{"approach": "Spread 4 only: split a new bid into two cent-quantized halves when it is exactly four ticks below a real rival ask (ca<=99), strictly above the rival bid, and worth at least 4*P['m'] ($20). Total placed quantity and the original size rule are retained. When the target shrinks, manage() can cancel a tail tranche while preserving an earlier tranche ahead of the rival's replenishment; both tranches are placed before any rival bid at that tick. Merged spread-4 order scans and cached lookups/prior-grid pairs reduce overhead; exact pilot result comparisons checked these changes. Discovery: 128 target-spread runs from 8000 onward, paired gain 0.16017483584296874; independent validation: 488 target-spread runs in 14000..15999, paired gain 0.16580731308913887. File fixed at 2026-11-06T09:25:18.180991+00:00 before unseen scoring, with no later edits. Simulator b244530ee2a6247b5f930830eeca9dd950da0e7e, default 2000 steps, no sandbox. Required CLI from clone: python3 -m orderbook_pm_challenge run ../base.py --simulations 4000 --seed-start 4000 --workers 4 --json; mean 98.4694596619638, zero failures. Each selected seed uses sample_config(ChallengeConfig(), ParameterVariance(), seed=seed) and SimulationEngine(..., seed=seed). Unseen metrics cover all 2009 spread-4 seeds in 60000..67999; differences are paired per seed, arb is signed edge. Non-target checks compare all SimulationResult fields for 300 seeds of each spread 1/2/3 starting at 60000. Timing is 200 spread-4 runs starting at seed 11200, paired in one process with alternating execution order and one warmup per strategy; reported seconds are wall time around engine construction and run, excluding configuration sampling and loading. CPU times are separately recorded in tried. Local measurements, no leaderboard submission.", "blocked": null, "spread": 4, "base_mean_edge": 98.4694596619638, "base_unseen": {"mean_edge": 163.47813945343543, "retail_edge": 191.20763318061364, "arb_edge": -27.729493727194825}, "holdout": {"mean_edge": 163.6506990531904, "retail_edge": 191.4293026862936, "arb_edge": -27.778603633119662, "failures": 0}, "diff": {"mean": 0.17255959975495247, "standard_error": 0.05609359681034755, "retail": 0.2216695056799397, "arb": -0.0491099059248383}, "others_identical": {"runs": 900, "identical": true}, "seconds": {"base": 294.15425222413614, "yours": 291.83430307451636}, "code": "\"\"\"Crossing-cover strategy with large spread-four destination bid tranches.\"\"\"\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)\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        self.flow_notional = 0.0\n        self.flow_exposure = 0\n        self.last_ask_tick = None\n        self.last_quote_exposed = False\n        self.last_flow_ca = None\n        self.last_flow_cap = 0.0\n        self.last_flow_correction = 1.0\n        self.params = P.copy()\n\n    def _oid(self, tag):\n        self.n += 1\n        return tag + str(self.n)\n\n    def on_step(self, state):\n        BUY, SELL = Side.BUY, Side.SELL\n        probe_limit = PROBE + 1e-9\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            if self.s == 1:\n                self.params.update({'dmin': 0.3, 'pfl': 1e-06})\n            if self.s == 3:\n                self.params.update({'flow_prior': 200, 'flow_level': 0.6, 'flow_strength': 1.0, 'flow_safety': 2.0, 'flow_exposure_min': 5.0})\n        P = self.params\n        if self.s == 3 and self.last_quote_exposed and ca <= self.last_flow_ca + 1:\n            self.flow_exposure += 1\n            self.flow_notional += min(self.last_flow_cap, state.sell_filled_quantity * self.last_ask_tick / 100.0) * self.last_flow_correction\n        lo, hi = cb, ca\n        if self.s == 4:\n            bids, asks, sb, sa = [], [], set(), set()\n            for o in state.own_orders:\n                if o.side is BUY:\n                    if o.price_ticks > lo:\n                        lo = o.price_ticks\n                    if o.remaining_quantity <= probe_limit:\n                        sb.add(o.price_ticks)\n                    else:\n                        bids.append(o)\n                else:\n                    if o.price_ticks < hi:\n                        hi = o.price_ticks\n                    if o.remaining_quantity <= probe_limit:\n                        sa.add(o.price_ticks)\n                    else:\n                        asks.append(o)\n        else:\n            for o in state.own_orders:\n                if o.side is BUY:\n                    lo = max(lo, o.price_ticks)\n                else:\n                    hi = min(hi, o.price_ticks)\n        if P['pinf'] > 0:\n            if self.s != 4:\n                sb = {o.price_ticks for o in state.own_orders if o.side is BUY and o.remaining_quantity <= probe_limit}\n                sa = {o.price_ticks for o in state.own_orders if o.side is SELL and o.remaining_quantity <= probe_limit}\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            previous = tuple(zip(self.bc, self.bw)) if self.s == 4 else None\n            exp = math.exp\n            for c in cs:\n                t = uni\n                for ci, wi in (previous if previous is not None else zip(self.bc, self.bw)):\n                    d = c - ci\n                    d2 = d * d * k\n                    if d2 > -12.5:\n                        t += wi * 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            return qsize(b, a, t / 100.0)\n\n        if self.s != 4:\n            bids = [o for o in state.own_orders if o.side is BUY and o.remaining_quantity > probe_limit]\n            asks = [o for o in state.own_orders if o.side is SELL and o.remaining_quantity > probe_limit]\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(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            stock_rate = P['ydf1']\n            if self.s == 3:\n                flow = (self.flow_notional + P['flow_prior'] * P['flow_level']) / (P['flow_prior'] + self.flow_exposure)\n                scale = max(0.5, min(2.0, flow / P['flow_level']))\n                stock_rate *= 1.0 + P['flow_strength'] * (scale - 1.0)\n            q1 = max(0.0, stock_rate * min(H, P['Wf']) / (self.s / 100.0) - Y)\n\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 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_limit:\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 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=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=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            total_q = q\n            if self.s == 4 and ca <= 99 and bid_t > cb and bid_t == ca - 4 and q >= 4.0 and q * bid_t / 100 >= 4 * P['m']:\n                head = fl(q / 2)\n                oid = self._oid('b')\n                out.append(PlaceOrder(side=BUY, price_ticks=bid_t, quantity=head, client_order_id=oid))\n                self.ahead.add(oid)\n                q = fl(q - head)\n            oid = self._oid('b')\n            out.append(PlaceOrder(side=BUY, price_ticks=bid_t, quantity=q, client_order_id=oid))\n            free -= total_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                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=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=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=SELL, price_ticks=t, quantity=PROBE))\n                    self.pa.add(t)\n        if self.s == 3:\n            self.last_ask_tick = ask_t\n            self.last_flow_ca = ca\n            exposed_qty = ka + sum(o.quantity for o in out if o.side is SELL and o.price_ticks == ask_t and o.quantity > PROBE)\n            self.last_flow_cap = exposed_qty * ask_t / 100.0\n            self.last_quote_exposed = ask_t / 100.0 > phat + max(0.005, P['flow_safety'] * self.st / 100.0) and self.last_flow_cap >= P['flow_exposure_min']\n            if self.last_quote_exposed:\n                logcap = math.log(self.last_flow_cap)\n                mu_flow = math.log(P['m']) - 0.5 * P['sig_n'] ** 2\n                clipped = P['m'] * cdf((logcap - mu_flow - P['sig_n'] ** 2) / P['sig_n']) + self.last_flow_cap * (1.0 - cdf((logcap - mu_flow) / P['sig_n']))\n                self.last_flow_correction = min(3.0, P['m'] / max(clipped, 0.01))\n        return actions + out\n", "code_sha256": "04ef81f760b2ae1c3802ba120998ae4621d8e9c8e04478395671ecbebd36ef3d", "tried": ["Additional real-source destination orders, distance >=3.5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.0330306949632814,\"standard_error\":0.02296946485447079,\"retail\":-6.562500000018012e-05,\"arb\":-0.03296506996328131}", "Additional