{"approach":"Grafted demand-fit's censored compound-Poisson retail inference and stock sizing onto the exactly reproduced seat-845 base, keeping its ordinary quote-sizing formulas and tick-2-to-9 crossing cover. Diagnosed retail stock shortages in 129/300 discovery runs: a hard surviving-bid requirement could suppress the bulk purchase needed before a prolonged low-price retail run. Removed that requirement, used a 0.45 BUY-notional-per-step floor for tick-one stock, and a 0.5 floor for the near-price reservoir's max(0.5,0.3+0.5*fitted_flow) target. Purchases retain the base cash caps. For speed, weighted 5x5 quadrature preserves the original 9x9 demand prior's first four centered moments; Gaussian factors are shared along grid diagonals, likelihood counts are batched, posterior refresh is deferred until stock sizing uses it, and unchanged probes are retained except where they could affect main-order priority. The probability filter keeps the base's eight bins/tick when its observable upper bound is at most ten ticks and uses four elsewhere. Discovery used only seeds 4000-5999. This exact strategy's 500-run discovery gain was 0.278523068440002; independent seeds 6000-6999 gained 0.4712146440644995, with retail +0.24950134830440004 and arbitrage +0.22171329574769985. Selected before inspecting any unseen score; no seed recognition or strategy access to simulator internals. Frozen before examining unseen scores. Richest tenth means the 400 seeds with greatest realized exogenous retail BUY notional, independently replayed from the simulator's RetailFlow, with identical seed membership for base and candidate: tuning retail before=182.88509095529497, after=184.88645592302402; unseen retail before=192.918195187389, after=196.18402107417975. Eligibility (unseen retail no lower, zero failures, 200-run wall time no slower): True. Simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e; standard configuration, unsandboxed.","blocked":null,"python_version":"3.12.3","base_mean_edge":108.63974289615767,"base_unseen":{"mean_edge":97.54703502577897,"retail_edge":126.92890288111944,"arb_edge":-29.381867855334075},"score":{"mean_edge":98.54458145702871,"retail_edge":125.40044140541545,"arb_edge":-26.85585994840265,"simulations":4000,"seed_start":4000,"failures":0},"holdout":{"mean_edge":98.09550702346218,"retail_edge":127.27673164043607,"arb_edge":-29.181224616963178,"simulations":4000,"seed_start":50000,"failures":0},"diff":{"mean":0.5484719976832001,"standard_error":0.11612940188936807,"retail":0.34782875931662516,"arb":0.20064323837089992},"seconds":{"base":115.75908906990662,"yours":113.57789771584794},"code":"\"\"\"Demand-floor stock sizing with the base probability grid retained below ten ticks. Bulk tick-one stock uses max(0.45, fitted BUY notional per step) times the remaining horizon divided by the competitor spread price. Near-price stock retains a 0.5 flow floor. Ordinary quote sizing and crossing cover come from the base. Five-point quadrature in each demand parameter preserves the original prior through fourth centered moments. Higher-price belief grids use four bins per tick. Reuse unchanged probes while preserving priority at main quote prices.\"\"\"\nimport math\nfrom operator import mul, add\nfrom orderbook_pm_challenge.strategy import BaseStrategy\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\n\nP = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02,\n         qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05,\n         ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02,\n         fb=0.9, fu=0.7, xu=0.1, sticky=1.0, min_ahead=2.0, keep_hi=1.5, keep_lo=0.7, Hu=300.0, bins=8.0, filt=1.0, uni=0.01, jumpinfo=1.0, dmin=0.0, kLb=0.08, kLf=0.08, pinf=1.0, pre=0.0, H1=100.0,\n         ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0,\n         ydf1=0.6, g1=5.0, f1=0.6, a1=100.0, nb1=0.0, gs=1.0, p1t=1.0, m1=1.0, zd=1.0, pfl=0.0005)\nSQ2 = math.sqrt(2.0)\nSQ2PI = math.sqrt(2.0 * math.pi)\ndef cdf(x): return 0.5 * (1.0 + math.erf(x / SQ2))\ndef pdf(x): return math.exp(-0.5 * x * x) / SQ2PI\ndef inv(p):\n    p = min(max(p, 1e-9), 1 - 1e-9)\n    t = math.sqrt(-2.0 * math.log(min(p, 1 - p)))\n    x = t - (2.515517 + 0.802853 * t + 0.010328 * t * t) / (1 + 1.432788 * t + 0.189269 * t * t + 0.001308 * t ** 3)\n    x = -x if p < 0.5 else x\n    for _ in range(2):\n        d = pdf(x)\n        if d < 1e-12:\n            break\n        x -= (cdf(x) - p) / d\n    return x\ndef lpm(k):\n    return pdf(k) + k * cdf(k)\ndef fl(q):\n    return math.floor(q * 100 + 1e-9) / 100\n\nNODES = [(-2.2, .06), (-1.3, .17), (-0.6, .27), (0.6, .27), (1.3, .17), (2.2, .06)]\nPROBE = 0.01\n\n\n\n# Conditional compound-Poisson likelihood for front-ask retail demand.\n# Bins contain total BUY notional in one step, including multiple arrivals.\n# A surviving front ask excludes arbitrage. Conditioning on V < quoted\n# capacity, INCLUDING zero-fill steps, removes partial-fill selection bias.\n# CDF table generated offline by FFT convolution of LogNormal(-.72,1.2).\n# Quantities and numerical convolution are discretized, not exact inference.\nDEMAND_RATE = [0.0825, 0.0935, 0.1045, 0.11549999999999999, 0.1265, 0.1375, 0.1485, 0.15949999999999998, 0.17049999999999998]\nDEMAND_MEAN = [2.8453333333333335, 3.2560000000000002, 3.666666666666667, 4.077333333333334, 4.488, 4.898666666666667, 5.309333333333333, 5.720000000000001, 6.1306666666666665]\nDEMAND_BINS = [0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 25.6, 51.2, 102.4, 204.8]\nDEMAND_CDF = [[0.92105888795, 0.92196113275, 0.924969202738, 0.932357807565, 0.945624667991, 0.963044967501, 0.979774193119, 0.991444400227, 0.997284251979, 0.999362623017, 0.999889869489, 0.999986021068, 0.999998701659], [0.920990592034, 0.921680317133, 0.924116873963, 0.930474227014, 0.942608055188, 0.959538272415, 0.976821491766, 0.989663227695, 0.996524794859, 0.999134577171, 0.999841266784, 0.999978600843, 0.999997887215], [0.920945012846, 0.921485151702, 0.92348952474, 0.929003887314, 0.940110233753, 0.956459487114, 0.974070049074, 0.987897259483, 0.99572208432, 0.998877738246, 0.999783052142, 0.999969162137, 0.99999678734], [0.920913751033, 0.921344787913, 0.923015908505, 0.927834183187, 0.938016424137, 0.953740992626, 0.971509510561, 0.986161548487, 0.994887167467, 0.998595033334, 0.999715353125, 0.999957581514, 0.999995364165], [0.920892286499, 0.921241004804, 0.922650360076, 0.926888826178, 0.936242509662, 0.951327643403, 0.969126878904, 0.984465721977, 0.99402899512, 0.998289236206, 0.999638392601, 0.999943764683, 0.999993582937], [0.920875459017, 0.921163058236, 0.922363381432, 0.926114128789, 0.934725532927, 0.949174359247, 0.966908468941, 0.982815715266, 0.993154809394, 0.99796292452, 0.999552457759, 0.999927641925, 0.999991411996], [0.920863935648, 0.92110328027, 0.92213405742, 0.925471903383, 0.933417637455, 0.947244128395, 0.964840878478, 0.981214974808, 0.992270479813, 0.997618465854, 0.999457877851, 0.999909164149, 0.999988822676], [0.920854888191, 0.921055988959, 0.921949132384, 0.924933659885, 0.932281742864, 0.945506399904, 0.962911468045, 0.979665200306, 0.991380757461, 0.997258018363, 0.999355008083, 0.999888299565, 0.999985789148], [0.920847052834, 0.921018315954, 0.921797428174, 0.924478933961, 0.931288674603, 0.943935630405, 0.961108570736, 0.978166924312, 0.990489488325, 0.996883539619, 