{
  "approach": "Returned pieces: priced, q1-bids, q1-ahead. Start with the supplied priced spread-1 ask block: fivefold inside tick-2 asks below 1 cent and retention of front asks at ticks <=2. Test q1-bids queue banking and s1-bids intermediate-tick stock suppression alone and together, without tuning constants. q1-bids lowers the bid front threshold to 0.02 shares, preserves tracked bids at the rival and safer lower ticks under the existing risk-size bound, and tracks auxiliary stock bids placed ahead. s1-bids zeros bulk amt/qam at ticks 2-5 before the unchanged bridge can replenish inventory. Add the highest supplied-gain remaining ask piece, q1-ahead, to the choosing winner: transfer safer dormant ask retention with the original 15000 tick-2 dormant capacity cap and ten-times-size current-front ask retention; keep the priced fivefold sizing once and preserve its indefinite low-tick retention. The choosing winner was priced_q1b_ahead. Returned priced_q1b_ahead. All trading additions are guarded by self.s == 1. Other spreads retain base arithmetic and actions. No seed recognition or external dependencies. Scoring: CPython 3.12.3, official unmodified simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e, default 2000 steps, no sandbox. Each seed uses sample_config(ChallengeConfig(), ParameterVariance(), seed=seed), fresh strategy and SimulationEngine(config, factory, seed=seed). All 2016 choosing and 2053 confirming spread-1 seeds are used; none from the earlier seats ranges. Confirmation was not used to tune or compare alternative policies; priced fallback is predeclared. Metrics are local paired simulator results, not a Paradigm submission or a claim about its private 200 seeds. Arb edge is signed. The standard error does not adjust for choosing-set selection. The code was frozen before confirming scores; exact hashes are recorded in tried. Runtime compares the returned code to the supplied base in one process.",
  "blocked": null,
  "spread": 1,
  "base_mean_edge": 108.63974289615767,
  "contains": [
    "priced",
    "q1-bids",
    "q1-ahead"
  ],
  "holdout": {
    "mean_edge": 34.32608522353142,
    "retail_edge": 57.629912983634,
    "arb_edge": -23.303827760101704,
    "failures": 0
  },
  "diff": {
    "mean": 1.7861548137700443,
    "standard_error": 0.3038745811145147,
    "retail": 1.991669698763176,
    "arb": -0.20551488499035536
  },
  "vs_priced": {
    "mean": 0.4797752608084268,
    "standard_error": 0.27459321069697257
  },
  "others_identical": {
    "runs": 900,
    "identical": true
  },
  "seconds": {
    "base": 210.5617640367709,
    "yours": 216.10719215264544
  },
  "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 == 1:\n                prior = tuple(zip(self.bc, self.bw))\n                exp = math.exp\n                for c in cs:\n                    t = uni\n                    for ci, wi in prior:\n                        d = c - ci\n                        d2 = d * d * k\n                        if d2 > -12.5:\n                            t += wi * 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, cap=None):\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'] if cap is None else cap), 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            if self.s == 1 and phat * 100 - t < P['dmin']:\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, dormant=False):\n            if not 1 <= t <= 99:\n                return 0.0\n            if self.s == 1 and t - phat * 100 < P['dmin']:\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, 15000 if dormant and self.s == 1 and t == 2 and phat < 0.01 else None)\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) >= (PROBE + 0.01 if self.s == 1 and step == 1 else 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        if self.s == 1 and ask_t == 2 and ask_t < ca and phat < 0.01:\n            qa *= 5\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            bound_t, bound_q = t, q\n            banked = {}\n            for o in orders:\n                if self.s == 1 and o.side