real-source destination orders, distance >=3.9 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.0,\"standard_error\":0.0,\"retail\":0.0,\"arb\":0.0}", "Additional real-source destination orders when inferred interval is within one tick of source lot [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.011223863545310936,\"standard_error\":0.8134644452684049,\"retail\":0.43623232192031014,\"arb\":-0.4474561854914063}", "Ask-only additional destination orders, >=3 ticks, real source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.11545249566875224,\"standard_error\":0.7905826651110023,\"retail\":0.2180838593562475,\"arb\":-0.10263136369453126}", "Two equal destination tranches on bids only, total notional >=$20 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.15971233629687498,\"standard_error\":0.05659787371084956,\"retail\":0.1571710546421853,\"arb\":0.002541281643749873}", "Same bid-only $5 rule with lookup caching; same pilot results [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.3036144196914076,\"standard_error\":0.0935671836766393,\"retail\":0.2660576336710936,\"arb\":0.03755678600703097}", "Bid-only additional destination orders, >=3 ticks, real source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.020593121137498317,\"standard_error\":0.299830791736327,\"retail\":0.47222129437890403,\"arb\":-0.49281441552968747}", "Raise cap to 12000 only for real lowest-ask future/front seat; no pilot changes [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.0,\"standard_error\":0.0,\"retail\":0.0,\"arb\":0.0}", "Double real tick-5 pre/front asks when estimated p<0.02 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.3710243109460941,\"standard_error\":0.49736763516727733,\"retail\":0.41878669501796884,\"arb\":-0.04776238405546873}", "Quadruple real tick-5 pre/front asks when estimated p<0.02 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.15212853467031318,\"standard_error\":0.48943058079641216,\"retail\":0.333903498196093,\"arb\":-0.18177496350703126}", "Boundary cap plus stand aside at p<0.002 and H>600; no pilot changes [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.0,\"standard_error\":0.0,\"retail\":0.0,\"arb\":0.0}", "Bid-only equal tranches at real future destination ca-4, source ask <=99, total notional >=$20; fixed before unseen [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.16017483584296874,\"standard_error\":0.05659637196501902,\"retail\":0.15763355418827904,\"arb\":0.002541281643749873}", "Same two-sided $5 rule with lookup caching; same pilot results [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5340464372085949,\"standard_error\":0.1510781193665596,\"retail\":0.5750142540648416,\"arb\":-0.040967816866406145}", "Allow front orders to remain up to 3x current target [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.10079300043984227,\"standard_error\":0.42932088106429106,\"retail\":1.5258221829828116,\"arb\":-1.626615183440625}", "Same two-tranche rule, exact aligned-grid convolution reuse; REJECT: slower timing [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5340464372085949,\"standard_error\":0.1510781193665596,\"retail\":0.5750142540648416,\"arb\":-0.040967816866406145}", "Two equal destination tranches on both sides, total notional >=$10 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.501892280925001,\"standard_error\":0.14229579540226592,\"retail\":0.5449339230078096,\"arb\":-0.043041642090624915}", "Two equal destination tranches on both sides, total notional >=$20 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.3548215345875008,\"standard_error\":0.10600699944482604,\"retail\":0.2951506830390591,\"arb\":0.059670851530468756}", "Same two-tranche $5 rule with direct order placement [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5340464372085949,\"standard_error\":0.1510781193665596,\"retail\":0.5750142540648416,\"arb\":-0.040967816866406145}", "Halve pre/front capacity [pilot]: {\"n\":128,\"failures\":0,\"mean\":-7.773222135541406,\"standard_error\":1.7667134781383762,\"retail\":-16.158512831150784,\"arb\":8.385290695670312}", "Double pre/front capacity at distance >=1 