Sources: [f-deep-tranche](../../items/f-deep-tranche/result.json), [f-cover-stock](../../items/f-cover-stock/result.json), [f-cancel-option](../../items/f-cancel-option/result.json), [f-shortfall-cap](../../items/f-shortfall-cap/result.json), [f-ambiguity](../../items/f-ambiguity/result.json), [f-toxic-fill](../../items/f-toxic-fill/result.json). Measurements are copied as reported, without independent replication. The task reports one unreliable or unreadable external source each for f-deep-tranche, f-cover-stock, f-shortfall-cap, and f-toxic-fill; their external-source claims are not relied on here. Zero-SE spreads have no defined mean_diff/se ratio and are excluded from top selection. ```json { "rows": [{ "key": "f-deep-tranche", "spreads_acted": [2], "off_equals_n": true, "code_sha256": "c9c946bce997656e44fce5b23d206ffea4a10d001842e2f3b37448705e3fc687", "assembled_sha256": "4c990d169f2792ce0dfcf5be68dc7adfccb8fd9ddd897afdeafb355e4b9182e0", "tuning": { "mean_diff": -0.23518141310349996, "se": 0.062367556219318825 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.39217582931512, "mean_diff": -0.12893707315260003, "se": 0.03574586920269902, "sum_d": -64.46853657630001, "sum_d2": 327.1152920981678, "retail_diff": 0.013649168325999992, "arb_diff": -0.14258624147780002, "failures": 0 }, "by_spread": { "1": { "runs": 115, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "2": { "runs": 116, "mean_diff": -0.5557632463474139, "se": 0.14776852134772675, "sum_d": -64.46853657630001, "sum_d2": 327.1152920981678 }, "3": { "runs": 138, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "4": { "runs": 131, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 } }, "flag": "incumbent_mean unlike other rows" }, { "key": "f-cover-stock", "spreads_acted": [4], "off_equals_n": true, "code_sha256": "29b3e7adf3e3315f2da261c2eace235bd22e42fbc748b18d658b8746836f7b0d", "assembled_sha256": "13f346f104d0706dc0e47adc5668afbfddca50124c0482ceef4256cc2c5eac26", "tuning": { "mean_diff": 0.082082, "se": 0.087628 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.413753, "mean_diff": 0.033797, "se": 0.05254, "sum_d": 16.898488, "sum_d2": 689.296134, "retail_diff": 0.077842, "arb_diff": -0.044045, "failures": 0 }, "by_spread": { "1": { "runs": 115, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "2": { "runs": 116, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "3": { "runs": 138, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "4": { "runs": 131, "mean_diff": 0.128996, "se": 0.200867, "sum_d": 16.898488, "sum_d2": 689.296134 } }, "flag": "" }, { "key": "f-cancel-option", "spreads_acted": [1], "off_equals_n": true, "code_sha256": "6a1180ff62873ae5d0c5ab61af4a05383e107744812c38b29cfb496d63d88f14", "assembled_sha256": "a3a91fc9cc756f0843db5ffcb262e37523e032c8b31fdda0df32fea7e9a850d3", "tuning": { "mean_diff": -5.891633870315499, "se": 1.8612645729822324 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.4137531881468, "mean_diff": -5.220709118934, "se": 1.4712629988686723, "sum_d": -2610.354559467, "sum_d2": 553699.29740635, "retail_diff": -8.0537476222348, "arb_diff": 2.8330385032884, "failures": 0 }, "by_spread": { "1": { "runs": 115, "mean_diff": -22.698735299713043, "se": 6.141278164992826, "sum_d": -2610.354559467, "sum_d2": 553699.29740635 }, "2": { "runs": 116, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "3": { "runs": 138, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "4": { "runs": 131, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 } }, "flag": "" }, { "key": "f-shortfall-cap", "spreads_acted": [4], "off_equals_n": true, "code_sha256": "9950f6ddd03922021040cd9c4226f473e4037f6ea25c26cc5ee23c014cdd1bbd", "assembled_sha256": "b133143385083e27719171d849732d131f3aa9fb15d4ba36f1a02611972bef8d", "tuning": { "mean_diff": -0.772573, "se": 1.133779 