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docs + audit sweep 2026-07-23: fill-model lesson, five-test day, archive pass
FINDINGS: 'The fill model is the next scorer' section (A2 chain grade -$7.54/fill x1344 confirms the surge kill; oracle harness chain grade vetoes the ledger-positive tiers; virtual-book +26% variance footnote; the five tandem tests and the makers-on-the-wall through-line). HANDOFF: snapshot -> 07-23 (7-wallet rev 5, dark flags, Friday agenda incl #20/#21). READMEs: /test consolidation row, measurement-harness research row, study statuses + new script inventory. Archive: value/ (closed 07-19) + its test, ETHERSCAN_MIGRATION.md, order_probe v1 -> archive/; replay_out/ gitignored; links repaired. Tests: all 7 active scripts pass post-move. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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jaxperro
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@@ -39,9 +39,26 @@ Layout:
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win-biased; see SCORER LAW) · title parsers · chain_overlay()
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sim.py execution replayer calibrated on OUR live fills ledger
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(lag, FAK no-match, protected band, 3% taker fee)
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study_flow.py Study A — surge momentum (KILLED 2026-07-22, #16)
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study_oracle.py Study B — crypto oracle fair value (#17: E0.04 killed,
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E≥0.07 accumulating)
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study_flow.py Study A — surge momentum (KILLED 2026-07-22, #16; A2
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chain grade −$7.54/fill × 1,344 independently confirms)
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study_oracle.py Study B — crypto oracle fair value (#17: E0.04 killed;
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E≥0.07/0.1 ledger-positive but the harness chain grade
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2026-07-23 reads −$8.51/−$5.90 per fill at real latency
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— taker arm evidence-dead, tiers accrue to their formal
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bars, maker pivot is the successor hypothesis)
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copy_edge_slices.py T5 — parity-era copy edge by niche/lag/band/wallet
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(esports carries it; feeds #14)
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copy_maker_entry.py T3 — resting-bid copy entries beat taker FAKs
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(+$17.45 vs +$12.86/signal; basis of #20)
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sell_mirror_study.py sharp exits are bankroll ops, not signal
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(basis of #21 hold-through)
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maker_sharps.py T2 — 673 improbably-winning MAKERS in
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orders_matched (86% invisible to taker screens);
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follow-on: inventory-lean signal
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sibling_sum_scan.py T4 — print-substrate sum-arb scan (artifact-
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dominated; needs standing-book data; parked)
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maker_quote_sim.py T1 — crypto maker quoting at fair−m (stale-quote
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latency model; re-run pending)
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requote.py crater→requote timing (feeds the bots' per-niche retry)
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forward.py scores frozen studies on new tape days → forward_ledger
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(payouts_for chain overlay mandatory; controls + sub5c
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@@ -0,0 +1,117 @@
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#!/usr/bin/env python3
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"""T1 EXPLORATORY (2026-07-23) — crypto maker quoting on the oracle feed.
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Every taker study died at the requote wall (60-83% craters; makers repriced
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in <4s). This sims BEING the maker: a resting bid pinned to oracle-fair
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minus a margin, refreshed with latency R (the stale-quote window is the
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risk), filled when a tape print crosses it, graded to chain truth.
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Model per print at t: our active bid = fair(S(t-R), vol(t)) - m (vol drift
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over R<=4s is negligible; S(t-R) is the staleness that matters). Fill if
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print px <= bid (the book crossed our level — queue-position optimism
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stated). Fill price = our bid. Maker pays no taker fee. Per-token cooldown
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60s, max 5 lots. Sprints only inside their window (no s0 lookahead —
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harness rule, not the tape scorer's).
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Grid (pre-declared, not tuned after): m in {2c, 4c, 7c} x R in {1s, 4s}.
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Readouts: EV/fill (chain), hit, fills/day, fair-markout at +60s (adverse
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selection: how far fair moves against us right after we're filled).
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NOT pre-registered — Stage 1 of the maker pivot; a live paper maker arm
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only if this survives its own optimism caveats."""