0.999244217885, 0.999865030849, 0.999982288256], [0.911015393227, 0.91202676244, 0.915398852192, 0.923683802715, 0.938573205914, 0.958164698941, 0.977045025765, 0.990270372821, 0.99690960856, 0.999275188478, 0.999874909321, 0.999984137561, 0.999998527586], [0.910938837773, 0.911711980722, 0.914443340761, 0.92157134034, 0.935185781023, 0.954216200139, 0.9737067051, 0.988248324346, 0.996045399111, 0.999015661953, 0.999819663444, 0.999975713709, 0.999997603736], [0.910887746387, 0.911493210227, 0.913740065925, 0.919922528256, 0.932381833873, 0.950751675304, 0.970598418574, 0.986244861081, 0.995132202668, 0.99872333238, 0.999753475247, 0.999964996232, 0.99999635597], [0.91085270389, 0.911335870128, 0.913209140571, 0.91861095414, 0.930031998285, 0.947694131152, 0.96770782133, 0.984276965071, 0.994182646727, 0.998401542246, 0.999676486805, 0.99995184415, 0.999994741251], [0.910828643526, 0.911219535232, 0.91279936534, 0.917551004111, 0.928041559719, 0.94498092456, 0.965019717212, 0.982355432861, 0.993206954014, 0.998053461537, 0.999588949514, 0.999936149582, 0.999992720063], [0.910809781001, 0.911132161761, 0.912477669161, 0.916682440875, 0.926339682127, 0.942560940358, 0.962518245592, 0.980486863234, 0.992213386966, 0.997682037669, 0.999491188774, 0.999917832576, 0.999990256392], [0.910796864047, 0.91106515429, 0.912220604318, 0.915962426656, 0.924872550884, 0.940392273583, 0.960187955834, 0.978675033418, 0.9912086336, 0.997289978664, 0.999383578573, 0.999896836658, 0.999987317608], [0.910786722433, 0.911012143639, 0.912013310526, 0.915359005735, 0.923598489015, 0.938440375567, 0.958014329781, 0.97692174727, 0.990198099766, 0.996879753344, 0.999266523042, 0.999873125058, 0.9999838743], [0.910777939504, 0.910969914546, 0.911843256686, 0.914849227461, 0.922484718874, 0.936676391159, 0.955984002912, 0.975227495313, 0.989186154422, 0.996453601066, 0.999140443007, 0.999846677505, 0.99997990008], [0.901081414622, 0.902199415354, 0.905927255918, 0.915088709511, 0.931567794602, 0.953296025539, 0.974308790694, 0.989088636586, 0.996531994403, 0.999187169072, 0.999859883387, 0.999982249395, 0.999998353287], [0.900996788476, 0.901851442981, 0.904870896674, 0.912752344413, 0.927816630714, 0.948911636334, 0.970586818571, 0.986824984063, 0.995562217264, 0.998895905894, 0.999797955478, 0.999972818528, 0.999997319839], [0.900940310933, 0.901609606607, 0.904093419216, 0.910928987821, 0.924712608966, 0.945067029715, 0.967124082228, 0.984583618901, 0.994537732949, 0.998567789553, 0.999723743198, 0.999960817458, 0.999995923888], [0.900901574204, 0.9014356781, 0.903506487786, 0.90947869185, 0.922111943958, 0.941675765478, 0.963906106167, 0.982383430016, 0.993472774438, 0.998206584149, 0.999637402547, 0.9999460874, 0.999994117201], [0.900874977369, 0.901307078282, 0.903053492688, 0.908306707177, 0.91990946569, 0.938667676578, 0.960915389766, 0.980236353468, 0.992378854321, 0.997815862377, 0.999539213179, 0.999928506641, 0.99999185547], [0.900854126337, 0.901210493391, 0.902697870177, 0.907346383142, 0.918026577442, 0.935985612129, 0.958133818397, 0.978149619512, 0.991265267558, 0.997398947779, 0.999429538708, 0.999907984771, 0.999989098292], [0.900839847669, 0.901136421686, 0.902413697149, 0.90655033071, 0.916403602673, 0.933582786883, 0.955543835134, 0.976127304679, 0.990139528354, 0.996958894844, 0.999308798077, 0.999884457729, 0.999985809043], [0.900828636919, 0.901077822453, 0.902184545019, 0.905883203054, 0.914994346605, 0.931420674118, 0.95312900087, 0.97417128754, 0.989007701572, 0.996498488588, 0.999177444748, 0.999857883589, 0.999981954693], [0.900818928088, 0.901031141439, 0.901996560421, 0.905319617381, 0.913762493352, 0.929467130002, 0.950874222813, 0.972281989775, 0.987874680808, 0.996020255134, 0.999035951481, 0.999828238979, 0.999977505591], [0.891255757962, 0.892477938773, 0.896553397947, 0.906571831841, 0.924608220065, 0.948439102628, 0.97156565953, 0.987899230706, 0.996151403653, 0.999098562208, 0.999844791432, 0.99998035656, 0.999998178761], [0.891163246829, 0.892097538306, 0.895398487266, 0.904016464283, 0.920500293587, 0.943624690215, 0.967462023842, 0.985393267467, 0.995075245531, 0.998775305423, 0.999776142454, 0.999969915277, 0.999997035521], [0.89110150707, 0.891833166273, 0.894548501709, 0.902022428121, 0.917102162082, 0.939405606846, 0.963647242268, 0.982913615703, 0.993938675182, 0.998411105239, 0.999693855328, 0.999956625776, 0.999995491092], [0.891059161123, 0.891643030807, 0.893906845678, 0.900436507561, 0.914255787845, 0.935685896328, 0.960104569481, 0.980481047699, 0.992757556081, 0.99801015369, 0.99959809939, 0.999940311204, 0.999993492013], [0.891030086188, 0.891502448161, 0.893411620959, 0.899155005029, 0.911845686879, 0.932387843667, 0.956814096025, 0.978108608422, 0.991544708476, 0.997576432764, 0.999489182284, 0.999920835766, 0.999990989154], [0.891007292413, 0.891396863746, 0.893022850195, 0.89810499087, 0.909785618713, 0.929448262997, 0.953755375997, 0.975804127023, 0.990310471826, 0.997113648576, 0.999367505846, 0.999898098372, 0.999987937691], [0.890991683367, 0.891315890329, 0.892712191124, 0.897234622142, 0.908010140111, 0.926815502609, 0.95090868949, 0.973571947532, 0.989063193972, 0.996625208155, 0.99923353421, 0.99987202717, 0.999984296973], [0.890979428087, 0.891251831094, 0.892461682597, 0.896505234275, 0.90646861638, 0.924447077939, 0.948255635164, 0.971413993627, 0.987809602799, 0.996114218284, 0.999087770576, 0.999842574898, 0.999980030318], [0.890968814662, 0.891200800596, 0.89225617915, 0.895889065656, 0.905121257434, 0.922307579854, 0.945779362161, 0.969330591281, 0.986555117979, 0.99558349686, 0.998930740192, 0.999809714928, 0.999975104775], [0.881537242096, 0.882861192393, 0.887276272726, 0.898132479461, 0.917694267994, 0.943594082249, 0.968815802614, 0.986702194611, 0.995767830544, 0.999009365295, 0.999829633201, 0.999978459043, 0.999998004007], [0.881437028584, 0.882449113693, 0.886025069079, 0.89536293197, 0.913236459787, 0.938355467807, 0.964332510753, 0.983953235334, 0.994584480265, 0.998653856968, 0.999754223941, 0.999967003937, 0.999996750782], [0.881370148485, 0.882162727389, 0.885104241895, 0.893202019128, 0.909550098159, 0.933767460298, 0.960168098636, 0.981234934238, 0.993335029571, 0.99825327492, 0.99966381097, 0.999952421149, 0.99999505758], [0.881324276914, 0.881956760056, 0.884409121484, 0.891483521104, 0.906463059504, 0.92972452195, 0.956303413006, 0.978569922279, 0.992036997367, 0.997812245536, 0.999558576371, 0.9999345155, 0.999992865684], [0.881292781279, 0.881804471972, 0.883872640944, 0.890094976505, 0.903849686343, 0.926141368064, 0.952716032491, 0.975972321873, 0.990704529173, 0.99733516677, 0.999438855515, 0.999913136864, 0.99999012111], [0.881268089758, 0.881690096398, 0.88345148707, 0.888957309016, 0.90161621033, 0.922948779972, 0.949383103889, 0.973450527947, 0.989349020694, 0.996826133831, 0.999305088475, 0.999888173242, 0.999986774583], [0.881251181151, 0.881602381081, 0.883114953749, 0.888014317635, 0.899691515761, 0.92009025422, 0.946282688752, 0.971009119894, 0.987979660618, 0.996288912423, 0.999157784814, 0.999859544788, 0.99998278139], [0.881237905535, 0.88153298828, 0.882843582411, 0.88722409227, 0.898020605, 0.917519369134, 0.943394383223, 0.968650036737, 0.986603843611, 0.995726936711, 0.998997497898, 0.999827198723, 0.999978101161], [0.88122640847, 0.881477709026, 0.882620965169, 