{"approach":"Blind fresh mechanism: estimate probability as the visible competitor quote midpoint (missing sides bounded by 0/1). Estimate uncertainty V as squared quote width / 12 plus an EWMA (alpha=0.05) of squared midpoint changes. S0 quotes one tick inside the competitor subject to a one-tick estimated edge, skips a side when that competitor side is missing, and sizes from typical retail quantity 4.5/max(p,0.05) times margin/(margin+sqrt(move_variance)+0.001), subject to collateral. S1 changes only the variance_budget constructor default: multiply S0 size by min(3, budget/max(V,1e-6)). This is capped Kelly-like inverse estimator-variance sizing, not a fitted or calibrated posterior. Source: pinned simulator and rules listed in sources.json; reference artifact used only as numeric fixture.H. No other strategy source was read.","tried":[{"variant":"s0pilot","flow_scale":1,"variance_budget":0,"missing_side_guard":false,"seeds":[115000,115499],"n":500,"mean":-32.222786657742,"retail_mean":40.4674398991584,"arb_mean":-72.6902265569056,"paired_vs_frozen_s0":-35.4278664343896,"paired_se":3.3923833025448764,"failures":[],"code_sha256":"ba01d0cddcb7314a5fab7d284f7000cfbc9cd8c276a5406c28ad4ba0bd21d966"},{"variant":"s0guard","flow_scale":1,"variance_budget":0,"missing_side_guard":true,"seeds":[115000,115499],"n":500,"mean":-1.6365471231101998,"retail_mean":39.828917662377,"arb_mean":-41.4654647854916,"paired_vs_frozen_s0":-4.8416268997578,"paired_se":0.8989770543756983,"failures":[],"code_sha256":"9778b2637abe82d3f18200c020c1a099708dbd94f42d7506135f1cf021e0af88"},{"variant":"s0half","flow_scale":0.5,"variance_budget":0,"missing_side_guard":true,"seeds":[115000,115499],"n":500,"mean":3.2050797766476,"retail_mean":24.6011989240664,"arb_mean":-21.396119147422,"paired_vs_frozen_s0":0.0,"paired_se":0.0,"failures":[],"code_sha256":"735e910386e95c8d655dd60eecb8b84d34cb58a186d6b3bd133a4ec823359230"},{"variant":"s1low","flow_scale":0.5,"variance_budget":0.0001,"missing_side_guard":true,"seeds":[115000,115499],"n":500,"mean":1.9177033359274,"retail_mean":15.563332218533,"arb_mean":-13.645628882598,"paired_vs_frozen_s0":-1.2873764407202,"paired_se":0.6044395255869526,"failures":[],"code_sha256":"60fe920718e3f1f356adfe8ac006b1c50a8011471d129ab1e8ef638f435e4a2b"},{"variant":"s1mid","flow_scale":0.5,"variance_budget":0.0003,"missing_side_guard":true,"seeds":[115000,115499],"n":500,"mean":-1.1894631233978001,"retail_mean":34.8834970573612,"arb_mean":-36.072960180757796,"paired_vs_frozen_s0":-4.3945429000454,"paired_se":0.6908239344682217,"failures":[],"code_sha256":"33da41c2771dfdd5b1658c6b2ef64350dfee10c7292e179c1f01f9525e1f0de5"},{"variant":"s1high","flow_scale":0.5,"variance_budget":0.001,"missing_side_guard":true,"seeds":[115000,115499],"n":500,"mean":-6.8565973321902,"retail_mean":48.6394502647716,"arb_mean":-55.4960475969664,"paired_vs_frozen_s0":-10.0616771088378,"paired_se":1.6217865261009095,"failures":[],"code_sha256":"e480aeeffcde42f6f8c3f2d4003d725972af99ca37cc3ed38d174d13836550f2"}],"next":"Reject this standalone mechanism: holdout S1-S0 is -2.159537 (SE 0.408913). Arbitrage edge improves by 7.203276 but retail edge falls by 9.362813. Do not graft this variance proxy unchanged. Future experiments should calibrate estimator-error variance, then repeat a frozen paired comparison. Reserved finalist seeds were untouched; no promotion.","code":"\"\"\"Public-state-only passive maker; no simulator internals or dependencies.