{
 "key": "d5-s23-cancel-time-gate",
 "idea": "d5-s23-cancel-time-gate: apply RC's one-tick-retreat cancel (spreads 2,3) only when state.step > T0; at or before T0 return N's actions unchanged.",
 "approach": "Wrapper subclass at end of RC file: if state.step <= T0 it updates RC's retreat memory, zeroes its block counters and returns N's on_step; otherwise RC runs unchanged. Off-target spreads are untouched (d exactly 0). Tuned T0 in {100,300,600,30} on seeds 330000-330199; every gate lost, monotonically in T0. Froze T0=30 (least loss) and ran holdout 440000-440499 once: -0.0029 (se 0.0017) on spreads 2,3. The cancel already pays in early steps; gating it does not help.",
 "tried": [
  {
   "name": "T0=100",
   "code_sha256": "a98082f2309d9a9b5d1ea52a195f8847ef40aa44949b66d2114dc56e7ac50b78",
   "mean_diff": -0.029042412774524944,
   "se": 0.011878263958630382,
   "n": 106,
   "scope": "target spreads 2,3"
  },
  {
   "name": "T0=300",
   "code_sha256": "909a85bb0cc045e1a52007396a15115ceccb8d6322ab28dcf33c19431e74e57f",
   "mean_diff": -0.089259784850943,
   "se": 0.033869194581043045,
   "n": 106,
   "scope": "target spreads 2,3"
  },
  {
   "name": "T0=600",
   "code_sha256": "ae66817b2e92855156b11d98cdf24b1440d20bd113a8a5bde6f8bc4d22a09b3c",
   "mean_diff": -0.2964509779773574,
   "se": 0.16761914797369512,
   "n": 106,
   "scope": "target spreads 2,3"
  },
  {
   "name": "T0=30",
   "code_sha256": "ad20a135ec3782556cff3febd2a3487b5d89ec9bf0c77632e5f9f22ce6ca68c9",
   "mean_diff": -0.005462103231132792,
   "se": 0.0034815622777842267,
   "n": 106,
   "scope": "target spreads 2,3"
  }
 ],
 "next": "Drop time gating of the retreat cancel: the loss grows with T0 (-0.006 at 30, -0.03 at 100, -0.09 at 300, -0.30 at 600 on targets), so early retreats are as informative as late ones. Deepen the cancel by other conditions (retreat size, inventory, distance to fair value) or a longer suppression, not by time.",
 "code": 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",
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 "target_spreads": [
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  "selected": "T0=30",
  "selection_rule": "best paired mean_diff on target spreads 2,3",
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    "by_spread": {
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   }
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 },
 "paired": {
  "seeds": {
   "start": 440000,
   "end": 440499
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  "n": 500,
  "complete": true,
  "missing_seeds": [],
  "overall": {
   "n": 500,
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   "mean_diff": -0.0013454422431997415,
   "se": 0.000767615472536012,
   "comparator_mean": 101.4369248623788,
   "candidate_mean": 101.43557942013561,
   "retail_diff": 0.00014765662739999285,
   "arb_diff": -0.0014930988701999661
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  "by_spread": {
   "1": {
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    "sum_d": 0.0,
    "sum_d2": 0.0,
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    "se": 0.0,
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   "2": {
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    "candidate_mean": 88.78236584593091,
    "retail_diff": 5.5970601817779585e-05,
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   "3": {
    "n": 119,
    "sum_d": 0.08759829010014841,
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    "se": 0.0026976208237346365,
    "comparator_mean": 143.37780997585293,
    "candidate_mean": 143.37854609593782,
    "retail_diff": 0.0005686684663868964,
    "arb_diff": 0.00016745162016820956
   },
   "4": {
    "n": 129,
    "sum_d": 0.0,
    "sum_d2": 0.0,
    "mean_diff": 0.0,
    "se": 0.0,
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    "candidate_mean": 153.5829305298,
    "retail_diff": 0.0,
    "arb_diff": 0.0
   }
  }
 },
 "failures": [],
 "check_values": {
  "310000": 17.5126554022,
  "310001": 20.6298693268,
  "310002": 2.3903113616,
  "310003": 248.006347259
 },
 "check_values_note": "RC total_edge on 310000..310003. The RC artifact's own check_values (17.1160017503, 20.0171113118, 2.3903113616, 241.4847522404) were reproduced exactly here by N (RC minus its wrapper), so those are N's values, not RC's; RC differs on the spread-2/3 seeds.",
 "python_version": "3.12.3",
 "seconds_per_simulation": 0.4466,
 "notes": "Screen, not a promotion. Simulator at b244530ee2a6247b5f930830eeca9dd950da0e7e, unmodified, default ChallengeConfig/ParameterVariance, single-process runs (separate processes of <=50 seeds), no --workers, no --sandbox.",
 "code_encoding": "gzip+base64"
}