{ "key": "sjb-d", "approach": "Legacy side-jump-budget transfer audit of immutable C against incumbent B, assigned block d. C and B sources are unchanged. This measures the supplied complete strategies, not an isolated modification to B.", "tried": [ "Verified exact UTF-8 SHA256 of C and B before and after execution.", "Verified downloaded simulator files against Git blob hashes in the pinned commit tree.", "Ran C then B serially in one Python OS process for every attempted seed; one shared default sample_config per pair, 2000 steps, fresh strategy instance for every simulation.", "Observed first callback rival bid/ask, labeled s=(ask-bid+1)//2, and checked against sampled config and both returned regimes.", "Checked returned seeds, finite total/retail/arb edges, failed flags and exceptions. Retained first ten timing pairs without rerunning." ], "next": "Combine this block with the other disjoint transfer-audit blocks before ranking C. This block alone does not authorize promotion; no seeds 900000..903999 were run.", "code": "\"\"\"Stale-quote defence with tick probes, a sub-tick belief and cheap YES stock.\n\nEvery step the arbitrageur removes any order on the wrong side of p, so tiny\ntwo-sided probes around the estimate locate p to a tick; a grid filter narrows\nthat further. Quotes sit one tick inside the competitor and are sized by the\nmarginal rule: one more share is worth quoting while the chance a retail order\nreaches it, times its edge, exceeds the expected arbitrage loss. Near p=0 a\nretail buy pays almost its whole notional as edge, so YES is held to cover low\nasks. That YES is bought when it is cheap: a large bid rests at tick 1 while p\nis above it, so the arbitrageur fills it on the crossing for about 0.1 tick a\nshare instead of nearly a full tick once p is near zero, and smaller bids sit on\nwhichever tick p is within 0.15 of. Below tick 1 the estimate of p decays with\nthe time spent there, since no probe can see it.\n\"\"\"\nimport math\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)\nP.update({'W': 50, 'ammo_pmax': 0.02, 'fy': 0.7, 'fu': 0.25, 'Hu': 500})\nC = {'W': 50, 'ammo_pmax': 0.02, 'fy': 0.7, 'reserve': 200, 'scar': 0.0025, 'fu': 0.25, 'Hu': 500}\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\nclass Strategy(BaseStrategy):\n def __init__(self):\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 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 for o in state.own_orders:\n if o.side is Side.BUY:\n lo = max(lo, o.price_ticks)\n else:\n hi = min(hi, o.price_ticks)\n if P['pinf'] > 0:\n sb = {o.price_ticks for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity <= PROBE + 1e-9}\n sa = {o.price_ticks for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity <= PROBE + 1e-9}\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 nb = max(2, min(48, int(round(wid * P['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 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(c * w for c, w in zip(cs, ws))\n var_t = max(sum(c * c * w for c, w in zip(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 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 = sum(w * max(0.0, x - p) for p, w in jp)\n jb = sum(w * max(0.0, p - x) for p, w in jp)\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 jump_charge(t, is_bid):\n x = t / 100.0\n # Adverse jump payoff, with the existing transition nodes unchanged.\n adverse = sum(w * max(0.0, x - p if is_bid else p - x) for p, w in jp)\n # Endpoint asymmetry: buying high and selling low expose more jump loss.