{"approach":"Blind boundary-distance collateral scaling. S0 estimates probability from competitor best-quote midpoint (missing bid=0, ask=100), quotes at least 1.5 cents away with a recent-move volatility buffer, and sizes both sides from a retail-notional target divided by estimated probability and an adverse-selection penalty. S1 changes only BOUNDARY_POWER: below p=0.25 multiply bids by min(8,(0.25/max(p,0.01))**power); above p=0.75 apply the reflected factor to asks. Positive powers expand quantity on the cheap boundary-facing side; negative powers taper it in proportion to boundary distance. Both directions were tested before freezing. Both enforce collateral budgets. No other strategies or previous network results were read; reference fixture.H was used only as numbers.","tried":[{"strategy":"s0","flow":2,"boundary_power":0.0,"seeds":[115000,115499],"n":500,"mean":-9.7344572057308,"retail_mean":15.0431559008066,"arb_mean":-24.7776131065266,"paired_vs_selected_s0":0.0,"paired_se":0.0,"code_sha256":"2a2f68ced8c684840dd0948112c17f1fb5b08acc8b8badd9396f620fedc5dbc6","selected":true},{"strategy":"s0","flow":4,"boundary_power":0.0,"seeds":[115000,115499],"n":500,"mean":-18.8886318560448,"retail_mean":24.2127356674558,"arb_mean":-43.1013675234968,"paired_vs_selected_s0":-9.154174650314,"paired_se":1.2222189018556648,"code_sha256":"543ad3212a9e4ea0951719473a45352942f0f1ba1cbd531a1f2dbe789c0bb3e9","selected":false},{"strategy":"s0","flow":6,"boundary_power":0.0,"seeds":[115000,115499],"n":500,"mean":-21.7987422637602,"retail_mean":29.9725487658498,"arb_mean":-51.7712910296058,"paired_vs_selected_s0":-12.0642850580294,"paired_se":1.9959578196919725,"code_sha256":"3fc91246d607f1f3a2883d6b878b0b82e11df2b2dce700bdfdd2f3202c2613d3","selected":false},{"strategy":"s0","flow":8,"boundary_power":0.0,"seeds":[115000,115499],"n":500,"mean":-25.6857841484418,"retail_mean":33.9932948032322,"arb_mean":-59.679078951674,"paired_vs_selected_s0":-15.951326942710999,"paired_se":2.2369700918944546,"code_sha256":"82823591c710fcdca021e814a938a7aa0d38f426cf2f46e0cf2d70cefa10438f","selected":false},{"strategy":"s1","flow":2,"boundary_power":-1.0,"seeds":[115000,115499],"n":500,"mean":3.4456503542476002,"retail_mean":14.1553648364472,"arb_mean":-10.709714482175,"paired_vs_selected_s0":13.1801075599784,"paired_se":2.3834026444613934,"code_sha256":"0f382e329f1f9209a13e774ffd827d4558899c2eaa4649c6e2d127b6b6a079bb","selected":true},{"strategy":"s1","flow":2,"boundary_power":-0.5,"seeds":[115000,115499],"n":500,"mean":0.18491160251440003,"retail_mean":14.5609107492996,"arb_mean":-14.375999146787,"paired_vs_selected_s0":9.919368808245201,"paired_se":1.80225674138654,"code_sha256":"9d21e2b079ff183d5cae938c6bb3f50f5a9ff788769d084bd17d46f3eb44f1de","selected":false},{"strategy":"s1","flow":2,"boundary_power":0.5,"seeds":[115000,115499],"n":500,"mean":-21.5471824348588,"retail_mean":15.7204693102998,"arb_mean":-37.2676517451566,"paired_vs_selected_s0":-11.812725229128,"paired_se":2.367376000572908,"code_sha256":"3ca6d63463617021587f2173fdaf3aae28a1b3c1f473e20953f48c4ced273818","selected":false},{"strategy":"s1","flow":2,"boundary_power":1.0,"seeds":[115000,115499],"n":500,"mean":-21.6477519412106,"retail_mean":16.2316866562404,"arb_mean":-37.8794385974322,"paired_vs_selected_s0":-11.9132947354798,"paired_se":2.2911375340726496,"code_sha256":"4031eb5004a5e5c6861c8ebd507c4313b9e8df6c0c27d32663aac6866e9607c7","selected":false}],"next":"Test a boundary-facing quantity taper as an isolated graft onto a stronger strategy using new assigned seeds. The blind baseline is intentionally simple; this standalone S1 is not a replacement for B. No promotion is claimed and reserved finalist seeds were not run. Do not retune on this holdout.","code":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # Retail notional target, tuned before the holdout.\n    FLOW = 2.0\n    BOUNDARY_POWER = -1.0\n\n    def __init__(self):\n        self.previous = None\n        self.move2 = 0.000025\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            move = p - self.previous\n            self.move2 = 0.97 * self.move2 + 0.03 * min(move * move, 0.01)\n        self.previous = p\n        # A volatility buffer prices diffusion and recent adverse moves.\n        risk = math.sqrt(self.move2)\n        margin = max(0.015, 1.5 * risk)\n        buy_tick = min(hi - 1, int(math.floor(100 * (p - margin))))\n        sell_tick = max(lo + 1, int(math.ceil(100 * (p + margin))))\n        # Lognormal retail flow is small; avoid exposing a large stale order.\n        q = self.FLOW / max(p, 0.05) / (1 + 30 * risk)\n        buy_q = q\n        sell_q = q\n        # S1's sole change: bounded-loss scaling of the boundary-facing side.