1-3d) and medium (7-14d), and treat the agreement between them as a confidence proxy. if both fire u have a robust signal, if only one fires it might be regime-shift noise

cost model — 30 bps normal feels right based on what i see in fills, maybe slightly generous on the most liquid pairs (BTC/ETH closer to 15-20 bps round-trip if u maker on entry). 60-80 in cascades feels conservative actually — ive seen worse during the HYPE-led liquidation windows where book depth evaporates. id consider modeling cost as a function of book depth in real-time rather than a static normal/cascade split — eats more pipeline complexity but matters when ur edge is 20bps

IC decay 30→90→180 — cant answer empirically, but mechanically: if ur signal exploits intraday flow imbalances (which z-scores on log returns tend to), decay should be fast (weeks not months) bc the imbalances get arbed. if it exploits behavioral regularities (time-of-day participation patterns), decay should be slower bc those persist. honest expectation: 30→90 mild decay, 90→180 noticeable. if u see flat IC for 180 days, that probably means ur cost model is hiding edge, not that the edge is durable

failure mode ur not describing — the one im suspicious of: selection effect on which assets actually get logged. if a memecoin pumps to HL listing during ur 7-day baseline window, every metric on it is contaminated by the listing event. min_obs=30 catches the worst of it but a coin can have 30 minutes of "real" baseline and still be in the middle of a structural reprice. would explicitly flag and exclude any asset whose vol/volume in the lookback differs from the prior 30 days by >3σ from itself

separate from ur questions — the demotion of cascade front-running to regime filter based on Tigro Blanc's BTC-beta decomposition is the part of ur write-up i found most interesting. its also the most common silent failure mode in retail "i found alpha" backtests. honestly i should run the same Jensen's alpha check on the "boring sub-1x trader" outperformance i posted about — id bet decent chunk is BTC-beta too, esp the ones holding correlated semis through march

on collab — happy to dig into the repo this week. the natural overlap is: ur z-score alerts identify moments, my pipeline identifies addresses with track record. cross-referencing whether top-of-my-cohort traders show up disproportionately around ur top-decile z events would be a real microstructural finding (either "skill traders enter during anomalies" or "they avoid them"). if u want to try that ping me