{
  "approach": "Blind competitor-replacement replay. Read only pinned simulator/README/rules and fixture.H numbers. S0 estimates probability as the current competitor midpoint, evaluates normal adverse-selection cost against expected capped lognormal retail flow, and posts the best positive-expectation order on each side. S1 changes only estimate(): infer ladder spread from the initial 2*s-1 gap; replay fully consumed ask k as bid k-s and bid k as ask k+s; outward quote changes identify the last crossed tick interval. Propagate a Gaussian belief and truncate on crossing/survival intervals, with a jump-contamination reset. Its posterior mean has sub-tick precision. Retail depletion is an acknowledged confounder; this is an approximate inverse model, not exact recovery of the true price. S0 remains below 150 lines. Non-abstaining S0 was retained even though three conservative variants scored zero by never trading. This deliberately weak standalone baseline tests the marginal estimator; no claim of beating or promotion over B.",
  "tried": [
    {
      "variant": "s0a",
      "code_sha256": "8712dada9c4a9e829e5a633885602c44c30a9d30ba5f10d36ad1b13870963e98",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": -53.4224168561904,
      "retail_mean": 69.805347408774,
      "arb_mean": -123.2277642649588,
      "paired_diff_vs_active_s0": 0.0,
      "paired_se": 0.0,
      "parameters": "size=12, risk=1; active baseline",
      "disposition": "frozen S0: only non-abstaining baseline"
    },
    {
      "variant": "s0b",
      "code_sha256": "277dabb5593d4a26a692009bfd961e61322d4e5f50ca908f61e3a4881679826f",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": 0.0,
      "retail_mean": 0.0,
      "arb_mean": 0.0,
      "paired_diff_vs_active_s0": 53.4224168561904,
      "paired_se": 4.308213227731528,
      "parameters": "size=12, risk=1, spread uncertainty floor and jump-loss penalty .012 ticks/share",
      "disposition": "rejected: abstains throughout tuning; baseline must actively quote"
    },
    {
      "variant": "s0c",
      "code_sha256": "1298f66ff456b2179bdbd8cb4487faca68658fd7bad1cbdf8872e3eafcd44f43",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": 0.0,
      "retail_mean": 0.0,
      "arb_mean": 0.0,
      "paired_diff_vs_active_s0": 53.4224168561904,
      "paired_se": 4.308213227731528,
      "parameters": "size=12, risk=2, spread uncertainty floor and jump-loss penalty .012 ticks/share",
      "disposition": "rejected: abstains throughout tuning; baseline must actively quote"
    },
    {
      "variant": "s0d",
      "code_sha256": "f641fe1daeefe04d99f9be6aac45e7cf77c71a1068dd6dd7808b4a1760e685f2",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": 0.0,
      "retail_mean": 0.0,
      "arb_mean": 0.0,
      "paired_diff_vs_active_s0": 53.4224168561904,
      "paired_se": 4.308213227731528,
      "parameters": "size=6, risk=2, spread uncertainty floor and jump-loss penalty .012 ticks/share",
      "disposition": "rejected: abstains throughout tuning; baseline must actively quote"
    },
    {
      "variant": "s1a",
      "code_sha256": "835dba46a877aacea78aeb5aa3d5f7d094147c0984b3b573aab3a7f5c72e4259",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": -18.4578072594764,
      "retail_mean": 66.5960963547832,
      "arb_mean": -85.053903614241,
      "paired_diff_vs_active_s0": 34.964609596714,
      "paired_se": 4.018398028613983,
      "parameters": "s0a quote policy; replay diffusion factor .7",
      "disposition": "frozen S1: highest 500-seed tuning mean"
    },
    {
      "variant": "s1b",
      "code_sha256": "68e672cdaf23a3cf90c84381a0f313c1060a991b24723d268dcd44d0b6880dbf",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": -26.4719101299196,
      "retail_mean": 70.1829910620774,
      "arb_mean": -96.6549011919718,
      "paired_diff_vs_active_s0": 26.950506726270802,
      "paired_se": 3.9192989839183507,
      "parameters": "s0a quote policy; replay diffusion factor 1.0",
      "disposition": "not selected"
    },
    {
      "variant": "s1c",
      "code_sha256": "54e75064cabf1089a7e291a19b5f982d369cabe7332a8fc4fa75f8e7a55cbe90",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": -33.6048603837186,
      "retail_mean": 71.5669446437798,
      "arb_mean": -105.1718050274732,
      "paired_diff_vs_active_s0": 19.8175564724718,
      "paired_se": 3.0813368573778,
      "parameters": "s0a quote policy; replay diffusion factor 1.4",
