{
  "approach": "Only self.s == 4 changes. Preserve FIFO on existing front SELL orders at the rival own tick by tolerating remaining quantity up to 3 times current desired size rather than 1.5 times. The base cancels oversized orders and loses priority; the changed order can remain through a temporary one-step sizing reduction, retiring when desired size is zero, its tick is no longer selected, or its quantity exceeds the bounded tolerance. Wider retention at 6 times or unlimited lost edge in discovery; bid-side retention also lost. Simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e, default 2000 steps, no sandbox, standard-library Python. Requested 4000-run CLI scorer exactly reproduced 98.4694596619638. Spread-specific seeds generated with sample_config; each run used the unmodified SimulationEngine as run_batch does. Discovery used the first 300 spread-4 seeds in 8000..15999; validation used the other 1743. Code frozen at 2026-11-06T08:43:21.220165+00:00 before evaluating any modified candidate on target-spread seeds in 60000..67999. Spread 4 reuses materialized prior belief-grid pairs in its inner convolution loop, preserving arithmetic order. Every field of all 2043 tuning results equals the original retention candidate; this performance-only change brings the measured 200-run runtime below base. The reported holdout covers all 2009 unseen spread-4 runs. Every SimulationResult field matches on 300 independent runs of each other spread, 900 total. Timing uses 200 runs of each code in one process, alternating execution order by seed.",
  "blocked": null,
  "python_version": "3.12.3",
  "spread": 4,
  "base_mean_edge": 98.4694596619638,
  "runs": {
    "tuning": 2043,
    "unseen": 2009
  },
  "base_unseen": {
    "mean_edge": 163.47813945343543,
    "retail_edge": 191.20763318061364,
    "arb_edge": -27.729493727194825
  },
  "holdout": {
    "mean_edge": 163.75574160072088,
    "retail_edge": 191.79291091539915,
    "arb_edge": -28.03716931469846,
    "failures": 0
  },
  "diff": {
    "mean": 0.2776021472854653,
    "standard_error": 0.06968053234050084,
    "retail": 0.5852777347855154,
    "arb": -0.3076755875036336
  },
  "others_identical": {
    "runs": 900,
    "identical": true
  },
  "seconds": {
    "base": 164.64734957250766,
    "yours": 163.9134922872763
  },
  "code": "\"\"\"Crossing-cover base with isolated, untuned spread overrides.\"\"\"\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        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        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            if self.s == 4:\n                prior_pairs = tuple(zip(self.bc, self.bw))\n                for c in cs:\n                    t = uni\n                    for ci, wi in prior_pairs:\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            else:\n                for c in cs:\n                    t = uni\n                    for ci, wi in zip(self.bc, self.bw):\n                        d = c - ci\n                        d2 = d * d * k\n                        if d2 > -12.5:\n                            t += wi * math.exp(d2)\n                    ws.append(t)\n            tot = sum(ws)\n            ws = [w / tot for w in ws]\n        self.bc, self.bw = cs, ws\n        mean_t = sum(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        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 = 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 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 o.price_ticks == t and q > 0 and kept + o.remaining_quantity <= max(q * (3.0 if self.s == 4 and o.side is Side.SELL and o.order_id in self.ahead and o.price_ticks == comp else 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                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        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 Side.