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-7.934378824296093,\"standard_error\":2.3848160945741705,\"retail\":5.788141156810935,\"arb\":-13.722519981144531}", "Double ask-only pre/front capacity at distance >=1 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-7.027693290800779,\"standard_error\":2.36222754621284,\"retail\":1.42362873103125,\"arb\":-8.451322021846876}", "Double bid-only pre/front capacity at distance >=1 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-1.8717391264890626,\"standard_error\":0.6543995813728642,\"retail\":2.946631503138279,\"arb\":-4.818370629649219}", "Double pre/front capacity at distance >=3 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-1.9722002287328113,\"standard_error\":1.2402887088688188,\"retail\":1.922290845391407,\"arb\":-3.8944910741296876}", "Quadruple pre/front capacity at distance >=2 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-18.426707080616406,\"standard_error\":3.004944646720914,\"retail\":5.037651630912498,\"arb\":-23.464358711571094}", "Eightfold pre/front capacity at distance >=3 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-17.957834721142188,\"standard_error\":3.442994887414335,\"retail\":2.6950135661281243,\"arb\":-20.65284828728047}", "Two equal destination tranches on both sides, total notional >=$48 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.01147038707421888,\"standard_error\":0.02859735403358513,\"retail\":-0.016915530461718564,\"arb\":0.0054451433843749715}", "Move main quote to real future tick instead of adding second order; distance 2 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-9.333516566192186,\"standard_error\":0.8726971140155804,\"retail\":-8.228425249265628,\"arb\":-1.1050913169421874}", "Move main quote to real future tick instead of adding second order; distance 3 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-4.6010454326499985,\"standard_error\":0.7789772539897948,\"retail\":-3.9426101409109395,\"arb\":-0.6584352917609374}", "Move main quote to real future tick instead of adding second order; distance 3; existing front seats only [pilot]: {\"n\":128,\"failures\":0,\"mean\":-4.413243646893748,\"standard_error\":0.7809703188394016,\"retail\":-3.7263693677484384,\"arb\":-0.6868742791625}", "Same two-tranche $5 rule plus merged order scans and cached prior-grid tuples [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5340464372085949,\"standard_error\":0.1510781193665596,\"retail\":0.5750142540648416,\"arb\":-0.040967816866406145}", "Additional future-tick orders, distance >=1 tick, full base size [pilot]: {\"n\":128,\"failures\":0,\"mean\":-8.270175120897655,\"standard_error\":0.9483092246364468,\"retail\":-2.959729202769533,\"arb\":-5.310445918115625}", "Additional future-tick orders, distance >=2, quarter size [pilot]: {\"n\":128,\"failures\":0,\"mean\":-4.2062608809828115,\"standard_error\":0.8803526183285102,\"retail\":-5.923903241744534,\"arb\":1.7176423608101563}", "Additional future-tick orders, distance >=3; REJECT: missing source lots treated as ticks 0/100 [pilot]: {\"n\":128,\"failures\":0,\"mean\":1.779811214136722,\"standard_error\":0.7654038911644607,\"retail\":2.3074818551710914,\"arb\":-0.5276706410390626}", "Ask-only >=3 additional-order rule; REJECT: missing source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":1.7814950714171884,\"standard_error\":0.7296946416082744,\"retail\":1.7736648036499978,\"arb\":0.007830267780468775}", "Bid-only >=3 additional-order rule; REJECT: missing source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.07997334369687362,\"standard_error\":0.2970427026880433,\"retail\":0.44546234923984157,\"arb\":-0.5254356929507813}", "Same >=3 additional-order rule with idle-loop optimization; REJECT: missing source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":1.779811214136722,\"standard_error\":0.7654038911644607,\"retail\":2.3074818551710914,\"arb\":-0.5276706410390626}", "Additional destination orders >=3 ticks away, actual rival source lots only [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.18846389627265842,\"standard_error\":0.8046008517869557,\"retail\":0.8041231572859354,\"arb\":-0.6156592610328127}", "Additional future-tick orders, distance >=1, one-tenth size [pilot]: {\"n\":128,\"failures\":0,\"mean\":-8.326102203445313,\"standard_error\":1.0597588359890027,\"retail\":-10.403997086899219,\"arb\":2.077894883482031}", "Enlarge only before rival arrival (size reverts on arrival): seat05 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-6.310214316696875,\"standard_error\":1.773070829009367,\"retail\":-14.932858920535159,\"arb\":8.622644603894532}", "Enlarge only before rival arrival (size reverts on arrival): seat2 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-13.652549059176561,\"standard_error\":2.217496899949547,\"retail\":-0.2505199783789064,\"arb\":-13.40202908080625}", "Enlarge only before rival arrival (size reverts on arrival): seat2k [pilot]: {\"n\":128,\"failures\":0,\"mean\":-13.040021449796093,\"standard_error\":2.4307262268523515,\"retail\":5.532735052352342,\"arb\":-18.572756502173437}", "Enlarge only before rival arrival (size reverts on arrival): seat4 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-41.38450999544922,\"standard_error\":4.561210539522662,\"retail\":-3.034204165361723,\"arb\":-38.35030583004141}", "Enlarge only before rival arrival (size reverts on arrival): seat4ask [pilot]: {\"n\":128,\"failures\":0,\"mean\":-29.052724717938283,\"standard_error\":4.183439653087198,\"retail\":-8.89724490563594,\"arb\":-20.155479812260158}", "Enlarge only before rival arrival (size reverts on arrival): seat4bid [pilot]: {\"n\":128,\"failures\":0,\"mean\":-15.816428976439843,\"standard_error\":1.888657591772254,\"retail\":-0.5142490370929693,\"arb\":-15.302179939353126}", "Enlarge only before rival arrival (size reverts on arrival): seat4d0 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-49.473280927178905,\"standard_error\":4.202273885194395,\"retail\":-1.6193445886046907,\"arb\":-47.85393633854687}", "Enlarge only before rival arrival (size reverts on arrival): seat4d2 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-25.75604578831328,\"standard_error\":2.632910786686583,\"retail\":-5.256895061138285,\"arb\":-20.499150727149217}", "Enlarge only before rival arrival (size reverts on arrival): seat8 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-92.4855986961539,\"standard_error\":7.466498514592308,\"retail\":-10.546543782001564,\"arb\":-81.93905491412188}", "Enlarge only before rival arrival (size reverts on arrival): seat8d3 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-18.71665906314531,\"standard_error\":3.0390255809609013,\"retail\":-2.5464284501585945,\"arb\":-16.17023061296328}", "Additional destination orders only while holding current front seat; distance >=2; REJECT: missing source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":-3.441084017809374,\"standard_error\":0.9746232767439736,\"retail\":-0.024724413497657582,\"arb\":-3.4163596042570314}", "Additional destination orders only while holding current front seat; distance >=2; actual source lots only [pilot]: {\"n\":128,\"failures\":0,\"mean\":-5.257823764128905,\"standard_error\":0.9775805265535427,\"retail\":-1.8426682012890643,\"arb\":-3.415155562811719}", "Additional destination orders only while holding current front seat; distance >=3; REJECT: missing source lots [pilot]: {\"n\":128,\"failures\":0,\"mean\":1.906089814894532,\"standard_error\":0.7614760094214625,\"retail\":2.3788436113007787,\"arb\":-0.4727537963945314}", "Additional destination orders only while holding current front seat; distance >=3; actual source lots only [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.32069510314687666,\"standard_error\":0.8092263931604664,\"retail\":0.8814762307054665,\"arb\":-0.5607811275664063}", "Smoothly grow real destination/front capacity from 1x at distance 3 to 2x at distance 4 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.2577731685046857,\"standard_error\":0.7926233490242619,\"retail\":1.7098330309242187,\"arb\":-1.96760619945}", "Smoothly grow real destination/front capacity from 1x at distance 3 to 3x at distance 4 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-1.9022853282414047,\"standard_error\":1.1478324726322795,\"retail\":2.0234246408453136,\"arb\":-3.9257099691007813}", "Future-destination order split into 10/30/60 percent tranches [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.2844628441765636,\"standard_error\":0.14624062406126553,\"retail\":0.4457787358492169,\"arb\":-0.16131589166718782}", "Same