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.413753, "mean_diff": -4.004254, "se": 0.978793, "sum_d": -2002.127014, "sum_d2": 247047.070252, "retail_diff": -2.284456, "arb_diff": -1.719798, "failures": 0 }, "by_spread": { "1": { "runs": 115, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "2": { "runs": 116, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "3": { "runs": 138, "mean_diff": 0.0, "se": 0.0, "sum_d": 0.0, "sum_d2": 0.0 }, "4": { "runs": 131, "mean_diff": -15.283412, "se": 3.565079, "sum_d": -2002.127014, "sum_d2": 247047.070252 } }, "flag": "" }, { "key": "f-ambiguity", "spreads_acted": [1, 2, 3, 4], "off_equals_n": true, "code_sha256": "55807d6950c8a9aa8105822e0c347abc3bb420d2e1bf7dbaa7f55082f48989ec", "assembled_sha256": "e304da6d9f3ba864b9dcf9d9d525dad5e841b3cc515d560460aa4d934ad1c2cb", "tuning": { "mean_diff": -39.441586404894004, "se": 4.698952741427669 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.4137531881468, "mean_diff": -44.386158788816, "se": 3.013138214895132, "sum_d": -22193.079394408, "sum_d2": 3250276.520577319, "retail_diff": -30059.7390416989, "arb_diff": 7866.6596472897, "failures": [] }, "by_spread": { "1": { "runs": 115, "mean_diff": -4.268163457178261, "se": 2.124740438470509, "sum_d": -490.8387975755, "sum_d2": 61280.362732907015 }, "2": { "runs": 116, "mean_diff": -28.794084633164655, "se": 5.195940853272968, "sum_d": -3340.1138174471, "sum_d2": 456326.1899624595 }, "3": { "runs": 138, "mean_diff": -59.03529701012681, "se": 6.1781910257594825, "sum_d": -8146.8709873975, "sum_d2": 1202595.8069399488 }, "4": { "runs": 131, "mean_diff": -77.97905184723588, "se": 6.562845829741553, "sum_d": -10215.2557919879, "sum_d2": 1530074.1609420036 } }, "flag": "" }, { "key": "f-toxic-fill", "spreads_acted": [1], "off_equals_n": true, "code_sha256": "bc07db5d2bc2068ce1c176373773bca90b4c9eed7e656126265a146bfc8aba5d", "assembled_sha256": "f708d8ceb2244cf0ce1231d92750a506b7d913bda6907d602fc0c1bc039f91ae", "tuning": { "mean_diff": -0.5376291733805002, "se": 0.32959362826456823 }, "paired": { "n": 500, "complete": true, "incumbent_mean": 104.4137531881468, "mean_diff": -0.02702767253780022, "se": 0.17711691423117318, "sum_d": -13.51383626890011, "sum_d2": 7827.280373581208, "retail_diff": -0.07862376439319982, "arb_diff": 0.05159609185000004, "failures": 0 }, "by_spread": { "1": { "runs": 115, "mean_diff": -0.11751161972956618, "se": 0.7726094915625336, "sum_d": -13.51383626890011, "sum_d2": 7827.280373581208 }, "2": { "runs": 116, "mean_diff": 0, "se": 0, "sum_d": 0, "sum_d2": 0 }, "3": { "runs": 138, "mean_diff": 0, "se": 0, "sum_d": 0, "sum_d2": 0 }, "4": { "runs": 131, "mean_diff": 0, "se": 0, "sum_d": 0, "sum_d2": 0 } }, "flag": "" }], "top": [{ "key": "f-cover-stock", "spread": 4, "code": "import dataclasses as _dc\nimport math as _m\nfrom orderbook_pm_challenge.types import CancelOrder as _CO, PlaceOrder as _PO, Side as _SD\n\n_SPREADS = (4,)\n_OFFSET = 1\n_CAP = 5.0\n\n\nclass Strategy(BaseStrategy):\n \"\"\"f-cover-stock: joint cash/inventory value of one more YES share.\n\n A YES share covers one of N's uncovered asks, freeing (1-ask) cash of\n collateral for the remaining steps. Value it at a shadow cost of cash\n that rises as free cash runs low, add the expected retail edge of the\n cover bid, and subtract its expected one-step arbitrage loss. Post a\n small hidden cover bid when the sum is positive.