{"key":"side-jump-budget","approach":"Add only a side-specific jump charge to sizing loss on observed spreads 2 and 3. At x=t/100 add multiplier*lj*sum(w*max(0,x-p))*phat*2/(1+self.s)*sqrt(min(1,500/H)) to bid loss, and multiplier*lj*sum(w*max(0,p-x))*(1-phat)*2/(1+self.s)*sqrt(min(1,500/H)) to ask loss, using existing jp. Added immediately before qsize (after the existing low-probability bid loss scaling); existing qsize kA then applies. AB, gains and queue code unchanged.","tried":[{"multiplier":0.5,"code_sha256":"9b2dd6c581608bd034d722c0a2e10a17054fb0a9c468035a6f8a402b902a29df","target_spreads":[2,3]},{"multiplier":1.0,"code_sha256":"8c249a7d2ea89675599b3605807a195ee3c960f7db34650935c1f158ab210f14","target_spreads":[2,3]}],"next":"Keep B as incumbent. This is screening only; no finalist seeds were run and no promotion is claimed. Extend the frozen candidate on independent paired seeds only if the coordinator considers its screening evidence worthwhile.","code":"\"\"\"Exact per-spread merge: c1-pairs, c2-queue, a3-ledger, c4-all; no fixes.\"\"\"\nimport math\nfrom orderbook_pm_challenge.strategy import BaseStrategy\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\n\ndef _spread1():\n    P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02, 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, ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0, 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)\n    SQ2 = math.sqrt(2.0)\n    SQ2PI = math.sqrt(2.0 * math.pi)\n\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / SQ2))\n\n    def pdf(x):\n        return math.exp(-0.5 * x * x) / SQ2PI\n\n    def inv(p):\n        p = min(max(p, 1e-09), 1 - 1e-09)\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\n\n    def lpm(k):\n        return pdf(k) + k * cdf(k)\n\n    def fl(q):\n        return math.floor(q * 100 + 1e-09) / 100\n    NODES = [(-2.2, 0.06), (-1.3, 0.17), (-0.6, 0.27), (0.6, 0.27), (1.3, 0.17), (2.2, 0.06)]\n    PROBE = 0.01\n    F_AH = 1\n    F_FL = 1\n    F_QB = 1\n    F_LK = 0\n    F_SB = 0\n    FLOW_CAPS = (0.03125, 0.0625, 0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0)\n    FLOW_GRID = []\n    FLOW_PRIORS = []\n    for _rn, _rw in ((-math.sqrt(0.6), 5 / 18), (0.0, 4 / 9), (math.sqrt(0.6), 5 / 18)):\n        _r = 0.1265 + 0.0495 * _rn\n        for _mn, _mw in ((-0.906179845938664, 0.118463442528095), (-0.538469310105683, 0.239314335249683), (0.0, 0.284444444444444), (0.538469310105683, 0.239314335249683), (0.906179845938664, 0.118463442528095)):\n            _m = 4.488 + 1.848 * _mn\n            _parts = []\n            for _n in (1, 2, 3):\n                _v = math.log(1 + (math.exp(1.44) - 1) / _n)\n                _sg = math.sqrt(_v)\n                _mu = math.log(_n * _m) - _v / 2\n                _w = _r ** _n / math.factorial(_n)\n                _parts.append((_mu, _sg, _w))\n            _norm = []\n            for _c in FLOW_CAPS:\n                _prob = sum((w * (cdf((math.log(_c) - mu) / sg) - cdf((math.log(0.03) - mu) / sg)) for mu, sg, w in _parts))\n                _norm.append(math.log1p(_prob))\n            _tails = []\n            for _c in FLOW_CAPS:\n                _cdf = 1 + sum((w * cdf((math.log(_c) - mu) / sg) for mu, sg, w in _parts))\n                _tails.append(math.log(max(1e-100, 1 - math.exp(-_r) * _cdf)))\n            FLOW_GRID.append((_r, _m, _parts, _norm, _tails))\n            FLOW_PRIORS.append(math.log(_rw * _mw))\n    return (P, cdf, pdf, inv, lpm, fl, NODES, PROBE, F_AH, F_FL, F_QB, F_LK, F_SB, FLOW_CAPS, FLOW_GRID, FLOW_PRIORS)\n\ndef _spread2():\n    P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02, 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, ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0, 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)\n    SQ2 = math.sqrt(2.0)\n    SQ2PI = math.sqrt(2.0 * math.pi)\n\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / SQ2))\n\n    def pdf(x):\n        return math.exp(-0.5 * x * x) / SQ2PI\n\n    def inv(p):\n        p = min(max(p, 1e-09), 1 - 1e-09)\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\n\n    def lpm(k):\n        return pdf(k) + k * cdf(k)\n\n    def fl(q):\n        return math.floor(q * 100 + 1e-09) / 100\n    NODES = [(-2.2, 0.06), (-1.3, 0.17), (-0.6, 0.27), (0.6, 0.27), (1.3, 0.17), (2.2, 0.06)]\n    PROBE = 0.01\n    return (P, cdf, pdf, inv, lpm, fl, NODES, PROBE)\n\ndef _spread3():\n    P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02, 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, ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0, 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)\n    SQ2 = math.sqrt(2.0)\n    SQ2PI = math.sqrt(2.0 * math.pi)\n\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / SQ2))\n\n    def pdf(x):\n        return math.exp(-0.5 * x * x) / SQ2PI\n\n    def inv(p):\n        p = min(max(p, 1e-09), 1 - 1e-09)\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\n\n    def lpm(k):\n        return pdf(k) + k * cdf(k)\n\n    