is Side.SELL and o.order_id in self.ahead and o.remaining_quantity >= P['min_ahead'] and o.price_ticks > t:\n                    enough = bound_q > 0 and o.remaining_quantity * o.price_ticks <= min(bound_q, P['qmax']) * bound_t\n                    if enough:\n                        kept_ids.add(o.order_id)\n                        continue\n                    if o.price_ticks == bound_t:\n                        capacity = bound_q\n                    else:\n                        capacity = qask(o.price_ticks, dormant=True)\n                        bound_t, bound_q = o.price_ticks, capacity\n                    if capacity > 0 and o.remaining_quantity <= max(capacity, 1.0):\n                        kept_ids.add(o.order_id)\n                        continue\n                if self.s == 1 and o.side is Side.SELL and o.order_id in self.ahead and o.price_ticks == t and q > 0 and kept + o.remaining_quantity <= max(q * 10.0, 1.0):\n                    kept += o.remaining_quantity\n                    kept_ids.add(o.order_id)\n                    continue\n                if self.s == 1 and o.side is Side.BUY and o.order_id in self.ahead and o.price_ticks < t and banked.get(o.price_ticks, 0.0) + o.remaining_quantity <= P['keep_hi'] * qbid(o.price_ticks):\n                    banked[o.price_ticks] = banked.get(o.price_ticks, 0.0) + o.remaining_quantity\n                    kept_ids.add(o.order_id)\n                elif o.price_ticks == t and q > 0 and (kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0) or (self.s == 1 and o.order_id in self.ahead and ((o.side is Side.SELL and o.price_ticks <= 2) or (o.side is Side.BUY and o.price_ticks == comp)))):\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            if self.s == 1:\n                oid = self._oid('m')\n                out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q, client_order_id=oid))\n                if amt > cb:\n                    self.ahead.add(oid)\n            else:\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": "6e96989063548d73f4b791241e4c57be6c964243a3e64e88f9a560734a208fee",
  "tried": [
    "Scorer: parent CLI, 600 seeds 1000-1599, workers=4, no sandbox, default 2000 steps; {\"failures\":0,\"mean_edge\":108.63974289615767,\"runs\":600}; elapsed_seconds=209.11576465889812. Exact reference match. No counts halved.",
    "Choosing: all 2016 sampled spread-1 seeds in [20000,28000); no constants tuned; all variants constructed before scoring. Paired SE is sample stdev(delta)/sqrt(n).",
    "priced_q1b choosing n=2016 scores={\"arb_edge\":-26.032381049816617,\"failures\":0,\"retail_edge\":63.30555371739142,\"total_edge\":37.273172667584774} vs_priced={\"arb\":0.025411366640178474,\"different\":1823,\"mean\":0.6979942217213789,\"retail\":0.6725828550776292,\"standard_error\":0.06667020423953877}",
    "priced_s1b choosing n=2016 scores={\"arb_edge\":-25.56692181978517,\"failures\":0,\"retail_edge\":61.71990288782505,\"total_edge\":36.152981068042806} vs_priced={\"arb\":0.49087059667162697,\"different\":783,\"mean\":-0.42219737782058525,\"retail\":-0.9130679744887401,\"standard_error\":0.46890393120525037}",
    "priced_both choosing n=2016 scores={\"arb_edge\":-25.51166629703666,\"failures\":0,\"retail_edge\":62.39485501145367,\"total_edge\":36.88318871442564} vs_priced={\"arb\":0.546126119420139,\"different\":1897,\"mean\":0.308010268562252,\"retail\":-0.23811585086011894,\"standard_error\":0.4733106646296329}",
    "priced_both choosing incremental over priced_q1b: {\"arb\":0.5207147527799605,\"different\":787,\"mean\":-0.38998395315912693,\"retail\":-0.9106987059377482,\"standard_error\":0.46727745299982537}",
    "priced_both choosing incremental over priced_s1b: {\"arb\":0.055255522748512066,\"different\":1819,\"mean\":0.7302076463828373,\"retail\":0.6749521236286212,\"standard_error\":0.07023426605706007}",
    "Bid selection chain: priced -> priced_q1b. Chosen by maximum mean on the complete choosing set; every retained addition has positive paired mean.",