\n \"\"\"\n\n def __init__(self, spreads=None, offset=None, cap=None):\n self._n = _N()\n self._sp = _SPREADS if spreads is None else tuple(spreads)\n self._off = _OFFSET if offset is None else offset\n self._cap = _CAP if cap is None else cap\n self._on = None\n self._oid = None\n self._orem = 0.0\n self._otick = 0\n self._ores = 0.0\n self._k = 0\n self._dy = 0.0\n self._dc = 0.0\n self._p = None\n self._v = 1e-4\n self._fr = 0.05\n self._cash0 = None\n\n def on_step(self, state):\n if self._on is None:\n b, a = state.competitor_best_bid_ticks, state.competitor_best_ask_ticks\n s = max(1, (a - b + 1) // 2) if (a is not None and b is not None) else 0\n self._on = s in self._sp\n self._cash0 = state.cash\n if not self._on:\n return self._n.on_step(state)\n\n mine = None\n others = []\n for o in state.own_orders:\n if o.order_id == self._oid:\n mine = o\n else:\n others.append(o)\n myfill = 0.0\n if self._oid is not None:\n rem = mine.remaining_quantity if mine is not None else 0.0\n myfill = round(max(0.0, self._orem - rem), 10)\n if myfill > 0:\n self._dy += myfill\n self._dc += myfill * self._otick / 100.0\n self._dy = min(self._dy, state.yes_inventory)\n myres = mine.remaining_quantity * self._otick / 100.0 if mine is not None else 0.0\n nview = _dc.replace(\n state,\n own_orders=tuple(others),\n yes_inventory=round(state.yes_inventory - self._dy, 10),\n reserved_cash=round(state.reserved_cash - myres, 10),\n free_cash=round(state.free_cash + myres, 10),\n buy_filled_quantity=round(max(0.0, state.buy_filled_quantity - myfill), 10),\n )\n acts = list(self._n.on_step(nview))\n\n pre = []\n if mine is not None:\n pre.append(_CO(mine.order_id))\n self._oid = None\n self._orem = 0.0\n\n # N's resulting book, from its view and actions\n book = {o.order_id: (o.side, o.price_ticks, o.remaining_quantity) for o in others}\n used = 0.0\n for x in acts:\n if isinstance(x, _CO):\n book.pop(x.order_id, None)\n elif isinstance(x, _PO):\n self._k += 1\n book[\"_new%d\" % self._k] = (x.side, x.price_ticks, x.quantity)\n pr = x.price_ticks / 100.0\n used += x.quantity * (pr if x.side == _SD.BUY else 1.0 - pr)\n else:\n book = {}\n bids = [t for (sd, t, q) in book.values() if sd == _SD.BUY and q >= 0.5]\n asks = [(t, q) for (sd, t, q) in book.values() if sd == _SD.SELL]\n ra = [t for t, q in asks if q >= 0.5]\n bb = max(bids) if bids else state.competitor_best_bid_ticks\n ba = min(ra) if ra else state.competitor_best_ask_ticks\n if bb is None or ba is None:\n return pre + acts\n p = (bb + ba) / 200.0\n if self._p is not None:\n d = p - self._p\n self._v = 0.97 * self._v + 0.03 * d * d\n self._p = p\n sig = max(0.004, _m.sqrt(self._v))\n self._fr = 0.99 * self._fr + 0.01 * (1.0 if state.buy_filled_quantity > 0 else 0.0)\n\n tick = bb - self._off\n if tick < 1 or tick >= ba:\n return pre + acts\n price = tick / 100.0\n askq = sum(q for t, q in asks)\n unc = askq - max(0.0, state.yes_inventory - self._dy)\n if unc <= 0.01 or self._dc > 100.0:\n return pre + acts\n a_px = ba / 100.0\n free = state.free_cash + myres - used\n lam = 2e-4 * max(0.0, 1.0 - free / (0.4 * self._cash0))\n v_coll = (1.0 - a_px) * lam * state.steps_remaining\n g = (p - price) / sig\n arb = sig * (_m.exp(-0.5 * g * g) / 2.5066282746 - g * 0.5 * _m.erfc(g / 1.41421356))\n ret = self._fr * 0.5 * max(0.0, p - price)\n if v_coll + ret - arb <= 0.0:\n return pre + acts\n q = int(min(self._cap, unc) * 100) / 100.0\n if q <= 0 or price * q > 0.2 * free - 1.0:\n return pre + acts\n self._k += 1\n self._oid = \"zcv%d\" % self._k\n self._orem = q\n self._otick = tick\n return pre + acts + [_PO(_SD.BUY, tick, q, self._oid)]\n" }, { "key": "f-toxic-fill", "spread": 1, "code": "from collections import defaultdict\n\nfrom orderbook_pm_challenge.strategy import BaseStrategy\nfrom orderbook_pm_challenge.types import CancelAll, CancelOrder, PlaceOrder, Side\n\n\n# These constants are replaced only while selecting one of the two tuning variants.\nACTIVE_SPREADS = frozenset((1,))\nTOXIC_K = 1\nTOXIC_M = 1\n\n\nclass Strategy(BaseStrategy):\n \"\"\"Suppress newly quoted near-touch orders briefly after a toxic fill.