def fl(q):\n        return math.floor(q * 100 + 1e-09) / 100\n    NODES = [(-2.2, 0.06), (-1.3, 0.17), (-0.6, 0.27), (0.6, 0.27), (1.3, 0.17), (2.2, 0.06)]\n    PROBE = 0.01\n    return (P, cdf, pdf, inv, lpm, fl, NODES, PROBE)\n\ndef _spread4():\n    P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02, 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, ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0, 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)\n    SQ2 = math.sqrt(2.0)\n    SQ2PI = math.sqrt(2.0 * math.pi)\n\n    def cdf(x):\n        return 0.5 * (1.0 + math.erf(x / SQ2))\n\n    def pdf(x):\n        return math.exp(-0.5 * x * x) / SQ2PI\n\n    def inv(p):\n        p = min(max(p, 1e-09), 1 - 1e-09)\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\n\n    def lpm(k):\n        return pdf(k) + k * cdf(k)\n\n    def fl(q):\n        return math.floor(q * 100 + 1e-09) / 100\n    NODES = [(-2.2, 0.06), (-1.3, 0.17), (-0.6, 0.27), (0.6, 0.27), (1.3, 0.17), (2.2, 0.06)]\n    PROBE = 0.01\n    return (P, cdf, pdf, inv, lpm, fl, NODES, PROBE)\n\nclass Strategy(BaseStrategy):\n\n    def __init__(self):\n        self.n = 0\n        self.ahead = set()\n        self.res = {}\n        self.bc = None\n        self.bw = None\n        self.st = 0.5\n        self.pcb = self.pca = None\n        self.pb = set()\n        self.pa = set()\n        self.tb = 0\n        self.s = None\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 = None\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        initializing = self.s is None\n        if initializing:\n            self.s = max(1, (ca - cb + 1) // 2)\n            if self.s == 1:\n                self._scope = _spread1()\n            elif self.s == 2:\n                self._scope = _spread2()\n            elif self.s == 3:\n                self._scope = _spread3()\n            elif self.s == 4:\n                self._scope = _spread4()\n            self.params = self._scope[0].copy()\n        if self.s == 1:\n            _unused, cdf, pdf, inv, lpm, fl, NODES, PROBE, F_AH, F_FL, F_QB, F_LK, F_SB, FLOW_CAPS, FLOW_GRID, FLOW_PRIORS = self._scope\n        elif self.s == 2:\n            _unused, cdf, pdf, inv, lpm, fl, NODES, PROBE = self._scope\n        elif self.s == 3:\n            _unused, cdf, pdf, inv, lpm, fl, NODES, PROBE = self._scope\n        elif self.s == 4:\n            _unused, cdf, pdf, inv, lpm, fl, NODES, PROBE = self._scope\n        if initializing:\n            if self.s == 1:\n                self.fl_obs = ()\n                self.fl_scores = FLOW_PRIORS.copy()\n                self.fl_bins = [0] * len(FLOW_CAPS)\n                self.fl_rate = 0.568734\n                self.fl_r = 0.1265\n                self.fl_m = 4.488\n                self.fl_buy_sum = self.fl_sell_sum = 0.0\n                self.fl_buy_exp = self.fl_sell_exp = 0.0\n                self.fl_exp = 0\n                self.fl_dirty = False\n                self.fl_ahead = set()\n                self.fl_safe_n = 0\n                self.fl_sbins = [0] * len(FLOW_CAPS)\n            elif self.s == 2:\n                self.params.update(dict(flow_prior=200, flow_level=0.6, flow_strength=1.0, flow_safety=2.0, flow_exposure_min=5.0))\n                self.params.update({'kLf': 0.16})\n            elif self.s == 4:\n                self.f4_exposure = [0, 0]\n                self.f4_events = [0, 0]\n                self.f4_weights = [[0.0] * 9, [0.0] * 9]\n                self.f4_means = [2.64 + i * (6.336 - 2.64) / 8 for i in range(9)]\n                self.f4_mu = [math.log(m) - 0.72 + 0.065 for m in self.f4_means]\n                self.f4_last = [None, None]\n                self.f4_rate = 0.1265\n                self.f4_mean = 4.488\n                self.f4_flow = self.f4_rate * self.f4_mean\n                self.f4_extreme = inv(1 - 1e-06)\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 == 1:\n            s1 = self.s == 1\n            AH = s1 and F_AH\n            FL = s1 and F_FL\n            QB = s1 and F_QB\n            LK = s1 and F_LK\n            SB = s1 and F_SB\n        elif self.s == 2:\n            if 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        elif self.s == 3:\n            if 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        elif self.s == 4:\n            for side, filled, bound in ((0, state.sell_filled_quantity, ca), (1, state.buy_filled_quantity, cb)):\n                last = self.f4_last[side]\n                if last is None:\n                    continue\n                tick, conv, cap, old_bound, ids = last\n                if side == 0 and bound > old_bound + 1 or (side == 1 and bound < old_bound - 1):\n                    continue\n                remaining = sum((o.remaining_quantity for o in state.own_orders if o.order_id in ids))\n                value = max(0.0, cap - remaining * conv)\n                self.f4_exposure[side] += 1\n                if value > 0.01:\n                    self.f4_events[side] += 1\n                    