{
  "approach": "Combines the base crossing-inventory quoter with the belief result's censored latent-score diffusion/jump Gaussian-mixture filter, replacing the probability-grid posterior and manual below-one-cent decay. Surviving orders and fill-size likelihoods constrain the posterior through an assumed compound-Poisson terminal-probability mapping. The selected calibration is assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=2.0. The base's quotes inside the competitor, queue management, crossing bids, low-price stock targets and other trading constants are retained. Tuning gains are small relative to paired run-to-run uncertainty. On the untouched holdout, this calibration underperformed the unchanged base; no code changes or reselection followed that result.",
  "blocked": null,
  "python_version": "3.14.4",
  "base_mean_edge": 102.331987422326,
  "score": {
    "mean_edge": 107.7287098973095,
    "retail_edge": 134.15892612772782,
    "arb_edge": -26.430216230414665,
    "simulations": 600,
    "seed_start": 1000,
    "failures": 0
  },
  "holdout": {
    "mean_edge": 94.98058276501934,
    "simulations": 600,
    "seed_start": 30000,
    "failures": 0
  },
  "code": "\"\"\"Base crossing-inventory quoter with a censored latent-score belief filter.\"\"\"\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\nBLAM = 0.0019\nBJS = 0.4\nLAT_LIK = 1.0\nLAT_JUMPINFO = 1.0\nMIXES = {}\nZTICKS = {}\ndef mixture(H):\n    if H not in MIXES:\n        v=math.exp(-H*BLAM)\n        arr=[]\n        for n in range(16):\n            arr.append((v,1.0/math.sqrt(max(H,1)*0.0004+n*BJS*BJS)))\n            v*=H*BLAM/(n+1)\n        MIXES[H]=arr\n    return MIXES[H]\ndef latent_tick(H,t):\n    if t<=0: return -12.0\n    if t>=100: return 12.0\n    key=(H,t)\n    if key not in ZTICKS:\n        mix=mixture(H)\n        z=inv(t/100.0)*math.sqrt(H*(0.0004+BLAM*BJS*BJS))\n        for _ in range(10):\n            prob=sum(w*cdf(z*r) for w,r in mix)\n            d=sum(w*r*pdf(z*r) for w,r in mix)\n            dz=max(-0.5,min(0.5,(prob-t/100.0)/max(d,1e-12)))\n            z-=dz\n            if abs(dz)<1e-8: break\n        ZTICKS[key]=z\n    return ZTICKS[key]\ndef normal_piece(mean,var,lo,hi):\n    sd=math.sqrt(max(var,1e-12))\n    a=(lo-mean)/sd;b=(hi-mean)/sd\n    if a>0:\n        mass=0.5*(math.erfc(a/SQ2)-math.erfc(b/SQ2))\n    elif b<0:\n        mass=0.5*(math.erfc(-b/SQ2)-math.erfc(-a/SQ2))\n    else:\n        mass=cdf(b)-cdf(a)\n    if mass<1e-200: return 0.0,mean,var\n    pa=pdf(a);pb=pdf(b)\n    shift=(pa-pb)/mass\n    m=mean+sd*shift\n    v=var*max(1e-8,1+(a*pa-b*pb)/mass-shift*shift)\n    return mass,m,v\n\n\ndef inverse_survival(q):\n    q=min(0.5,max(1e-300,q))\n    t=math.sqrt(-2.0*math.log(q))\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    for _ in range(5):\n        x+=(0.5*math.erfc(x/SQ2)-q)/max(pdf(x),1e-300)\n    return max(0.0,x)\n\ndef truncated_quantile(mean,sd,lo,hi,u):\n    a=(lo-mean)/sd;b=(hi-mean)/sd\n    if a>0:\n        qa=0.5*math.erfc(a/SQ2);qb=0.5*math.erfc(b/SQ2)\n        z=inverse_survival(qa+u*(qb-qa))\n    elif b<0:\n        qa=0.5*math.erfc(-a/SQ2);qb=0.5*math.erfc(-b/SQ2)\n        z=-inverse_survival(qa+u*(qb-qa))\n    else:\n        z=inv(cdf(a)+u*(cdf(b)-cdf(a)))\n    return min(hi,max(lo,mean+sd*z))\n\n\nclass Strategy(BaseStrategy):\n    def __init__(self):\n        self.n = 0\n        self.ahead = set()     # main orders placed strictly ahead of any competitor order at their tick\n        self.res = {}          # order id -> uncovered quantity at placement\n        self.bc = None         # belief: sub-tick grid centres (ticks)\n        self.bw = None         # belief: weights\n        self.st = 0.5          # last one-step sd in ticks\n        self.pcb = self.pca = None\n        self.pb = set()\n        self.pa = set()\n        self.tb = 0\n        self.s = None          # competitor spread parameter\n\n    def _oid(self, tag):\n        