{"approach":"Diagnostic oracle only: replace B phat with the exact probability after the coming process.step(), immediately before z = inv(phat). All other strategy logic, including its uncertainty model, final-step AB override, queue management and collateral constraints, is unchanged. The simulator engine on disk is unmodified; a runtime copy adds an observer before on_step. The observer peeks one process step and restores PRNG, score and step exactly. No candidate was built or promoted.","tried":[{"name":"instrumentation_invariance","seeds":{"start":237000,"end_inclusive":237019},"n":20,"identical_full_simulation_results":20,"comparison":"Exact equality of dataclasses.asdict(SimulationResult), not a floating-point tolerance."},{"name":"timing_and_seed_count","pilot_n":20,"baseline_seconds_per_simulation":0.9080607365904143,"oracle_seconds_per_simulation":1.0846987740020269,"estimated_1000_pair_serial_seconds":1992.759510592441,"decision":"Use first 500 seeds because the 20-seed timing projects more than 1800 seconds for 1000 incumbent/oracle pairs. Four independent serial OS processes handle disjoint remaining seed ranges."},{"name":"oracle_phat_substitution","n":500,"paired":{"n":500,"mean_diff":16.521563462571,"se":2.8664084038396918,"retail_diff":-1.0754618001346,"arb_diff":17.5970252626674}}],"next":[{"change":"Improve sub-tick probability and jump estimation before quote sizing; use fill/probe evidence to reduce stale-price errors.","edge_at_stake":16.521563462571,"se":2.8664084038396918,"interpretation":"Oracle-minus-B diagnostic headroom on this seed block, not a forecast of achievable strategy gain."},{"change":"Use the improved probability forecast to cancel or reduce orders vulnerable to adverse selection, then test on separate seeds.","edge_at_stake":17.5970252626674,"interpretation":"Measured arbitrage component of the oracle gain; retail component is -1.0754618001346. Components interact and are not independently attainable."}],"code":{"harness":"\"\"\"Run from a directory containing the pinned simulator as sim/ and b.py.\nOnly Python's standard library is required. All simulations use serial run_batch.\n\"\"\"\nimport argparse, dataclasses, hashlib, json, pathlib, sys, time, types\nROOT = pathlib.Path(__file__).resolve().parent\nsys.path.insert(0, str(ROOT / 'sim'))\nfrom orderbook_pm_challenge.config import ChallengeConfig, ParameterVariance\nfrom orderbook_pm_challenge.loader import load_strategy_factory\nfrom orderbook_pm_challenge.runner import run_batch\nimport orderbook_pm_challenge.engine as engine\n\ncontext = types.ModuleType('oracle_context')\ncontext.probability = None\nsys.modules['oracle_context'] = context\nORIGINAL_RUN = engine.SimulationEngine.run\n\ndef observe(process):\n    # Restore the complete PRNG state, including Random.gauss's cached sample.\n    rng_state = process._rng.getstate()\n    score, step = process._score, process._step\n    try:\n        process.step()\n        context.probability = process.current_true_probability()\n    finally:\n        process._rng.setstate(rng_state)\n        process._score, process._step = score, step\n\nsource = (ROOT / 'sim/orderbook_pm_challenge/engine.py').read_text()\nneedle = '                actions = strategy.on_step(state)'\nassert source.count(needle) == 1\ninstrumented = source.replace(needle, '                observe(process)\\n' + needle)\nnamespace = dict(engine.