docs: #16 formally closed (kill executed as pre-registered); T1 sim hardened (per-second vol memo + streamed progress)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

HANDOFF.md

@@ -25,11 +25,11 @@ LOW urgency, the platform auto-redeems winners itself. auto_redeem OFF.
 Open now: #1 Signal A, #2 tape research, #4 redeem builder-key, #13 Fri
 bench, #14 edge verdict, #15 tape-pull batching (Stage-0 fold+mirror
 superseded the bulk path; sync now sftp-with-base64-fallback), #16 surge
-momentum (**KILL CRITERIA MET 2026-07-22** — resolution-timing scorer bug,
-FINDINGS round-3 section; formal close at Friday's read), #17 oracle fair
-value (sample ×30 under chain scoring; E0.04 tier killed, E≥0.07 slightly
-positive and accumulating), #19 surge sprint plan (Friday decision matrix;
-surgebot stays paper through the read). #18 (empty-cond copies
+momentum **CLOSED 2026-07-23** (kill executed as pre-registered — both
+instruments chain-true; A2 runs through Friday's #19 read), #17 oracle fair
+value (E0.04 killed; higher tiers ledger-positive but harness-vetoed —
+taker arm evidence-dead, decision Friday), #19 surge sprint plan (Friday:
+disposition + #20/#21 flip decisions). #18 (empty-cond copies
 unsettleable) closed same day: RTDS seed enrichment + falsy-cond repair
 pass + 1h alarm.
 

research/README.md

@@ -39,7 +39,7 @@ Layout:
               win-biased; see SCORER LAW) · title parsers · chain_overlay()
   sim.py      execution replayer calibrated on OUR live fills ledger
               (lag, FAK no-match, protected band, 3% taker fee)
-  study_flow.py    Study A — surge momentum (KILLED 2026-07-22, #16; A2
+  study_flow.py    Study A — surge momentum (#16 CLOSED 2026-07-23; A2
                    chain grade −$7.54/fill × 1,344 independently confirms)
   study_oracle.py  Study B — crypto oracle fair value (#17: E0.04 killed;
                    E≥0.07/0.1 ledger-positive but the harness chain grade

research/maker_quote_sim.py

@@ -49,6 +49,18 @@ def main():
     print(f"universe {len(uni)} tokens · tick span {span_d:.1f}d")
     cells = {(m, R): dict(fills=[], last=0.0, n_tok={})
              for m in MARGINS for R in LATENCIES}
+    # per-(sym, second) vol memo: ticks are ~1/s, so vol_1s within one
+    # second is identical — this turns the O(window)-per-print killer into
+    # a dict hit (the un-memoized run needed hours and died unwitnessed)
+    vol_cache = {}
+
+    def vol_at(s, sym, ts):
+        k = (sym, int(ts))
+        if k not in vol_cache:
+            vol_cache[k] = s.vol_1s(ts)
+        return vol_cache[k]
+
+    done = 0
     for u in uni:
         mkt = u["mkt"]
         prints = db.execute("""SELECT ts, price FROM trades
@@ -63,7 +75,7 @@ def main():
             px = float(px)
             if mkt["kind"] == "sprint" and ts < (mkt["t0"] or 0):
                 continue                     # no pre-window quoting
-            sig = s.vol_1s(ts)
+            sig = vol_at(s, mkt["sym"], ts)
             if sig is None:
                 continue
             for R in LATENCIES:
@@ -88,6 +100,11 @@ def main():
                     cells[k]["fills"].append(
                         {"asset": u["asset"], "ts": ts, "bid": bid,
                          "fair": f, "mo60": (f60 - bid) if f60 else None})
+        done += 1
+        if done % 500 == 0:
+            nf = sum(len(c["fills"]) for c in cells.values())
+            print(f"  … {done}/{len(uni)} tokens · {nf} fills so far",
+                  flush=True)
     filled_assets = {f["asset"] for c in cells.values() for f in c["fills"]}
     pays = fwd.payouts_for(db, list(filled_assets))
     print(f"grading {len(filled_assets)} filled tokens (chain overlay)…")