validate_timing.py now writes name, conv_win, conv_won/lost, conv_pnl,
realized_pnl, last_conv_bet and last_trade per sharp, so the dashboard
reads everything from the feed in ONE request instead of 3 data-api
calls per wallet (no more rate-limit storms). Stats come from the cache
(survivorship-correct): conviction win%/record/P&L over ALL of the
wallet's top-20%-stake bets; realized P&L over the last 500 resolved
bets. Resolved P&L per bet = stake*(1-p)/p if won else -stake.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Swap the flat $200 conviction cutoff for a per-wallet percentile (top
20% of each wallet's own stake sizes) everywhere it was used:
- cache.py: canonical CONV_PCTILE=0.80 + conv_cutoff() helper (matches
the dashboard's pctl: filter >0, sort, linear interp)
- conviction_scan.py: per-wallet quantile_cont(size,0.8) in SQL, was
`size >= 200`
- validate_timing.py, pnl_focused.py: use cache.conv_cutoff
Rationale + validation: p80 reproduces flat-$200's win-rate lift on the
sharps while adapting to scale (a whale's $200 isn't conviction, a
minnow's is). Re-running the pipeline under p80: scan finds 218 profile
wallets (was 69), forward 62/83 profitable (p~0), +16% pooled ROI — edge
persists out-of-sample. Regenerated conviction_wallets.json /
watch_sharps.json; docs updated. skill.py/strategy.py/insider.py
untouched (score over all bets / size as copyability heuristic only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
conviction_scan.py finds wallets whose high-conviction (>=$200) bets win on
uncertain (~0.4-0.6) markets — real edge, not favorite-riding — trained pre-June
and validated June (25/37 stayed profitable forward, p=0.024). validate_timing.py
applies the entry->resolution lead-time gate that separates copyable sharps from
uncopyable insiders: of 69 matches, 21 were insiders (lead <6h), leaving 23
validated copyable sharps (watch_sharps.json) now shown on jaxperro.com/trading.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
jaxperro