censored = remaining < 0.011\n                    logv = math.log(max(value, 0.01))\n                    weights = self.f4_weights[side]\n                    for i, mu4 in enumerate(self.f4_mu):\n                        z4 = (logv - mu4) / 1.2\n                        weights[i] += math.log(max(1e-12, 1.0 - cdf(z4))) if censored else -0.5 * z4 * z4\n            if state.step % 25 == 0:\n                exposure = sum(self.f4_exposure)\n                events = sum(self.f4_events)\n                event_probability = (events + 80.0 * (1.0 - math.exp(-0.1265))) / (exposure + 80.0)\n                self.f4_rate = max(0.077, min(0.176, -math.log(max(1e-06, 1.0 - event_probability))))\n                weights = [a + b for a, b in zip(*self.f4_weights)]\n                peak = max(weights)\n                weights = [math.exp(w - peak) for w in weights]\n                self.f4_mean = sum((m * w for m, w in zip(self.f4_means, weights))) / sum(weights)\n                self.f4_flow = self.f4_rate * self.f4_mean\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-09}\n            sa = {o.price_ticks for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity <= PROBE + 1e-09}\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        wid = max(hi - lo, 1e-06)\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            elif self.s == 2:\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            elif self.s == 3:\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            elif self.s == 4:\n                exp = math.exp\n                if self.bc == cs and 1.0 <= wid <= 6.0 and (nb == 8 * wid):\n                    kernel = []\n                    c0 = cs[0]\n                    for ci in self.bc:\n                        d = c0 - ci\n                        d2 = d * d * k\n                        kernel.append(exp(d2) if d2 > -12.5 else 0.0)\n                    kernel = kernel[:0:-1] + kernel\n                    for i in range(nb):\n                        t = uni\n                        start = nb - 1 - i\n                        for wi, e in zip(self.bw, kernel[start:start + nb]):\n                            if e:\n                                t += wi * e\n                        ws.append(t)\n                else:\n                    previous = tuple(zip(self.bc, self.bw))\n                    for c in cs:\n                        t = uni\n                        for ci, wi in previous:\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            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        if self.s == 1:\n            if FL:\n                remaining = {o.order_id: o.remaining_quantity for o in state.own_orders} if self.fl_obs else {}\n                for px, ci, buying, previous, tick in self.fl_obs:\n                    qty = max(0.0, sum((q - remaining.get(oid, 0.0) for oid, q in previous)))\n                    x = qty * px\n                    cap = FLOW_CAPS[ci]\n                    gap = tick / 100.0 - phat if buying else phat - tick / 100.0\n                    safe = gap > max(0.005, 2.0 * math.sqrt(var_t) / 100.0)\n                    if 0 < x < 0.03:\n                        continue\n                    if x >= cap:\n                        if safe:\n                            self.fl_sbins[ci] += 1\n                            self.fl_exp += 1\n                            self.fl_dirty = True\n                        continue\n                    if safe:\n                        self.fl_safe_n += 1\n                    else:\n                        self.fl_bins[ci] += 1\n                    self.fl_exp += 1\n                    self.fl_dirty = True\n                    if buying:\n                        self.fl_buy_exp += 1\n                    else:\n                        self.fl_sell_exp += 1\n                    if x > 0:\n                        lx = math.log(x)\n                        for i, (r, m, parts, norm, tails) in enumerate(FLOW_GRID):\n                            density = sum((w * pdf((lx - mu) / sg) / sg for mu, sg, w in parts))\n                            self.fl_scores[i] += math.log(max(density, 1e-100))\n                if state.step % 100 == 0 and (self.fl_dirty or state.step == 0):\n                    bins = [(i, n) for i, n in enumerate(self.fl_bins) if n]\n                    sbins = [(i, n) for i, n in enumerate(self.fl_sbins) if n]\n                    logs = [score - sum((n * g[3][i] for i, n in bins)) - g[0] * self.fl_safe_n + sum((n * g[4][i] for i, n in sbins)) for score, g in zip(self.fl_scores, FLOW_GRID)]\n                    mx = max(logs)\n                    weights = [math.exp(v - mx) for v in logs]\n                    den = sum(weights)\n                    self.fl_dirty = False\n                    self.fl_rate = sum((w * g[0] * g[1] for w, g in zip(weights, FLOW_GRID))) / den\n                    self.fl_r = sum((w * g[0] for w, g in zip(weights, FLOW_GRID))) / den\n                    self.fl_m = sum((w * g[1] for w, g in