{"approach":"Blind construction from pinned simulator only. Estimate probability by clipping the previous estimate to public competitor quotes, smoothing toward their midpoint, and applying a bounded fill correction. Quote a probability half-width with a late-horizon allowance; size by mean retail notional divided by estimated probability and cap collateral. S1 differs from frozen S0 only in its POLICIES constant: select a half-width/size pair once using the initial gap, spread=(ask-bid+1)//2. Four S0 variants total include the unguarded pilot; four S1 tables were preset before guarded tuning finished. Table tuning uses exact per-seed equivalence to the selected constant-policy run, not extra simulations. No prior strategy or network report read; reference fixture.H used only as numbers.","tried":[{"strategy":"S0","variant":0,"policy":[0.5,1.0],"boundary_guard":false,"n":500,"mean":-81.291819013311,"paired_vs_selected_s0":-26.1460502937512,"paired_se":3.147653680741702},{"strategy":"S0","variant":1,"policy":[0.5,1.0],"boundary_guard":true,"n":500,"mean":-55.1457687195598,"paired_vs_selected_s0":0.0,"paired_se":0.0},{"strategy":"S0","variant":2,"policy":[1.0,1.5],"boundary_guard":true,"n":500,"mean":-66.2965912180334,"paired_vs_selected_s0":-11.1508224984736,"paired_se":1.6093759095882998},{"strategy":"S0","variant":3,"policy":[1.5,2.0],"boundary_guard":true,"n":500,"mean":-81.7791090218892,"paired_vs_selected_s0":-26.6333403023294,"paired_se":2.5638104796996926},{"strategy":"S1","variant":0,"policy_indices_by_spread":[1,1,2,3],"n":500,"mean":-72.8744280214844,"paired_vs_selected_s0":-17.7286593019246,"paired_se":1.8043988686828814},{"strategy":"S1","variant":1,"policy_indices_by_spread":[1,2,2,3],"n":500,"mean":-78.0545260953168,"paired_vs_selected_s0":-22.908757375757002,"paired_se":1.8417968836837684},{"strategy":"S1","variant":2,"policy_indices_by_spread":[1,2,3,3],"n":500,"mean":-86.0028957375794,"paired_vs_selected_s0":-30.8571270180196,"paired_se":2.3381154199995873},{"strategy":"S1","variant":3,"policy_indices_by_spread":[2,2,3,3],"n":500,"mean":-83.6393727793582,"paired_vs_selected_s0":-28.4936040597984,"paired_se":2.41405968093213}],"next":"Do not promote this strategy: compare the measured reference gap below. The simple probability estimator remains vulnerable to stale competitor quotes. Test spread switching as a graft onto a stronger estimator in an independently seeded follow-up; do not retune on this holdout. The first unguarded pilot was retained as a failed tuning idea, not silently discarded.","code":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # (probability half-width in ticks, retail size multiplier)\n    POLICIES = [(0.5, 1.0), (0.5, 1.0), (1.0, 1.5), (1.5, 2.0)]\n\n    def __init__(self):\n        self.p = None\n        self.spread = None\n\n    def on_step(self, s):\n        b, a = s.competitor_best_bid_ticks, s.competitor_best_ask_ticks\n        if self.p is None:\n            if b is None or a is None:\n                return []\n            self.p = (b + a) / 200.0\n            self.spread = max(1, min(4, (a - b + 1) // 2))\n        width, size = self.POLICIES[self.spread - 1]\n        # The arbitrage sweep leaves a public probability interval. Retail may\n        # widen it, so retain the previous estimate inside that interval.\n        lo = (b or 0) / 100.0\n        hi = (a if a is not None else 100) / 100.0\n        self.p = max(lo, min(hi, self.p))\n        mid = (lo + hi) / 2.0\n        self.p = 0.8 * self.p + 0.2 * mid\n        # Recent fills give a weak directional correction, bounded to one tick.\n        signal = s.sell_filled_quantity - s.buy_filled_quantity\n        self.p = max(0.005, min(0.995, self.p + 0.002 * math.tanh(signal / 10.0)))\n        # Late diffusion widens the adverse-selection allowance.\n        late = min(2.0, math.sqrt(200.0 / max(20, s.steps_remaining)))\n        half = width * max(1.0, late)\n        bid = max(1, min(99, math.floor(100 * self.p - half)))\n        ask = max(1, min(99, math.ceil(100 * self.p + half)))\n        # Avoid sitting behind an entire competitor level unless our estimate\n        # requires it; strict price improvement wins FIFO priority.\n        if b is not None:\n            bid = min(bid, b + 1)\n        if a is not None:\n            ask = max(ask, a - 1)\n        actions = [CancelAll()]\n        cash = max(0.0, s.cash) * 0.95\n        # Mean retail notional ~4.5, arrival probability per side ~0.126.\n        # Large quotes have diminishing retail benefit but linear jump loss.