\n        if p < 0.25:\n            buy_q *= min(8.0, (0.25 / max(p, 0.01)) ** self.BOUNDARY_POWER)\n        if p > 0.75:\n            sell_q *= min(8.0, (0.25 / max(1 - p, 0.01)) ** self.BOUNDARY_POWER)\n        actions = [CancelAll()]\n        cash = max(0.0, state.cash - 0.01)\n        if 1 <= buy_tick <= 99:\n            price = buy_tick / 100.0\n            buy_q = math.floor(min(buy_q, 0.45 * cash / price) * 100) / 100\n            if buy_q >= 0.01:\n                actions.append(PlaceOrder(Side.BUY, buy_tick, buy_q))\n        if 1 <= sell_tick <= 99:\n            price = sell_tick / 100.0\n            sell_q = math.floor(min(sell_q, state.yes_inventory + 0.45 * cash / (1 - price)) * 100) / 100\n            if sell_q >= 0.01:\n                actions.append(PlaceOrder(Side.SELL, sell_tick, sell_q))\n        return actions\n","code_sha256":"0f382e329f1f9209a13e774ffd827d4558899c2eaa4649c6e2d127b6b6a079bb","code_s0":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # Retail notional target, tuned before the holdout.\n    FLOW = 2.0\n    BOUNDARY_POWER = 0.0\n\n    def __init__(self):\n        self.previous = None\n        self.move2 = 0.000025\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            move = p - self.previous\n            self.move2 = 0.97 * self.move2 + 0.03 * min(move * move, 0.01)\n        self.previous = p\n        # A volatility buffer prices diffusion and recent adverse moves.\n        risk = math.sqrt(self.move2)\n        margin = max(0.015, 1.5 * risk)\n        buy_tick = min(hi - 1, int(math.floor(100 * (p - margin))))\n        sell_tick = max(lo + 1, int(math.ceil(100 * (p + margin))))\n        # Lognormal retail flow is small; avoid exposing a large stale order.\n        q = self.FLOW / max(p, 0.05) / (1 + 30 * risk)\n        buy_q = q\n        sell_q = q\n        # S1's sole change: bounded-loss scaling of the boundary-facing side.\n        if p < 0.25:\n            buy_q *= min(8.0, (0.25 / max(p, 0.01)) ** self.BOUNDARY_POWER)\n        if p > 0.75:\n            sell_q *= min(8.0, (0.25 / max(1 - p, 0.01)) ** self.BOUNDARY_POWER)\n        actions = [CancelAll()]\n        cash = max(0.0, state.cash - 0.01)\n        if 1 <= buy_tick <= 99:\n            price = buy_tick / 100.0\n            buy_q = math.floor(min(buy_q, 0.45 * cash / price) * 100) / 100\n            if buy_q >= 0.01:\n                actions.append(PlaceOrder(Side.BUY, buy_tick, buy_q))\n        if 1 <= sell_tick <= 99:\n            price = sell_tick / 100.0\n            sell_q = math.floor(min(sell_q, state.yes_inventory + 0.45 * cash / (1 - price)) * 100) / 100\n            if sell_q >= 0.01:\n                actions.append(PlaceOrder(Side.SELL, sell_tick, sell_q))\n        return actions\n","tuning_score":{"seeds":[115000,115499],"n":500,"s1_mean":3.4456503542476002,"s0_mean":-9.7344572057308,"mechanism_diff":13.1801075599784,"mechanism_se":2.3834026444613934,"selected_flow":2,"selected_boundary_power":-1.0,"selection":"Four S0 FLOW values selected by mean total edge, then four S1 powers at the selected FLOW selected by mean total edge. Both frozen before holdout.","s0_code_sha256":"2a2f68ced8c684840dd0948112c17f1fb5b08acc8b8badd9396f620fedc5dbc6"},"paired":{"seeds":[200000,200999],"n":1000,"complete":true,"mechanism_diff":15.3591320844349,"mechanism_se":1.647981277225783,"mechanism_retail_diff":-0.9206800697662,"mechanism_arb_diff":16.2798121542086,"reference_diff":-95.5945598859886,"reference_se":5.177436735687836,"s1_mean":4.1013182663937,"s0_mean":-11.2578138180412,"b_mean":99.6958781523823,"failures":[],"samples_columns":["s1_minus_s0_total","s1_minus_s0_retail","s1_minus_s0_arb","s1_minus_b_total"],"samples_encoding":"base64(zlib(little-endian float64)); row-major, four columns, rows in ascending seed order 200000..200999. Preserve all rows. SE=sample standard deviation/sqrt(n).","samples_sha256":"c90989ee3b2c01f6da0e48db9f6cd53adcdf27ee39dc0a47c47722d86259e435","samples_f64le_zlib_b64":"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","reference_scores_sha256":"bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470","runner":"Unmodified pinned simulator b244530ee2a6247b5f930830eeca9dd950da0e7e. run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1); default 2000 steps. Four independent OS processes on disjoint ranges; no sandbox."},"graft":"Keep the host probability, quotes and base quantities. If p<0.25 multiply bid quantity by min(8,(0.25/max(p,0.01))**power); if p>0.75 multiply ask quantity by min(8,(0.25/max(1-p,0.01))**power). Use the tuned power and reapply host collateral limits. Change no other rule.","seconds_per_simulation":0.5855160760926083,"timing_note":"20 initial baseline runs on 115000..115019, serial and before the four-variant tuning. seconds_per_simulation excludes download/setup; repeated tuning rows are not independent additional seeds."}