zip(weights, FLOW_GRID))) / den\n            s = math.sqrt(sp * sp + var_t / 10000.0 + 1e-08)\n        s = math.sqrt(sp * sp + var_t / 10000.0 + 1e-08)\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            if self.s == 1:\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            elif self.s == 2:\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            elif self.s == 3:\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            elif self.s == 4:\n                ja = jb = 0.0\n                for p, w in jp:\n                    if x > p:\n                        ja += w * (x - p)\n                    else:\n                        jb += w * (p - x)\n            return ((1 - lj) * a + lj * ja, (1 - lj) * b + lj * jb)\n        if H == 1:\n\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        if self.s == 1:\n\n            def qsize(loss, gain, px, flow=False, cap=None):\n                loss *= kA\n                if gain <= 0:\n                    return 0.0\n                rr = 0.125 + 0.5 * (self.fl_r - 0.125) if flow and FL else rho\n                mm = math.log(5.0 + 0.5 * (self.fl_m - 5.0)) - 0.5 * P['sig_n'] ** 2 if flow and FL else mu\n                tail = loss / (rr * gain)\n                if tail >= 1.0:\n                    return 0.0\n                return min(cap if cap is not None else P['qmax'], math.exp(mm + P['sig_n'] * inv(1 - max(tail, 1e-06))) / px)\n        elif self.s == 2:\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-06))) / px)\n        elif self.s == 3:\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-06))) / px)\n        elif self.s == 4:\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                if tail <= 1e-06:\n                    return min(P['qmax'], math.exp(mu + P['sig_n'] * self.f4_extreme) / px)\n                return min(P['qmax'], math.exp(mu + P['sig_n'] * inv(1 - max(tail, 1e-06))) / px)\n        ptk = phat * 100\n\n        def qbid(t):\n            if not 1 <= t <= 99:\n                return 0.0\n            if self.s == 4:\n                if 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            if self.s in (2, 3):\n                x = t / 100.0\n                a += 1.0 * lj * sum(w * max(0.0, x - p) for p, w in jp) * phat * 2 / (1 + self.s) * math.sqrt(min(1.0, 500 / H))\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            if self.s == 4:\n                if 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            if self.s in (2, 3):\n                x = t / 100.0\n                b += 1.0 * lj * sum(w * max(0.0, p - x) for p, w in jp) * (1 - phat) * 2 / (1 + self.s) * math.sqrt(min(1.0, 500 / H))\n            if self.s == 1:\n                return qsize(b, a, t / 100.0, flow=True, cap=15000 if AH and t == 2 and (phat < 0.01) else None)\n            elif self.s == 2:\n                return qsize(b, a, t / 100.0)\n            elif self.s == 3:\n                return qsize(b, a, t / 100.0)\n            elif self.s == 4:\n                if P['pL'] <= phat <= 0.95:\n                    b *= 1.4\n                tail = b * kA / max(self.f4_rate * a, 1e-30)\n                if a <= 0 or tail >= 1.0:\n                    return 0.0\n                return min(P['qmax'], math.exp(math.log(self.f4_mean) - 0.72 + 1.2 * (self.f4_extreme if tail <= 1e-06 else inv(1 - tail))) / (t / 100.0))\n                return qsize(b, a, t / 100.0)\n        bids = [o for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity > PROBE + 1e-09]\n        asks = [o for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity > PROBE + 1e-09]\n        sticky = P['sticky'] > 0\n        if self.s == 4:\n            ma_ask = 50.0 if self.s == 4 and phat < 0.05 else P['min_ahead']\n\n        def top(orders, comp, step):\n            if self.s == 1:\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 QB and step == 1 else P['min_ahead']):\n                    return comp\n            elif self.s == 2:\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'] if self.s != 2 else max(P['min_ahead'], 400.0 / max(comp, 1))):\n                    return comp\n            elif self.s == 3:\n                threshold = max(P['min_ahead'], 20 * 100.0 / comp) if self.s == 3 and step == -1 and (1 <= comp <= 5) else P['min_ahead']\n                if sticky and sum((o.remaining_quantity for o in orders if o.price_ticks == comp and o.order_id in self.ahead)) >= threshold:\n                    return comp\n            elif self.s == 4:\n                if sticky and sum((o.remaining_quantity for o in orders if o.price_ticks == comp and o.order_id in self.ahead)) >= (ma_ask if step < 0 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        if self.s == 2:\n            s2f = self.s == 2 and bid_t == cb and (qb > 0) and (cb / 100.0 < phat)\n        elif self.s == 4:\n            if phat < 0.05 and bid_t >= 2:\n                qb *= 0.5\n        qa = qask(ask_t)\n        if self.s == 1:\n            if ask_t == 2 and ask_t < ca and (phat < 