{
  "key": "d7-s1-ask-cancel-on-bid-up",
  "idea": "After an exact one-tick rival best-bid rise, cancel all own asks and suppress ask placements for a tuned number of callbacks.",
  "spread": 1,
  "base_sha256": "5d1bd5272884f513b19f0e66a7c235f6c188a08e4f7787ac9346f1614dda3ed3",
  "approach": "Append-only subclass wrapper; call super().on_step first, then guard self.s == 1. Track the prior rival bid. An exact +1 tick resets a 2-callback countdown including the trigger callback; repeated +1 moves restart it. During suppression filter all SELL PlaceOrder actions and append cancellations for every existing ask not already cancelled. BUY actions are preserved; base body is unchanged. Select by all-seed paired tuning mean; freeze before holdout.",
  "tried": [
    {
      "callbacks": 1,
      "n": 150,
      "mean_diff": 0.07184667665266632,
      "se": 0.2887351869107106,
      "target_n": 41,
      "target_mean_diff": 0.2628536950707304,
      "target_se": 1.0653112734910588
    },
    {
      "callbacks": 2,
      "n": 150,
      "mean_diff": 0.3410693944059999,
      "se": 0.24873500042655836,
      "target_n": 41,
      "target_mean_diff": 1.2478148575829264,
      "target_se": 0.9027008680831701
    },
    {
      "callbacks": 3,
      "n": 150,
      "mean_diff": 0.24540626262733325,
      "se": 0.24527764584816325,
      "target_n": 41,
      "target_mean_diff": 0.8978277900999997,
      "target_se": 0.8973486903156055
    }
  ],
  "next": "Retain MA and deprioritize this unconditional cancellation rule because the holdout gain is smaller than twice its paired standard error.",
  "code": "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",
  "code_sha256": "a806ad6b45ea1f8509b895b27066ea3ec3914985a73b4dad48f1e2260e7efc8a",
  "tuning": {
    "n": 150,
    "mean_diff": 0.3410693944059999,
    "se": 0.24873500042655836
  },
  "holdout": {
    "n": 500,
    "complete": true,
    "missing_seeds": [],
    "failures": [],
    "comparator_mean": 95.40629995253512,
    "mean_diff": 0.16185453158580004,
    "se": 0.1413299800348969,
    "sum_d": 80.92726579290002,
    "sum_d2": 4996.652177235186,
    "retail_diff": 0.0936178648872,
    "arb_diff": 0.06823666669779996
  },
  "by_spread": {
    "1": {
      "n": 128,
      "complete": true,
      "missing_seeds": [],
      "failures": [],
      "comparator_mean": 30.21701806900548,
      "mean_diff": 0.6322442640070314,
      "se": 0.5515662189618245,
      "sum_d": 80.92726579290002,
      "sum_d2": 4996.652177235186,
      "retail_diff": 0.365694784715625,
      "arb_diff": 0.2665494792882811
    },
    "2": {
      "n": 123,
      "complete": true,
      "missing_seeds": [],
      "failures": [],
      "comparator_mean": 99.00925043335212,
      "mean_diff": 0.0,
      "se": 0.0,
      "sum_d": 0.0,
      "sum_d2": 0.0,
      "retail_diff": 0.0,
      "arb_diff": 0.0
    },
    "3": {
      "n": 131,
      "complete": true,
      "missing_seeds": [],
      "failures": [],
      "comparator_mean": 104.90118179050612,
      "mean_diff": 0.0,
      "se": 0.0,
      "sum_d": 0.0,
      "sum_d2": 0.0,
      "retail_diff": 0.0,
      "arb_diff": 0.0
    },
    "4": {
      "n": 118,
      "complete": true,
      "missing_seeds": [],
      "failures": [],
      "comparator_mean": 151.8235512336966,
      "mean_diff": 0.0,
      "se": 0.0,
      "sum_d": 0.0,
      "sum_d2": 0.0,
      "retail_diff": 0.0,
      "arb_diff": 0.0
    }
  },
  "off_target_zero": true,
  "worst_seeds": [
    {
      "seed": 460419,
      "s": 1,
      "d": -20.04166269890004
    },
    {
      "seed": 460313,
      "s": 1,
      "d": -10.069471403399987
    },
    {
      "seed": 460352,
      "s": 1,
      "d": -8.386026975100002
    },
    {
      "seed": 460370,
      "s": 1,
      "d": -6.963837234
    },
    {
      "seed": 460660,
      "s": 1,
      "d": -4.773435634099997
    }
  ],
  "seconds_per_simulation": {
    "candidate": 1.2739650356800212,
    "base": 1.2913970398199126
  },
  "check_values": {
    "310000": 17.1160017503,
    "310001": 23.8203924577,
    "310002": 2.3761435866,
    "310003": 243.8721397061
  },
  "python_version": null,
  "notes": "Python version is not collected under operator policy. SE is sample standard deviation of paired differences divided by sqrt(n). Timings cover engine.run only. Spread is measured from first competitor quotes. Current-best scoreboard comparator is the verified MA hash; no promotion is made. Wrapper unit checks passed; reserved seeds were not run."
}