\n exposure = phat if is_bid else 1.0 - phat\n spread_weight = 2.0 / (1.0 + self.s)\n horizon_weight = min(1.0, 500.0 / H) ** 0.5\n return 1.0 * lj * adverse * exposure * spread_weight * horizon_weight\n\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 + jump_charge(t, True), 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 if phat < C.get('scar_p', .1):\n a = max(0.0, a - C.get('scar', 0.0) * (1 - min(1.0, state.yes_inventory / C.get('stock', 3000.0))))\n return min(C.get('askcap', P['qmax']), qsize(b + jump_charge(t, False), a, t / 100.0))\n\n bids = [o for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity > PROBE + 1e-9]\n asks = [o for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity > PROBE + 1e-9]\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 = min(C.get('bidcap', 3000.0), 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(P['ydf'] * 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 q1 = 0.0\n if P['ydf1'] > 0 and P['m1'] < ptick < P['g1'] + 1.0 and amt != 1:\n q1 = max(0.0, P['ydf1'] * min(H, P['Wf']) / (self.s / 100.0) - 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 for o in state.own_orders:\n if o.remaining_quantity <= PROBE + 1e-9:\n cancel(o)\n kept_ids = set()\n def manage(orders, t, q, comp):\n kept = 0.0\n for o in orders:\n if not (C.get('recover', 0) and o.side is Side.SELL and t < comp and self.res.get(o.order_id, 0.0) > 1 and state.yes_inventory > C.get('recover_yes', 1) and state.free_cash < C.get('recover_cash', 200)) and 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 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 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 ub = min(ub, max(0.0, free - C.get('reserve', 0.0) * min(1.0, H / C.get('release', 50.0))))\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\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 if free > 1.0:\n for t in range(f, f + r + 1):\n if cb < t < ca and 1 <= t <= 99:\n out.append(PlaceOrder(side=Side.BUY, price_ticks=t, quantity=PROBE))\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 out.append(PlaceOrder(side=Side.SELL, price_ticks=t, quantity=PROBE))\n self.pa.add(t)\n return actions + out\n", "code_sha256": "3b7236c98d52efed9617e4cf6d8183012c98b8b61b18eb59b5afae937c99c724", "incumbent_code_sha256": "bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae", "tuning": null, "paired": { "seeds": { "start": 421500, "end": 421999, "inclusive": true }, "role": "Assigned independent audit block d; no tuning, no finalist seeds.", "n": 500, "sum_d": -947.8026994086001, "sum_d2": 169998.17288959754, "mean_diff": -1.8956053988172001, "se": 0.821069035236129, "C_mean_edges": { "total_edge": 104.6096928249566, "retail_edge": 128.5231500747018, "arb_edge": -23.9134572497742 }, "B_mean_edges": { "total_edge": 106.5052982237738, "retail_edge": 132.7298084633162, "arb_edge": -26.224510239563397 }, "retail_diff": -4.206658388614399, "arb_diff": 2.3110529897892, "complete": true, "by_spread": { "1": { "n": 120, "sum_d": -339.1403828149, "sum_d2": 59068.983345973735, "mean_diff": -2.8261698567908335, "se": 2.0172664169210917, "C_mean_edges": { "total_edge": 22.713891764706666, "retail_edge": 41.621731558181665, "arb_edge": -18.9078397935375 }, "B_mean_edges": { "total_edge": 25.5400616214975, "retail_edge": 45.59330544563083, "arb_edge": -20.05324382419583 }, "retail_diff": -3.971573887449167, "arb_diff": 1.145404030658334 }, "2": { "n": 122, "sum_d": -53.80845771009995, "sum_d2": 11726.30142034628, "mean_diff": -0.4410529320499996, "se": 0.8903648479954758, "C_mean_edges": { "total_edge": 87.0086275244295, "retail_edge": 107.24866742768116, "arb_edge": -20.240039903340165 }, "B_mean_edges": { "total_edge": 87.44968045647951, "retail_edge": 112.16747939372542, "arb_edge": -24.71779893732377 }, "retail_diff": -4.918811966044262, "arb_diff": 4.477759033983607 }, "3": { "n": 130, "sum_d": -115.74597618280009, "sum_d2": 59745.85187187566, "mean_diff": -0.890353662944616, "se": 1.8858731169193947, "C_mean_edges": { "total_edge": 129.8640879452523, "retail_edge": 155.4944149453123, "arb_edge": -25.63032699992077 }, "B_mean_edges": { "total_edge": 130.75444160819694, "retail_edge": 160.51485926194923, "arb_edge": -29.760417653634615 }, "retail_diff": -5.0204443166369215, "arb_diff": 4.130090653713846 }, "4": { "n": 128, "sum_d": -439.1078827008001, "sum_d2": 39457.03625140187, "mean_diff": -3.4305303336000006, "se": 1.5279278267169956, "C_mean_edges": { "total_edge": 172.51402663945547, "retail_edge": 202.87778032266718, "arb_edge": -30.363753683323438 }, "B_mean_edges": { "total_edge": 175.94455697305546, "retail_edge": 205.7995577194883, "arb_edge": -29.855000746501563 }, "retail_diff": -2.921777396821093, "arb_diff": -0.5087529368218745 } } }, "failures": [], "missing_seeds": [], "seconds_per_simulation": 0.9883316181248519, "timing": { "first10": { "pairs": [ { "seed": 421500, "C_seconds": 1.1506296310108155, "B_seconds": 0.8909671900328249, "pair_seconds": 2.0416101759765297 }, { "seed": 421501, "C_seconds": 0.823368398938328, "B_seconds": 1.0536692179739475, "pair_seconds": 1.8770509830210358 }, { "seed": 421502, "C_seconds": 0.9184637570288032, "B_seconds": 0.8576317969709635, "pair_seconds": 1.776101295137778 }, { "seed": 421503, "C_seconds": 1.1565762558020651, "B_seconds": 1.2604707300197333, "pair_seconds": 2.4170526158995926 }, { "seed": 421504, "C_seconds": 1.0031402138993144, "B_seconds": 0.9116473221220076, "pair_seconds": 1.9147946489974856 }, { "seed": 421505, "C_seconds": 0.9662456770893186, "B_seconds": 0.8629614238161594, "pair_seconds": 1.8292127009481192 }, { "seed": 421506, "C_seconds": 0.8031224389560521, "B_seconds": 0.6713140539359301, "pair_seconds": 1.4744413709267974 }, { "seed": 421507, "C_seconds": 0.7446187408640981, "B_seconds": 1.004100892925635, "pair_seconds": 1.7487255949527025 }, { "seed": 421508, "C_seconds": 0.9533532380592078, "B_seconds": 0.9702754740137607, "pair_seconds": 1.9236352741718292 }, { "seed": 421509, "C_seconds": 0.9016893059015274, "B_seconds": 0.8789710060227662, "pair_seconds": 1.780665883095935 } ], "seconds_per_simulation": 0.9391645271563902 }, "total_elapsed_seconds": 988.9060011510737, "simulation_seconds": { "C": 489.05822210013866, "B": 499.2733960247133 }, "deadline_seconds": 2700 }, "simulator_commit": "b244530ee2a6247b5f930830eeca9dd950da0e7e", "reproduction_method": "Use the unmodified orderbook_pm_challenge files at the pinned commit with Python 3 and no external dependencies. Extract JSON.code from the two artifact URLs in sources.json; verify their UTF-8 hashes. Import ChallengeConfig, ParameterVariance, sample_config, SimulationEngine and load_strategy_factory. For seed in range(421500,422000), cfg=sample_config(ChallengeConfig(),ParameterVariance(),seed=seed); call SimulationEngine(cfg,factory_C,seed=seed).run(), then the same with factory_B; each factory creates a new Strategy. A forwarding on_step observer records the first StepState rival bid/ask without changing actions or passing config/seed to strategies. No multiprocessing, workers or sandbox. Record all results and timings once. d=C.total_edge-B.total_edge; sum_d=math.fsum(d), sum_d2=math.fsum(d*d), mean=sum_d/n, SE=statistics.stdev(d)/sqrt(n). Retail and arb differences are signed C-minus-B means. Group by observed s and verify sampled regime. Stop starting pairs at 2700 elapsed seconds; report missing seeds explicitly. No tuning or other seed blocks.", "validation": { "strategy_hashes_before_after": true, "simulator_git_blobs_verified": 18, "first_observed_spreads_match_sampled_regimes": true, "failed_flags": { "C": [], "B": [] }, "attempted_pairs": 500, "completed_pairs_in_statistics": 500, "failure_handling": "All returned finite pairs are included even if failed=True; exceptions and invalid pairs are explicitly reported, with incomplete status. No failed pairs are silently dropped.", "untouched_finalist_seed_range": [ 900000, 903999 ] } }