0.01):\n                qa *= 5\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\n        ptick = phat * 100\n        if self.s == 1:\n            flow_scale = max(0.4, min(1.9, 1.0 + 0.5 * (self.fl_rate / 0.568734 - 1))) if FL else 1.0\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            if self.s == 1:\n                target = min(flow_scale * P['ydem'] * min(H, P['W']) / x, capw)\n                full = min(flow_scale * P['ydf'] * min(H, P['Wf']) / x, capw) if phat < P['pmaxf'] else 0.0\n            elif self.s == 2:\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            elif self.s == 3:\n                flow_scale = self.f4_flow / 0.6 if self.s == 4 else 1.0\n                target = min(flow_scale * P['ydem'] * min(H, P['W']) / x, capw)\n                target = min(target, qa)\n                full = min(flow_scale * P['ydf'] * min(H, P['Wf']) / x, capw) if phat < P['pmaxf'] else 0.0\n            elif self.s == 4:\n                flow_scale = self.f4_flow / 0.6 if self.s == 4 else 1.0\n                target = min(flow_scale * P['ydem'] * min(H, P['W']) / x, capw)\n                full = min(flow_scale * 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-09))))\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))\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))\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            if self.s == 1:\n                stock_rate = flow_scale * P['ydf1']\n            elif self.s == 2:\n                stock_rate = P['ydf1']\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            elif self.s == 3:\n                stock_rate = P['ydf1']\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            elif self.s == 4:\n                stock_rate = P['ydf1']\n                stock_rate *= self.f4_flow / 0.6\n            q1 = max(0.0, stock_rate * min(H, P['Wf']) / (self.s / 100.0) - Y)\n        if 0.02 < phat < 0.1:\n            ct = max(2, min(99, int(math.floor(ptick))))\n            wealth = state.cash + Y * phat\n            if self.s == 1:\n                goal = min(flow_scale * qa, 0.3 * max(wealth, 0.0) / phat)\n            elif self.s == 2:\n                goal = min(1 * qa, 0.3 * max(wealth, 0.0) / phat)\n            elif self.s == 3:\n                goal = min(1 * qa, 0.3 * max(wealth, 0.0) / phat)\n            elif self.s == 4:\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        if self.s == 1:\n            if SB and 2 <= amt <= 5:\n                amt, qam = (0, 0.0)\n        actions = []\n        free = state.free_cash - 2.0\n        keep_ask = 0.0\n        new_b = []\n        new_a = []\n\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-09:\n                cancel(o)\n        kept_ids = set()\n\n        def manage(orders, t, q, comp):\n            kept = 0.0\n            if self.s == 1:\n                banked = {}\n                bound_t, bound_q = (t, q)\n            elif self.s == 2:\n                buy = comp == cb\n                held = {}\n                for o in orders:\n                    x = o.price_ticks\n                    gain = ptk - x if buy else x - ptk\n                    if x == t and q > 0 and (kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0) or (x == comp and o.order_id in self.ahead and (ptk <= 10.0) and (gain >= 0.0))):\n                        kept += o.remaining_quantity\n                        kept_ids.add(o.order_id)\n                        continue\n                    if x == comp and x != t and (o.order_id in self.ahead) and (gain >= 1.0):\n                        if x not in held:\n                            held[x] = [0.0, qbid(x) if buy else qask(x)]\n                        h = held[x]\n                        if h[0] + o.remaining_quantity <= max(h[1], 1.0) and h[1] > 0:\n                            h[0] += o.remaining_quantity\n                            kept_ids.add(o.order_id)\n                            continue\n                    cancel(o)\n                return kept\n            for o in orders:\n                if self.s == 1:\n                    ah = o.order_id in self.ahead\n                    if AH and ah and (o.remaining_quantity >= P['min_ahead']) and (o.price_ticks != t):\n                        if o.side is Side.SELL and o.price_ticks > t or (o.side is Side.BUY and o.price_ticks < t):\n                            enough = bound_q > 0 and (o.remaining_quantity <= bound_q if o.side is Side.BUY else 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) if o.side is Side.SELL else qbid(o.price_ticks)\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 QB and ah and (o.side is Side.BUY) 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                        continue\n                    khi = 10.0 if AH and ah else P['keep_hi']\n                    if o.price_ticks == t and q > 0 and (kept + o.remaining_quantity <= max(q * khi, 1.0) or (s1 and ah and (o.side is Side.SELL) and (o.price_ticks <= 2)) or (LK and ah) or (QB and ah and (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                elif self.s == 2:\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                elif self.s == 3:\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                elif self.s == 4:\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        if self.s == 1:\n            kb = manage(bids, bid_t, qb, cb)\n        elif self.s == 2:\n            if s2f and 1:\n                kb = 0.0\n                for o in bids:\n                    if o.price_ticks == bid_t and o.order_id in self.ahead:\n                        kb += o.remaining_quantity\n                        kept_ids.add(o.order_id)\n                    else:\n                        cancel(o)\n            elif 1 and bid_t == cb + 1:\n                qf = qbid(cb) if 1 else 1.0\n                fr = [o for o in bids if o.price_ticks == cb and o.order_id in self.ahead and (cb / 100.0 < phat - 0.0) and (qf > 0)]\n                for o in fr:\n                    kept_ids.add(o.order_id)\n                kb = manage([o for o in bids if o not in fr], bid_t, qb, cb)\n            else:\n                kb = manage(bids, bid_t, qb, cb)\n        elif self.s == 3:\n            kb = manage(bids, bid_t, qb, cb)\n        elif self.s == 4:\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        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-06\n        if amt and qam >= 0.01:\n            q = fl(qam)\n            if self.s == 1:\n                if QB:\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            elif self.s == 2:\n                out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n            elif self.s == 3:\n                out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n            elif self.s == 4:\n                out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n            free -= q * amt / 100.0 + 1e-06\n        if bid_t >= 1 and add_b >= 0.01:\n            q = fl(add_b)\n            if self.s == 4:\n                total_q = q\n                if 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=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=Side.BUY, price_ticks=bid_t, quantity=q, client_order_id=oid))\n            if self.s == 1:\n                free -= q * bid_t / 100.0 + 1e-06\n            elif self.s == 2:\n                free -= q * bid_t / 100.0 + 1e-06\n            elif self.s == 3:\n                free -= q * bid_t / 100.0 + 1e-06\n            elif self.s == 4:\n                free -= total_q * bid_t / 100.0 + 1e-06\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) 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-06\n                if ask_t < ca:\n                    self.ahead.add(oid)\n        f = int(math.floor(ptick + 1e-09))\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 == 1:\n            if FL:\n                if not kept_ids and (not any((o.quantity > PROBE for o in out))):\n                    self.fl_obs = ()\n                    self.fl_ahead.clear()\n                    return actions + out\n                for i, o in enumerate(out):\n                    if o.client_order_id is None and o.quantity > PROBE:\n                        out[i] = PlaceOrder(side=o.side, price_ticks=o.price_ticks, quantity=o.quantity, client_order_id=self._oid('f'))\n                live = []\n                oldids = {o.order_id for o in state.own_orders if o.order_id in kept_ids}\n                self.fl_ahead.intersection_update(oldids)\n                for o in state.own_orders:\n                    if o.order_id in kept_ids and o.remaining_quantity > PROBE:\n                        live.append((o.side, o.price_ticks, o.remaining_quantity, o.order_id))\n                for o in out:\n                    if o.quantity > PROBE:\n                        live.append((o.side, o.price_ticks, o.quantity, o.client_order_id))\n                        if o.side is Side.SELL and o.price_ticks < ca or (o.side is Side.BUY and o.price_ticks > cb):\n                            self.fl_ahead.add(o.client_order_id)\n                obs = []\n                for buying, side, comp in ((True, Side.SELL, ca), (False, Side.BUY, cb)):\n                    orders = [o for o in live if o[0] is side]\n                    if not orders:\n                        continue\n                    tick = min((o[1] for o in orders)) if buying else max((o[1] for o in orders))\n                    orders = [o for o in orders if o[1] == tick]\n                    if not all((o[3] in self.fl_ahead for o in orders)):\n                        continue\n                    if buying and tick > comp or (not buying and tick < comp):\n                        continue\n                    px = tick / 100.0 if buying else max(phat, 0.05)\n                    cap = sum((o[2] for o in orders)) * px\n                    if cap < 0.04:\n                        continue\n                    ci = 0\n                    while ci + 1 < len(FLOW_CAPS) and FLOW_CAPS[ci + 1] < cap - 