\n        q = size * 4.5 / max(0.08, self.p)\n        qb = min(q, cash * 0.45 / (bid / 100.0))\n        qa = min(q, s.yes_inventory + cash * 0.45 / (1.0 - ask / 100.0))\n        qb, qa = math.floor(qb * 100) / 100.0, math.floor(qa * 100) / 100.0\n        if qb >= 0.01 and bid / 100.0 < self.p - 0.003:\n            actions.append(PlaceOrder(Side.BUY, bid, qb))\n        if qa >= 0.01 and ask / 100.0 > self.p + 0.003:\n            actions.append(PlaceOrder(Side.SELL, ask, qa))\n        return actions\n","code_sha256":"e1aaf387e8544537596cdda831eb98042bf1199fc203817ffc9c43a245961891","code_s0":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # (probability half-width in ticks, retail size multiplier)\n    POLICIES = [(0.5, 1.0), (0.5, 1.0), (0.5, 1.0), (0.5, 1.0)]\n\n    def __init__(self):\n        self.p = None\n        self.spread = None\n\n    def on_step(self, s):\n        b, a = s.competitor_best_bid_ticks, s.competitor_best_ask_ticks\n        if self.p is None:\n            if b is None or a is None:\n                return []\n            self.p = (b + a) / 200.0\n            self.spread = max(1, min(4, (a - b + 1) // 2))\n        width, size = self.POLICIES[self.spread - 1]\n        # The arbitrage sweep leaves a public probability interval. Retail may\n        # widen it, so retain the previous estimate inside that interval.\n        lo = (b or 0) / 100.0\n        hi = (a if a is not None else 100) / 100.0\n        self.p = max(lo, min(hi, self.p))\n        mid = (lo + hi) / 2.0\n        self.p = 0.8 * self.p + 0.2 * mid\n        # Recent fills give a weak directional correction, bounded to one tick.\n        signal = s.sell_filled_quantity - s.buy_filled_quantity\n        self.p = max(0.005, min(0.995, self.p + 0.002 * math.tanh(signal / 10.0)))\n        # Late diffusion widens the adverse-selection allowance.\n        late = min(2.0, math.sqrt(200.0 / max(20, s.steps_remaining)))\n        half = width * max(1.0, late)\n        bid = max(1, min(99, math.floor(100 * self.p - half)))\n        ask = max(1, min(99, math.ceil(100 * self.p + half)))\n        # Avoid sitting behind an entire competitor level unless our estimate\n        # requires it; strict price improvement wins FIFO priority.\n        if b is not None:\n            bid = min(bid, b + 1)\n        if a is not None:\n            ask = max(ask, a - 1)\n        actions = [CancelAll()]\n        cash = max(0.0, s.cash) * 0.95\n        # Mean retail notional ~4.5, arrival probability per side ~0.126.\n        # Large quotes have diminishing retail benefit but linear jump loss.\n        q = size * 4.5 / max(0.08, self.p)\n        qb = min(q, cash * 0.45 / (bid / 100.0))\n        qa = min(q, s.yes_inventory + cash * 0.45 / (1.0 - ask / 100.0))\n        qb, qa = math.floor(qb * 100) / 100.0, math.floor(qa * 100) / 100.0\n        if qb >= 0.01 and bid / 100.0 < self.p - 0.003:\n            actions.append(PlaceOrder(Side.BUY, bid, qb))\n        if qa >= 0.01 and ask / 100.0 > self.p + 0.003:\n            actions.append(PlaceOrder(Side.SELL, ask, qa))\n        return actions\n","tuning_score":{"seeds":[115000,115499],"n":500,"s1_mean":-72.8744280214844,"s0_mean":-55.1457687195598},"paired":{"seeds":[200000,200999],"n":1000,"complete":true,"mechanism_diff":-13.1981522195026,"mechanism_se":1.2279953771157022,"mechanism_retail_diff":-4.3786226169557,"mechanism_arb_diff":-8.8195296025401,"reference_diff":-166.4452238593074,"reference_se":6.325636383554475,"s1_mean":-66.7493457069251,"s0_mean":-53.5511934874225,"b_mean":99.6958781523823,"failures":[]},"graft":"At initialization infer spread=(competitor_ask-competitor_bid+1)//2; freeze a 1..4 regime index. Select a separately tuned quote-width/size pair from a four-entry table, leaving probability estimation and all other logic unchanged. Initial two-sided quotes are required; do not infer spread from later depleted books.","seconds_per_simulation":0.15719422599021346,"measurement":{"simulator_commit":"b244530ee2a6247b5f930830eeca9dd950da0e7e","configuration":"Unmodified run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1); 2000 steps; no sandbox. Sequential OS process per strategy.","pilot_20_seconds":3.267814553808421,"reference_scores_sha256":"bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470","s0_code_sha256":"d64300a14a818939c4c683aa6fcaf813c3e18d401ab00c5dec005b51801e41f1","selected_s0_variant":1,"selected_s1_variant":0,"s1_policy_indices_by_spread":[1,1,2,3],"tuning_note":"All eight variant scores use all 500 seeds 115000..115499. The 20-seed timing pilot is a repeated subset of the first baseline variant, not a fifth variant. No seeds outside the two permitted intervals were simulated. Selection used tuning mean only; sources frozen before holdout.","by_spread":[{"spread":1,"n":248,"mechanism_diff":0.0,"mechanism_se":0.0,"s0_mean":-45.56979547582419,"s1_mean":-45.56979547582419},{"spread":2,"n":252,"mechanism_diff":0.0,"mechanism_se":0.0,"s0_mean":-64.37029666259762,"s1_mean":-64.37029666259762},{"spread":3,"n":238,"mechanism_diff":-18.87958843198109,"mechanism_se":2.0599499437755813,"s0_mean":-59.45437871128655,"s1_mean":-78.33396714326764},{"spread":4,"n":262,"mechanism_diff":-33.224466307981295,"mechanism_se":3.950606654476813,"s0_mean":-45.33750884411183,"s1_mean":-78.56197515209313}]},"per_seed":{"start":200000,"n":1000,"columns":["s0_total_edge","s1_total_edge","mechanism_retail_diff","mechanism_arb_diff"],"encoding":"base64(zlib(little-endian float64 rows)); four values per seed, ascending seed; exact unrounded totals. Mechanism total = column 1 - column 0. Reference difference = column 1 - fixture.H value for the same 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"}}