jaxperro
3d0bc7f001
live/: operationalizes the LBS/Yale "skilled ~3%" result against the live data-api. Enumerate recent liquid markets -> top traders -> candidate pool; cache every wallet's resolved bets once in DuckDB (~26k wallets / 12.5M bets, keyed by per-bet resolution time so any cutoff re-scores in seconds); 5-gate skill funnel (n>=15, z>0, BH-FDR, split-half OOS, MM/bot cap); dashboard + daily refresh. Key finding: copying the high-win-rate "favorite-rider" cohort looks +23.6% in-sample but loses -7.4% once selected on pre-June-1 data only (99% -> 68% win rate) — selection bias, reproducing the paper's "lucky winners revert" result on live data. Win rate != edge, again. wide/: bulk subgraph->DuckDB scanner (survivorship-bias-free over all wallets), but the public subgraph is frozen at Jan 2026 -> historical tool only. Large local data (*.duckdb, candidates.json, *_scored.json, history/) gitignored. README + FINDINGS updated with the current logic and the clean result. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
27 lines
1.3 KiB
Bash
Executable File
27 lines
1.3 KiB
Bash
Executable File
#!/bin/bash
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# Daily skilled-wallet refresh, cache-backed:
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# 1) discover — enumerate traders of markets resolved in the last ~14 days,
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# merged into the accumulating candidate pool
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# 2) freshen — force a re-pull of the current watchlist (forward tracking),
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# then top up the cache (collect re-pulls only new + >14d-stale
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# wallets, so it's cheap after the initial collection)
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# 3) re-score — the 5-gate funnel, instant from cache -> watch_skilled.json
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# 4) dashboard — regenerate + snapshot for auditable forward history
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#
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# Schedule with launchd/cron (Mac must be awake). Logs to daily.log.
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set -u
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cd "$(dirname "$0")"
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echo "[daily] $(date '+%F %T') 1/4 discover (enumerate last 14d)"
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python3 enumerate.py 14
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echo "[daily] $(date '+%F %T') 2/4 freshen cache (watchlist forced + new wallets)"
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python3 -c "import json,os,cache
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if os.path.exists('watch_skilled.json'):
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cache.invalidate([w['wallet'] for w in json.load(open('watch_skilled.json'))])" 2>/dev/null || true
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python3 collect.py
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echo "[daily] $(date '+%F %T') 3/4 re-score (cache-backed, instant)"
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python3 skill.py
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echo "[daily] $(date '+%F %T') 4/4 dashboard"
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python3 dashboard.py
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mkdir -p history && cp watch_skilled.json "history/watch_$(date '+%Y%m%d').json" 2>/dev/null
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echo "[daily] $(date '+%F %T') done -> watch_skilled.json + dashboard.html"
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