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T11: maker-entry patience curve — 60s TTL already optimal; patience past 60s harvests adverse selection (loser fill-rate 100% at 5m+)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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jaxperro
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#!/usr/bin/env python3
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"""T11 EXPLORATORY (2026-07-23) — the PATIENCE CURVE for maker copy
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entries: #20 rests at the sharp's price for 60s because that is what T3
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tested, not because 60s is optimal. Same universe and fill convention as
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T3 (copy_maker_entry.py — fill = later tape print <= our bid; queue
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optimism stated), extended to the full TTL grid, with the two numbers T3
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did not produce:
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1. time-to-touch distribution among fills (median/p75/p90 seconds) —
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where the fills actually live on the clock;
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2. HYBRID EV/signal — maker fill inside the TTL, else taker fallback at
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the first print AFTER TTL expiry (within 10 min; approximates the
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then-current ask from the tape; miss if no print) — the policy a bot
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with a fallback would actually run. #20 as deployed has NO fallback
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(pure-maker column is the deployed comparator).
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Chain-true grading (payouts_for — scorer law), refunds excluded.
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Adverse-selection split (fill rate among eventual winners vs losers) per
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TTL — T3's signature stat — decides where patience turns toxic.
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NOT pre-registered — exploration to tune #20's maker_ttl_s knob.
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VERDICT (2026-07-23 run, n=58 chain-graded): 60s IS ALREADY OPTIMAL.
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Touch median 1-2s, p90 3-7s; fill rate 90% @60s -> 97% @5m and FLAT
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thereafter (zero touches after 5m). The marginal 60s->5m fills LOWER
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EV/signal (+16.62 @60s vs +15.36 @5m+) — at 5m+ the loser fill-rate is
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100% (every eventual loser returns to the bid; the 3% never-filled are
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winners running away). Hybrid taker-fallback adds nothing (<=4 events).
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Taker baseline +12.58. #20's maker_ttl_s=60 stands; patience past 60s
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only harvests adverse selection."""
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import json
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import os
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import statistics as st
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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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import forward as fwd # noqa: E402
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ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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TTLS = [(60, "60s"), (300, "5m"), (1800, "30m"), (14400, "4h"),
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(86400, "24h"), (None, "to-res")]
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FEE = 0.03
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FALLBACK_S = 600
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def main():
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db = tape.connect()
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t_lo, t_hi = db.execute("SELECT min(ts), max(ts) FROM trades").fetchone()
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tape.build_resolved(db)
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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"),
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"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 not r.get("their_price") or not r.get("my_price")):
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continue
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sig_ts = r["ts"] - (r.get("detect_lag_s") or 0)
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if not (t_lo + 60 <= sig_ts <= t_hi - 1800):
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continue
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fills.append({"book": book, "token": str(r["token"]),
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"sig_ts": sig_ts, "p": r["their_price"],
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"my_px": r["my_price"]})
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print(f"copy signals with tape coverage: {len(fills)}", flush=True)
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pays = fwd.payouts_for(db, [f["token"] for f in fills])
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graded = [f for f in fills if pays.get(f["token"]) is not None
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and pays.get(f["token"]) != 0.5]
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print(f"chain-graded (refunds excluded): {len(graded)}", flush=True)
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tk_pnl = 0.0
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for f in graded:
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pay = pays[f["token"]]
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sh = 100.0 / f["my_px"]
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tk_pnl += sh * (pay - f["my_px"]) \
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- FEE * sh * min(f["my_px"], 1 - f["my_px"])
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print(f"TAKER baseline @$100/signal: n={len(graded)} · "
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f"EV/signal {tk_pnl/len(graded):+.2f}\n", flush=True)
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# one touch-time query per signal covers every TTL (first touch ever)
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touch = {}
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for i, f in enumerate(graded):
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r = db.execute("""SELECT min(ts) FROM trades WHERE asset = ?
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AND ts > ? AND price <= ?""",
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[f["token"], f["sig_ts"], f["p"]]).fetchone()
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touch[i] = r[0]
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if i % 200 == 0:
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print(f" touch scan {i}/{len(graded)}", flush=True)
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for ttl_s, tag in TTLS:
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mk = hy = 0.0
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n_fill = n_fb = n_miss = 0
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waits = []
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win_fill = lose_fill = win_all = lose_all = 0
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for i, f in enumerate(graded):
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pay = pays[f["token"]]
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win_all += pay == 1
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lose_all += pay == 0
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t_t = touch[i]
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hi = f["sig_ts"] + ttl_s if ttl_s else t_hi
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if t_t is not None and t_t <= hi:
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n_fill += 1
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waits.append(t_t - f["sig_ts"])
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sh = 100.0 / f["p"]
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pnl = sh * (pay - f["p"]) # maker: no taker fee
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mk += pnl
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hy += pnl
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win_fill += pay == 1
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lose_fill += pay == 0
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else:
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# hybrid: taker fallback at first print after TTL expiry
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if ttl_s:
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fb = db.execute("""SELECT price FROM trades
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WHERE asset = ? AND ts > ? AND ts <= ?
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ORDER BY ts LIMIT 1""",
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[f["token"], hi, hi + FALLBACK_S]).fetchone()
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if fb is not None:
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fp = float(fb[0])
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if 0.01 <= fp <= 0.99:
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sh = 100.0 / fp
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hy += sh * (pay - fp) \
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- FEE * sh * min(fp, 1 - fp)
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n_fb += 1
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else:
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n_miss += 1
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else:
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n_miss += 1
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else:
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n_miss += 1
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n = len(graded)
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fr = n_fill / n
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med = int(st.median(waits)) if waits else 0
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p90 = int(sorted(waits)[int(0.9 * len(waits))]) if waits else 0
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fr_w = win_fill / max(win_all, 1)
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fr_l = lose_fill / max(lose_all, 1)
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print(f"TTL {tag:>6}: fill {fr:5.0%} ({n_fill}) · "
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f"touch med {med}s p90 {p90}s · "
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f"MAKER EV/sig {mk/n:+6.2f} · "
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f"HYBRID EV/sig {hy/n:+6.2f} (fb {n_fb}, miss {n_miss}) · "
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f"fill-rate W {fr_w:.0%} vs L {fr_l:.0%}"
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f"{' <- adverse' if fr_l > fr_w + 0.1 else ''}", flush=True)
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if __name__ == "__main__":
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main()
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