mirror of
https://github.com/jaxperro/winning-wallet-finder.git
synced 2026-08-03 11:17:47 +00:00
jaxperro
1cbe1a67b9
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>
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""Does a FOCUSED copy strategy clear where the broad 10-wallet basket didn't?
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Same $1000 capital-constrained engine + missed-trade accounting (pnl_basket.sim),
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but on narrower signal sets: fewer wallets, and/or only the wallet's higher-
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conviction (larger-stake) bets — so $1000 isn't spread across 1,210 markets.
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"""
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import time
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import cache
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import pnl_basket as pb
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JUN1 = time.mktime(time.strptime("2026-06-01", "%Y-%m-%d"))
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E8 = "0xe8ca3f758c93f44f3ec210542ab78afb7c0bcccb"
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A0 = "0x0a7aaf83341b52df34e8ffef52aa295538d6df1b"
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def gather(wallets, conviction=False):
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"""One signal per market. conviction=True filters to each wallet's high-
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conviction bets — the top 20% of its own stake sizes (p80) — instead of a flat
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dollar cutoff; in that mode we only use resolved bets (open ones have no known
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stake to filter on)."""
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pos = {}
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for w in wallets:
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ent = cache.get_entries(w)
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bets = cache.get_bets(w)
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resolved = {b["cond"]: b for b in bets}
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cut = cache.conv_cutoff(b["size"] for b in bets) if conviction else None
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for cond, ets in ent.items():
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if ets < JUN1:
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continue
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b = resolved.get(cond)
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if b:
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if cut is not None and (b["size"] or 0) < cut:
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continue
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rec = dict(ets=ets, p=max(0.001, min(0.999, b["p"])),
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won=b["won"], res_t=b["res_t"])
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else:
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if cut is not None:
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continue
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rec = dict(ets=ets, p=None, won=None, res_t=None)
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if cond not in pos or ets < pos[cond]["ets"]:
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pos[cond] = rec
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return sorted(pos.values(), key=lambda r: r["ets"])
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def run(label, wallets, conviction=False):
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ev = gather(wallets, conviction)
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res = sum(1 for e in ev if e["res_t"] is not None)
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print(f"\n### {label} — {len(ev)} markets ({res} resolved)")
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h = f"{'stake':>6}{'entered':>8}{'missed':>7}{'open':>5}{'realized':>11}{'equity':>9}"
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print(h)
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for s in (50, 100, 200):
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r = pb.sim(ev, s)
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print(f"${s:>4}{r['entered']:>8}{r['missed']:>7}{r['open_left']:>5}"
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f"{r['realized']:>+10,.0f}{r['equity']:>9,.0f}")
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def main():
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run("0xe8 only — all June+ entries", [E8])
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run("0xe8 only — conviction (top 20% by stake)", [E8], conviction=True)
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run("0xe8 + 0x0a — all June+ entries", [E8, A0])
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run("0xe8 + 0x0a — conviction (top 20% by stake)", [E8, A0], conviction=True)
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print("\nrealized = settled-bet P&L · equity = $1000 + realized (open at cost)")
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if __name__ == "__main__":
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main()
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