      "disposition": "not selected"
    },
    {
      "variant": "s1d",
      "code_sha256": "46e2e26e40856951a273959bc0b8561984618e718114a322f466f028b7956b2b",
      "seeds": [
        115000,
        115499
      ],
      "n": 500,
      "mean": -31.0847018430834,
      "retail_mean": 72.8311062065142,
      "arb_mean": -103.9158080495746,
      "paired_diff_vs_active_s0": 22.337715013107,
      "paired_se": 3.33612317951856,
      "parameters": "s0a quote policy; replay diffusion factor 1.0, 6% broad-interval contamination",
      "disposition": "not selected"
    }
  ],
  "next": "Graft the frozen replay estimator into a competitive quoting policy and measure it afresh on permitted independent seeds. Do not promote this standalone strategy: consult its negative or positive reference difference, not just the improvement over weak S0. Improve the observation likelihood to separate retail depletion from informed sweeps and infer partially consumed ladder depth. No other strategies, earlier network results, finalist seeds, or seeds outside the two assigned ranges were read/run.",
  "code": "import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    SIZE = 12\n    RISK = 1\n\n    def __init__(self):\n        self.previous = None\n        self.mean = 50.0\n        self.variance = 4.0\n        self.spread = 2\n\n    def estimate(self, state, bid, ask):\n        # At initialization the 2*s-1 tick gap identifies the ladder offset.\n        if self.previous is None:\n            self.spread = max(1, min(4, (ask-bid+1)//2))\n            self.mean = (bid+ask)*0.5\n            self.variance = max(0.1, (ask-bid)**2/12.0)\n        lo, hi = float(bid), float(ask)\n        if self.previous is not None:\n            old_bid, old_ask = self.previous\n            # A swept ask k is replayed as bid k-s; the first surviving ask\n            # is k+1. The symmetric rule applies to a swept bid.\n            up = ask > old_ask and bid >= ask-1-self.spread\n            down = bid < old_bid and ask <= bid+1+self.spread\n            if up and not down:\n                lo = max(lo, ask-1.0)\n            elif down and not up:\n                hi = min(hi, bid+1.0)\n            # Quote survival is also informative: predict then truncate.\n            pp = min(0.999, max(0.001, self.mean/100.0))\n            diffusion = 0.7 * 140.0*pp*(1-pp)/math.sqrt(state.steps_remaining+10)\n            variance = self.variance + max(0.08, diffusion)**2\n            sd = math.sqrt(variance)\n            a, b = (lo-self.mean)/sd, (hi-self.mean)/sd\n            mass = self.cdf(b)-self.cdf(a)\n            if mass > 1e-8:\n                pa = math.exp(-0.5*a*a)/math.sqrt(2*math.pi)\n                pb = math.exp(-0.5*b*b)/math.sqrt(2*math.pi)\n                shift = (pa-pb)/mass\n                filtered = self.mean + sd*shift\n                var = variance*(1+(a*pa-b*pb)/mass-shift*shift)\n                # A small uniform contamination component allows jumps/retail sweeps.\n                mix = 0.02 / (0.02 + 0.98*mass)\n                self.mean = (1-mix)*filtered + mix*(lo+hi)*0.5\n                self.variance = max(0.02, (1-mix)*max(0.0,var)+mix*(hi-lo)**2/12)\n            else:\n                self.mean = (lo+hi)*0.5\n                self.variance = max(0.02, (hi-lo)**2/12)\n        self.previous = (bid, ask)\n        return self.mean\n\n    @staticmethod\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks or 0\n        ask = state.competitor_best_ask_ticks or 100\n        p = self.estimate(state, bid, ask) / 100.0\n        p = min(0.9999, max(0.0001, p))\n        # Diffusion scale in ticks, larger near expiry and near probability 1/2.\n        sd = max(0.12, 140.0 * p * (1.0-p) / math.sqrt(state.steps_remaining + 10))\n        actions = [CancelAll()]\n        budget = max(0.0, state.cash) * 0.48\n        for side in (Side.BUY, Side.SELL):\n            best = None\n            # Only orders ahead of the competitor receive ordinary retail flow.