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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 study_oracle as so # noqa: E402
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import forward as fwd # noqa: E402
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MARGINS = (0.02, 0.04, 0.07)
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LATENCIES = (1.0, 4.0)
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COOLDOWN_S = 60
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MAX_LOTS = 5
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STAKE = 100.0
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def main():
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db = tape.connect()
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series = {s: so.TickSeries(tape.load_ticks(db, s))
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for s in ("btcusdt", "ethusdt", "solusdt", "xrpusdt",
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"bnbusdt", "dogeusdt")}
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outcomes = so.outcome_map(db)
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tape.build_resolved(db)
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uni = so.crypto_universe(db, outcomes, series)
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tick_lo = min(s.ts[0] for s in series.values() if s.ts)
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tick_hi = max(s.ts[-1] for s in series.values() if s.ts)
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span_d = (tick_hi - tick_lo) / 86400
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print(f"universe {len(uni)} tokens · tick span {span_d:.1f}d")
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cells = {(m, R): dict(fills=[], last=0.0, n_tok={})
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for m in MARGINS for R in LATENCIES}
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for u in uni:
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mkt = u["mkt"]
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prints = db.execute("""SELECT ts, price FROM trades
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WHERE asset = ? AND ts >= ? ORDER BY ts""",
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[u["asset"], tick_lo]).fetchall()
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if not prints:
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continue
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s = series[mkt["sym"]]
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last_fill = {k: 0.0 for k in cells}
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n_tok = {k: 0 for k in cells}
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for ts, px in prints:
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px = float(px)
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if mkt["kind"] == "sprint" and ts < (mkt["t0"] or 0):
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continue # no pre-window quoting
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sig = s.vol_1s(ts)
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if sig is None:
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continue
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for R in LATENCIES:
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S_stale = s.at(ts - R)
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f = so.fair_value(mkt, u["up"], S_stale, sig, ts)
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if f is None:
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continue
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for m in MARGINS:
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k = (m, R)
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bid = f - m
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if not (0.02 <= bid <= 0.95):
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continue
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if px > bid:
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continue # print didn't reach our level
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if ts - last_fill[k] < COOLDOWN_S or n_tok[k] >= MAX_LOTS:
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continue
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last_fill[k] = ts
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n_tok[k] += 1
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# adverse selection: where is fair 60s after our fill
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f60 = so.fair_value(mkt, u["up"], s.at(ts + 60), sig,
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ts + 60)
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cells[k]["fills"].append(
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{"asset": u["asset"], "ts": ts, "bid": bid,
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"fair": f, "mo60": (f60 - bid) if f60 else None})
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filled_assets = {f["asset"] for c in cells.values() for f in c["fills"]}
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pays = fwd.payouts_for(db, list(filled_assets))
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print(f"grading {len(filled_assets)} filled tokens (chain overlay)…")
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for (m, R), c in sorted(cells.items()):
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fs = c["fills"]
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graded = [(f, pays.get(f["asset"])) for f in fs]
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graded = [(f, p) for f, p in graded if p is not None and p != 0.5]
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if not graded:
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print(f"m={m:.2f} R={R:.0f}s: {len(fs)} fills, none graded")
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continue
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pnl = wins = 0.0
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for f, p in graded:
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sh = STAKE / f["bid"]
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pnl += sh * (p - f["bid"]) # maker: no taker fee
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wins += p == 1.0
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mo = [f["mo60"] for f, _ in graded if f["mo60"] is not None]
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n = len(graded)
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print(f"m={m:.2f} R={R:.0f}s: fills {len(fs)} ({n} graded) · "
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f"{len(fs)/span_d:.0f}/day · EV/fill {pnl/n:+7.2f} · "
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f"hit {wins/n:.2f} · avg bid "
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f"{sum(f['bid'] for f,_ in graded)/n:.2f} · "
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f"fair-markout60 {sum(mo)/len(mo)*100:+.1f}c"
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if mo else "")
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if __name__ == "__main__":
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main()
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