{ "approach": "In spread-3 runs only (guard self.s != 3 added to one condition), drop the first ammunition branch: the bid for YES stock placed at ceil(p) when p has just crossed below a tick (g <= g_hi). That bid sits above the probability, so the arbitrageur fills it in full whenever p stays below the tick after the move: it buys stock at a sure loss of up to 0.15 cents a share. Without it, the stock still comes from the cheaper tick-1 stock bid, the under-p branch C, the target branch B and the bridge, and the retail edge is unchanged (-0.036). The gain is almost all arbitrage (+0.32 of +0.28) and broad: 299 runs up, 83 down. Every other run is bit-identical to the start file.", "blocked": null, "spread": 3, "base_mean_edge": 108.63974289615767, "found": [ "Logging copy of the simulator scored identically to the real one (start file on 1938 spread-3 choosing seeds 30000-37999: mean 126.39937568895708, retail 154.7018870176436, arb -28.30251132871765).", "Arb loss per run by side/band/step on choosing runs: plain-step bids at 1-4c -6.62 (largest), jump-step asks at 1-4c -4.12, jump asks 5-9c -2.74, jump asks 10-29c -1.85, jump bids 1-4c -1.63, jump asks 30-70c -1.35, plain asks 30-70c -1.30, plain bids 30-70c -1.26, jump bids 30-70c -1.16, plain bids 5-9c -1.13; 3.879 jump steps a run.", "Total by step type: jump steps -14.705744521050049, plain steps -13.596766807614229 a run; by side: our bids -14.65363548916862, our asks -13.648875839495673.", "Low bids by tick (arb edge plain/jump, shares bought by arb a run): tick 1 -2.18/-1.22 (2295/234 shares), tick 2 -1.48/-0.16 (961), tick 3 -1.31/-0.09 (713), tick 4 -1.65/-0.18 (719); retail sells into them earn only +1.18, +1.55, +1.13, +1.32.", "That stock pays for itself: retail buying our ask at 3c earns +65.13 a run (2393 shares), at 4c +18.05, at 5c +6.77, while arb on asks at 3-5c costs -5.33 on jump steps and -0.50 on plain steps.", "Ablations on choosing runs (diff vs start, even/odd halves): no bridge -1.22/-1.21; no tick-1 stock bid q1 -4.44/-3.86; no ammo block +0.25/+0.70 (arb +1.28, retail -0.81); ammo branch A only (bid at ceil(p) just after a crossing) +0.215/+0.324 (arb +0.24); branch B only -0.17/-0.07 (arb +1.04, retail -1.16); branch C only +0.02/+0.05." ], "holdout": { "mean_edge": 127.42084810738282, "retail_edge": 155.47204541774, "arb_edge": -28.051197310322326, "failures": 0 }, "diff": { "mean": 0.28400077523163414, "standard_error": 0.04205212786714992, "retail": -0.03587290157287119, "arb": 0.3198736768049999, "trimmed": 0.2541545390681504, "runs_up": 299, "runs_down": 83 }, "others_identical": { "runs": 900, "identical": true }, "seconds": { "start": 44.98937749862671, "yours": 45.02668595314026 }, "code": "\"\"\"Crossing-cover base with isolated, untuned per-spread overrides stacked: each acts only in runs of its own competitor spread.\"\"\"\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 == 4:\n # Censored compound-Poisson flow likelihood, using only our fills.\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 # Positive aggregate flow is approximately lognormal; +0.065\n # accounts for the occasional second arrival in a timestep.\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-6)\n if self.s == 2:\n self.params.update(dict(W=150, ammo_pmax=0.02, fy=0.7, fu=0.25, Hu=500))\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 == 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 # Track only the main quote, excluding probes and stock orders.\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-6, 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 if self.s == 3 and self.last_quote_exposed and ca <= self.last_flow_ca + 1:\n self.flow_exposure += 1\n self.flow_notional += min(self.last_flow_cap, state.sell_filled_quantity * self.last_ask_tick / 100.0) * self.last_flow_correction\n lo, hi = cb, ca\n for o in state.own_orders:\n if o.side is Side.BUY:\n lo = max(lo, o.price_ticks)\n else:\n hi = min(hi, o.price_ticks)\n if P['pinf'] > 0:\n sb = {o.price_ticks for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity <= PROBE + 1e-9}\n sa = {o.price_ticks for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity <= PROBE + 1e-9}\n ea = self.pa - sa\n eb = self.pb - sb\n if sa and ea:\n lo = max(lo, min(max(ea), hi))\n if eb and (sb or min(eb) <= P['p1t']):\n hi = min(hi, max(min(eb), lo))\n if P['jumpinfo'] > 0 and self.pcb is not None:\n if ca > self.pca + 1:\n lo = max(lo, ca - 1)\n if cb < self.pcb - 1:\n hi = min(hi, cb + 1)\n self.pcb, self.pca = cb, ca\n if lo > hi:\n lo, hi = hi, lo\n # sub-tick belief over p_(t-1): predict with one-step noise, truncate to [lo, hi]\n wid = max(hi - lo, 1e-6)\n nb = max(2, min(48, int(round(wid * P['bins']))))\n cs = [lo + (i + 0.5) * wid / nb for i in range(nb)]\n if self.bc is None or P['filt'] <= 0:\n ws = [1.0 / nb] * nb\n else:\n sg = max(self.st, 0.6 * wid / nb)\n k = -0.5 / (sg * sg)\n ws = []\n uni = P['uni'] * sg\n if self.s == 4:\n exp = math.exp\n if self.bc == cs and 1.0 <= wid <= 6.0 and nb == 8 * wid:\n # On this stationary grid, spacing is exactly 1/8 tick.