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": "8e808793991199c72a4224401e2c63cf9a2b8a6f5f2ebfeb5df38e629e4e703f",
  "tried": [
    "Fourfold ask depth at ticks <=8 with estimated probability at least 2 cents below ask: {\"discovery_300\": {\"arb\": -24.361149304878666, \"failures\": 0, \"losses\": 106, \"mean\": -21.698658594216333, \"n\": 300, \"retail\": 2.6624907106710007, \"se\": 9.696819512571576, \"wins\": 45}}",
    "Join the rival ask at tick 5 below 1-cent estimated probability and >300 steps remaining; track priority after rival departure: {\"discovery_300\": {\"arb\": 0.21955820322800024, \"failures\": 0, \"losses\": 47, \"mean\": -7.570314904513333, \"n\": 300, \"retail\": -7.789873107735667, \"se\": 1.2064191911131712, \"wins\": 1}}",
    "Fourfold ask depth restricted to future-replenishment ticks 5..8 with a 2-cent cushion: {\"discovery_300\": {\"arb\": -5.244340691500333, \"failures\": 0, \"losses\": 103, \"mean\": -3.362660921837, \"n\": 300, \"retail\": 1.881679769673, \"se\": 1.3564846560096622, \"wins\": 42}}",
    "Retain same-tick front orders despite any oversizing, provided desired quantity remains positive: {\"discovery_300\": {\"arb\": -3.1535355190666663, \"failures\": 0, \"losses\": 132, \"mean\": -1.5912092850173336, \"n\": 300, \"retail\": 1.562326234036332, \"se\": 1.0559032311813847, \"wins\": 165}}",
    "Retain any same-tick front ask while desired quantity stays positive, without an oversizing cap: {\"discovery_300\": {\"arb\": -1.2632284804190002, \"failures\": 0, \"losses\": 155, \"mean\": -0.7402199386900006, \"n\": 300, \"retail\": 0.5230085417276652, \"se\": 0.4914981655787038, \"wins\": 140}}",
    "Allow up to 3x desired quantity only on same-tick front bids: {\"discovery_300\": {\"arb\": -1.0390774049509997, \"failures\": 0, \"losses\": 72, \"mean\": -0.6497248246116666, \"n\": 300, \"retail\": 0.38935258033900005, \"se\": 0.9214203279688314, \"wins\": 191}}",
    "Tick-5 depth uses expected time until the next jump or settlement instead of the entire horizon: {\"discovery_300\": {\"arb\": 0.06949566006533332, \"failures\": 0, \"losses\": 19, \"mean\": 0.41072136521199953, \"n\": 300, \"retail\": 0.34122570515533457, \"se\": 0.3581452899908975, \"wins\": 26}, \"validation_next_400\": {\"arb\": -0.54808719276025, \"failures\": 0, \"losses\": 25, \"mean\": -0.5398885704500002, \"n\": 400, \"retail\": 0.008198622313248869, \"se\": 0.5166115460642662, \"wins\": 41}}",
    "Tick-5 lifetime demand depth capped at twice the base desired quantity: {\"discovery_300\": {\"arb\": 0.10175669217266665, \"failures\": 0, \"losses\": 15, \"mean\": 0.48576501543299994, \"n\": 300, \"retail\": 0.3840083232683341, \"se\": 0.36601636569848583, \"wins\": 30}, \"validation_next_400\": {\"arb\": -0.5266678506245, \"failures\": 0, \"losses\": 27, \"mean\": -0.4108646774649998, \"n\": 400, \"retail\": 0.11580317316199977, \"se\": 0.4824109250756326, \"wins\": 42}}",
