{ "key": "s2-flow-jump-mask", "approach": "Spread 2 only (guarded by self.s == 2). B's s2 retail-flow estimator adds each step's exposed ask fill to flow_notional/flow_exposure, and already drops an observation when the rival ask rose by more than 1 tick. C drops (masks) an observation whenever the rival best ask rose at all, or the rival best bid moved by more than 1 tick. Our exposed ask sits ahead of the rival ask, so the rival ask can only rise after our order was swept, and that pattern fits arbitrage after a price move better than a retail-only fill. The masked step is left out of both the numerator and the exposure count, so the estimator prior (flow_prior=200, flow_level=0.6) and every ordinary eligible observation are unchanged. Spreads 1, 3 and 4 run byte-identical paths.", "tried": [ { "variant": "CA: mask when ca > last_flow_ca (any rise of the rival ask)", "tuning_overall_mean_diff": 0.010166289797499992, "tuning_overall_se": 0.04683738013498318, "tuning_s2_mean_diff": 0.035671192271929796, "tuning_s2_se": 0.1653383796122695, "chosen": false }, { "variant": "CB: CA plus mask when |cb - last_flow_cb| > 1 (adds the state field last_flow_cb, s2 only)", "tuning_overall_mean_diff": 0.016099481380500033, "tuning_overall_se": 0.04239950071391896, "tuning_s2_mean_diff": 0.0564894083526317, "tuning_s2_se": 0.14958077540972065, "chosen": true } ], "next": "The holdout gain is small: +0.35 per s2 seed (SE 0.22), +0.08 overall (SE 0.05), all of it on the arb side. It is under 2 SE, so it is screening evidence only. Extending it to thousands of seeds would settle whether it is real. B's s2 estimator only scales the 1-tick YES stock (ydf1), so flow-estimate noise has little effect on edge. Using the masked flow estimate for s2 ask sizing could be what makes it matter.", "code": "\"\"\"Exact per-spread merge: c1-pairs, c2-queue, a3-ledger, c4-all; s2 flow jump mask.\"\"\"\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 and abs(cb - self.last_flow_cb) <= 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 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 == 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 self.last_flow_cb = cb\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": "91ce2de42cda0eb65638767a546b272549638ee7cf7fb94ed45a9650e5959db7", "incumbent_code_sha256": "bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae", "incumbent_verified": "Downloaded JSON.code from https://api.imdnotdead.fun/artifacts/db22aff7167a4798251ed3d20432509f30e511e4958799aba5b17b41bb39b4b6; the SHA256 of its exact UTF-8 bytes matched bf0d179f...ffae.", "target_spreads": [ 2 ], "diff_vs_incumbent": "Line 1 docstring text changed. Line 256: 'ca <= self.last_flow_ca + 1' became 'ca <= self.last_flow_ca and abs(cb - self.last_flow_cb) <= 1'. Inserted 'self.last_flow_cb = cb' after 'self.last_flow_ca = ca' in the s==2 end-of-step block.", "tuning": { "seeds": "310000..310199", "variant_CA": { "seeds": "310000..310199", "n_seeds": 200, "groups": { "overall": { "n": 200, "sum_d": 2.0332579594999984, "sum_d2": 87.33152977042425, "mean_diff": 0.010166289797499992, "se": 0.04683738013498318, "C_mean_edge": 85.1728194548395, "B_mean_edge": 85.16265316504199, "retail_diff": -0.026259554460011714, "arb_diff": 0.036425844257497175 }, "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": 2.0332579594999984, "sum_d2": 87.33152977042425, "mean_diff": 0.035671192271929796, "se": 0.1653383796122695, "C_mean_edge": 81.39280327487018, "B_mean_edge": 81.35713208259824, "retail_diff": -0.09213878757896055, "arb_diff": 0.12780997985087694 }, "3": { "n": 53, "sum_d": 0.0, "sum_d2": 0.0, "mean_diff": 0.0, "se": 0.0, "C_mean_edge": 115.06691980369057, "B_mean_edge": 115.06691980369057, "retail_diff": 