0.886556544958, 0.896560277004, 0.915197473935, 0.940699549414, 0.966373481837, 0.985227516633, 0.995143321396, 0.998824806026, 0.999791105011, 0.999972697614], [0.871924698753, 0.873348048187, 0.878094885015, 0.889769967952, 0.910825723789, 0.938761113188, 0.966059389, 0.985497567972, 0.995381269397, 0.998919575735, 0.999814408437, 0.999976556834, 0.999997829025], [0.871816962417, 0.872905028606, 0.876749609515, 0.886790986402, 0.906024820292, 0.933104071993, 0.961198467328, 0.982504948584, 0.994089917952, 0.998531556959, 0.999732199504, 0.999964084484, 0.999996465623], [0.871745061817, 0.872597140714, 0.875759579532, 0.884466938598, 0.902056024025, 0.928152640826, 0.956686848999, 0.979547657241, 0.992726796504, 0.998094294081, 0.999633609453, 0.999948203539, 0.999994623352], [0.87169574682, 0.872375710342, 0.87501223402, 0.882618860809, 0.898733291352, 0.923791638162, 0.952502835833, 0.976650157688, 0.991311104231, 0.997612854369, 0.999518832523, 0.999928700225, 0.999992238213], [0.871661886924, 0.872211989578, 0.874435455254, 0.881125709529, 0.895920930878, 0.919928190071, 0.948621392742, 0.973827617497, 0.989858329356, 0.997092058497, 0.999388231555, 0.999905409838, 0.999989251337], [0.871635341905, 0.872089027728, 0.87398267067, 0.879902392113, 0.893517761212, 0.916487048674, 0.945017184454, 0.971088963718, 0.988380935355, 0.996536397367, 0.999242284875, 0.999878209241, 0.999985608964], [0.871617164037, 0.871994727652, 0.873620864696, 0.878888443863, 0.891447087415, 0.913406874352, 0.94166599955, 0.96843897866, 0.986888958717, 0.995950001547, 0.99908154773, 0.999847010388, 0.999981262286], [0.871602891875, 0.871920125611, 0.873329115902, 0.878038780246, 0.88964962499, 0.910637329561, 0.938545392352, 0.965879586774, 0.985390464414, 0.99533663824, 0.998906624085, 0.999811754803, 0.999976167212], [0.871590531773, 0.871860696643, 0.873089783159, 0.877321038588, 0.888078824382, 0.908136545779, 0.935634909809, 0.963410841873, 0.983891927668, 0.994699724008, 0.998718145867, 0.999772408883, 0.999970284092], [0.8624169724, 0.863937390277, 0.869008249778, 0.881483618668, 0.904002372588, 0.93394034102, 0.963296586465, 0.984285390699, 0.994991714626, 0.998829190931, 0.999799116882, 0.999974649922, 0.999997653816], [0.862301889791, 0.863464154837, 0.867571086725, 0.878299873356, 0.898865066929, 0.927870602558, 0.958060079789, 0.981048468276, 0.993591555222, 0.998408401823, 0.999710068708, 0.999961156897, 0.999996180042], [0.862225086521, 0.863135269465, 0.866513465537, 0.875816372004, 0.8946195484, 0.922561196377, 0.953203688763, 0.977851867426, 0.992113976556, 0.997934158218, 0.999603250104, 0.999943972908, 0.999994188406], [0.862172408921, 0.862898738711, 0.865715113563, 0.87384166343, 0.891066018656, 0.917887238395, 0.948703034425, 0.974721857621, 0.990579882833, 0.997411974886, 0.999478866877, 0.999922865318, 0.999991609598], [0.862136240257, 0.86272385346, 0.865098978201, 0.872246301028, 0.888058890995, 0.913748248102, 0.944530367456, 0.971674618492, 0.989006122225, 0.996847102076, 0.99933730908, 0.999897654595, 0.999988379831], [0.862107885246, 0.862592506788, 0.864615302748, 0.870939304182, 0.885489684771, 0.910062953373, 0.940657796989, 0.968719575024, 0.987406237264, 0.996244433051, 0.999179093323, 0.999868206233, 0.999984440828], [0.86208846791, 0.862491776464, 0.86422881567, 0.869856037386, 0.883276218061, 0.906765194853, 0.937058785332, 0.965861679699, 0.985791118956, 0.995608469492, 0.999004820794, 0.999834423777, 0.999979739653], [0.862073222594, 0.862412087425, 0.863917166664, 0.868948311637, 0.881354994696, 0.903800740852, 0.933708806646, 0.963102812338, 0.984169505847, 0.994943317331, 0.998815146505, 0.999796242874, 0.999974228459], [0.862060019713, 0.862348606131, 0.863661510057, 0.868181540363, 0.879676178266, 0.901124529261, 0.930585565403, 0.960442850252, 0.982548402175, 0.99425270008, 0.998610756596, 0.999753626198, 0.999967864194], [0.853012920103, 0.8546281148, 0.860015392079, 0.873272758699, 0.897223999292, 0.929131908149, 0.960527561505, 0.983065702936, 0.994599160733, 0.998738208281, 0.999783758278, 0.999972738295, 0.999997478378], [0.852890664809, 0.854125376379, 0.8584884895, 0.869888845399, 0.891756892388, 0.922655156231, 0.954917532562, 0.979583855603, 0.993089388837, 0.998284387991, 0.999687831118, 0.999958221154, 0.999995894039], [0.852809074725, 0.853775989183, 0.857364861859, 0.867249512461, 0.887240281902, 0.916993172144, 0.949718811092, 0.976147647478, 0.991496570487, 0.997772862833, 0.999572732251, 0.999939729216, 0.999993752742], [0.852753113987, 0.853524714623, 0.856516701731, 0.865151074059, 0.883460779525, 0.91201131374, 0.944904202643, 0.972785125538, 0.989843339557, 0.997209601805, 0.999438678463, 0.999917010716, 0.999990979836], [0.852714691119, 0.85333892858, 0.855862135668, 0.863455856842, 0.880263040906, 0.907601478717, 0.940443144439, 0.969513447569, 0.988147921229, 0.996600291671, 0.999286086768, 0.999889871038, 0.999987506588], [0.852684568891, 0.853199395161, 0.855348296815, 0.862067118628, 0.87753139889, 0.903676377023, 0.936305117745, 0.966342501804, 0.986424948141, 0.995950234801, 0.999115512092, 0.999858164076, 0.999983270169], [0.85266394138, 0.853092386508, 0.854937710281, 0.860916144551, 0.875178275843, 0.90016504681, 0.932461206399, 0.963277377855, 0.984686172274, 0.995264310293, 0.99892760184, 0.999821784759, 0.999978213482], [0.85264774591, 0.853007730665, 0.854606630317, 0.859951710001, 0.873136038234, 0.897009384443, 0.928884767028, 0.960319880736, 0.982941008781, 0.99454696854, 0.99872306252, 0.999780662672, 0.99997228489], [0.852633720168, 0.852940292796, 0.854335034922, 0.859137054511, 0.871351623679, 0.894161158616, 0.925551635118, 0.957469684274, 0.981196991434, 0.993802245109, 0.998502635096, 0.999734756609, 0.999965437903], [0.843711411394, 0.845419129782, 0.851115346983, 0.86513672082, 0.89049038859, 0.924335953846, 0.957752479339, 0.981838545053, 0.99420360231, 0.99864662518, 0.999768332365, 0.999970821942, 0.999997302711], [0.843582154083, 0.844887589288, 0.849500817159, 0.861557161829, 0.884699990237, 0.917457826732, 0.951771008281, 0.978111171887, 0.992583415701, 0.998159511892, 0.999665486294, 0.999955277233, 0.999995607612], [0.843495891092, 0.844518187601, 0.848312741368, 0.858765560661, 0.879917837048, 0.911448610603, 0.946232406922, 0.974435080048, 0.990874579238, 0.997610403433, 0.999542055216, 0.999935472427, 0.999993316357], [0.843436725345, 0.844252519821, 0.847415951356, 0.856546246048, 0.875917114905, 0.906163852985, 0.941106531766, 0.970840064652, 0.989101481009, 0.997005729859, 0.999398266306, 0.999911136357, 0.999990348925], [0.843396101918, 0.844056092249, 0.846723864987, 0.854753491639, 0.8725328585, 0.901487816668, 0.936359908659, 0.967344226952, 0.987283740064, 0.996351621478, 0.99923456329, 0.999882059072, 0.999986631604], [0.84336425453, 0.843908566828, 0.846180578011, 0.853284918156, 0.869642325888, 0.897327201302, 0.931959319957, 0.96395788324, 0.985437089961, 0.995653796585, 0.999051539455, 0.999848082632, 0.999982096983], [0.843342445645, 0.843795429212, 0.845746463916, 0.852067821394, 0.867152634025, 0.893606260585, 0.927873419944, 0.960686226948, 0.983574149863, 0.994917518053, 0.998849888695, 0.99980909314, 0.999976683767], [0.843325322633, 0.843705924736, 0.845396414373, 0.851048008916, 0.864992085454, 0.890263041598, 0.924073411289, 0.957530957974, 0.981705014312, 0.99414758651, 0.998630369493, 0.999765013933, 0.999970336494], [0.843310493614, 0.843634624433, 0.845109258803, 0.85018659556, 0.863104451923, 0.887246168453, 0.920533234786, 0.954491519679, 0.979837746907, 0.993348354707, 0.998393778244, 0.999715799769, 0.999963005203]]\n# Five-point quadrature preserves the original nine-point uniform\n# rate/size priors through their fourth centered moments.