self.n += 1\n        return tag + str(self.n)\n\n    def remember(self, state, actions):\n        prev = {o.order_id:(o.side,o.price_ticks,o.remaining_quantity) for o in state.own_orders}\n        for action in actions:\n            if isinstance(action, CancelOrder):\n                prev.pop(action.order_id,None)\n            elif isinstance(action, PlaceOrder):\n                prev[action.client_order_id]=(action.side,action.price_ticks,action.quantity)\n        self.previous_orders = prev\n\n    def belief(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.pcb is None:\n            spread=max(1,(ca-cb+1)//2)\n            lo,hi=cb+spread-1,ca-spread+1\n        else:\n            lo,hi=min(cb,self.pcb),max(ca,self.pca)\n        for o in state.own_orders:\n            if o.side is Side.BUY: lo=max(lo,o.price_ticks)\n            else: hi=min(hi,o.price_ticks)\n        if LAT_JUMPINFO and self.pcb is not None:\n            if ca>self.pca+1: lo=max(lo,ca-1)\n            if cb<self.pcb-1: hi=min(hi,cb+1)\n        self.pcb,self.pca=cb,ca\n        if lo>=hi: lo,hi=min(lo,hi)-0.001,max(lo,hi)+0.001\n        prev=getattr(self,'previous_orders',{})\n        survivors={o.order_id:o.remaining_quantity for o in state.own_orders}\n        filled=[[],[]]\n        cuts={lo,hi}\n        for oid,(side,tick,qty) in prev.items():\n            rest=survivors.get(oid,0.0)\n            if qty-rest>0.005:\n                filled[0 if side is Side.BUY else 1].append((tick,qty-rest,rest))\n                if lo<tick<hi: cuts.add(tick)\n        bounds=sorted(cuts)\n        if not hasattr(self,'zm'):\n            zl=latent_tick(H,lo);zh=latent_tick(H,hi)\n            self.zm=(zl+zh)/2; self.zv=(zh-zl)**2/12\n        components=[]\n        points=[]\n        for l,h in zip(bounds,bounds[1:]):\n            c=(l+h)/2\n            like=1.0\n            if LAT_LIK:\n                for side,records in enumerate(filled):\n                    rq=cash=0.0\n                    for tick,qty,rest in records:\n                        isarb=tick>c if side==0 else tick<c\n                        if not isarb:\n                            rq+=qty;cash+=qty*tick/100.0\n                    if rq<=0.005:\n                        like*=0.875\n                    else:\n                        value=rq*max(c/100.0,0.05) if side==0 else cash\n                        k=(math.log(max(value,1e-9))-(math.log(4.5)-0.72))/1.2\n                        tail=1-cdf(k)\n                        if any(rest>0.005 for tick,qty,rest in records):\n                            tail=pdf(k)/max(value*1.2,1e-9)\n                        like*=max(1e-8,0.125*tail)\n            zl=latent_tick(H,l);zh=latent_tick(H,h)\n            for jump,weight in [(0,1-BLAM),(BJS*BJS,BLAM)]:\n                mass,m,v=normal_piece(self.zm,self.zv+0.0004+jump,zl,zh)\n                components.append((weight*mass*like,m,v))\n                if mass > 1e-100:\n                    sd=math.sqrt(self.zv+0.0004+jump)\n                    aa=cdf((zl-self.zm)/sd)\n                    bb=cdf((zh-self.zm)/sd)\n                    for u,qw in [(0.1127016654,5/18),(0.5,4/9),(0.8872983346,5/18)]:\n                        zz=truncated_quantile(self.zm,sd,zl,zh,u)\n                        pp=sum(w*cdf(zz*r) for w,r in mixture(H))\n                        points.append((weight*mass*like*qw,pp))\n        total=sum(w for w,m,v in components)\n        if total<=1e-150:\n            zl=latent_tick(H,lo);zh=latent_tick(H,hi)\n            self.zm=max(zl,min(zh,self.zm));self.zv=0.0004\n        else:\n            m=sum(w*m for w,m,v in components)/total\n            v=sum(w*(v+(mm-m)**2) for w,mm,v in components)/total\n            self.zm,self.zv=m,max(v,1e-10)\n        mean=sum(w*cdf(self.zm/math.sqrt(1/r**2+self.zv)) for w,r in mixture(H))\n        if total > 1e-150 and points:\n            mean=sum(w*pp for w,pp in points)/total\n            v=max(0,sum(w*(pp-mean)**2 for w,pp