    "Allow up to 6x desired quantity only on same-tick front asks: {\"discovery_300\": {\"arb\": -0.9893447610280001, \"failures\": 0, \"losses\": 113, \"mean\": -0.3944142495043326, \"n\": 300, \"retail\": 0.5949305115186664, \"se\": 0.4517522244808093, \"wins\": 167}}",
    "Stage remaining expected demand at tick 5 only with estimated probability below 2 cents: {\"discovery_300\": {\"arb\": 0.016859190252999964, \"failures\": 0, \"losses\": 19, \"mean\": 0.3731295698756665, \"n\": 300, \"retail\": 0.3562703796296675, \"se\": 0.3702226878966735, \"wins\": 26}, \"validation_next_400\": {\"arb\": -0.755733339215, \"failures\": 0, \"losses\": 26, \"mean\": -0.3613485801734997, \"n\": 400, \"retail\": 0.3943847590472494, \"se\": 0.6631834769217264, \"wins\": 43}}",
    "Allow up to 3x desired quantity on same-tick front orders on both sides instead of 1.5x: {\"discovery_300\": {\"arb\": -1.1656150565376664, \"failures\": 0, \"losses\": 70, \"mean\": -0.18921778310566686, \"n\": 300, \"retail\": 0.9763972734323321, \"se\": 0.8370435435399021, \"wins\": 217}}",
    "Ignore oversizing for front asks only when estimated probability remains 2 cents below ask: {\"discovery_300\": {\"arb\": -0.22769402461366672, \"failures\": 0, \"losses\": 35, \"mean\": -0.13311034506733357, \"n\": 300, \"retail\": 0.09458367954633283, \"se\": 0.1912531787483974, \"wins\": 73}}",
    "At ticks 5..8 with a 2-cent cushion, stage covered YES up to remaining expected retail demand: {\"discovery_300\": {\"arb\": -0.7120921841233333, \"failures\": 0, \"losses\": 24, \"mean\": 0.41962771366033286, \"n\": 300, \"retail\": 1.1317198977896665, \"se\": 0.6230930679256264, \"wins\": 51}, \"validation_next_400\": {\"arb\": -1.25054951083225, \"failures\": 0, \"losses\": 46, \"mean\": -0.07930804687750104, \"n\": 400, \"retail\": 1.171241463954499, \"se\": 0.7847398199849439, \"wins\": 73}}",
    "If management cancels all orders selected at the rival tick, immediately recompute and quote one tick inside: {\"discovery_300\": {\"arb\": -0.011744259524666638, \"failures\": 0, \"losses\": 84, \"mean\": 0.01040721834166628, \"n\": 300, \"retail\": 0.02215147786699994, \"se\": 0.00855375111192123, \"wins\": 83}, \"full_2043\": {\"arb\": -0.005607834289182595, \"failures\": 0, \"losses\": 654, \"mean\": -0.0032485696051397592, \"n\": 2043, \"retail\": 0.0023592646829658373, \"se\": 0.011496187341701084, \"wins\": 550}, \"validation_1743\": {\"arb\": -0.004551650944004618, \"failures\": 0, \"losses\": 570, \"mean\": -0.005598963399770747, \"n\": 1743, \"retail\": -0.0010473124571433027, \"se\": 0.013394196401195078, \"wins\": 467}, \"validation_final_1343\": {\"arb\": 0.0002919340556961435, \"failures\": 0, \"losses\": 449, \"mean\": -0.008328894172077954, \"n\": 1343, \"retail\": -0.008620828229337732, \"se\": 0.017247948653670266, \"wins\": 355}}",
    "Stage covered YES for remaining expected demand at ticks 5..8 only if base one-step quantity already hits qmax: {\"discovery_300\": {\"arb\": -0.0013416550113333277, \"failures\": 0, \"losses\": 2, \"mean\": 0.058813561494000056, \"n\": 300, \"retail\": 0.06015521650533344, \"se\": 0.07523269440638322, \"wins\": 2}, \"full_2043\": {\"arb\": 0.000126255170288796, \"failures\": 0, \"losses\": 7, \"mean\": 0.028929906792853607, \"n\": 2043, \"retail\": 0.028803651623446058, \"se\": 0.021123536415839236, \"wins\": 12}, \"validation_1743\": {\"arb\": 0.0003789075251290927, \"failures\": 0, \"losses\": 5, \"mean\": 0.023786420613654564, \"n\": 1743, \"retail\": 0.023407513089558386, \"se\": 0.021113026086299596, \"wins\": 10}, \"validation_final_1343\": {\"arb\": -0.00024451250342516246, \"failures\": 0, \"losses\": 4, \"mean\": 0.019794134923901665, \"n\": 1343, \"retail\": 0.020038647428592887, \"se\": 0.02285639720592449, \"wins\": 6}, \"validation_next_400\": {\"arb\": 0.0024720402710000046, \"failures\": 0, \"losses\": 1, \"mean\": 0.03719051981699991, \"n\": 400, \"retail\": 0.03471847954625005, \"se\": 0.05079682116797489, \"wins\": 4}}",