    "priced_q1b_ahead choosing n=2016 incremental over priced_q1b: {\"arb\":-0.1936321021070435,\"different\":1408,\"mean\":0.06696707173377987,\"retail\":0.26059917384007913,\"standard_error\":0.16368161803715153}; kept. Ask-only q1-ahead transfer; use its 15000 tick-2 cap only for dormant-lot capacity, while new target quotes keep the priced fivefold sizing once; no q1-ahead bid rules copied.",
    "Frozen 2026-11-06T14:49:04.617438+00:00 candidate=priced_q1b_ahead sha256=6e96989063548d73f4b791241e4c57be6c964243a3e64e88f9a560734a208fee. No policy edits or constants changed after freezing. Only this choosing winner was tested on confirmation seeds.",
    "Confirmation n=2053 sampled spread-1 seeds in [80000,88000): {\"diffs\":{\"frozen - base\":{\"arb\":-0.20551488499035536,\"different\":1921,\"mean\":1.7861548137700443,\"retail\":1.991669698763176,\"standard_error\":0.3038745811145147},\"frozen - priced\":{\"arb\":-0.05424889424490972,\"different\":1919,\"mean\":0.4797752608084268,\"retail\":0.5340241550547492,\"standard_error\":0.27459321069697257},\"priced - base\":{\"arb\":-0.15126599074544564,\"different\":131,\"mean\":1.3063795529616173,\"retail\":1.4576455437084266,\"standard_error\":0.2452432031092503}},\"runs\":2053,\"scores\":{\"base\":{\"arb_edge\":-23.098312875111347,\"failures\":0,\"retail_edge\":55.638243284870825,\"total_edge\":32.539930409761375},\"frozen\":{\"arb_edge\":-23.303827760101704,\"failures\":0,\"retail_edge\":57.629912983634,\"total_edge\":34.32608522353142},\"priced\":{\"arb_edge\":-23.249578865856797,\"failures\":0,\"retail_edge\":57.09588882857925,\"total_edge\":33.84630996272299}}}",
    "Delivery frozen; frozen vs priced={\"arb\":-0.05424889424490972,\"different\":1919,\"mean\":0.4797752608084268,\"retail\":0.5340241550547492,\"standard_error\":0.27459321069697257}. Positive confirmation gain retained.",
    "Other spreads: first 300 sampled seeds of each spread 2, 3, 4 from 80000; all 900 full SimulationResult objects exactly identical to base.",
    "Timing: 200 same spread-1 confirming seeds for each file, single process after other jobs completed, one warmup per file, alternating order by seed, engine construction/run only; base_seconds=210.5617640367709, yours_seconds=216.10719215264544, ratio=1.026336349057686.",
    "Initial smoke priced_q1b n=8, first choosing seeds, no policy selection; ahead smoke used the base dormant-cap bound: {\"arb\":0.07872650983749828,\"different\":8,\"mean\":0.19011641215000002,\"retail\":0.11138990231250681,\"standard_error\":0.15873009658616574}",
    "Initial smoke priced_s1b n=8, first choosing seeds, no policy selection; ahead smoke used the base dormant-cap bound: {\"arb\":0.03642202041249995,\"different\":2,\"mean\":0.036422020400000046,\"retail\":0.0,\"standard_error\":0.025309287449980327}",
    "Initial smoke priced_both n=8, first choosing seeds, no policy selection; ahead smoke used the base dormant-cap bound: {\"arb\":0.035031827924998316,\"different\":8,\"mean\":0.14642173022500002,\"retail\":0.11138990231250681,\"standard_error\":0.10368168939099835}",
    "Initial smoke priced_both_ahead n=8, first choosing seeds, no policy selection; ahead smoke used the base dormant-cap bound: {\"arb\":-0.07937388080000196,\"different\":8,\"mean\":0.049698680412499134,\"retail\":0.12907256125000213,\"standard_error\":0.06738181541122742}",
    "Ask merge review before full ask scoring: {\"note\":\"Before full ask scoring, code review restored q1-ahead tick-2 capacity cap 15000 for dormant-order retention only. New target quotes keep the priced multiplier once. Initial 8-seed smoke used retention rules with the base 3000 dormant cap; it was a smoke test, not a selection or constant search. The two source caps are unchanged.\",\"utc\":\"2026-11-06T14:26:14.259148+00:00\"}"
  ],
  "next": "Test the supplied s1-flow estimator as one unchanged addition to this block on fresh spread-1 choosing and confirming ranges. Its flow adaptation may interact with the larger tick-2 asks and retained bids; price the frozen result on Paradigm before trusting the local gain."
}