in points)/total)\n        else:\n            deriv=sum(w*r*pdf(self.zm*r) for w,r in mixture(H))\n            v=deriv**2*self.zv\n        return min(0.999999,max(0.000001,mean)),v\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        phat, var_p = self.belief(state)\n        var_t = 20000.0 * var_p\n        var = P['sig'] ** 2 + P['lam_j'] * P['sig_j'] ** 2\n        sd = math.sqrt(H * var)\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            q1 = max(0.0, P['ydf1'] * min(H, P['Wf']) / (self.s / 100.0) - Y)\n        # order management\n        actions = []\n        free = state.free_cash - 2.0\n        keep_ask = 0.0\n        new_b = []\n        new_a = []\n        def cancel(o):\n            nonlocal free\n            actions.append(CancelOrder(o.order_id))\n            x = o.price_ticks / 100.0\n            if o.side is Side.BUY:\n                free += x * o.remaining_quantity\n            else:\n                free += (1 - x) * min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n            self.ahead.discard(o.order_id)\n            self.res.pop(o.order_id, None)\n        for o in state.own_orders:\n            if o.remaining_quantity <= PROBE + 1e-9:\n                cancel(o)\n        kept_ids = set()\n        def manage(orders, t, q, comp):\n            kept = 0.0\n            for o in orders:\n                if o.price_ticks == t and q > 0 and kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0):\n                    kept += o.remaining_quantity\n                    kept_ids.add(o.order_id)\n                else:\n                    cancel(o)\n            return kept\n        kb = manage(bids, bid_t, qb, cb)\n        ka = manage(asks, ask_t, qa, ca)\n        add_b = (qb - kb) if (kb < qb * P['keep_lo'] and bid_t != cb) else 0.0\n        add_a = (qa - ka) if (ka < qa * P['keep_lo'] and ask_t != ca) else 0.0\n\n        budget = P['fb'] * max(free, 0.0)\n        cost = add_b * bid_t / 100.0 + qam * amt / 100.0\n        if cost > budget:\n            f = budget / cost\n            add_b *= f\n            qam *= f\n            cost = budget\n        out = []\n        q1 = min(q1, P['f1'] * max(budget - cost, 0.0) * 100.0)\n        if q1 >= 0.01:\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=1, quantity=fl(q1), client_order_id=self._oid('c')))\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, client_order_id=self._oid('m')))\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, client_order_id=self._oid('p')))\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, client_order_id=self._oid('p')))\n                    self.pa.add(t)\n        self.remember(state, actions + out)\n        return actions + out\n",
  "code_sha256": "b497c32538a607acd52ab646c91c7c876372b6ba27c617e89dd48a9573b8feee",
  "tried": [
    "Unmodified public simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e; Python 3.14.4. All tuning used only seeds 1000-1599. Candidate selection was frozen before either final evaluation; holdout seeds 30000-30599 were not used to tune.",
    "Base reproduction on seeds 0-599: mean_edge=102.331987422326, retail_edge=127.3199510028995, arb_edge=-24.987963580578832, failures=0. Supplied reference mean_edge=102.33197908899267; measured difference=8.333333326504544e-06.",
    "Unchanged base on tuning seeds 1000-1599: mean_edge=107.2442092237515, retail_edge=135.801656522888, arb_edge=-28.557447299150834, failures=0.",
    "--workers 4 was refused by the environment: PermissionError: [Errno 1] Operation not permitted while multiprocessing created a Unix socket. All reported results use serial official simulator execution; independent research processes ran alongside each other.",
    "Numerical checks passed for tick-to-score inversion, monotonicity, truncated-normal moments and tail quantiles; maximum tested inversion error=1.9761969838327786e-14.",