0.01:\n                        ci += 1\n                    obs.append((px, ci, buying, [(o[3], o[2]) for o in orders], tick))\n                self.fl_obs = obs\n        elif self.s == 2:\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        elif 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        elif self.s == 4:\n            self.f4_last = [None, None]\n            for side, tick, bound, conv, distance in ((0, ask_t, ca, ask_t / 100.0, ask_t / 100.0 - phat), (1, bid_t, cb, max(phat, 0.05), phat - bid_t / 100.0)):\n                if distance < max(0.01, 3.0 * self.st / 100.0):\n                    continue\n                if side == 0:\n                    if not (ask_t < ca or any((o.price_ticks == ask_t and o.order_id in self.ahead for o in asks))):\n                        continue\n                    orders = asks\n                    order_side = Side.SELL\n                else:\n                    if not (bid_t > cb or any((o.price_ticks == bid_t and o.order_id in self.ahead for o in bids))):\n                        continue\n                    if qam > 0 and amt >= bid_t or (q1 > 0 and bid_t <= 1):\n                        continue\n                    orders = bids\n                    order_side = Side.BUY\n                tracked = {o.order_id: o.remaining_quantity for o in orders if o.order_id in kept_ids and o.price_ticks == tick}\n                tracked.update({o.client_order_id: o.quantity for o in out if o.side is order_side and o.price_ticks == tick and (o.client_order_id is not None)})\n                cap = sum(tracked.values()) * conv\n                if cap >= 0.5:\n                    self.f4_last[side] = (tick, conv, cap, bound, tracked)\n        return actions + out\n","code_sha256":"8c249a7d2ea89675599b3605807a195ee3c960f7db34650935c1f158ab210f14","incumbent_code_sha256":"bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae","target_spreads":[2,3],"tuning":{"seed_start":310000,"seed_end":310199,"expected_pairs_per_variant":200,"complete":true,"variants":{"0.5":{"overall":{"n":200,"sum_d":-33.19454799540015,"sum_d2":2761.2682979260194,"mean_diff":-0.16597273997700074,"se":0.2631352761677776,"C_mean_edge":84.99668042506501,"B_mean_edge":85.16265316504199,"retail_diff":-0.6071940141590001,"arb_diff":0.4412212741870004},"by_spread":{"1":{"n":47,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":30.59600304578298,"B_mean_edge":30.59600304578298,"retail_diff":0.0,"arb_diff":0.0},"2":{"n":57,"sum_d":-23.640582593599966,"sum_d2":195.03860421508344,"mean_diff":-0.41474706304561343,"se":0.24089545643751156,"C_mean_edge":80.94238501955263,"B_mean_edge":81.35713208259824,"retail_diff":-0.6391271834491237,"arb_diff":0.2243801204157896},"3":{"n":53,"sum_d":-9.553965401800182,"sum_d2":2566.229693710936,"mean_diff":-0.18026349814717324,"se":0.9646335818336266,"C_mean_edge":114.8866563055434,"B_mean_edge":115.06691980369057,"retail_diff":-1.6039349693433955,"arb_diff":1.423671471201888},"4":{"n":43,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":112.9910514314628,"B_mean_edge":112.9910514314628,"retail_diff":0.0,"arb_diff":0.0}}},"1.0":{"overall":{"n":200,"sum_d":-3.8999034361000273,"sum_d2":7180.666054671397,"mean_diff":-0.019499517180500137,"se":0.4247550286728196,"C_mean_edge":85.14315364786151,"B_mean_edge":85.16265316504199,"retail_diff":-0.9455321801715003,"arb_diff":0.9260326630065004},"by_spread":{"1":{"n":47,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":30.59600304578298,"B_mean_edge":30.59600304578298,"retail_diff":0.0,"arb_diff":0.0},"2":{"n":57,"sum_d":-52.831454163399926,"sum_d2":749.8669234214161,"mean_diff":-0.9268676169017531,"se":0.4685935735330723,"C_mean_edge":80.4302644656965,"B_mean_edge":81.35713208259824,"retail_diff":-1.673202724749123,"arb_diff":0.7463351078684216},"3":{"n":53,"sum_d":48.93155072729989,"sum_d2":6430.799131249981,"mean_diff":0.9232368061754697,"se":1.522166134069477,"C_mean_edge":115.99015660986605,"B_mean_edge":115.06691980369057,"retail_diff":-1.7685637872377367,"arb_diff":2.6918005934490576},"4":{"n":43,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":112.9910514314628,"B_mean_edge":112.9910514314628,"retail_diff":0.0,"arb_diff":0.0}}}},"selected_multiplier":1.0,"selection_rule":"Maximum overall paired mean on all 200 T seeds; mask fixed to [2,3]. No H results