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research: copy_edge_slices (T5) — parity-era copy edge by niche/lag/band/wallet, chain-true
Paper pooled +18.6% (n=35, clears hurdle+2pp). Concentration: esports +22.0% (n=29, 83% of stake) and gkmgkldfmg +33.4% (n=19). Live pooled ~breakeven at $1-2 stakes (rounding drag, concentrated in the esports cell) — the #14 size-up tension in one table. Slippage by lag: <=3s fills BETTER than their print (-0.66c mean). Exploratory measurement; cells small; meta-niche coverage 0% until snapshots accrue (title fallback used). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""T5 EXPLORATORY (2026-07-23) — niche x lead-time x entry-band decomposition
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of the copybot's parity-era edge. WHERE does copy alpha live?
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Feeds the #14 sizing verdict: the pooled edge fights a measured ~1.9%-of-
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stake live fee hurdle (+2pp comfort, live/edge.py decision rule). If the
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edge concentrates (informed-niche prior: ITF/esports), per-niche follow
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filters beat one global rule.
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Substrate: fills ledgers (paper + live), BUYs opened >= PARITY_T0
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(2026-07-16 03:49Z, same boundary as live/edge.py), graded with
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forward.payouts_for (tape proxy + mandatory chain overlay — scorer law).
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Slight substrate difference vs edge.py (feed bets, bot-graded) is
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deliberate: chain truth + per-fill lag/price fields; the pooled number is
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printed next to edge.py's for reconciliation. Niches: meta_snap category/
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tags where the token appears in a snapshot, title heuristic fallback
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(coverage % reported — snapshots only start 2026-07-23, so early-era
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closed markets fall back). NOT pre-registered; measurement of a live edge."""
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import glob
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import gzip
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import json
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import os
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import sys
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import time
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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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import forward as fwd # noqa: E402
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HERE = os.path.dirname(os.path.abspath(__file__))
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ROOT = os.path.dirname(HERE)
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PARITY_T0 = 1784260140 # keep == live/edge.py
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HURDLE = 0.019 # measured live round-trip fee drag
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LAG_BUCKETS = [(0, 3, "<=3s"), (3, 10, "3-10s"), (10, 30, "10-30s"),
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(30, 1e9, ">30s")]
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PX_BANDS = [(0, .30, "<30c"), (.30, .50, "30-50c"), (.50, .70, "50-70c"),
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(.70, .96, "70-95c")]
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def load_meta():
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"""token -> niche from ALL meta snapshots (accrete over days)."""
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tok2niche = {}
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for f in sorted(glob.glob(os.path.join(HERE, "meta", "meta_*.jsonl.gz"))):
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for ln in gzip.open(f, "rt"):
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try:
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m = json.loads(ln)
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except Exception:
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continue
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tags = " ".join(str(t) for t in (m.get("tags") or [])).lower()
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cat = (m.get("category") or "").lower()
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niche = ("esports" if "esports" in tags
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else "tennis" if "tennis" in tags
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else cat or None)
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if not niche:
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continue
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try:
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toks = json.loads(m.get("clobTokenIds") or "[]")
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except Exception:
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toks = []
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for t in toks:
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tok2niche[str(t)] = niche
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return tok2niche
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def main():
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fills = []
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for path, book in ((os.path.join(ROOT, "copybot_fills.jsonl"), "paper"),
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(os.path.join(ROOT, "copybot_fills.live.jsonl"), "live")):
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for ln in open(path):
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r = json.loads(ln)
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if (r.get("side") == "SELL" or r.get("untracked")
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or r.get("ts", 0) < PARITY_T0 or not r.get("my_price")):
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continue
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r["_book"] = book
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fills.append(r)
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db = tape.connect()
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tape.build_resolved(db)
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pays = fwd.payouts_for(db, [str(f["token"]) for f in fills])
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tok2niche = load_meta()
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meta_hit = 0
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rows = []
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for f in fills:
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pay = pays.get(str(f["token"]))
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if pay is None:
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continue
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niche = tok2niche.get(str(f["token"]))
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if niche:
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meta_hit += 1
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else:
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niche = tape.niche(f.get("title") or "")
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rows.append({"book": f["_book"], "niche": niche,
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"lag": f.get("detect_lag_s"),
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"px": f["my_price"], "cost": f.get("cost") or
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f["my_price"] * f.get("shares", 0),
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"slip": (f["my_price"] - f["their_price"])
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if f.get("their_price") else None,
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"name": f.get("name") or "?",
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"pnl": f.get("shares", 0) * (pay - f["my_price"])
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- (f.get("fee") or 0),
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"win": pay == 1.0, "refund": pay == 0.5})
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print(f"parity-era BUY fills: {len(fills)} · chain-graded {len(rows)} "
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f"({len(fills)-len(rows)} unresolved) · meta-niche coverage "
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f"{100*meta_hit/max(len(rows),1):.0f}% (title fallback rest)")
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def cell(tag, rs, show_hurdle=True):
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if not rs:
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return
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n = len(rs)
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staked = sum(r["cost"] for r in rs)
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pnl = sum(r["pnl"] for r in rs)
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ret = pnl / staked if staked else 0
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hit = sum(r["win"] for r in rs) / n
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mark = ""
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if show_hurdle and n >= 15:
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mark = (" ✅ clears hurdle+2pp" if ret > HURDLE + 0.02
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else " ❌ under hurdle" if ret < HURDLE else " ~ marginal")
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print(f" {tag:<22} n={n:<4} ret {ret*100:+6.1f}% · EV/fill "
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f"{pnl/n:+6.2f} · hit {hit:.2f} · staked ${staked:,.0f}{mark}")
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for book in ("paper", "live"):
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rs = [r for r in rows if r["book"] == book]
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print(f"\n== {book.upper()} pooled ==")
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cell("ALL", rs)
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print(f"-- by niche --")
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for niche in sorted({r['niche'] for r in rs}):
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cell(niche, [r for r in rs if r["niche"] == niche])
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print(f"-- by detection lag --")
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for lo, hi, tag in LAG_BUCKETS:
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cell(tag, [r for r in rs
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if r["lag"] is not None and lo <= r["lag"] < hi])
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print(f"-- by entry band --")
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for lo, hi, tag in PX_BANDS:
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cell(tag, [r for r in rs if lo <= r["px"] < hi])
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print(f"-- by wallet --")
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for nm in sorted({r['name'] for r in rs}):
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cell(nm, [r for r in rs if r["name"] == nm], show_hurdle=False)
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# slippage by lag (execution cost of latency — T3 motivation)
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print("\n== slippage paid vs their print, by lag (both books) ==")
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for lo, hi, tag in LAG_BUCKETS:
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sl = [r["slip"] for r in rows
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if r["slip"] is not None and r["lag"] is not None
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and lo <= r["lag"] < hi]
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if sl:
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sl.sort()
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print(f" {tag:<8} n={len(sl):<4} mean {sum(sl)/len(sl)*100:+5.2f}c"
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f" · p90 {sl[int(len(sl)*.9)]*100:+5.2f}c")
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if __name__ == "__main__":
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main()
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