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https://github.com/jaxperro/winning-wallet-finder.git
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jaxperro
50f1f0ae12
T2: the maker species is real and distinct — 673 wallets clear the taker screen's z>=2.5 discipline on the orders_matched stream (~33 expected by chance), pooled +$5.9M over 47,693 resolved bets in ~6 tape days; only 96/673 overlap the taker informed set, 3/37 watch_sharps. Not copyable by taking (their edge IS the spread) — follow-ons: inventory-lean signal, T1 generic maker sim. T4: print-substrate sibling-sum scan reads 2.8%/1.5% violation minutes but the top examples expose the artifact (resolution-time dust prints at bids, not standing offers); persistence p50 1min. Verdict: inconclusive at v0, needs standing-book data (L2 recording / live paired scanner) — parked behind the T1 decision. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""T2 EXPLORATORY (2026-07-23) — maker-sharp selection: mine the maker side
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of every match (aux orders_matched, ~6M wallet-attributed rows the screens
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have never touched). Do improbably-winning MAKERS exist — the species
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farming the crater wall — and are they a distinct, followable cohort?
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Method mirrors the taker screen (study_flow.informed_set): per wallet-asset
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net maker position (maker BUY = resting bid filled), entry vwap, resolved
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via tape proxy (chain-validated 742/742 method); wallet improbability
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z = (wins − Σp)/sqrt(Σp(1−p)) on n≥6 resolved bets with net≥5sh, vwap in
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[0.05,0.95], pnl>0; z≥2.5 qualifies. Readouts: cohort size, top wallets,
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overlap vs the taker informed set + watch_sharps (distinct species?),
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pooled per-bet EV of qualifying makers' resolved bets. NOT pre-registered;
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a forward table row + copyability (lag) study only if a cohort exists."""
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import json
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import tape # noqa: E402
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HERE = os.path.dirname(os.path.abspath(__file__))
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SET_MIN_Z, SET_MIN_BETS = 2.5, 6
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def main():
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db = tape.connect()
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tape.build_resolved(db)
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rows = db.execute("""
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WITH mk AS (
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SELECT lower(json_extract_string(payload,'$.proxyWallet')) wallet,
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json_extract_string(payload,'$.asset') asset,
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json_extract_string(payload,'$.side') side,
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cast(json_extract(payload,'$.price') AS DOUBLE) price,
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cast(json_extract(payload,'$.size') AS DOUBLE) size,
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any_value(json_extract_string(payload,'$.name')) OVER
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(PARTITION BY lower(json_extract_string(payload,'$.proxyWallet'))) nm
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FROM aux WHERE type = 'orders_matched'
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), bets AS (
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SELECT wallet, any_value(nm) nm, mk.asset,
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any_value(tk.payout) payout,
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sum(CASE WHEN side='BUY' THEN size ELSE -size END) net,
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sum(CASE WHEN side='BUY' THEN size*price END)
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/ nullif(sum(CASE WHEN side='BUY' THEN size END),0) vwap
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FROM mk JOIN res_tok tk ON mk.asset = tk.asset
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GROUP BY wallet, mk.asset
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HAVING net >= 5 AND vwap BETWEEN 0.05 AND 0.95
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)
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SELECT wallet, any_value(nm), count(*) n,
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sum(CASE WHEN payout=1.0 THEN 1 ELSE 0 END) wins,
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sum(vwap) exp_w, sum(vwap*(1-vwap)) var_s,
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sum(net*(payout - vwap)) pnl,
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avg(vwap) avg_entry, sum(net*vwap) staked
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FROM bets GROUP BY wallet
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HAVING n >= ? AND var_s > 0
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""", [SET_MIN_BETS]).fetchall()
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print(f"maker wallets with >= {SET_MIN_BETS} resolved conviction-ish "
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f"positions: {len(rows)}")
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scored = []
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for w, nm, n, wins, exp_w, var_s, pnl, avg_e, staked in rows:
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z = (wins - exp_w) / (var_s ** 0.5)
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if z >= SET_MIN_Z and pnl > 0:
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scored.append((z, w, nm, n, wins, pnl, avg_e, staked))
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scored.sort(reverse=True)
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print(f"qualifying maker-sharps (z>={SET_MIN_Z}, pnl>0): {len(scored)}")
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# overlap with the taker screens
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taker = set()
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try:
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d = json.load(open(os.path.join(HERE, "params", "informed_set.json")))
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taker = {x.lower() for x in d["wallets"]}
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except Exception:
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pass
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watch = set()
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try:
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for r in json.load(open(os.path.join(
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os.path.dirname(HERE), "live", "watch_sharps.json"))):
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watch.add(r["wallet"].lower())
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except Exception:
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pass
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ol_t = sum(1 for z, w, *_ in scored if w in taker)
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ol_w = sum(1 for z, w, *_ in scored if w in watch)
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print(f"overlap: {ol_t} in taker informed set (n={len(taker)}) · "
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f"{ol_w} in watch_sharps (n={len(watch)})")
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pooled_pnl = sum(s[5] for s in scored)
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pooled_n = sum(s[3] for s in scored)
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print(f"cohort pooled: {pooled_n} resolved bets · "
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f"${pooled_pnl:+,.0f} maker pnl\n")
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print(f"{'z':>5} {'name':<20} {'bets':>5} {'wins':>5} {'hit':>5} "
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f"{'pnl':>10} {'avg entry':>9}")
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for z, w, nm, n, wins, pnl, avg_e, staked in scored[:15]:
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print(f"{z:5.1f} {(nm or w[:12]):<20} {n:>5} {int(wins):>5} "
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f"{wins/n:>5.2f} {pnl:>+10,.0f} {avg_e:>9.2f}")
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if __name__ == "__main__":
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main()
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