\n # Equal index distances therefore have bit-identical d*d*k.\n # Reuse exp values while retaining the original sum order.\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 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 if 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 else:\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 if self.s == 4 and tail <= 1e-6:\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-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 == 4 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):\n if not 1 <= t <= 99:\n return 0.0\n if self.s == 4 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 if self.s == 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-6 else inv(1 - tail))) / (t / 100.0))\n if self.s == 2 and phat < 0.1:\n a = max(0.0, a - 0.0015 * (1 - min(1.0, state.yes_inventory / 3000.0)))\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 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 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 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-9))))\n g = t - ptick\n if self.s != 3 and 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 == 4:\n stock_rate *= self.f4_flow / 0.6\n if self.s == 3:\n flow = (self.flow_notional + P['flow_prior'] * P['flow_level']) / (P['flow_prior'] + self.flow_exposure)\n scale = max(0.5, min(2.0, flow / P['flow_level']))\n stock_rate *= 1.0 + P['flow_strength'] * (scale - 1.0)\n q1 = max(0.0, stock_rate * min(H, P['Wf']) / (self.s / 100.0) - Y)\n\n # Bridge expected retail demand until another tick crossing is available.\n if 0.02 < phat < 0.1:\n ct = max(2, min(99, int(math.floor(ptick))))\n wealth = state.cash + Y * phat\n goal = min(1 * qa, 0.3 * max(wealth, 0.0) / phat)\n if Y < goal and ct < ask_t:\n amt, qam = ct, min(3000.0, goal - Y)\n # order management\n actions = []\n free = state.free_cash - 2.0\n keep_ask = 0.0\n new_b = []\n new_a = []\n def cancel(o):\n nonlocal free\n actions.append(CancelOrder(o.order_id))\n x = o.price_ticks / 100.0\n if o.side is Side.BUY:\n free += x * o.remaining_quantity\n else:\n free += (1 - x) * min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n self.ahead.discard(o.order_id)\n self.res.pop(o.order_id, None)\n for o in state.own_orders:\n if o.remaining_quantity <= PROBE + 1e-9:\n cancel(o)\n kept_ids = set()\n def manage(orders, t, q, comp):\n kept = 0.0\n for o in orders:\n if o.price_ticks == t and q > 0 and (kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0) or (self.s == 1 and (o.side is Side.SELL) and o.price_ticks <= 2 and o.order_id in self.ahead)):\n kept += o.remaining_quantity\n kept_ids.add(o.order_id)\n else:\n cancel(o)\n return kept\n kb = manage(bids, bid_t, qb, cb)\n ka = manage(asks, ask_t, qa, ca)\n add_b = (qb - kb) if (kb < qb * P['keep_lo'] and bid_t != cb) else 0.0\n add_a = (qa - ka) if (ka < qa * P['keep_lo'] and ask_t != ca) else 0.0\n\n budget = P['fb'] * max(free, 0.0)\n cost = add_b * bid_t / 100.0 + qam * amt / 100.0\n if cost > budget:\n f = budget / cost\n add_b *= f\n qam *= f\n cost = budget\n out = []\n q1 = min(q1, P['f1'] * max(budget - cost, 0.0) * 100.0)\n if q1 >= 0.01:\n out.append(PlaceOrder(side=Side.BUY, price_ticks=1, quantity=fl(q1)))\n free -= fl(q1) / 100.0 + 1e-6\n if amt and qam >= 0.01:\n q = fl(qam)\n out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n free -= q * amt / 100.0 + 1e-6\n if bid_t >= 1 and add_b >= 0.01:\n q = fl(add_b)\n oid = self._oid('b')\n out.append(PlaceOrder(side=Side.BUY, price_ticks=bid_t, quantity=q, client_order_id=oid))\n free -= q * bid_t / 100.0 + 1e-6\n if bid_t > cb:\n self.ahead.add(oid)\n if ask_t <= 99 and add_a >= 0.01:\n x = ask_t / 100.0\n covk = sum(o.remaining_quantity - min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n for o in asks if o.order_id in kept_ids)\n cov = max(0.0, Y - covk - 0.1)\n unc = max(0.0, add_a - cov)\n if unc > 0:\n ub = max(0.0, free - (1 - P['fu']) * max(state.cash, 0.0)) if x >= P['xu'] else 0.0\n if H < P['Hu'] and phat < 0.05:\n ub = max(0.0, free * (1 - H / P['Hu']))\n if self.s == 2:\n ub = min(ub, max(0.0, free - 200 * min(1.0, H / 50.0)))\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 if self.s == 4:\n self.f4_last = [None, None]\n # Eligibility is decided before observing fills, so depleted quotes\n # remain in the sample as right-censored observations.\n for side, tick, bound, conv, distance in (\n (0, ask_t, ca, ask_t / 100.0, ask_t / 100.0 - phat),\n (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": "32136198da9799afcc587e08a219f58ead7df9848a046e43a369e5ea28c83490", "tried": [ "Scorer check: earlier file, 600 runs from seed 1000, 4 workers: 108.63974289615767, exactly the author's figure, in 32 s; run counts not halved.", "Idea 1 (measure): remove the bridge buy at floor(p) for 0.02