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https://github.com/jaxperro/winning-wallet-finder.git
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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>
32 lines
1.2 KiB
Bash
Executable File
32 lines
1.2 KiB
Bash
Executable File
#!/bin/bash
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# Massive ingest of the wallet data we still need, from the FAST subgraph
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# (accounts + market_positions). The CLOB token map is already sufficient
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# (1.27M tokens covering every resolved condition), so it's NOT re-run here —
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# run `python3 clob_tokens.py` separately if you want to refresh it.
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#
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# Both steps are parallel (16 id-shards) and resumable (per-shard cursors), so
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# killing and re-running continues from the last checkpoint.
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#
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# nohup ./run_full.sh > run_full.log 2>&1 < /dev/null & disown
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# tail -f run_full.log
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set -u
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cd "$(dirname "$0")"
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until python3 -c "import duckdb;duckdb.connect('pmkt.duckdb',read_only=True).close()" 2>/dev/null; do
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sleep 5
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done
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echo "[run] $(date '+%F %T') 1/2 accounts (parallel, resumable) …"
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python3 ingest.py -p accounts
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echo "[run] $(date '+%F %T') 2/2 market_positions (parallel, resumable, the big one) …"
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python3 ingest.py -p market_positions
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echo "[run] $(date '+%F %T') DONE"
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python3 - <<'PY'
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import duckdb
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c = duckdb.connect("pmkt.duckdb", read_only=True)
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for t in ("conditions", "market_data", "accounts", "market_positions"):
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print(f" {t:18} {c.execute('select count(*) from '+t).fetchone()[0]:,}")
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PY
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