{ "key": "seg-b", "approach": "Immutable legacy stock-entry-gate transfer audit against the new incumbent B, fixed block b. C and B are measured without tuning or code changes.", "tried": [ "Exactly one serial C/B pair per seed 420500..420999, same default sampled configuration, fresh module factory and strategy instance per simulation.", "First 10 timing pairs retained in the final statistics; no substitute or finalist seeds used." ], "next": "Combine sufficient statistics with the other disjoint transfer-audit blocks. This block alone does not promote a strategy; reserved finalist seeds were not 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 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 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, 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 and cb <= 1 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": "a709c7e85c7dc5200cba83d854cc7ecc9cee8a70915ca79798504da648e9ebed", "incumbent_code_sha256": "bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae", "tuning": null, "paired": { "n": 500, "sum_d": -1049.7042022243004, "sum_d2": 119471.83298893126, "mean_diff": -2.0994084044486008, "se": 0.6855744491366377, "C_mean_edges": { "total_edge": 110.3228067970984, "retail_edge": 135.11716526959162, "arb_edge": -24.794358472486802 }, "B_mean_edges": { "total_edge": 112.422215201547, "retail_edge": 137.432105047327, "arb_edge": -25.009889845777202 }, "retail_diff": -2.3149397777354, "arb_diff": 0.21553137329040006, "seeds": { "start": 420500, "end": 420999, "inclusive": true, "role": "fixed transfer audit holdout; no tuning performed" }, "complete": true, "by_spread": { "1": { "n": 124, "sum_d": -407.7480590737001, "sum_d2": 48805.679431720484, "mean_diff": -3.2882907989814525, "se": 1.7640984537050095, "C_mean_edges": { "total_edge": 50.247284728503224, "retail_edge": 70.92674109813227, "arb_edge": -20.679456369655647 }, "B_mean_edges": { "total_edge": 53.53557552748468, "retail_edge": 73.65968673511856, "arb_edge": -20.124111207648387 }, "retail_diff": -2.7329456369862895, "arb_diff": -0.5553451620072581 }, "2": { "n": 114, "sum_d": -89.06176733970022, "sum_d2": 16752.668769135995, "mean_diff": -0.7812435731552652, "se": 1.1380113585463052, "C_mean_edges": { "total_edge": 91.37539293255087, "retail_edge": 112.92517932865, "arb_edge": -21.549786396161405 }, "B_mean_edges": { "total_edge": 92.15663650570615, "retail_edge": 116.30605891610351, "arb_edge": -24.149422410435967 }, "retail_diff": -3.3808795874535083, "arb_diff": 2.5996360142745614 }, "3": { "n": 133, "sum_d": -267.49095366950013, "sum_d2": 39080.455780828765, "mean_diff": -2.0112101779661664, "se": 1.4816888577855583, "C_mean_edges": { "total_edge": 130.2876496249278, "retail_edge": 156.81659782471803, "arb_edge": -26.52894819962707 }, "B_mean_edges": { "total_edge": 132.298859802894, "retail_edge": 159.48954370185638, "arb_edge": -27.19068389880827 }, "retail_diff": -2.672945877138346, "arb_diff": 0.6617356991812031 }, "4": { "n": 129, "sum_d": -285.4034221413998, "sum_d2": 14833.029007246016, "mean_diff": -2.212429629003099, "se": 0.9274034489971535, "C_mean_edges": { "total_edge": 164.2301387425473, "retail_edge": 194.0588277866186, "arb_edge": -29.82868904413411 }, "B_mean_edges": { "total_edge": 166.4425683715504, "retail_edge": 194.66086309865193, "arb_edge": -28.21829472720155 }, "retail_diff": -0.6020353120333339, "arb_diff": -1.6103943169325579 } } }, "failures": [], "missing_seeds": [], "seconds_per_simulation": 0.2585314241250744, "timing": { "first_10_pairs": [ { "seed": 420500, "C_seconds": 0.1966796809574589, "B_seconds": 0.22523311211261898 }, { "seed": 420501, "C_seconds": 0.277790496009402, "B_seconds": 0.28101837099529803 }, { "seed": 420502, "C_seconds": 0.19306436693295836, "B_seconds": 0.20152275706641376 }, { "seed": 420503, "C_seconds": 0.3044478470692411, "B_seconds": 0.3081432309700176 }, { "seed": 420504, "C_seconds": 0.28415406902786344, "B_seconds": 0.29369511001277715 }, { "seed": 420505, "C_seconds": 0.24885152000933886, "B_seconds": 0.26315725804306567 }, { "seed": 420506, "C_seconds": 0.19769816799089313, "B_seconds": 0.23191312898416072 }, { "seed": 420507, "C_seconds": 0.20454238099046052, "B_seconds": 0.22546900599263608 }, { "seed": 420508, "C_seconds": 0.26497499202378094, "B_seconds": 0.27373051701579243 }, { "seed": 420509, "C_seconds": 0.19654499099124223, "B_seconds": 0.20659026794601232 } ], "first_10_seconds_per_simulation": 0.24396106355707162, "total_simulation_seconds": 258.5314241250744, "stop_budget_seconds": 2700 }, "checks": { "hashes_verified_before_and_after": true, "returned_seeds_verified": true, "finite_total_retail_arb_edges_verified": true, "observed_spread_matches_sampled_regime": true, "failed_flag_counts": { "C": 0, "B": 0 }, "simulations": 1000, "single_os_process": true, "n_steps": 2000 }, "scoreboard": { "current_best_code_sha256": "bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae", "candidate_code_sha256": "a709c7e85c7dc5200cba83d854cc7ecc9cee8a70915ca79798504da648e9ebed", "paired_mean_diff": -2.0994084044486008, "paired_se": 0.6855744491366377, "promotion": false }, "simulator_commit": "b244530ee2a6247b5f930830eeca9dd950da0e7e", "reproduction_method": "Fetch the pinned simulator archive and JSON.code for C/B from the URLs in sources.json; verify exact UTF-8 hashes. On Python 3 use unmodified ChallengeConfig(), ParameterVariance(), and runner.sample_config(base, variance, seed=seed). For each integer seed 420500 through 420999, sample once, load each strategy afresh with loader.load_strategy_factory, then SimulationEngine(config, factory, seed=seed).run(), C then B, synchronously in one process without sandbox or multiprocessing. An observation-only on_step wrapper records the first StepState rival bid/ask and infers s=max(1,((ask or 100)-(bid or 0)+1)//2), checks against sampled and returned regimes, and forwards the identical state/actions. Check seed, finite edges, failed/error fields. d=C.total_edge-B.total_edge; math.fsum for sum_d and sum_d2, statistics.stdev(d)/sqrt(n) for SE. Means and signed retail/arb differences use the same completed pairs, including timing pairs; all four strata use observed s. No failed flag is silently excluded. Stop after a pair if accumulated simulation time reaches 2700 seconds; mark incomplete with missing seeds. No tuning or reserved 900000..903999 seeds." }