\n_DIDX = (0, 2, 4, 6, 8)\n_DW = (13.0/96.0, 7.0/24.0, 7.0/48.0, 7.0/24.0, 13.0/96.0)\nDEMAND_CDF = [DEMAND_CDF[r*9+m] for r in _DIDX for m in _DIDX]\nDEMAND_RATE = [DEMAND_RATE[i] for i in _DIDX]\nDEMAND_MEAN = [DEMAND_MEAN[i] for i in _DIDX]\nDEMAND_PRIOR = [r*m for r in _DW for m in _DW]\nDEMAND_PRIOR_LOG = [math.log(w) for w in DEMAND_PRIOR]\nDEMAND_LOG = []\nfor row in DEMAND_CDF:\n    masses = [row[0]] + [max(1e-14, row[i]-row[i-1]) for i in range(1,len(row))]\n    DEMAND_LOG.append([[math.log(max(1e-14, mass)/max(row[k],1e-14))\n                        for mass in masses[:k+1]] for k in range(len(row))])\nDEMAND_FLOW = [r*m for r in DEMAND_RATE for m in DEMAND_MEAN]\nDEMAND_LIKELIHOOD = [[tuple(table[k][b] for table in DEMAND_LOG)\n                       for b in range(k+1)] for k in range(len(DEMAND_BINS))]\n\nD = {'mult': 1.0, 'horizon': 2000.0, 'mode': 1, 'shrink': 1.0, 'extra': 0.0, 'floor': 0.45}\n\nclass Strategy(BaseStrategy):\n    def __init__(self):\n        self.dpost = DEMAND_PRIOR_LOG[:]\n        self.dcounts = {}\n        self.dprev = None\n        self.dbprev = None\n        self.dpending = None\n        self.dcount = 0\n        self.dflow = sum(DEMAND_FLOW)/len(DEMAND_FLOW)\n        self.probe_ids = set()\n        self.n = 0\n        self.ahead = set()     # main orders placed strictly ahead of any competitor order at their tick\n        self.res = {}          # order id -> uncovered quantity at placement\n        self.bc = None         # belief: sub-tick grid centres (ticks)\n        self.bw = None         # belief: weights\n        self.st = 0.5          # last one-step sd in ticks\n        self.pcb = self.pca = None\n        self.pb = set()\n        self.pa = set()\n        self.tb = 0\n        self.s = None          # competitor spread parameter\n\n    def _oid(self, tag):\n        self.n += 1\n        return tag + str(self.n)\n\n    def _demand_update(self, state, now):\n        if self.dprev is None:\n            return\n        tick, prev, cutoff = self.dprev\n        # One surviving order proves the arbitrageur did not sweep this tick.\n        if not any(oid in now for oid in prev):\n            return\n        spent = sum(q-now.get(oid,0.0) for oid,q in prev.items())*tick/100.0\n        k = 0\n        while k < len(DEMAND_BINS) and spent >= DEMAND_BINS[k]:\n            k += 1\n        if k > cutoff:\n            return\n        key = (cutoff, k)\n        self.dcounts[key] = self.dcounts.get(key, 0) + 1\n        self.dcount += 1\n        if self.dcount % 16 == 0:\n            for (cutoff, k), count in self.dcounts.items():\n                terms = DEMAND_LIKELIHOOD[cutoff][k]\n                self.dpost = [v + count*l for v,l in zip(self.dpost,terms)]\n            self.dcounts.clear()\n            self.dpending = self.dpost[:]\n\n    def _refresh_demand(self):\n        if self.dpending is not None:\n            mx = max(self.dpending)\n            weights = [math.exp(v-mx) for v in self.dpending]\n            z = sum(weights)\n            self.dflow = sum(w*f for w,f in zip(weights,DEMAND_FLOW))/z\n            self.dpending = None\n\n    def _demand_snapshot(self, buy, sell, unknown_tick, q1, amt, qam):\n        self.dprev = None\n        if sell:\n            tick = min(v[0] for v in sell.values())\n            front = {oid:q for oid,(t,q) in sell.items() if t == tick}\n            cap = sum(front.values())*tick/100.0\n            k = -1\n            for i,b in enumerate(DEMAND_BINS):\n                if b < cap-.01*tick/100.0:\n                    k = i\n            if k >= 0:\n                self.dprev = (tick,front,k)\n        self.dbprev = None\n        if buy:\n            tick = max(v[0] for v in buy.values())\n            unknown = unknown_tick >= tick or (tick == 1 and (q1 >= .01 or (amt == 1 and qam >= .01)))\n            if not unknown:\n                front = {oid:q for oid,(t,q) in buy.items() if t == tick}\n                cap = sum(front.values())*.05\n                k = -1\n                for i,b in enumerate(DEMAND_BINS):\n                    if b < cap-.0005:\n                        k = i\n                if k >= 0:\n                    self.dbprev = (5.,front,k)\n\n    def on_step(self, state):\n        H = max(1, state.steps_remaining)\n        cb = state.competitor_best_bid_ticks or 0\n        ca = state.competitor_best_ask_ticks or 100\n        if self.s is None:\n            self.s = max(1, (ca - cb + 1) // 2)\n        lo, hi = cb, ca\n        sb, sa = set(), set()\n        bids, asks, probes = [], [], []\n        now = {}\n        has_bid = low_ask = False\n        for o in state.own_orders:\n            now[o.order_id] = o.remaining_quantity\n            is_probe = o.remaining_quantity <= PROBE + 1e-9\n            if is_probe:\n                probes.append(o)\n            if o.side is Side.BUY:\n                has_bid = True\n                lo = max(lo, o.price_ticks)\n                if is_probe:\n                    sb.add(o.price_ticks)\n                else:\n                    bids.append(o)\n            else:\n                hi = min(hi, o.price_ticks)\n                if o.price_ticks <= 5:\n                    low_ask = True\n                if is_probe:\n                    sa.add(o.price_ticks)\n                else:\n                    asks.append(o)\n        self._demand_update(state, now)\n        ask_prev = self.dprev\n        if low_ask:\n            self.dprev = self.dbprev\n            self._demand_update(state, now)\n        self.dprev = ask_prev\n        if P['pinf'] > 0:\n            ea = self.pa - sa\n            eb = self.pb - sb\n            if sa and ea:\n                lo = max(lo, min(max(ea), hi))\n            if eb and (sb or min(eb) <= P['p1t']):\n                hi = min(hi, max(min(eb), lo))\n        if P['jumpinfo'] > 0 and self.pcb is not None:\n            if ca > self.pca + 1:\n                lo = max(lo, ca - 1)\n            if cb < self.pcb - 1:\n                hi = min(hi, cb + 1)\n        self.pcb, self.pca = cb, ca\n        if lo > hi:\n            lo, hi = hi, lo\n        # sub-tick belief over p_(t-1): predict with one-step noise, truncate to [lo, hi]\n        wid = max(hi - lo, 1e-6)\n        grid_bins = 8.0 if hi <= 10 else 4.0\n        nb = max(2, min(48, int(round(wid * grid_bins))))\n        cs = [lo + (i + 0.5) * wid / nb for i in range(nb)]\n        if self.bc is None or P['filt'] <= 0:\n            ws = [1.0 / nb] * nb\n        else:\n            sg = max(self.st, 0.6 * wid / nb)\n            k = -0.5 / (sg * sg)\n            ws = []\n            uni = P['uni'] * sg\n            oldn = len(self.bc)\n            spacing = wid / nb\n            if nb >= 8 and oldn >= 8 and abs(self.bc[1] - self.bc[0] - spacing) < 1e-12:\n                # Equal-spaced grids share Gaussian factors along diagonals.\n                # Evaluate each distinct factor once, preserving the same filter.