three tranches plus doubling before arrival [pilot]: {\"n\":128,\"failures\":0,\"mean\":-9.182581900766406,\"standard_error\":2.240468878079268,\"retail\":3.289959263574997,\"arb\":-12.472541164365625}", "Two destination tranches: 25 percent then 75 percent, >=$5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.12040183048281487,\"standard_error\":0.09778717703454312,\"retail\":0.2254721643289045,\"arb\":-0.1050703338421876}", "Same three tranches plus 4x pre/post-arrival size at distance >=2 [pilot]: {\"n\":128,\"failures\":0,\"mean\":-18.24834287252578,\"standard_error\":3.1014746997146467,\"retail\":5.428805177773435,\"arb\":-23.677148050328125}", "Two destination tranches: 75 percent then 25 percent, >=$5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.35441505724375016,\"standard_error\":0.1641319996111408,\"retail\":0.5843445063265613,\"arb\":-0.2299294490796875}", "Two equal destination tranches on asks only, total notional >=$5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.295555285997657,\"standard_error\":0.12454952329374087,\"retail\":0.31957190525937307,\"arb\":-0.024016619263281268}", "Two equal destination tranches on bids only, total notional >=$5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.3036144196914076,\"standard_error\":0.0935671836766393,\"retail\":0.2660576336710936,\"arb\":0.03755678600703097}", "Two equal future-destination tranches, minimum four shares [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5322364972265642,\"standard_error\":0.1537998319581469,\"retail\":0.5751218681703103,\"arb\":-0.04288537095312492}", "Four equal future-destination tranches, minimum eight shares [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.40263024390937724,\"standard_error\":0.18154223777320078,\"retail\":0.9215735767242179,\"arb\":-0.5189433328148441}", "Eight equal future-destination tranches, minimum sixteen shares [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.3444188403203135,\"standard_error\":0.20313840161217298,\"retail\":1.0592748633671858,\"arb\":-0.7148560230312501}", "Two equal destination tranches, total notional >= assumed mean retail order $5 [pilot]: {\"n\":128,\"failures\":0,\"mean\":0.5340464372085949,\"standard_error\":0.1510781193665596,\"retail\":0.5750142540648416,\"arb\":-0.040967816866406145}", "Two-share anchor followed by the rest of the future-destination order [pilot]: {\"n\":128,\"failures\":0,\"mean\":-0.07959382450781187,\"standard_error\":0.06558524609476954,\"retail\":-0.057749923289062866,\"arb\":-0.02184390121093735}", "Double real tick-5 pre/front asks when estimated p<0.02 [validation]: {\"n\":661,\"failures\":0,\"mean\":0.06986006839924357,\"standard_error\":0.39021532346315563,\"retail\":0.5830906700871406,\"arb\":-0.5132306016848714}", "Same two-tranche $5 rule with direct order placement [validation]: {\"n\":661,\"failures\":0,\"mean\":0.2863755793948557,\"standard_error\":0.11663516717835355,\"retail\":0.5662314794022699,\"arb\":-0.2798559000152797}", "Additional future-tick orders, distance >=3; REJECT: missing source lots treated as ticks 0/100 [validation]: {\"n\":661,\"failures\":0,\"mean\":-0.3914835281956126,\"standard_error\":0.8198868963008447,\"retail\":2.8763384996750387,\"arb\":-3.267822027872617}", "Future-destination order split into 10/30/60 percent tranches [validation]: {\"n\":661,\"failures\":0,\"mean\":-0.07002059900998485,\"standard_error\":0.19253402006509446,\"retail\":0.5315582748748863,\"arb\":-0.6015788738931922}", "Two equal future-destination tranches, minimum four shares [validation]: {\"n\":661,\"failures\":0,\"mean\":0.26488349885491647,\"standard_error\":0.11582979619251661,\"retail\":0.5544642085237527,\"arb\":-0.2895807096739787}", "Two equal destination tranches on both sides, total notional >=$48 [validation2]: {\"n\":738,\"failures\":0,\"mean\":0.034844010853387314,\"standard_error\":0.026663028524228572,\"retail\":0.039856788844173756,\"arb\":-0.005012777992412017}", "Two equal destination tranches on bids only, total notional >=$5 [validation2]: {\"n\":738,\"failures\":0,\"mean\":0.32621537635420006,\"standard_error\":0.07156603076126304,\"retail\":0.2962651674348237,\"arb\":0.029950208922086607}", "Two equal destination tranches on bids