{"approach":"Blind construction from pinned simulator only. S0 estimates probability by smoothing the competitor interval, its movement, and fill imbalance; it posts two passive quotes sized from retail share demand and reduced near expiry. S1 changes only sizing: retain full size on the side with larger estimated per-share edge, cap the other side at a 0.05-share probe. This is estimated edge skew, not observed true probability. Both use identical prices and estimator equations, although their different fills subsequently change their estimates.","tried":[{"width_ticks":0.2,"seeds":[115000,115499],"n":500,"s0_mean":-88.0548390096944,"s1_mean":-31.1227548268838,"mechanism_diff":56.9320841828106,"mechanism_se":1.9949001560708672,"failures":[]},{"width_ticks":0.7,"seeds":[115000,115499],"n":500,"s0_mean":-52.0404564201558,"s1_mean":-13.5960542387034,"mechanism_diff":38.4444021814524,"mechanism_se":1.6171280381227877,"failures":[]},{"width_ticks":1.2,"seeds":[115000,115499],"n":500,"s0_mean":-37.5354587003956,"s1_mean":-2.0509728514098,"mechanism_diff":35.4844858489858,"mechanism_se":1.81771917230006,"failures":[]},{"width_ticks":1.7,"seeds":[115000,115499],"n":500,"s0_mean":-32.8931017047284,"s1_mean":-4.0069131151262,"mechanism_diff":28.8861885896022,"mechanism_se":1.8375634176107902,"failures":[]}],"selection":"Four widths per strategy; selected width using S0 tuning mean only, then froze both at that width before holdout. S1 tuning variants are reported but do not determine selection. Timing seeds 115000..115019 were reused within tuning, not extra observations.","next":"Do not promote this standalone strategy: holdout S1 mean -2.81 is far below reference B 99.70. The one-sided gate improves its own baseline by 28.47 (SE 1.29), mainly reducing arbitrage loss by 30.48 while losing 2.01 retail edge. Next, graft the gate onto a stronger estimator and evaluate on separately authorized disjoint seeds; no finalist evaluation or promotion occurred here.","code":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\n\nclass Strategy:\n    WIDTH = 1.7\n    ONE_SIDED = True\n    PROBE = 0.05\n\n    def __init__(self):\n        self.p = None\n        self.last_bid = None\n        self.last_ask = None\n\n    def on_step(self, state):\n        b = state.competitor_best_bid_ticks\n        a = state.competitor_best_ask_ticks\n        lo = (b if b is not None else 0) / 100\n        hi = (a if a is not None else 100) / 100\n        mid = (lo + hi) / 2\n        if self.p is None:\n            self.p = mid\n        else:\n            # Bound the old estimate by current information, then relax to midpoint.\n            p = min(hi, max(lo, self.p))\n            if self.last_bid is not None and self.last_ask is not None:\n                # A swept ask is evidence of an upward move; a swept bid, downward.\n                delta = ((b or 0) - self.last_bid + (a or 100) - self.last_ask) / 200\n                p += 0.5 * delta\n            if state.sell_filled_quantity > 0 and state.buy_filled_quantity == 0:\n                p += 0.002\n            elif state.buy_filled_quantity > 0 and state.sell_filled_quantity == 0:\n                p -= 0.002\n            self.p = min(hi, max(lo, 0.75 * p + 0.25 * mid))\n        self.last_bid, self.last_ask = b or 0, a or 100\n        p = min(0.995, max(0.005, self.p))\n        bid = min(99, max(1, math.floor(100 * p - self.WIDTH)))\n        ask = min(99, max(1, math.ceil(100 * p + self.WIDTH)))\n        # Retail share demand, tempered as one-step uncertainty rises near expiry.\n        horizon_scale = max(0.15, math.sqrt(state.steps_remaining / 2000))\n        size = min(50.0, 8.0 / max(0.08, p)) * horizon_scale\n        qb = qa = size\n        if self.ONE_SIDED:\n            # The only mechanism change: retain normal size on the better side.\n            buy_edge = p - bid / 100\n            sell_edge = ask / 100 - p\n            if buy_edge >= sell_edge:\n                qa = min(qa, self.PROBE)\n            else:\n                qb = min(qb, self.PROBE)\n        cash = max(0.0, state.cash - 0.01)\n        qb = min(qb, cash * 0.45 / (bid / 100))\n        qa = min(qa, state.yes_inventory + cash * 0.45 / (1 - ask / 100))\n        actions = [CancelAll()]\n        if qb >= 0.01:\n            actions.append(PlaceOrder(Side.BUY, bid, qb))\n        if qa >= 0.01:\n            actions.append(PlaceOrder(Side.SELL, ask, qa))\n        return actions\n","code_sha256":"c7a2294db5148572b5225ecdc42c786a4da3d7cbce872ffc2dfa5a1c9febbaf7","code_s0":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\n\nclass Strategy:\n    WIDTH = 1.7\n    ONE_SIDED = False\n    PROBE = 0.05\n\n    def __init__(self):\n        self.p = None\n        self.last_bid = None\n        self.last_ask = None\n\n    def on_step(self, state):\n        b = state.competitor_best_bid_ticks\n        a = state.competitor_best_ask_ticks\n        lo = (b if b is not None else 0) / 100\n        hi = (a if a is not None else 100) / 100\n        mid = (lo + hi) / 2\n        if self.p is None:\n            self.p = mid\n        else:\n            # Bound the old estimate by current information, then relax to midpoint.