\n                offset = oldn - 1\n                d0 = cs[0] - self.bc[0]\n                kernel = []\n                for j in range(-offset, nb):\n                    d = d0 + j * spacing\n                    d2 = d * d * k\n                    kernel.append(math.exp(d2) if d2 > -12.5 else 0.0)\n                for i in range(nb):\n                    ix = i + offset\n                    ws.append(sum(map(mul, self.bw, kernel[ix::-1]), uni))\n            else:\n                for c in cs:\n                    t = uni\n                    for ci, wi in zip(self.bc, self.bw):\n                        d = c - ci\n                        d2 = d * d * k\n                        if d2 > -12.5:\n                            t += wi * math.exp(d2)\n                    ws.append(t)\n            tot = sum(ws)\n            ws = [w / tot for w in ws]\n        self.bc, self.bw = cs, ws\n        mean_t = sum(map(mul, cs, ws))\n        var_t = max(sum(map(mul, map(mul, cs, cs), ws)) - mean_t * mean_t, 0.0)\n        phat = min(max(mean_t / 100.0, 0.002), 0.998)\n        var = P['sig'] ** 2 + P['lam_j'] * P['sig_j'] ** 2\n        sd = math.sqrt(H * var)\n        self.tb = self.tb + 1 if hi <= 1 else 0\n        if self.tb > 0 and P['zd'] > 0:\n            # below the last tick there is no probe: the score keeps walking away from it\n            phat = min(phat, max(P['pfl'], cdf(inv(0.01) - P['zd'] * 0.02 * math.sqrt(self.tb) / sd)))\n        if phat < 0.1:\n            self._refresh_demand()\n        z = inv(phat)\n        Z = z * sd\n        sp = pdf(z) / sd * P['sig']\n        self.st = sp * 100.0\n        s = math.sqrt(sp * sp + var_t / 1e4 + 1e-8)\n        sd1 = math.sqrt(max(1.0, H - 1.0) * var)\n        jp = [(cdf((Z + n * P['sig_j']) / sd1), w) for n, w in NODES]\n        lj = P['lam_j']\n\n        def AB(x):\n            k = (x - phat) / s\n            a = s * lpm(k)\n            b = a - (x - phat)\n            ja = jb = 0.0\n            for p, w in jp:\n                d = x - p\n                if d > 0.0:\n                    ja += w * d\n                else:\n                    jb -= w * d\n            return (1 - lj) * a + lj * ja, (1 - lj) * b + lj * jb\n\n        if H == 1:\n            def AB(x):\n                return (1 - phat) * x, phat * (1 - x)\n        rho = P['lam_r'] * 0.5\n        mu = math.log(P['m']) - 0.5 * P['sig_n'] ** 2\n        kA = P['kL'] if phat < P['pL'] else P['kA']\n\n        def qsize(loss, gain, px):\n            loss *= kA\n            if gain <= 0:\n                return 0.0\n            tail = loss / (rho * gain)\n            if tail >= 1.0:\n                return 0.0\n            return min(P['qmax'], math.exp(mu + P['sig_n'] * inv(1 - max(tail, 1e-6))) / px)\n\n        ptk = phat * 100\n        def qbid(t):\n            if not 1 <= t <= 99:\n                return 0.0\n            a, b = AB(t / 100.0)\n            if phat * 100 - t < P['dmin']:\n                return 0.0\n            if P['nb1'] > 0 and self.s == 1 and t == 1 and ptk < 1.0:\n                return 0.0\n            if phat < P['pL']:\n                ytg = P['ydem'] * min(H, P['W']) / max(ca - 1, ptk + 1) * 100.0\n                a *= (P['kLb'] if state.yes_inventory < ytg else P['kLf']) / P['kL']\n            return qsize(a, b, max(phat, 0.05))\n\n        def qask(t):\n            if not 1 <= t <= 99:\n                return 0.0\n            a, b = AB(t / 100.0)\n            if t - phat * 100 < P['dmin']:\n                return 0.0\n            return qsize(b, a, t / 100.0)\n\n        sticky = P['sticky'] > 0\n        def top(orders, comp, step):\n            if sticky and sum(o.remaining_quantity for o in orders if o.price_ticks == comp and o.order_id in self.ahead) >= P['min_ahead']:\n                return comp\n            return comp + step\n        bid_t = top(bids, cb, 1)\n        ask_t = top(asks, ca, -1)\n        qb = qbid(bid_t)\n        qa = qask(ask_t)\n\n        Y = state.yes_inventory\n        if ask_t == 1 and H > P['H1'] and P['pre'] > 0:\n            qa = qa if Y >= P['a1'] > 0 else 0.0          # let the competitor's 2-tick ask erode instead of fronting it for nothing\n        # ammunition: hold YES so that low asks are covered\n        ptick = phat * 100\n        amt = 0\n        qam = 0.0\n        if P['ammo'] > 0 and ask_t >= 2 and phat < max(P['ammo_pmax'], P['pmaxf']):\n            wealth = state.cash + Y * phat\n            x = max(ask_t, ptick + 1) / 100.0\n            capw = P['fy'] * wealth / max(phat, 0.005)\n            target = min(P['ydem'] * min(H, P['W']) / x, capw)\n            full = min(max(0.5, .3+.5*self.dflow) * min(H, P['Wf']) / x, capw) if phat < P['pmaxf'] else 0.0\n            t = max(1, min(99, int(math.ceil(ptick + 1e-9))))\n            g = t - ptick\n            if Y < full and g <= P['g_hi'] and bid_t <= t < ask_t:\n                amt, qam = t, min(P['qf'], full - Y)     # a tick p has just crossed: nearly free\n            elif Y < target and phat < P['ammo_pmax'] and bid_t <= t < ask_t and t / 100.0 - phat < P['ammo_maxpay']:\n                amt, qam = t, min(P['ammo_q'], target - Y)\n            elif Y < full and t >= 2 and 1.0 - g <= P['g_lo']:\n                amt, qam = t - 1, min(P['qf'], full - Y)  # rest just under p: filled only on the crossing, or by retail\n\n        if P['pre'] > 0 and ca == 2 and phat < 0.01 and H > P['H1'] and Y < P['pre'] and not qam and 1.0 - ptick < P['pre_gap']:\n            amt, qam = 1, min(P['ammo_q'], P['pre'] - Y)\n        demand_flow = max(D['floor'], self.dflow * D['mult'])\n        q1 = 0.0\n        if P['ydf1'] > 0 and P['m1'] < ptick < P['g1'] + 1.0 and amt != 1:\n            q1 = max(0.0, (demand_flow * min(H, D['horizon'])) / (self.s / 100.0) - Y)\n        # Bridge expected retail demand until another tick crossing is available.\n        if 0.02 < phat < 0.1:\n            ct = max(2, min(99, int(math.floor(ptick))))\n            wealth = state.cash + Y * phat\n            goal = min(1 * qa, 0.3 * max(wealth, 0.0) / phat)\n            if Y < goal and ct < ask_t:\n                amt, qam = ct, min(3000.0, goal - Y)\n        # order management\n        actions = []\n        free = state.free_cash - 2.0\n        keep_ask = 0.0\n        new_b = []\n        new_a = []\n        def cancel(o):\n            nonlocal free\n            actions.append(CancelOrder(o.order_id))\n            x = o.price_ticks / 100.0\n            if o.side is Side.BUY:\n                free += x * o.remaining_quantity\n            else:\n                free += (1 - x) * min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n            self.ahead.discard(o.order_id)\n            self.res.pop(o.order_id, None)\n        live_probes = {}\n        for o in probes:\n            if o.order_id in self.probe_ids:\n                # Match the original budget's release accounting. Actual cancels\n                # are decided after computing the desired probe price set.