only, total notional >=$20 [validation3]: {\"n\":488,\"failures\":0,\"mean\":0.16583784444323724,\"standard_error\":0.06292183124768809,\"retail\":0.15496302901536915,\"arb\":0.010874815418442381}", "Bid-only equal tranches at real future destination ca-4, source ask <=99, total notional >=$20; fixed before unseen [validation3]: {\"n\":488,\"failures\":0,\"mean\":0.16580731308913887,\"standard_error\":0.0629219927693215,\"retail\":0.15496302901536915,\"arb\":0.010844284064344019}", "Same >=3 additional-order rule with idle-loop optimization; REJECT: missing source lots [tune]: {\"n\":2043,\"failures\":0,\"mean\":-1.4265934815550658,\"standard_error\":0.8261636205825369,\"retail\":2.600184849869554,\"arb\":-4.026778331427509}", "bid-timing pilot timing: {\"wall\":{\"base\":61.643766783643514,\"yours\":61.57911495119333},\"cpu\":{\"base\":13.822515086999994,\"yours\":13.868329329999995,\"runs\":20}}", "bid20-timing pilot timing: {\"wall\":{\"base\":61.34124193061143,\"yours\":60.31905086012557},\"cpu\":{\"base\":13.667095317000001,\"yours\":13.381397484999988,\"runs\":20}}", "bidfast-timing pilot timing: {\"wall\":{\"base\":62.22642746241763,\"yours\":63.44133620429784},\"cpu\":{\"base\":13.624254605000003,\"yours\":13.596684846,\"runs\":20}}", "fullfast-timing pilot timing: {\"wall\":{\"base\":66.36208059033379,\"yours\":67.31026616180316},\"cpu\":{\"base\":13.585730130999996,\"yours\":13.945817172999996,\"runs\":20}}", "kernel-timing pilot timing: {\"wall\":{\"base\":101.80883803172037,\"yours\":104.8974393629469},\"cpu\":{\"base\":13.705857461999994,\"yours\":14.200055347000005,\"runs\":20}}", "lot48-timing pilot timing: {\"wall\":{\"base\":61.44434935040772,\"yours\":58.997285183053464},\"cpu\":{\"base\":13.382345948,\"yours\":13.340585814999997,\"runs\":20}}", "opt-timing pilot timing: {\"wall\":{\"base\":103.57655311888084,\"yours\":103.16528169158846},\"cpu\":{\"base\":13.887074632000008,\"yours\":14.055095081999996,\"runs\":20}}", "pre-timing pilot timing: {\"wall\":{\"base\":137.37058035843074,\"yours\":139.27380474703386},\"cpu\":{\"base\":13.780055360999995,\"yours\":14.091148544000003,\"runs\":20}}", "Diagnostic on first 64 spread-4 tuning seeds: {\".cache/base.py\":{\"runs\":64,\"steps\":128000,\"reserved_cash\":22.336927876562495,\"future_reserved_cash\":8.943800659374997,\"front_bid_steps\":0.166671875,\"front_ask_steps\":0.1713671875},\".cache/kernel.py\":{\"runs\":64,\"steps\":128000,\"reserved_cash\":22.426416690625,\"future_reserved_cash\":8.86831994765624,\"front_bid_steps\":0.1765859375,\"front_ask_steps\":0.179859375}}", "Arithmetic check: 299601 cent-quantity sizes from 4 to 3000 shares conserve total quantity after equal splitting; aligned-grid arithmetic alternative passed 10000 randomized exact-equality cases but was rejected as slower.", "Frozen real-source bid-only $20 candidate unseen evaluation: {\"n\":2009,\"mean\":0.17255959975495247,\"standard_error\":0.05609359681034755,\"retail\":0.2216695056799397,\"arb\":-0.0491099059248383,\"failures\":0}", "Frozen candidate final 200-run timing, alternating order in one process: {\"wall\":{\"base\":294.15425222413614,\"yours\":291.83430307451636},\"cpu\":{\"base\":131.15759489399977,\"yours\":130.01038047599997,\"runs\":200}}", "Frozen real-source bid-tranche candidate 64-run collateral/queue diagnostic: {\".cache/base.py\":{\"runs\":64,\"steps\":128000,\"reserved_cash\":22.336927876562495,\"future_reserved_cash\":8.943800659374997,\"front_bid_steps\":0.166671875,\"front_ask_steps\":0.1713671875},\".cache/final.py\":{\"runs\":64,\"steps\":128000,\"reserved_cash\":22.23691695,\"future_reserved_cash\":8.858086147656255,\"front_bid_steps\":0.174,\"front_ask_steps\":0.171171875}}", "Final decision: eligible: positive discovery, independent tuning-validation and unseen gains; zero failures; all 900 non-target records identical; wall time <= base"], "next": "Try extending destination tranches to asks only if the extra order overhead can be removed: two-sided $5 tranches gained in tuning but were slower. Also test skipping splits on ammunition bids already predicted above fair value, since they are likely to be swept immediately. Keep real-source guards, unchanged aggregate size, and fresh unseen seeds beyond the ranges already evaluated."}