    "Partial screening branches were pruned and excluded from candidate selection: additional probability floors, likelihood power 0.75, uniform regularization 0.25, crossing threshold 0.9, original-scale zero-variance hard-probe restoration, jump sigma 0.15, and unused queued sweep combinations. No incomplete or failed simulation batch is reported as a candidate.",
    "Control gridvar0 on seeds 1000-1599: mean_edge=105.19729813998417, retail_edge=138.26200980250516, arb_edge=-33.0647116625325, failures=0.",
    "Control gridvarhalf on seeds 1000-1599: mean_edge=107.331518061488, retail_edge=137.01536921746768, arb_edge=-29.683851156007666, failures=0.",
    "400-seed screen floor00001 on seeds 1000-1399: mean_edge=110.45472149668124, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; probability floor=0.0001.",
    "400-seed screen floor00005 on seeds 1000-1399: mean_edge=110.34009495808776, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; probability floor=0.0005.",
    "400-seed screen floor0001 on seeds 1000-1399: mean_edge=110.3364312327845, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; probability floor=0.001.",
    "200-seed screen swap_gridbounds on seeds 1000-1199: mean_edge=111.682529651854, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; current-only competitor bounds.",
    "200-seed screen swap_nolik on seeds 1000-1199: mean_edge=109.373782663732, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; fill likelihood disabled.",
    "200-seed screen swap_quad30 on seeds 1000-1199: mean_edge=113.12523692709699, failures=0; assumed jump sigma=0.3; three-node quadrature point estimate; posterior variance sizing weight=0.",
    "200-seed screen swap_varfull on seeds 1000-1199: mean_edge=113.09998665082499, failures=0; assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=1.0.",
    "starter --sandbox on 20 simulations, seeds 1000-1019: successes=20, failures=0. Framework used Python-level sandboxing because nsjail was unavailable.",
    "frozen candidate --sandbox on 20 simulations, seeds 1000-1019: successes=20, failures=0. Framework used Python-level sandboxing because nsjail was unavailable.",
    "Read-only fill attribution on tuning seeds 1400-1599: unchanged base mean_edge=97.747615433798; direct swap mean_edge=95.476403150669; quarter-variance latent quadrature mean_edge=97.931997204259. Direct swap extra arbitrage losses mainly occurred at ticks 2-5 and 6-94; tick-1 arbitrage loss improved. Diagnostics never exposed hidden state to submitted strategy code.",
    "Frozen candidate versus unchanged base on the untouched holdout: base mean_edge=96.04052032617767, candidate mean_edge=94.98058276501934, paired gain=-1.0599375611583355, paired standard error=0.49740626976596103; both 600 simulations, seeds 30000-30599, failures=0. This comparison was recorded after selection and did not change the code.",
    "600-seed candidate like05: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; fill likelihood power=0.5; seeds 1000-1599; mean_edge=104.83708942114684, retail_edge=138.33711071924066, arb_edge=-33.50002129809067, failures=0.",
    "600-seed candidate moment_exactprocess: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.25; compound-Poisson local derivative; compound-Poisson jump mapping; seeds 1000-1599; mean_edge=105.31793860692733, retail_edge=136.06623523994134, arb_edge=-30.748296633026, failures=0.",
    "600-seed candidate swap: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; seeds 1000-1599; mean_edge=105.47736546872316, retail_edge=138.131417599906, arb_edge=-32.65405213119983, failures=0.",
    "600-seed candidate cross_m08: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; tick-1 crossing threshold=0.8 ticks; seeds 1000-1599; mean_edge=105.654214535345, retail_edge=138.34462969691367, arb_edge=-32.6904151615855, failures=0.",
    "600-seed candidate riskfloor01: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; quote-risk probability noise floor=0.001; seeds 1000-1599; mean_edge=105.69778161074066, retail_edge=138.01143380867282, arb_edge=-32.313652197946666, failures=0.",