\n                if o.side is Side.BUY:\n                    free += o.price_ticks / 100.0 * o.remaining_quantity\n                live_probes[(o.side, o.price_ticks)] = o\n            else:\n                cancel(o)\n        kept_ids = set()\n        d_buy, d_sell = {}, {}\n        def manage(orders, t, q, comp):\n            kept = 0.0\n            for o in orders:\n                if o.price_ticks == t and q > 0 and kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0):\n                    kept += o.remaining_quantity\n                    kept_ids.add(o.order_id)\n                    if o.order_id in self.ahead:\n                        target = d_buy if o.side is Side.BUY else d_sell\n                        target[o.order_id] = (o.price_ticks, o.remaining_quantity)\n                else:\n                    cancel(o)\n            return kept\n        kb = manage(bids, bid_t, qb, cb)\n        ka = manage(asks, ask_t, qa, ca)\n        add_b = (qb - kb) if (kb < qb * P['keep_lo'] and bid_t != cb) else 0.0\n        add_a = (qa - ka) if (ka < qa * P['keep_lo'] and ask_t != ca) else 0.0\n\n        budget = P['fb'] * max(free, 0.0)\n        cost = add_b * bid_t / 100.0 + qam * amt / 100.0\n        if cost > budget:\n            f = budget / cost\n            add_b *= f\n            qam *= f\n            cost = budget\n        out = []\n        q1 = min(q1, P['f1'] * max(budget - cost, 0.0) * 100.0)\n        if q1 >= 0.01:\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=1, quantity=fl(q1)))\n            free -= fl(q1) / 100.0 + 1e-6\n        if amt and qam >= 0.01:\n            q = fl(qam)\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n            free -= q * amt / 100.0 + 1e-6\n        if bid_t >= 1 and add_b >= 0.01:\n            q = fl(add_b)\n            oid = self._oid('b')\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=bid_t, quantity=q, client_order_id=oid))\n            free -= q * bid_t / 100.0 + 1e-6\n            if bid_t > cb:\n                self.ahead.add(oid)\n                d_buy[oid] = (bid_t, q)\n        if ask_t <= 99 and add_a >= 0.01:\n            x = ask_t / 100.0\n            covk = sum(o.remaining_quantity - min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n                       for o in asks if o.order_id in kept_ids)\n            cov = max(0.0, Y - covk - 0.1)\n            unc = max(0.0, add_a - cov)\n            if unc > 0:\n                ub = max(0.0, free - (1 - P['fu']) * max(state.cash, 0.0)) if x >= P['xu'] else 0.0\n                if H < P['Hu'] and phat < 0.05:\n                    ub = max(0.0, free * (1 - H / P['Hu']))\n                unc = min(unc, ub / (1 - x))\n            q = fl(min(add_a, cov + unc))\n            if q >= 0.01:\n                oid = self._oid('a')\n                out.append(PlaceOrder(side=Side.SELL, price_ticks=ask_t, quantity=q, client_order_id=oid))\n                r = max(0.0, q - cov)\n                self.res[oid] = r\n                free -= r * (1 - x) + 1e-6\n                if ask_t < ca:\n                    self.ahead.add(oid)\n                    d_sell[oid] = (ask_t, q)\n\n        # probes: tiny orders that the arbitrageur removes when p crosses them\n        f = int(math.floor(ptick + 1e-9))\n        r = max(int(P['rmin']), int(math.ceil(2.0 * s * 100)))\n        self.pb = set()\n        self.pa = set()\n        self.probe_ids = set()\n        def probe(side, t):\n            old = live_probes.pop((side, t), None)\n            collision = (side is Side.BUY and (t == bid_t or (qam >= .01 and t == amt) or (q1 >= .01 and t == 1))) or (side is Side.SELL and t == ask_t)\n            if old is not None and not collision:\n                self.probe_ids.add(old.order_id)\n            else:\n                if old is not None:\n                    actions.append(CancelOrder(old.order_id))\n                oid = self._oid('p')\n                out.append(PlaceOrder(side=side, price_ticks=t, quantity=PROBE, client_order_id=oid))\n                self.probe_ids.add(oid)\n        if free > 1.0:\n            for t in range(f, f + r + 1):\n                if cb < t < ca and 1 <= t <= 99:\n                    probe(Side.BUY, t)\n                    self.pb.add(t)\n            for t in range(f + 1 - r, f + 2):\n                if cb < t < ca and 1 <= t <= 99:\n                    probe(Side.SELL, t)\n                    self.pa.add(t)\n        for old in live_probes.values():\n            actions.append(CancelOrder(old.order_id))\n        unknown_tick = max((o.price_ticks for o in bids if o.order_id not in self.ahead), default=0)\n        self._demand_snapshot(d_buy, d_sell, unknown_tick, q1, amt, qam)\n        return actions + out\n","code_sha256":"6aabf8e9f61ee7ea673b35c1c17a2ef49a2a08c750d6bfe147f2042e1acaa436","tried":["Scorer verification: exact base artifact code/hash, seeds 1000-1599, 600 simulations, mean_edge=108.63974289615767, retail_edge=136.2713209771645, arb_edge=-27.63157808101933, failures=0.","Stock-shortage diagnostic on the stock-only graft, seeds 4000-4299: 129 of 300 runs, 342 retail events; missed-edge proxy=1.6786387802305247 per run (diagnostic counterfactual, not realized edge). The hard bid certificate caused a large loss of retail in seed 4134; it was not special-cased in the strategy.","Implementation-only Gaussian, order-scan, posterior-refresh and action-snapshot changes matched all reported simulator fields exactly over 100 runs. The separate 300-run plan045 probe-retention experiment had paired edge effect=2.3051392999935417e-05 and failures=0.","Rejected inverse-normal memoization: 20-run CPU base=13.909627283000006, cached strategy=14.945339042999992; 2200 hits versus 79450 misses.","Five-node demand quadrature checked centered moments 0-4 against the original nine-point uniform grid to absolute tolerance 1e-12.","Hard bid-certificate and stock-floor controls/floor0.6_fast; seeds 4000-4299; n=300; edge=112.94963710727134; retail=139.44160232077567; arb=-26.491965213447333; failures=0","Hard bid-certificate and stock-floor controls/floor0.9_fast; seeds 4000-4299; n=300; edge=111.52682492675801; retail=139.54143824816435; arb=-28.014613321348666; failures=0","Hard bid-certificate and stock-floor controls/guard_fast; seeds 4000-4299; n=300; edge=113.12744837847434; retail=139.127042947982; arb=-25.999594569451336; failures=0","Hard bid-certificate and stock-floor controls/base_fast; seeds 4000-4299; n=300; edge=114.03723334826633; retail=140.60124106430266; arb=-26.56400771597667; failures=0","Hard bid-certificate and stock-floor controls/floor1.1_fast; seeds 4000-4299; n=300; edge=110.15819605241501; retail=139.55787997329233; arb=-29.399683920818667; failures=0","Fitted ordinary quote-size floors with crossing cover/fit_qfloor1.0; seeds 4000-4299; n=300; edge=112.819652228674; retail=140.60272187392667; arb=-27.783069645197; failures=0","Fitted ordinary quote-size floors with crossing cover/fitbridge; seeds 4000-4299; n=300; edge=113.97362446481165; retail=138.750420150063; arb=-24.776795685202334; failures=0","Fitted ordinary quote-size floors with crossing cover/fit_qfloor0.75; seeds 4000-4299; n=300; edge=112.96084914841866; retail=138.998245188755; arb=-26.037396040285667; failures=0","Fitted ordinary quote-size floors with crossing cover/fit_qfloor0.9; seeds 4000-4299; n=300; edge=112.98986919259866; retail=139.78067184856965; arb=-26.790802655923002; failures=0","Fitted ordinary quote-size floors with crossing cover/fit_stockfloor; seeds 4000-4299; n=300; edge=113.28376216907367; retail=138.78771023654633; arb=-25.503948067424666; failures=0","Small replenishment/reservoir rescue/bridge1.5; seeds 4000-4299; n=300; edge=113.01787822212134; retail=139.53823792068567; arb=-26.520359698502666; failures=0","Small replenishment/reservoir