used.","missing_seed_suffixes":{"0.5":[],"1.0":[]}},"paired":{"seed_start":410000,"seed_end":410499,"expected_pairs":500,"complete":true,"overall":{"n":500,"sum_d":249.48447364920014,"sum_d2":30624.774888752123,"mean_diff":0.4989689472984003,"se":0.3496364552445071,"C_mean_edge":104.91272213544521,"B_mean_edge":104.4137531881468,"retail_diff":-0.7259764881544005,"arb_diff":1.2249454354416},"by_spread":{"1":{"n":115,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":28.495154459731307,"B_mean_edge":28.495154459731307,"retail_diff":0.0,"arb_diff":0.0},"2":{"n":116,"sum_d":113.6588088609001,"sum_d2":12807.128551337022,"mean_diff":0.9798173177663801,"se":0.9755544182391407,"C_mean_edge":98.74607979724397,"B_mean_edge":97.76626247947758,"retail_diff":-2.132425071843966,"arb_diff":3.1122423895732765},"3":{"n":138,"sum_d":135.82566478830003,"sum_d2":17817.646337415103,"mean_diff":0.9842439477413045,"se":0.9671413332353783,"C_mean_edge":129.3896953504797,"B_mean_edge":128.4054514027384,"retail_diff":-0.8378763459659435,"arb_diff":1.8221202936978258},"4":{"n":131,"sum_d":0.0,"sum_d2":0.0,"mean_diff":0.0,"se":0.0,"C_mean_edge":151.67248160310686,"B_mean_edge":151.67248160310686,"retail_diff":0.0,"arb_diff":0.0}},"missing_seed_suffix":[],"screening_only":true},"complete":true,"failures":{"records":[],"failed_flags":0,"exceptions":[],"spread_mismatches":0,"missing_tuning_suffixes":{"0.5":[],"1.0":[]},"missing_holdout_suffix":[]},"seconds_per_simulation":0.6494659236661391,"timing":{"simulations":1600,"simulation_seconds":1039.1454778658226,"wall_seconds":1039.5316530540586,"first_10_assigned_seeds":[{"seed":310000,"s":3,"B_seconds":0.6804849952459335,"C_0_5_seconds":0.6348384078592062,"C_1_seconds":0.6833388842642307,"pair_0_5_seconds":1.3153234031051397,"pair_1_seconds":1.3638238795101643},{"seed":310001,"s":2,"B_seconds":0.6624867981299758,"C_0_5_seconds":0.6313109532929957,"C_1_seconds":0.6924933567643166,"pair_0_5_seconds":1.2937977514229715,"pair_1_seconds":1.3549801548942924},{"seed":310002,"s":1,"B_seconds":0.6411658702418208,"C_0_5_seconds":0.6058317478746176,"C_1_seconds":0.6204077010042965,"pair_0_5_seconds":1.2469976181164384,"pair_1_seconds":1.2615735712461174},{"seed":310003,"s":3,"B_seconds":0.664067714009434,"C_0_5_seconds":0.650456634350121,"C_1_seconds":0.682594477199018,"pair_0_5_seconds":1.314524348359555,"pair_1_seconds":1.346662191208452},{"seed":310004,"s":2,"B_seconds":0.6910703759640455,"C_0_5_seconds":0.646013215649873,"C_1_seconds":0.6386982686817646,"pair_0_5_seconds":1.3370835916139185,"pair_1_seconds":1.3297686446458101},{"seed":310005,"s":1,"B_seconds":0.4791827332228422,"C_0_5_seconds":0.45677153673022985,"C_1_seconds":0.5156273213215172,"pair_0_5_seconds":0.9359542699530721,"pair_1_seconds":0.9948100545443594},{"seed":310006,"s":2,"B_seconds":0.6325398399494588,"C_0_5_seconds":0.6397761879488826,"C_1_seconds":0.6471355729736388,"pair_0_5_seconds":1.2723160278983414,"pair_1_seconds":1.2796754129230976},{"seed":310007,"s":3,"B_seconds":0.6433333652094007,"C_0_5_seconds":0.691454207058996,"C_1_seconds":0.6304550850763917,"pair_0_5_seconds":1.3347875722683966,"pair_1_seconds":1.2737884502857924},{"seed":310008,"s":3,"B_seconds":0.7044049710966647,"C_0_5_seconds":0.6765026170760393,"C_1_seconds":0.6814682260155678,"pair_0_5_seconds":1.380907588172704,"pair_1_seconds":1.3858731971122324},{"seed":310009,"s":4,"B_seconds":0.6912632486782968,"C_0_5_seconds":0.7837520851753652,"C_1_seconds":0.7511347029358149,"pair_0_5_seconds":1.475015333853662,"pair_1_seconds":1.4423979516141117}],"first_10_note":"B run once per seed, shared across both tuning comparisons; each C run once. All first ten retained in tuning. Pair times include the same B duration; no simulation reruns."},"simulator_commit":"b244530ee2a6247b5f930830eeca9dd950da0e7e","reproduction_method":"Python3 standard library only. Fetch simulator at pinned commit. Extract B from incumbent URL JSON.code and verify incumbent SHA256; C is exact result.code. Import ChallengeConfig,ParameterVariance,sample_config,SimulationEngine. Use base=ChallengeConfig(),variance=ParameterVariance() unchanged (2000 steps). Load Strategy via exec of exact source into separate namespaces. In one OS process, for each T seed 310000..310199: cfg=sample_config(base,variance,seed=seed); run SimulationEngine(cfg,B,seed=seed).run(), then C0.5, then C1 with the identical cfg/seed and fresh Strategy instance for each simulation. C0.5 differs from C1 only by the two added 1.0 sizing multipliers replaced with 0.5. Select using T only and record freeze hash/mask before H. For each H seed 410000..410499 run B then selected C exactly once. A callback wrapper records the first observed max(1,((ask or 100)-(bid or 0)+1)//2), comparing with sampled config competitor.spread_ticks and result.regime; strategy receives only unchanged StepState. No workers, multiprocessing or sandbox. Measure monotonic elapsed for each engine run. For each pair d=C.total_edge-B.total_edge. Aggregate with math.fsum; SE=sqrt((sum_d2-sum_d*sum_d/n)/(n-1)/n). retail_diff and arb_diff are signed means C-B. All engine results including failed flags retained; no failures dropped. No seeds 900000..903999 run.","checks":["Exact incumbent UTF-8 SHA256 verified before use.","Both candidates compile as Python; removing precisely the two guarded three-line additions reconstructs exact B bytes.","No duplicate (seed,strategy) simulations; exact assigned seed sets checked.","Observed first spread verified against sampled regime for every simulation.","Untargeted spreads 1 and 4 have exact zero total, retail and arbitrage differences.","SE independently checked via centered sample variance."],"sources_file":"sources.json"}