    "600-seed candidate f_j35_v000_moment: assumed jump sigma=0.35; moment point estimate; posterior variance sizing weight=0.0; seeds 1000-1599; mean_edge=105.7279242013, retail_edge=138.36191168582417, arb_edge=-32.63398748457083, failures=0.",
    "600-seed candidate riskfloor02: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0; quote-risk probability noise floor=0.002; seeds 1000-1599; mean_edge=105.7950923821765, retail_edge=137.0114381062602, arb_edge=-31.216345724097998, failures=0.",
    "600-seed candidate f_j35_v000_quad: assumed jump sigma=0.35; three-node quadrature point estimate; posterior variance sizing weight=0.0; seeds 1000-1599; mean_edge=105.88988763638967, retail_edge=138.50609493571383, arb_edge=-32.616207299342335, failures=0.",
    "600-seed candidate f_j25_v100_quad: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=1.0; seeds 1000-1599; mean_edge=105.9151396919005, retail_edge=135.39885852960248, arb_edge=-29.483718837713997, failures=0.",
    "600-seed candidate riskpinf30: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=3.0; base hard probe bounds restored; seeds 1000-1599; mean_edge=106.0367873960875, retail_edge=133.01234705035418, arb_edge=-26.975559654280833, failures=0.",
    "600-seed candidate moment_derivative: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.25; compound-Poisson local derivative; seeds 1000-1599; mean_edge=106.0772951609285, retail_edge=135.82128863027216, arb_edge=-29.743993469370835, failures=0.",
    "600-seed candidate f_j30_v025_quad: assumed jump sigma=0.3; three-node quadrature point estimate; posterior variance sizing weight=0.25; seeds 1000-1599; mean_edge=106.13372279225884, retail_edge=137.28415416287183, arb_edge=-31.150431370622332, failures=0.",
    "600-seed candidate f_j25_v100_moment: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=1.0; seeds 1000-1599; mean_edge=106.14379423823749, retail_edge=135.3624822227525, arb_edge=-29.21868798451017, failures=0.",
    "600-seed candidate f_j25_v000_moment: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.0; seeds 1000-1599; mean_edge=106.19213323486366, retail_edge=138.42434715253717, arb_edge=-32.23221391769933, failures=0.",
    "600-seed candidate swap_varhalf: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=0.5; seeds 1000-1599; mean_edge=106.20498713486417, retail_edge=136.7082851814695, arb_edge=-30.503298046624, failures=0.",
    "600-seed candidate f_j30_v100_quad: assumed jump sigma=0.3; three-node quadrature point estimate; posterior variance sizing weight=1.0; seeds 1000-1599; mean_edge=106.207322698814, retail_edge=135.53593155850282, arb_edge=-29.32860885967133, failures=0.",
    "600-seed candidate swap_varfull: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=1.0; seeds 1000-1599; mean_edge=106.277997997313, retail_edge=136.0333607562915, arb_edge=-29.755362758969998, failures=0.",
    "600-seed candidate f_j30_v000_quad: assumed jump sigma=0.3; three-node quadrature point estimate; posterior variance sizing weight=0.0; seeds 1000-1599; mean_edge=106.29353587843049, retail_edge=138.39763942518817, arb_edge=-32.10410354678783, failures=0.",
    "600-seed candidate f_j30_v050_moment: assumed jump sigma=0.3; moment point estimate; posterior variance sizing weight=0.5; seeds 1000-1599; mean_edge=106.35735471285467, retail_edge=136.53910181900366, arb_edge=-30.1817471061635, failures=0.",
    "600-seed candidate f_j30_v025_moment: assumed jump sigma=0.3; moment point estimate; posterior variance sizing weight=0.25; seeds 1000-1599; mean_edge=106.41626132293584, retail_edge=137.35794477559133, arb_edge=-30.94168345269367, failures=0.",
    "600-seed candidate f_j25_v000_quad: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=0.0; seeds 1000-1599; mean_edge=106.43261733145916, retail_edge=138.45675207129065, arb_edge=-32.024134739856166, failures=0.",