\n            ticks = range(max(1, bid), min(99, ask) + 1)\n            for tick in ticks:\n                x = tick / 100.0\n                distance = (p-x if side == Side.BUY else x-p) * 100.0\n                if distance < 0.0:\n                    continue\n                if side == Side.BUY and tick < bid:\n                    continue\n                if side == Side.SELL and tick > ask:\n                    continue\n                flow = 0.12\n                if (side == Side.BUY and tick == bid) or (side == Side.SELL and tick == ask):\n                    flow *= 0.04\n                collateral = x if side == Side.BUY else 1.0-x\n                q = min(self.SIZE / max(p, 0.05), budget / max(collateral, 0.01))\n                if side == Side.SELL:\n                    q = min(self.SIZE / max(p, 0.05), state.yes_inventory + budget / max(collateral, 0.01))\n                q = math.floor(max(0.0, q) * 100.0) / 100.0\n                if q < 0.01:\n                    continue\n                # E[min(q, lognormal retail shares)], mean notional about $4.5.\n                mu = math.log(4.5 / max(p, 0.05)) - 0.72\n                z = (math.log(q) - mu) / 1.2\n                volume = math.exp(mu+0.72)*self.cdf(z-1.2) + q*(1-self.cdf(z))\n                u = distance / sd\n                adverse = sd * math.exp(-0.5*u*u) / math.sqrt(2*math.pi) - distance*self.cdf(-u)\n                edge = flow*volume*distance - self.RISK*q*adverse\n                if best is None or edge > best[0]:\n                    best = (edge, tick, q)\n            if best is not None and best[0] > 0:\n                actions.append(PlaceOrder(side, best[1], best[2]))\n        return actions\n",
  "code_sha256": "835dba46a877aacea78aeb5aa3d5f7d094147c0984b3b573aab3a7f5c72e4259",
  "code_s0": "import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    SIZE = 12\n    RISK = 1\n\n    def __init__(self):\n        self.previous = None\n        self.mean = 50.0\n        self.variance = 4.0\n        self.spread = 2\n\n    def estimate(self, state, bid, ask):\n        return (bid + ask) * 0.5\n\n    @staticmethod\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks or 0\n        ask = state.competitor_best_ask_ticks or 100\n        p = self.estimate(state, bid, ask) / 100.0\n        p = min(0.9999, max(0.0001, p))\n        # Diffusion scale in ticks, larger near expiry and near probability 1/2.\n        sd = max(0.12, 140.0 * p * (1.0-p) / math.sqrt(state.steps_remaining + 10))\n        actions = [CancelAll()]\n        budget = max(0.0, state.cash) * 0.48\n        for side in (Side.BUY, Side.SELL):\n            best = None\n            # Only orders ahead of the competitor receive ordinary retail flow.\n            ticks = range(max(1, bid), min(99, ask) + 1)\n            for tick in ticks:\n                x = tick / 100.0\n                distance = (p-x if side == Side.BUY else x-p) * 100.0\n                if distance < 0.0:\n                    continue\n                if side == Side.BUY and tick < bid:\n                    continue\n                if side == Side.SELL and tick > ask:\n                    continue\n                flow = 0.12\n                if (side == Side.BUY and tick == bid) or (side == Side.SELL and tick == ask):\n                    flow *= 0.04\n                collateral = x if side == Side.BUY else 1.0-x\n                q = min(self.SIZE / max(p, 0.05), budget / max(collateral, 0.01))\n                if side == Side.SELL:\n                    q = min(self.SIZE / max(p, 0.05), state.yes_inventory + budget / max(collateral, 0.01))\n                q = math.floor(max(0.0, q) * 100.0) / 100.0\n                if q < 0.01:\n                    continue\n                # E[min(q, lognormal retail shares)], mean notional about $4.5.\n                mu = math.log(4.5 / max(p, 0.05)) - 0.72\n                z = (math.log(q) - mu) / 1.2\n                volume = math.exp(mu+0.72)*self.cdf(z-1.2) + q*(1-self.cdf(z))\n                u = distance / sd\n                adverse = sd * math.exp(-0.5*u*u) / math.sqrt(2*math.pi) - distance*self.cdf(-u)\n                edge = flow*volume*distance - self.RISK*q*adverse\n                if best is None or edge > best[0]:\n                    best = (edge, tick, q)\n            if best is not None and best[0] > 0:\n                actions.append(PlaceOrder(side, best[1], best[2]))\n        return actions\n",
  "code_s0_sha256": "8712dada9c4a9e829e5a633885602c44c30a9d30ba5f10d36ad1b13870963e98",
  "tuning_score": {
    "seeds": [
      115000,
      115499
    ],
    "n": 500,
    "s1_mean": -18.4578072594764,
    "s0_mean": -53.4224168561904,
    "selected_variant": "s1a",
    "selection": "Four baseline and four replay variants maximum; all scored on the complete tuning range. S0 selection requires non-abstention. S1 selected by highest paired tuning gain; frozen before opening holdout outcomes."