0.0, "arb_diff": 0.0 }, "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 } }, "failures": [], "label_mismatch_seeds": [], "seconds_per_simulation": 0.5282087308168412, "first10_pair_seconds": [ 1.0884, 1.0582, 0.9522, 1.1131, 1.1253, 0.8937, 1.0632, 1.1413, 1.1044, 1.1345 ] }, "variant_CB_chosen": { "seeds": "310000..310199", "n_seeds": 200, "groups": { "overall": { "n": 200, "sum_d": 3.2198962761000067, "sum_d2": 71.60100155957086, "mean_diff": 0.016099481380500033, "se": 0.04239950071391896, "C_mean_edge": 85.17875264642251, "B_mean_edge": 85.16265316504199, "retail_diff": -0.011044900727497975, "arb_diff": 0.027144382107497478 }, "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": 3.2198962761000067, "sum_d2": 71.60100155957086, "mean_diff": 0.0564894083526317, "se": 0.14958077540972065, "C_mean_edge": 81.41362149095087, "B_mean_edge": 81.35713208259824, "retail_diff": -0.03875403764035923, "arb_diff": 0.09524344599122969 }, "3": { "n": 53, "sum_d": 0.0, "sum_d2": 0.0, "mean_diff": 0.0, "se": 0.0, "C_mean_edge": 115.06691980369057, "B_mean_edge": 115.06691980369057, "retail_diff": 0.0, "arb_diff": 0.0 }, "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 } }, "failures": [], "label_mismatch_seeds": [], "seconds_per_simulation": 0.5311472952365875, "first10_pair_seconds": [ 1.0923, 1.0273, 1.0292, 1.0638, 1.1073, 0.8498, 1.1058, 1.1545, 1.2011, 1.1135 ] }, "selection": "CB chosen on T alone (higher overall and s2 mean_diff). Code and hash frozen before H." }, "paired": { "seeds": "410000..410499", "n_seeds": 500, "groups": { "overall": { "n": 500, "sum_d": 40.703359575799965, "sum_d2": 663.9596087743716, "mean_diff": 0.08140671915159993, "se": 0.051457556174251844, "C_mean_edge": 104.4951599072984, "B_mean_edge": 104.4137531881468, "retail_diff": 6.039209949904034e-07, "arb_diff": 0.08140611523159791 }, "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": 40.703359575799965, "sum_d2": 663.9596087743716, "mean_diff": 0.35089103082586176, "se": 0.2206840199352422, "C_mean_edge": 98.11715351030344, "B_mean_edge": 97.76626247947758, "retail_diff": 2.6031077595689567e-06, "arb_diff": 0.3508884277224098 }, "3": { "n": 138, "sum_d": 0.0, "sum_d2": 0.0, "mean_diff": 0.0, "se": 0.0, "C_mean_edge": 128.4054514027384, "B_mean_edge": 128.4054514027384, "retail_diff": 0.0, "arb_diff": 0.0 }, "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 } }, "failures": [], "label_mismatch_seeds": [], "seconds_per_simulation": 0.5433347434997559, "first10_pair_seconds": [ 0.9945, 0.9897, 0.9295, 1.1559, 1.0235, 1.1134, 0.9873, 1.0895, 1.0289, 1.0755 ], "note": "H run once, with frozen C (sha above) vs B. d = C.total_edge - B.total_edge; se = sample_SD(d)/sqrt(n). s is labelled from the first observed rival spread (ca-cb+1)//2 and checked against config.competitor.spread_ticks: 0 mismatches." }, "complete": true, "failures": { "tuning": [], "holdout": [], "exceptions": [], "missing_seeds": [] }, "seconds_per_simulation": 0.5433347434997559, "simulator_commit": "b244530ee2a6247b5f930830eeca9dd950da0e7e", "reproduction": "Clone danrobinson/prediction-market-challenge at b244530 (unmodified). In one Python 3.14.4 process, for each seed: cfg = runner.sample_config(ChallengeConfig(), ParameterVariance(), seed=seed); SimulationEngine(cfg, factory, seed=seed).run() for B and then C, giving a fresh Strategy instance each time. No --workers, no --sandbox, default 2000 steps. Seeds 900000..903999 were never run.", "notes": "Before the official tuning run, B alone was run once on seeds 310000..310002 as a harness smoke test. That output was discarded, and those seeds were then run once in the official tuning pass (the simulator is deterministic). Total simulation time was about 15 minutes." }