\"\"\"\nimport math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\n\nclass Strategy:\n    def __init__(self, flow_scale=0.5, variance_budget=0.0001):\n        self.flow_scale = flow_scale\n        self.variance_budget = variance_budget\n        self.previous = None\n        self.move_var = 0.0001\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks\n        ask = state.competitor_best_ask_ticks\n        lo = 0 if bid is None else bid\n        hi = 100 if ask is None else ask\n        p = (lo + hi) / 200.0\n        if self.previous is not None:\n            change = p - self.previous\n            self.move_var = 0.95 * self.move_var + 0.05 * change * change\n        self.previous = p\n        # Uniform quote-interval uncertainty plus observed next-step motion.\n        variance = ((hi - lo) / 100.0) ** 2 / 12.0 + self.move_var\n        # Improve the competitor by one tick, keeping a one-tick fair-value margin.\n        buy_tick = min(lo + 1, math.floor(100 * p - 1.0))\n        sell_tick = max(hi - 1, math.ceil(100 * p + 1.0))\n        actions = [CancelAll()]\n        cash = max(0.0, state.cash - 0.01)\n        for side, tick in ((Side.BUY, buy_tick), (Side.SELL, sell_tick)):\n            if (side is Side.BUY and bid is None) or (side is Side.SELL and ask is None):\n                continue\n            if not 1 <= tick <= 99:\n                continue\n            price = tick / 100.0\n            margin = p - price if side is Side.BUY else price - p\n            if margin <= 0:\n                continue\n            # Typical retail quantity times flow/adverse-selection tradeoff.\n            flow_quantity = 4.5 / max(p, 0.05)\n            adverse = math.sqrt(self.move_var) + 0.001\n            quantity = self.flow_scale * flow_quantity * margin / (margin + adverse)\n            if self.variance_budget > 0.0:\n                quantity *= min(3.0, self.variance_budget / max(variance, 1e-6))\n            # Split spendable cash, including uncovered sell-side collateral.\n            unit_cost = price if side is Side.BUY else 1.0 - price\n            covered = 0.0 if side is Side.BUY else state.yes_inventory\n            cap = cash * 0.48 / max(unit_cost, 0.01) + covered\n            quantity = math.floor(100 * min(quantity, cap)) / 100.0\n            if quantity < 0.01:\n                continue\n            cash -= unit_cost * max(0.0, quantity - covered)\n            actions.append(PlaceOrder(side, tick, quantity))\n        return actions\n","code_sha256":"60fe920718e3f1f356adfe8ac006b1c50a8011471d129ab1e8ef638f435e4a2b","code_s0":"\"\"\"Public-state-only passive maker; no simulator internals or dependencies.\"\"\"\nimport math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\n\nclass Strategy:\n    def __init__(self, flow_scale=0.5, variance_budget=0.0):\n        self.flow_scale = flow_scale\n        self.variance_budget = variance_budget\n        self.previous = None\n        self.move_var = 0.0001\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks\n        ask = state.competitor_best_ask_ticks\n        lo = 0 if bid is None else bid\n        hi = 100 if ask is None else ask\n        p = (lo + hi) / 200.0\n        if self.previous is not None:\n            change = p - self.previous\n            self.move_var = 0.95 * self.move_var + 0.05 * change * change\n        self.previous = p\n        # Uniform quote-interval uncertainty plus observed next-step motion.\n        variance = ((hi - lo) / 100.0) ** 2 / 12.0 + self.move_var\n        # Improve the competitor by one tick, keeping a one-tick fair-value margin.\n        buy_tick = min(lo + 1, math.floor(100 * p - 1.0))\n        sell_tick = max(hi - 1, math.ceil(100 * p + 1.0))\n        actions = [CancelAll()]\n        cash = max(0.0, state.cash - 0.01)\n        for side, tick in ((Side.BUY, buy_tick), (Side.SELL, sell_tick)):\n            if (side is Side.BUY and bid is None) or (side is Side.SELL and ask is None):\n                continue\n            if not 1 <= tick <= 99:\n                continue\n            price = tick / 100.0\n            margin = p - price if side is Side.BUY else price - p\n            if margin <= 0:\n                continue\n            # Typical retail quantity times flow/adverse-selection tradeoff.