__dict__, observe=observe, __name__='instrumented_engine')\nexec(compile(instrumented, '<instrumented-engine>', 'exec'), namespace)\nINSTRUMENTED_RUN = namespace['SimulationEngine'].run\n\ndef run(factory, seed, instrument):\n    engine.SimulationEngine.run = INSTRUMENTED_RUN if instrument else ORIGINAL_RUN\n    start = time.perf_counter()\n    result = run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(),\n                       n_simulations=1, seed_start=seed, workers=1).simulation_results[0]\n    return dataclasses.asdict(result), time.perf_counter() - start\n\ndef main():\n    p = argparse.ArgumentParser()\n    p.add_argument('--start', type=int, default=237000)\n    p.add_argument('--n', type=int, default=20)\n    p.add_argument('--verify', action='store_true')\n    p.add_argument('--output', required=True)\n    args = p.parse_args()\n    assert 237000 <= args.start and args.start + args.n <= 238000\n    b = (ROOT / 'b.py').read_text()\n    assert hashlib.sha256(b.encode()).hexdigest() == 'baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273'\n    incumbent = load_strategy_factory(str(ROOT / 'b.py'))\n    oracle = load_strategy_factory(str(ROOT / 'oracle.py'))\n    with open(args.output, 'w') as out:\n        for seed in range(args.start, args.start + args.n):\n            baseline, bt = run(incumbent, seed, False)\n            same, st = (run(incumbent, seed, True) if args.verify else (None, 0))\n            if args.verify:\n                assert baseline == same, (seed, baseline, same)\n            diagnostic, ot = run(oracle, seed, True)\n            row = dict(seed=seed, incumbent=baseline, oracle=diagnostic,\n                       verified_identical=(baseline == same) if args.verify else None,\n                       baseline_seconds=bt, instrument_seconds=st, oracle_seconds=ot)\n            out.write(json.dumps(row) + '\\n'); out.flush()\n            print(seed, baseline['total_edge'], diagnostic['total_edge'], round(bt+st+ot, 3), flush=True)\nif __name__ == '__main__':\n    main()\n","oracle_strategy":"\"\"\"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        from oracle_context import probability\n        phat = probability  # exact coming-step probability; diagnostic only\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 < 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":"050d8833255e7d2e295208762ad1629bfa0e57a513bc4a66a51838788aa91439","code_hash_convention":"SHA-256 of UTF-8 json.dumps(code, sort_keys=True, separators=(comma,colon), ensure_ascii=False); comma and colon denote the literal separators \",\" and \":\".","source_code_sha256":{"harness":"588ed862b1d4785e8ffd88867e7abcab6710c85e72711f964eb885785d9111bd","oracle_strategy":"c911fe39ea27cb91f89cf3ef0757f9e3ef726c079433e3c630ba620e72517434"},"incumbent_code_sha256":"baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273","tuning":null,"seeds":{"start":237000,"end_inclusive":237499,"excluded":"237500..237999 not run; 900000..903999 untouched"},"n":500,"complete":true,"incumbent":{"mean":105.59281984014,"retail_mean":130.0047262930132,"arb_mean":-24.4119064528362,"se":8.289955364939026},"findings":[{"name":"incumbent","count":500,"mean":105.59281984014,"retail_mean":130.0047262930132,"arb_mean":-24.4119064528362,"se":8.289955364939026},{"name":"oracle","count":500,"mean":122.114383302711,"retail_mean":128.9292644928786,"arb_mean":-6.8148811901688005,"se":7.6711401273521425},{"name":"oracle_minus_B/competitor_spread_ticks=1","count":140,"mean":5.617950407692144,"retail_mean":-13.716580370888572,"arb_mean":19.334530778532145,"se":7.436974887471781},{"name":"oracle_minus_B/competitor_spread_