\n            p = min(hi, max(lo, self.p))\n            if self.last_bid is not None and self.last_ask is not None:\n                # A swept ask is evidence of an upward move; a swept bid, downward.\n                delta = ((b or 0) - self.last_bid + (a or 100) - self.last_ask) / 200\n                p += 0.5 * delta\n            if state.sell_filled_quantity > 0 and state.buy_filled_quantity == 0:\n                p += 0.002\n            elif state.buy_filled_quantity > 0 and state.sell_filled_quantity == 0:\n                p -= 0.002\n            self.p = min(hi, max(lo, 0.75 * p + 0.25 * mid))\n        self.last_bid, self.last_ask = b or 0, a or 100\n        p = min(0.995, max(0.005, self.p))\n        bid = min(99, max(1, math.floor(100 * p - self.WIDTH)))\n        ask = min(99, max(1, math.ceil(100 * p + self.WIDTH)))\n        # Retail share demand, tempered as one-step uncertainty rises near expiry.\n        horizon_scale = max(0.15, math.sqrt(state.steps_remaining / 2000))\n        size = min(50.0, 8.0 / max(0.08, p)) * horizon_scale\n        qb = qa = size\n        if self.ONE_SIDED:\n            # The only mechanism change: retain normal size on the better side.\n            buy_edge = p - bid / 100\n            sell_edge = ask / 100 - p\n            if buy_edge >= sell_edge:\n                qa = min(qa, self.PROBE)\n            else:\n                qb = min(qb, self.PROBE)\n        cash = max(0.0, state.cash - 0.01)\n        qb = min(qb, cash * 0.45 / (bid / 100))\n        qa = min(qa, state.yes_inventory + cash * 0.45 / (1 - ask / 100))\n        actions = [CancelAll()]\n        if qb >= 0.01:\n            actions.append(PlaceOrder(Side.BUY, bid, qb))\n        if qa >= 0.01:\n            actions.append(PlaceOrder(Side.SELL, ask, qa))\n        return actions\n","scoreboard":{"baseline_code_sha256":"0f3fb373bcc83d89a2d6093c55326acac874a2c3e9132e3708cf2ca8fab7f912","candidate_code_sha256":"c7a2294db5148572b5225ecdc42c786a4da3d7cbce872ffc2dfa5a1c9febbaf7","reference_scores_sha256":"bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470","reference_strategy_code_hash":null,"reference_strategy_code_hash_note":"Numeric fixture does not supply code; no reference strategy was read. No promotion claimed."},"tuning_score":{"seeds":[115000,115499],"n":500,"s1_mean":-4.0069131151262,"s0_mean":-32.8931017047284},"paired":{"seeds":[200000,200999],"n":1000,"complete":true,"mechanism_diff":28.4694987255857,"mechanism_se":1.291425530691067,"mechanism_retail_diff":-2.0145466948174,"mechanism_arb_diff":30.4840454203934,"reference_diff":-102.5048745195422,"reference_se":5.499450420418756,"s1_mean":-2.8089963671599,"s0_mean":-31.2784950927456,"b_mean":99.6958781523823,"failures":[],"mechanism_retail_se":0.14723926729288678,"mechanism_arb_se":1.3466648579421878},"graft":"After calculating normal bid/ask prices and sizes, compare estimated probability minus bid price with ask price minus estimated probability. Keep normal size on the larger-margin side; cap the other at 0.05 shares. Leave estimator, prices, and cash limits unchanged; fills will feed back differently.","seconds_per_simulation":0.15638344974063026,"timing":{"initial_20_seeds":[115000,115019],"initial_seconds_per_simulation":0.16515003782697021,"reported_seconds_include_concurrent_process_contention":true},"execution":"Unmodified pinned simulator; run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1), default 2000 steps; no sandbox. Four OS processes at most, each with serial run_batch calls. No external strategy or historical results read; reference consumed only as numbers.","per_seed":{"seeds":[200000,200999],"n":1000,"columns":["s0_total_edge","s1_total_edge","s1_minus_s0_retail_edge","s1_minus_s0_arb_edge"],"encoding":"base64(zlib(row-major little-endian float64)); row i corresponds to seed 200000+i. Reference total_edge is fixture.H value i, verified by reference_scores_sha256.","raw_sha256":"8c771de453062e66addd085bf99b0f3dc60da91cf788049905761fd203c240b0","data":"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"},"limitations":"The chosen side has the larger estimated margin to integer-tick quotes; that skew can come from rounding. This is not a calibrated probability of an upward or downward move. The estimator is deliberately simple and has poor absolute performance. The paired effect includes feedback from different fills into the otherwise identical estimator."}