    "600-seed candidate riskv15_pinf: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=1.5; base hard probe bounds restored; seeds 1000-1599; mean_edge=106.47764985307717, retail_edge=135.20170823234434, arb_edge=-28.724058379277, failures=0.",
    "600-seed candidate f_j25_v050_quad: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=0.5; seeds 1000-1599; mean_edge=106.4820540475075, retail_edge=136.52887426974416, arb_edge=-30.046820222250833, failures=0.",
    "600-seed candidate f_j25_v050_moment: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.5; seeds 1000-1599; mean_edge=106.64395840040866, retail_edge=136.54531649387215, arb_edge=-29.901358093472833, failures=0.",
    "600-seed candidate moment_j20: assumed jump sigma=0.2; moment point estimate; posterior variance sizing weight=0.25; seeds 1000-1599; mean_edge=106.69643830507634, retail_edge=137.63548340022334, arb_edge=-30.93904509514783, failures=0.",
    "600-seed candidate regularize01: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=0.25; uniform interval regularization=0.1; seeds 1000-1599; mean_edge=106.885444239444, retail_edge=137.703610568563, arb_edge=-30.818166329137668, failures=0.",
    "600-seed candidate f_j25_v025_quad: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=0.25; seeds 1000-1599; mean_edge=106.90135814138083, retail_edge=137.6466608811045, arb_edge=-30.74530273973717, failures=0.",
    "600-seed candidate f_j25_v025_quad_pinf: assumed jump sigma=0.25; three-node quadrature point estimate; posterior variance sizing weight=0.25; base hard probe bounds restored; seeds 1000-1599; mean_edge=107.01969755268449, retail_edge=137.56149303642917, arb_edge=-30.541795483757166, failures=0.",
    "600-seed candidate riskv25: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=2.5; seeds 1000-1599; mean_edge=107.0376169387925, retail_edge=133.44566906916117, arb_edge=-26.408052130371335, failures=0.",
    "600-seed candidate f_j25_v025_moment: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.25; seeds 1000-1599; mean_edge=107.220180867775, retail_edge=137.62777021075917, arb_edge=-30.40758934300583, failures=0.",
    "600-seed candidate riskpinf25: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=2.5; base hard probe bounds restored; seeds 1000-1599; mean_edge=107.32267899869166, retail_edge=133.62603983820281, arb_edge=-26.30336083953267, failures=0.",
    "600-seed candidate pinf_moment: assumed jump sigma=0.25; moment point estimate; posterior variance sizing weight=0.25; base hard probe bounds restored; seeds 1000-1599; mean_edge=107.43085148013216, retail_edge=137.58581567165035, arb_edge=-30.154964191517998, failures=0.",
    "600-seed candidate riskv15: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=1.5; seeds 1000-1599; mean_edge=107.69761777993199, retail_edge=135.2224299952355, arb_edge=-27.5248122153065, failures=0.",
    "Selected riskv20 before holdout: assumed jump sigma=0.4; three-node quadrature point estimate; posterior variance sizing weight=2.0. Frozen-file recheck on seeds 1000-1599: mean_edge=107.7287098973095, retail_edge=134.15892612772782, arb_edge=-26.430216230414665, paired gain over base=0.48450067355800286, paired standard error=1.045146739766478, failures=0. Untouched seeds 30000-30599: mean_edge=94.98058276501934, failures=0. No tuning followed the holdout."
  ],
  "next": "Keep the base pending evidence from fresh seeds. Run the latent filter in shadow to separate point-estimate calibration from sizing uncertainty and probe radius. Calibrate one-step quote losses by price band and side, especially ticks 2-5 and the middle range, while preserving the crossing-inventory policy. Restoring hard probe bounds helped the low-jump moment variant but hurt the original-scale high-uncertainty variant, so test that interaction explicitly. Use a new holdout: the selected tuning gain did not survive this holdout."
}