    "Quote tick 5 early when rival ask exceeds 5 and estimated probability is below 3 cents: {\"discovery_300\": {\"arb\": 1.4795805381256668, \"failures\": 0, \"losses\": 95, \"mean\": 0.1830302920263342, \"n\": 300, \"retail\": -1.2965502460910012, \"se\": 1.7996847844837185, \"wins\": 28}}",
    "Instrumentation of base on first 120 spread-4 tuning seeds: 121 individual front ask orders at tick 5; mean overlap steps 125.59504132231405; retail edge/run 13.593384467359762; arb edge/run -0.003105016524795309. Multiple own orders can belong to one front position.",
    "Actual front priority grouped by side/tick across all own orders; diagnostic edge is earned during front overlap, not a full-run score comparison: {\"trace_groups.json\": {\"arb_edge_per_run\": -0.6267940179577932, \"episodes\": 2267, \"mean_duration\": 5.8367887075430085, \"reasons\": {\"cancelled\": 2096, \"filled\": 166, \"settlement\": 5}, \"retail_edge_per_run\": 34.04989223070308, \"runs\": 40}, \"trace_groups_keep3_sell.json\": {\"arb_edge_per_run\": -0.9064613865009621, \"episodes\": 2247, \"mean_duration\": 6.238985313751669, \"reasons\": {\"cancelled\": 2056, \"filled\": 185, \"settlement\": 6}, \"retail_edge_per_run\": 34.76555471139104, \"runs\": 40}}",
    "Spread-4 performance-only prior-pair reuse: all 2043 optimized tuning SimulationResults equal the original candidate in every field; original slower benchmark {\"base\": 160.30550064332783, \"yours\": 164.7766307478305}",
    "Allow up to 3x desired quantity only on same-tick front asks: {\"discovery_300\": {\"arb\": -0.37908314133533294, \"failures\": 0, \"losses\": 83, \"mean\": 0.18726923959566616, \"n\": 300, \"retail\": 0.5663523809319985, \"se\": 0.22785616844094242, \"wins\": 185}, \"full_2043\": {\"arb\": -0.35020688796093985, \"failures\": 0, \"losses\": 578, \"mean\": 0.19231342411943203, \"n\": 2043, \"retail\": 0.5425203120807143, \"se\": 0.07226192132148135, \"wins\": 1237}, \"validation_1743\": {\"arb\": -0.34523679271577756, \"failures\": 0, \"losses\": 495, \"mean\": 0.19318161422679278, \"n\": 1743, \"retail\": 0.5384184069427996, \"se\": 0.0751052327564865, \"wins\": 1052}, \"validation_final_1343\": {\"arb\": -0.34645590257244996, \"failures\": 0, \"losses\": 395, \"mean\": 0.17428583584921792, \"n\": 1343, \"retail\": 0.520741738422189, \"se\": 0.07872056550664963, \"wins\": 799}}; frozen_sha256=8e808793991199c72a4224401e2c63cf9a2b8a6f5f2ebfeb5df38e629e4e703f; unseen_2009={\"arb\": -0.3076755875036336, \"failures\": 0, \"losses\": 561, \"mean\": 0.2776021472854653, \"n\": 2009, \"retail\": 0.5852777347855154, \"se\": 0.06968053234050084, \"wins\": 1233}; seconds_200_each={\"base\": 164.64734957250766, \"yours\": 163.9134922872763}"
  ],
  "next": "Try a state-dependent retirement threshold for front asks, using remaining quantity and estimated retail-flow exposure rather than one fixed oversizing multiple. Use fresh tuning/validation seeds. Bid-side retention and broad extra depth increased arbitrage losses here."
}