ticks=2","count":111,"mean":11.591363285066667,"retail_mean":5.791444147277478,"arb_mean":5.799919137822522,"se":5.422038255451445},{"name":"oracle_minus_B/competitor_spread_ticks=3","count":133,"mean":30.000102669306013,"retail_mean":5.524828781802258,"arb_mean":24.475273887369926,"se":4.364778255363708},{"name":"oracle_minus_B/competitor_spread_ticks=4","count":116,"mean":18.944945642659484,"retail_mean":0.04256744422068772,"arb_mean":18.90237819845431,"se":4.206573561263741}],"paired":{"n":500,"mean_diff":16.521563462571,"se":2.8664084038396918,"retail_diff":-1.0754618001346,"arb_diff":17.5970252626674},"failures":[],"seconds_per_simulation":1.123635781740013,"simulation_timing":{"incumbent":1.0438290745783598,"oracle":1.2034424889016664,"definition":"Mean perf_counter elapsed seconds inside each run_batch call; includes contention between independent OS processes. Pilot timing is reported separately."},"statistics":"Sample standard deviation divided by sqrt(n); paired SE uses per-seed oracle-minus-B differences. No tuning or finalist evaluation. Bucket means are conditional per-simulation means, not contributions divided by all 500 seeds. Complete means all 500 seeds in the timing-selected block finished.","reproducibility":{"simulator_commit":"b244530ee2a6247b5f930830eeca9dd950da0e7e","steps":2000,"configuration":"ChallengeConfig() and ParameterVariance(), n_simulations=1, seed_start=s, workers=1, no sandbox","setup":"Place pinned repository at sim/, exact incumbent code at b.py, and code.harness / code.oracle_strategy at harness.py / oracle.py in one directory. No third-party Python dependencies. The harness imports oracle_context dynamically in-process.","commands":["python3 harness.py --verify --start 237000 --n 20 --output pilot.jsonl","python3 harness.py --start 237020 --n 120 --output part0.jsonl","python3 harness.py --start 237140 --n 120 --output part1.jsonl","python3 harness.py --start 237260 --n 120 --output part2.jsonl","python3 harness.py --start 237380 --n 120 --output part3.jsonl"]},"paired_by_seed_columns":["seed","total_diff","retail_diff","arb_diff"],"paired_by_seed":[[237000,7.8695197729,2.6628049874,5.2067147854],[237001,12.5686149526,8.2973959167,4.2712190355],[237002,6.9025303151,-1.3177388918,8.2202692057],[237003,38.5246119729,17.7758029491,20.748809024],[237004,14.0020365367,8.2445905494,5.7574459869],[237005,25.8451664938,15.5730474577,10.2721190358],[237006,0.5965397983,-7.0646630112,7.6612028095],[237007,13.6598066919,13.5453195448,0.1144871472],[237008,16.0066377405,9.7593622805,6.2472754604],[237009,7.9855682303,6.55131061,1.4342576201],[237010,38.1340431509,28.090073174,10.0439699767],[237011,-21.2029489671,3.4442232978,-24.6471722653],[237012,16.5881222946,7.4048952745,9.1832270195],[237013,7.2612385166,3.056163378,4.2050751391],[237014,41.8438896976,8.4217792764,33.4221104211],[237015,13.8959471961,8.3264496304,5.5694975669],[237016,22.3343666168,22.3177550779,0.016611539],[237017,9.2121509576,8.5343357223,0.6778152346],[237018,22.8744270419,22.3761529084,0.4982741337],[237019,-197.4649833455,-57.590890711,-139.8740926343],[237020,30.2820317757,-5.8230359245,36.1050676995],[237021,18.3296131584,-2.7610335451,21.0906467033],[237022,15.6507312573,10.0479733425,5.6027579156],[237023,5.1974727218,5.1046908867,0.0927818353],[237024,0.7156495547,-5.8464084056,6.5620579607],[237025,6.608337946,3.2045160163,3.40382193],[237026,7.0581727007,2.401967936,4.6562047642],[237027,14.9795462025,9.3268820878,5.6526641131],[237028,6.1059715142,6.0314276519