\"\"\"\n\n def __init__(self, enabled=True):\n self._n = _N()\n self._enabled = enabled\n self._active = None\n self._previous_quote = None\n self._expected = ()\n self._blocked_until = {Side.BUY: 0, Side.SELL: 0}\n\n @staticmethod\n def _spread(state):\n bid, ask = state.competitor_best_bid_ticks, state.competitor_best_ask_ticks\n if bid is None or ask is None:\n return None\n return max(1, (ask - bid + 1) // 2)\n\n @staticmethod\n def _quantities(orders):\n result = defaultdict(float)\n for order in orders:\n quantity = getattr(order, \"remaining_quantity\", None)\n if quantity is None:\n quantity = order.quantity\n result[(order.side, order.price_ticks)] += quantity\n return result\n\n def _whole_order_filled(self, state, side):\n old = self._quantities(self._expected)\n new = self._quantities(state.own_orders)\n for (old_side, price), quantity in old.items():\n if old_side is side and new[(side, price)] + 1e-9 < quantity:\n # FIFO makes a loss at least as large as one queued order a full fill.\n return True\n return False\n\n def _toxic(self, state, side):\n filled = state.buy_filled_quantity if side is Side.BUY else state.sell_filled_quantity\n if filled <= 0.0:\n return False\n current = state.competitor_best_bid_ticks if side is Side.BUY else state.competitor_best_ask_ticks\n prior = None if self._previous_quote is None else self._previous_quote[0 if side is Side.BUY else 1]\n return current != prior or self._whole_order_filled(state, side)\n\n @staticmethod\n def _near_quote(action, state):\n quote = state.competitor_best_bid_ticks if action.side is Side.BUY else state.competitor_best_ask_ticks\n return quote is not None and abs(action.price_ticks - quote) <= TOXIC_K\n\n def _remember(self, state, actions):\n orders = list(state.own_orders)\n for action in actions:\n if isinstance(action, CancelAll):\n orders = []\n elif isinstance(action, CancelOrder):\n orders = [order for order in orders if order.order_id != action.order_id]\n elif isinstance(action, PlaceOrder):\n orders.append(action)\n self._expected = tuple(orders)\n self._previous_quote = (state.competitor_best_bid_ticks, state.competitor_best_ask_ticks)\n\n def on_step(self, state):\n actions = self._n.on_step(state)\n if self._active is None:\n self._active = self._enabled and self._spread(state) in ACTIVE_SPREADS\n if not self._active:\n return actions\n for side in (Side.BUY, Side.SELL):\n if self._toxic(state, side):\n self._blocked_until[side] = max(self._blocked_until[side], state.step + TOXIC_M)\n kept = [\n action for action in actions\n if not (\n isinstance(action, PlaceOrder)\n and state.step < self._blocked_until[action.side]\n and self._near_quote(action, state)\n )\n ]\n self._remember(state, kept)\n return kept\n" }, { "key": "f-ambiguity", "spread": 1, "code": "from dataclasses import replace\n\n\nclass Strategy(_N):\n \"\"\"Ambiguity-set filter with faster learning from the observed quote path.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._spread = None\n self._mid = []\n self._seen = 0\n\n def on_step(self, state):\n if self._spread is None:\n b = state.competitor_best_bid_ticks\n a = state.competitor_best_ask_ticks\n self._spread = 0 if b is None or a is None else max(1, (a - b + 1) // 2)\n actions = super().on_step(state)\n if self._spread not in (1, 2, 3, 4):\n return actions\n b = state.competitor_best_bid_ticks\n a = state.competitor_best_ask_ticks\n if b is None or a is None:\n return actions\n self._seen += 1\n self._mid.append((b + a) * 0.005)\n if len(self._mid) > 32:\n self._mid.pop(0)\n center = sum(self._mid) / len(self._mid)\n vol = 0.0 if len(self._mid) < 2 else (sum((x - center) ** 2 for x in self._mid) / len(self._mid)) ** 0.5\n width = 0.14 / (1.0 + self._seen / 80.0) ** 0.5 + 2.5 * vol\n lo, hi = max(0.0, center - width), min(1.0, center + width)\n out = []\n for action in actions:\n if isinstance(action, PlaceOrder):\n px = action.price_ticks * 0.01\n if action.side is Side.BUY and lo <= px:\n continue\n if action.side is Side.SELL and hi >= px:\n continue\n out.append(action)\n return out\n" }] } ```