  },
  "paired": {
    "seeds": [
      200000,
      200999
    ],
    "n": 1000,
    "complete": true,
    "mechanism_diff": 27.1178261652066,
    "mechanism_se": 2.5453271494017837,
    "mechanism_retail_diff": -0.9973068428755002,
    "mechanism_arb_diff": 28.1151330080911,
    "reference_diff": -117.7052176333529,
    "reference_se": 5.575313557927414,
    "s1_mean": -18.0093394809706,
    "s0_mean": -45.1271656461772,
    "b_mean": 99.6958781523823,
    "failures": []
  },
  "graft": "Replace only your probability estimator. Infer spread from the initial gap; an outward ask move implies the last consumed ask, replenished at k-spread on the bid; reverse for bids. Filter a diffusing belief through these crossing/survival intervals and use its fractional-tick posterior mean. Keep your existing risk, sizing, and cash controls; retail depletion can mimic crossings.",
  "seconds_per_simulation": 1.302918233843986,
  "measurement": {
    "api": "run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1)",
    "steps": 2000,
    "simulator_commit": "b244530ee2a6247b5f930830eeca9dd950da0e7e",
    "simulator_modified": false,
    "sandbox": false,
    "timing_pilot": {
      "strategy": "s0a",
      "seeds": [
        115000,
        115019
      ],
      "n": 20,
      "seconds_per_simulation": 0.6467481205938383
    },
    "standard_error": "sample standard deviation of paired differences (ddof=1) / sqrt(n); no trimming or exclusions",
    "reference_url": "https://api.imd.fun/artifacts/04e37cd4e1a420b91cd7a012f416e6ebf1002c5c56867981741a3a77c72b9c48",
    "reference_scores_sha256": "bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470",
    "reference_code_sha256": null,
    "reference_code_note": "Not supplied or inspected: blind task permits numbers only. Fixture.H first 1000 values are B for this holdout.",
    "frozen_code_sha256": "835dba46a877aacea78aeb5aa3d5f7d094147c0984b3b573aab3a7f5c72e4259",
    "checks": [
      "all downloaded simulator files match pinned Git blob hashes",
      "fixture.H decoded length and SHA-256 checked",
      "S0 under 150 lines",
      "source identical outside estimate()",
      "synthetic finite/bounded estimator checks",
      "all 1000 paired seeds retained, failures checked",
      "paired total difference equals retail plus arb difference",
      "output JSON, code hash, source quote bytes, size checked"
    ]
  },
  "per_seed": {
    "seeds": [
      200000,
      200999
    ],
    "n": 1000,
    "columns": [
      "s0_total_edge",
      "s1_total_edge",
      "mechanism_retail_diff",
      "mechanism_arb_diff"
    ],
    "encoding": "Row-major little-endian float64, zlib then base64. Consecutive rows correspond to consecutive seeds. Decode to 4000 doubles and reshape (1000,4). Reference B values are fixture.H at index seed-200000.",
    "sha256": "cb5c721150b9531f5c67c12e98c8cb9e1495e67b13085113fe75e7890af56abc",
    "data": 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"
  }
}