,0.0745438617],[237029,32.5792933696,28.70035789,3.878935479],[237030,11.2730645441,3.2875204614,7.9855440828],[237031,19.920480222,-9.8273870883,29.7478673096],[237032,1.9575614434,-16.1841810795,18.1417425229],[237033,11.4452039315,9.4717421584,1.9734617731],[237034,16.5935000065,15.4190832741,1.1744167323],[237035,16.7350534427,3.0278934116,13.7071600319],[237036,18.3878879209,6.1637338874,12.2241540318],[237037,3.4301342878,2.8046943539,0.6254399334],[237038,117.758639947,3.3858218246,114.3728181229],[237039,-35.9772347642,-20.4745370916,-15.5026976729],[237040,0.1637523972,-0.6865265469,0.8502789439],[237041,26.0563904043,20.890554782,5.1658356222],[237042,-15.3198653178,-41.2735603727,25.9536950536],[237043,49.12453418,12.6428195848,36.4817145958],[237044,52.044908983,19.7371034126,32.3078055704],[237045,16.0899775485,10.1197903503,5.9701871981],[237046,44.3593244393,31.6944750812,12.6648493576],[237047,12.7109433434,12.6416964296,0.0692469143],[237048,-27.5827823575,-81.2743643058,53.6915819485],[237049,6.2656840141,2.5795019197,3.6861820941],[237050,14.1207646303,2.3831732622,11.7375913688],[237051,8.6416781212,2.6297852064,6.0118929167],[237052,19.3180059688,5.7200919947,13.5979139736],[237053,31.1836714407,20.8354829718,10.3481884695],[237054,44.0128137621,23.5550895162,20.4577242447],[237055,7.490260304,-1.7117250826,9.201985386],[237056,42.7698565244,13.0880600178,29.6817965067],[237057,7.4778107296,2.671833693,4.8059770357],[237058,16.9048297635,11.6135172154,5.291312547],[237059,-157.2534872503,-180.1747921995,22.9213049506],[237060,8.8107459044,4.7056268648,4.1051190392],[237061,8.8246879473,6.4442499753,2.3804379715],[237062,104.4432015287,29.0905308118,75.3526707167],[237063,12.0979530592,2.7222198991,9.3757331599],[237064,4.8293143348,2.4033924445,2.4259218901],[237065,27.6939450063,2.9163191279,24.7776258794],[237066,5.5954556409,1.9952701444,3.6001854963],[237067,20.6105355174,8.6307378316,11.9797976849],[237068,19.0895973501,-4.0395777173,23.129175069],[237069,19.1045925194,6.6522912165,12.4523013037],[237070,15.2382951477,8.327864312,6.9104308358],[237071,9.2836603015,5.758812582,3.5248477205],[237072,19.0623275079,3.8955107042,15.1668168051],[237073,37.1909785681,11.9902830764,25.2006954921],[237074,20.1820486246,12.2286507896,7.9533978358],[237075,20.3158900204,3.5107235665,16.8051664542],[237076,14.3619922883,1.0158952923,13.3460969963],[237077,101.2842288553,-0.4986435389,101.7828723934],[237078,9.1857801528,8.9459783846,0.2398017681],[237079,36.1164811048,6.0595224607,30.0569586442],[237080,8.2043021588,7.9977621123,0.2065400467],[237081,8.1120273173,7.3169525428,0.7950747743],[237082,13.3726448555,7.049159079,6.3234857757],[237083,54.603731415,29.2556204533,25.348110961],[237084,11.0791151245,10.957810211,0.1213049136],[237085,21.7362487655,5.3143562077,16.4218925568],[237086,2.1743489207,0.4576230483,1.716725873],[237087,59.7058573278,-100.9143649173,160.6202222452],[237088,18.0284865694,6.9365399305,11.0919466388],[237089,15.2419345915,8.1846491386,7.057285453],[237090,18.969794664,8.9500236676,10.0197709976],[237091,0.787381469,0.2769785965,0.510402873],[237092,45.6879705309,26.331853356,19.356117175],[237093,5.72184159,1.4926279142,4.2292136763],[237094,-11.5506034066,-14.5169811495,2.9663777426],[237095,5.0487347388,2.3311721177,2.7175626207],[237096,15.2211077145,5.1256462042,10.0954615093],[237097,33.5170217868,31.4572283979,2.0597933898],[237098,14.9135286087,5.5896521611,9.3238764472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