rescue/stock_rescue1.0; seeds 4000-4299; n=300; edge=113.080532157959; retail=139.44883256654; arb=-26.368300408522668; failures=0","Small replenishment/reservoir rescue/stock_rescue3.0; seeds 4000-4299; n=300; edge=113.07777285794533; retail=139.44791329148234; arb=-26.370140433478333; failures=0","Small replenishment/reservoir rescue/floor_noguard; seeds 4000-4299; n=300; edge=113.37702421159733; retail=141.07240110925034; arb=-27.695376897594997; failures=0","Small replenishment/reservoir rescue/qfit_rescue3.0; seeds 4000-4299; n=300; edge=113.16883277502299; retail=139.78489729263; arb=-26.616064517557; failures=0","Small replenishment/reservoir rescue/bridge2.0; seeds 4000-4299; n=300; edge=113.09074218591299; retail=139.935871085841; arb=-26.845128899867003; failures=0","Small replenishment/reservoir rescue/spreadfloor0.8; seeds 4000-4299; n=300; edge=112.53697902819833; retail=139.44160232077567; arb=-26.90462329252; failures=0","Small replenishment/reservoir rescue/qfit_rescue1.0; seeds 4000-4299; n=300; edge=113.20913831592799; retail=139.78495695929365; arb=-26.575818643316335; failures=0","Small replenishment/reservoir rescue/spreadfloor1.0; seeds 4000-4299; n=300; edge=112.08839875346234; retail=139.45774517962565; arb=-27.369346426103004; failures=0","Small replenishment/reservoir rescue/fitq1_stock06; seeds 4000-4299; n=300; edge=113.37618732205965; retail=140.598741928857; arb=-27.222554606741333; failures=0","Demand- or spread-conditional bid certificate/spreadguard1; seeds 4000-4299; n=300; edge=113.28529126783134; retail=141.06583717270166; arb=-27.78054590481467; failures=0","Demand- or spread-conditional bid certificate/flowguard0.65; seeds 4000-4299; n=300; edge=113.235380897461; retail=140.961394016186; arb=-27.726013118667996; failures=0","Demand- or spread-conditional bid certificate/noguard_f0.6_m1.5; seeds 4000-4299; n=300; edge=112.35008066871167; retail=141.08184651115232; arb=-28.73176584238233; failures=0","Demand- or spread-conditional bid certificate/spreadguard2; seeds 4000-4299; n=300; edge=113.291124636499; retail=141.06611152517598; arb=-27.774986888621; failures=0","Demand- or spread-conditional bid certificate/flowguard0.55; seeds 4000-4299; n=300; edge=113.37771259932035; retail=141.07240110925034; arb=-27.694688509872; failures=0","Demand- or spread-conditional bid certificate/noguard_f0.6_m1.0; seeds 4000-4299; n=300; edge=113.89014945484934; retail=140.94714772685433; arb=-27.056998271947332; failures=0","Demand- or spread-conditional bid certificate/noguard_f0.45_m1.0; seeds 4000-4299; n=300; edge=114.08265126741166; retail=140.79527660766598; arb=-26.712625340196333; failures=0","Demand- or spread-conditional bid certificate/flowguard0.75; seeds 4000-4299; n=300; edge=113.24517556699634; retail=140.96141540281835; arb=-27.716239835765; failures=0","Demand- or spread-conditional bid certificate/noguard_f0.75_m1.25; seeds 4000-4299; n=300; edge=112.79875484316334; retail=141.07237972815201; arb=-28.273624884930335; failures=0","No-certificate bulk floor and forecast multiplier/noguard_f0.6_m0.75; seeds 4000-4299; n=300; edge=114.04548865536465; retail=140.851782388565; arb=-26.806293733140667; failures=0","No-certificate bulk floor and forecast multiplier/noguard_f0.45_m1.25; seeds 4000-4299; n=300; edge=113.46667291880534; retail=141.053387243659; arb=-27.586714324795; failures=0","No-certificate bulk floor and forecast multiplier/noguard_f0.3_m1.25; seeds 4000-4299; n=300; edge=113.44742315359734; retail=141.05338807366067; arb=-27.605964920005; failures=0","No-certificate bulk floor and forecast multiplier/noguard_f0.0_m1.25; seeds 4000-4299; n=300; edge=113.44742315359734; retail=141.05338807366067; arb=-27.605964920005; failures=0","No-certificate bulk floor and forecast multiplier/noguard_f0.45_m1.5; seeds 4000-4299; n=300; edge=112.39466289776166; retail=141.081849831159; arb=-28.687186933339; failures=0","Posterior confidence buffer/near-price stock floor/buffer1.0_slim; seeds 4000-4499; n=500; edge=108.3119469403508; retail=136.6203820394068; arb=-28.308435099024397; failures=0","Posterior confidence buffer/near-price stock floor/fullfloor0.5_slim; seeds 4000-4499; n=500; edge=108.5968241052328; retail=136.35833202273759; arb=-27.761507917474997; failures=0","Posterior confidence buffer/near-price stock floor/noguard_f0.2_m1.0_slim; seeds 4000-4499; n=500; edge=108.45719018563459; retail=136.30368487481138; arb=-27.846494689147598; failures=0","Posterior confidence buffer/near-price stock floor/fullfloor0.4_slim; seeds 4000-4499; n=500; edge=108.596975059543; retail=136.35833202273759; arb=-27.761356963165; failures=0","Posterior confidence buffer/near-price stock floor/buffer0.5_slim; seeds 4000-4499; n=500; edge=108.5547474393904; retail=136.5482326708096; arb=-27.9934852313892; failures=0","Posterior confidence buffer/near-price stock floor/buffer1.5_slim; seeds 4000-4499; n=500; edge=107.9932987523036; retail=136.6447502329754; arb=-28.6514514806386; failures=0","Posterior confidence buffer/near-price stock floor/noguard_f0.3_m1.0_slim; seeds 4000-4499; n=500; edge=108.4658464914516; retail=136.30368487481138; arb=-27.8378383833304; failures=0","Recent bid-certificate memory/memory_lower2; seeds 4000-4499; n=500; edge=108.5626779755018; retail=135.68316453697418; arb=-27.120486561442803; failures=0","Recent bid-certificate memory/memory_lower4; seeds 4000-4499; n=500; edge=108.63060153493359; retail=135.6832987613418; arb=-27.0526972263786; failures=0","Recent bid-certificate memory/memory_lower8; seeds 4000-4499; n=500; edge=108.63060153493359; retail=135.6832987613418; arb=-27.0526972263786; failures=0","Recent bid-certificate memory/memory_lower1; seeds 4000-4499; n=500; edge=108.5279174446866; retail=135.6895842176808; arb=-27.1616667729642; failures=0","Recent bid-certificate memory/memory_basefloor1; seeds 4000-4499; n=500; edge=107.93891376996919; retail=135.975472808117; arb=-28.036559038117; failures=0","Recent bid-certificate memory/memory_basefloor8; seeds 4000-4499; n=500; edge=108.00171567718479; retail=135.9797724861674; arb=-27.9780568089512; failures=0","Recent bid-certificate memory/memory_basefloor2; seeds 4000-4499; n=500; edge=107.9435552636416; retail=135.9797723051236; arb=-28.0362170414508; failures=0","Recent bid-certificate memory/memory_basefloor4; seeds 4000-4499; n=500; edge=108.00889177738; retail=135.9797724861674; arb=-27.970880708756003; failures=0","Smaller demand forecast multiplier/lowmult_f0.45_m0.75; seeds 4000-4499; n=500; edge=107.8377915135694; retail=135.7419981891338; arb=-27.9042066755298; failures=0","Smaller demand forecast multiplier/lowmult_f0.45_m0.9; seeds 4000-4499; n=500; edge=108.474176438854; retail=136.1498510340548; arb=-27.6756745951692; failures=0","Smaller demand forecast multiplier/lowmult_f0.5_m0.75; seeds 4000-4499; n=500; edge=108.0324745061578; retail=135.909364669346; arb=-27.8768901631562; failures=0","Smaller demand forecast multiplier/lowmult_f0.5_m0.9; seeds 4000-4499; n=500; edge=108.4684918733902; retail=136.2197958894196; arb=-27.751304015999402; failures=0","No-certificate stock sizing plus fitted quote floors/hybrid0.9; seeds 4000-4499; n=500; edge=108.73185247646899; retail=136.343137384623; arb=-27.6112849081304; failures=0","No-certificate stock sizing plus fitted quote floors/hybrid1.0; seeds 