\n            flow_quantity = 4.5 / max(p, 0.05)\n            adverse = math.sqrt(self.move_var) + 0.001\n            quantity = self.flow_scale * flow_quantity * margin / (margin + adverse)\n            if self.variance_budget > 0.0:\n                quantity *= min(3.0, self.variance_budget / max(variance, 1e-6))\n            # Split spendable cash, including uncovered sell-side collateral.\n            unit_cost = price if side is Side.BUY else 1.0 - price\n            covered = 0.0 if side is Side.BUY else state.yes_inventory\n            cap = cash * 0.48 / max(unit_cost, 0.01) + covered\n            quantity = math.floor(100 * min(quantity, cap)) / 100.0\n            if quantity < 0.01:\n                continue\n            cash -= unit_cost * max(0.0, quantity - covered)\n            actions.append(PlaceOrder(side, tick, quantity))\n        return actions\n","tuning_score":{"seeds":[115000,115499],"n":500,"s1_mean":1.9177033359274,"s0_mean":3.2050797766476,"mechanism_diff":-1.2873764407202,"mechanism_se":0.6044395255869526,"selection":"Highest tuning mean of three S0 variants, then highest tuning mean of three S1 budgets on frozen S0. Pilot timing reused the first 20 tuning seeds; no extra variants or seeds."},"paired":{"seeds":[200000,200999],"n":1000,"complete":true,"mechanism_diff":-2.1595373820516,"mechanism_se":0.40891258417564375,"mechanism_retail_diff":-9.3628134530752,"mechanism_arb_diff":7.2032760710273,"reference_diff":-97.143476221531,"reference_se":5.203482370206133,"s1_mean":2.5524019308513,"s0_mean":4.7119393129029,"b_mean":99.6958781523823,"failures":[],"mechanism_retail_se":0.6159326870646793,"mechanism_arb_se":0.3616326293434441},"graft":"Keep the recipient estimator and prices fixed. Track estimator variance V (e.g. interval-width squared/12 plus EWMA squared innovations). Multiply its base quote size by min(3, variance_budget/max(V,1e-6)), then apply existing cash/inventory caps. Tune budget on separate seeds; freeze and pair-test the graft.","seconds_per_simulation":0.3108839258435182,"evidence":{"simulator_commit":"b244530ee2a6247b5f930830eeca9dd950da0e7e","config":"Unmodified ChallengeConfig() and ParameterVariance(), 2000 steps; run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1); no sandbox. Four separate OS processes process disjoint 125-seed chunks.","se_definition":"Sample standard deviation of seedwise paired differences, ddof=1, divided by sqrt(n). All seeds retained.","scoreboard":{"frozen_s0_variant":"s0half","frozen_s0_sha256":"735e910386e95c8d655dd60eecb8b84d34cb58a186d6b3bd133a4ec823359230","frozen_s1_variant":"s1low","frozen_s1_sha256":"60fe920718e3f1f356adfe8ac006b1c50a8011471d129ab1e8ef638f435e4a2b","promoted":false,"reason":"This item estimates a fresh mechanism; it does not run the reserved finalist seeds."},"reference_fixture_scores_sha256":"bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470","timing":{"seeds":[115000,115019],"n":20,"seconds":6.2176785168703645,"strategy":"s0pilot"},"per_seed":{"start":200000,"n":1000,"columns":["s1_minus_s0_total_edge","s1_minus_s0_retail_edge","s1_minus_s0_arb_edge","s1_minus_B_total_edge"],"encoding":"base64(zlib(row-major little-endian float64)); row i is seed 200000+i; 4 doubles per row. B totals are the first 1000 values of the cited fixture.H; S1=B+column4; S0=S1-column1.","raw_sha256":"f731de1047bbc2f60fde08dc176fee3febd08c6f677c9f849f3df3b1ce4053c9","data":"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"},"limitations":"S0 is intentionally simple; observed midpoint motion conflates latent volatility, retail-induced competitor changes and estimation error. Scores test this exact heuristic only, not theoretical Kelly optimality."}}