4000-4499; n=500; edge=108.82342892224041; retail=137.3056355136564; arb=-28.4822065913884; failures=0","No-certificate stock sizing plus fitted quote floors/hybrid_adaptive; seeds 4000-4499; n=500; edge=108.60038289764441; retail=135.6120091994192; arb=-27.0116263017444; failures=0","No-certificate stock sizing plus fitted quote floors/hybrid0.75; seeds 4000-4499; n=500; edge=108.5395482044728; retail=135.6143430930874; arb=-27.0747948886052; failures=0","Six-bin probability grid and probe retention/fast6_plan045; seeds 4000-4299; n=300; edge=114.08913718941466; retail=140.86086199795167; arb=-26.77172480851233; failures=0","Six-bin probability grid and probe retention/fast6_plan_hybrid1; seeds 4000-4299; n=300; edge=114.66950427818199; retail=142.01073604217135; arb=-27.341231763955335; failures=0","Six-bin probability grid and probe retention/fast6_plan_fullfloor; seeds 4000-4299; n=300; edge=114.07657236207133; retail=140.86086366261898; arb=-26.784291300523666; failures=0","Six-bin probability grid and probe retention/fast6_plan_buffer; seeds 4000-4299; n=300; edge=114.01044343606166; retail=141.00779348315365; arb=-26.997350047067666; failures=0","Six-bin probability grid and probe retention/fast6_plan_hybrid09; seeds 4000-4299; n=300; edge=115.001124895992; retail=141.37094325505467; arb=-26.369818358999666; failures=0","Three-point demand quadrature and four-bin beliefs/tiny_plan_hybrid09; seeds 4000-4499; n=500; edge=108.20907865741559; retail=136.1386252245938; arb=-27.9295465671556; failures=0","Three-point demand quadrature and four-bin beliefs/tiny_plan_fullfloor; seeds 4000-4499; n=500; edge=108.68252646607979; retail=135.9355463089652; arb=-27.2530198428836; failures=0","Three-point demand quadrature and four-bin beliefs/tiny_plan_hybrid1; seeds 4000-4499; n=500; edge=108.303577116; retail=137.0793275214418; arb=-28.7757504054192; failures=0","Flow-regime quote sizing/tiny_regime; seeds 4000-4499; n=500; edge=109.0773386123502; retail=136.535008455302; arb=-27.4576698429176; failures=0","Five-point demand quadrature, preserved low-price filter/safe_stock; seeds 4000-4499; n=500; edge=108.610930330211; retail=136.370958172048; arb=-27.7600278418248; failures=0","extendedA/plan060; seeds 4500-5999; n=1500; edge=99.89279952960393; retail=125.6587666319344; arb=-25.7659671023454; failures=0; paired gain=0.3190709738042, SE=0.1174705271814098, retail delta=0.21702957518333357","extendedA/plan045; seeds 4500-5999; n=1500; edge=100.0608580207912; retail=125.61400653570574; arb=-25.553148514929532; failures=0; paired gain=0.4871294649914666, SE=0.14184790268805356, retail delta=0.17226947895466693","extendedB/plan_fullfloor; seeds 4500-5999; n=1500; edge=100.05984654659827; retail=125.6063252610566; arb=-25.54647871447327; failures=0; paired gain=0.4861179907985334, SE=0.14294790754351308, retail delta=0.16458820430553361","extendedB/plan_buffer; seeds 4500-5999; n=1500; edge=100.031834170942; retail=125.8330370248342; arb=-25.801202853907064; failures=0; paired gain=0.4581056151422661, SE=0.1686978649433088, retail delta=0.3912999680831338","extendedB/plan_hybrid1; seeds 4500-5999; n=1500; edge=99.74988398781399; retail=126.18002465992807; arb=-26.430140672131333; failures=0; paired gain=0.17615543201426628, SE=0.23241661693861557, retail delta=0.7382876031770008","extendedB/plan_hybrid09; seeds 4500-5999; n=1500; edge=99.44245884356575; retail=125.56050089671653; arb=-26.118042053169997; failures=0; paired gain=-0.13126971223399958, SE=0.4039310523624716, retail delta=0.11876383996546783","validationA/fast6_plan_hybrid09; seeds 6000-6999; n=1000; edge=97.0610922616708; retail=124.1242487509018; arb=-27.063156489248797; failures=0; paired gain=1.3436048046052, SE=0.366979639562926, retail delta=0.4385491767246008","validationA/fast6_plan_hybrid1; seeds 6000-6999; n=1000; edge=96.4519052643989; retail=124.78093777245479; arb=-28.3290325080817; failures=0; paired gain=0.7344178073332995, SE=0.32245193993943183, retail delta=1.0952381982776007","validationB/tiny_plan_hybrid09; seeds 6000-6999; n=1000; edge=96.899782959062; retail=124.0558849587461; arb=-27.156101999711797; failures=0; paired gain=1.1822955019963999, SE=0.7136637424735559, retail delta=0.3701853845689005","validationB/tiny_regime; seeds 6000-6999; n=1000; edge=96.802050012177; retail=124.31993678247011; arb=-27.5178867703233; failures=0; paired gain=1.0845625551113998, SE=0.7033181182750896, retail delta=0.6342372082929001","validationB/tiny_plan_fullfloor; seeds 6000-6999; n=1000; edge=96.1019915662018; retail=123.9543569506507; arb=-27.8523653844939; failures=0; paired gain=0.38450410913619965, SE=0.2236026813591924, retail delta=0.26865737647350035","validationB/tiny_plan_hybrid1; seeds 6000-6999; n=1000; edge=95.83642818059761; retail=124.7485069669449; arb=-28.9120787863952; failures=0; paired gain=0.11894072353200004, SE=0.4371514463321123, retail delta=1.062807392767701","validationSafe/safe_stock; seeds 6000-6999; n=1000; edge=96.18870210113009; retail=123.9352009224816; arb=-27.7464988213992; failures=0; paired gain=0.4712146440644995, SE=0.20944016060037068, retail delta=0.24950134830440004","Initial 200-run timing, during a changing background scoring load: wall={'base': 244.84542267676443, 'b_safe_stock': 246.45651053311303, 'b_tiny_regime': 247.61760969646275}; CPU={'base': 127.17256701900001, 'b_safe_stock': 123.2554417499999, 'b_tiny_regime': 124.33796579599989}; failures={'base': 0, 'b_safe_stock': 0, 'b_tiny_regime': 0}. A quiet 200-run repeat was predeclared before this timing finished and is reported in seconds.","Richest realized-BUY-notional tenth of discovery seeds 4000-4299, identical 30 seeds: base retail=202.23047111994998, uncorrected stock-graft retail=189.19730949780666, frozen corrected stock strategy retail=204.60329400144334.","Alternative richest-tenth definition, realized total BUY+SELL retail notional, all tuning, identical 400 seeds: base retail=181.80979415237525, selected retail=183.6526460369045.","Alternative richest-tenth definition, realized total BUY+SELL retail notional, unseen, identical 400 seeds: base retail=199.56801125171998, selected retail=202.47116930021727.","200 runs each in one process, alternating ten-run blocks, seeds 4100-4299: wall base=115.75908906990662, candidate=113.57789771584794; CPU base=115.75101165899999, candidate=113.56968744699998; failures={'base': 0, 'frozen': 0}.","Selected frozen strategy / all tuning; seeds 4000-7999; n=4000; edge=98.54458145702871; retail=125.40044140541545; arb=-26.85585994840265; failures=0; paired gain=0.44488577660942497, SE=0.10191717285465102, retail delta=0.22082396471705024","Selected frozen strategy / untouched unseen; seeds 50000-53999; n=4000; edge=98.09550702346218; retail=127.27673164043607; arb=-29.181224616963178; failures=0; paired gain=0.5484719976832001, SE=0.11612940188936807, retail delta=0.34782875931662516"],"next":"Assess the unseen-untested tiny_regime prototype on a fresh unseen set: quote 0.95 times the base size below fitted BUY flow 0.6, fitted compound-Poisson sizes above it; use bulk floor 0.45, near-stock floor 0.6, three-point prior quadrature and four belief bins/tick. Its 1000-run independent validation paired gain was 1.0845625551113998 (SE 0.7033181182750896), retail delta 0.6342372082929001. Preserve the selected stock-only file as the comparator and verify quiet runtime."}