{ "key": "f6-retreat-size-half", "mechanism": "Exactly one-tick rival retreat on one side with the opposite quote unchanged; cancel and resize that side at unchanged prices.", "spreads_acted": [ 1, 4 ], "approach": "Blind append-only subclass of MA. Call MA first; reconcile its cancellations against public own_orders, shrink surviving orders and new placements on the retreating side. Infer spread only from the first rival quotes. Select by target-spread paired tuning mean; freeze before holdout.", "tried": [ "half: h=0.5", "quarter: h=0.25", "half_drop_nearest: h=0.5 and omit one nearest order to the old rival quote; ties prefer resting order, then callback/action order" ], "next": "Retain MA: the holdout effect is negligible and below twice its SE. If revisiting, first measure retreat-side exposure from public callbacks. Reserve finalist seeds for stronger candidates; no promotion is claimed.", "overlay_code": "_IncumbentStrategy = Strategy\n\nfrom orderbook_pm_challenge.types import PlaceOrder as _F6Place, CancelOrder as _F6Cancel, CancelAll as _F6CancelAll, Side as _F6Side\nfrom orderbook_pm_challenge.utils import quantize_down as _F6Quantize\n\nclass Strategy(_IncumbentStrategy):\n def __init__(self):\n super().__init__()\n self._f6_previous = None\n self._f6_spread = None\n\n def on_step(self, state):\n actions = super().on_step(state)\n bid = state.competitor_best_bid_ticks\n ask = state.competitor_best_ask_ticks\n previous = self._f6_previous\n self._f6_previous = (bid, ask)\n if self._f6_spread is None:\n # Infer once from the rival's first quotes, never from later widths.\n self._f6_spread = max(1, (ask - bid + 1) // 2) if bid is not None and ask is not None else 0\n if self._f6_spread not in (1, 4) or previous is None:\n return actions\n old_bid, old_ask = previous\n side = None\n old_quote = None\n if old_bid is not None and bid == old_bid - 1 and ask == old_ask:\n side, old_quote = _F6Side.BUY, old_bid\n elif old_ask is not None and ask == old_ask + 1 and bid == old_bid:\n side, old_quote = _F6Side.SELL, old_ask\n if side is None:\n return actions\n # Resolve MA's cancellations using only the callback's public orders.\n actions = list(actions)\n surviving = {o.order_id: o for o in state.own_orders}\n for action in actions:\n if isinstance(action, _F6CancelAll):\n surviving.clear()\n elif isinstance(action, _F6Cancel):\n surviving.pop(action.order_id, None)\n resting = [o for o in surviving.values() if o.side == side]\n placements = [(i, a) for i, a in enumerate(actions) if isinstance(a, _F6Place) and a.side == side]\n # The optional deletion selects one nearest order after MA's actions.\n nearest = None\n if False:\n choices = [(abs(o.price_ticks - old_quote), 0, j, ('rest', j)) for j, o in enumerate(resting)]\n choices += [(abs(a.price_ticks - old_quote), 1, i, ('new', i)) for i, a in placements]\n if choices:\n nearest = min(choices)[3]\n output = [_F6Cancel(o.order_id) for o in resting]\n for i, action in enumerate(actions):\n if isinstance(action, _F6Place) and action.side == side:\n q = _F6Quantize(float(action.quantity) * 0.5, 0.01)\n if q >= 0.01 and nearest != ('new', i):\n output.append(_F6Place(action.side, action.price_ticks, q, action.client_order_id))\n else:\n output.append(action)\n for j, order in enumerate(resting):\n q = _F6Quantize(order.remaining_quantity * 0.5, 0.01)\n if q >= 0.01 and nearest != ('rest', j):\n output.append(_F6Place(order.side, order.price_ticks, q, order.order_id))\n return output\n", "overlay_sha256": "cfa1df206293d0bf711119784f358f38fd4eff2ec35ca41f5d608322213bd387", "code_sha256": "b54db960e6562ce53319d6d80419d93cc01c42fb4362a8e468df42fd4d236363", "base_sha256": "5d1bd5272884f513b19f0e66a7c235f6c188a08e4f7787ac9346f1614dda3ed3", "check_values": [ { "seed": 310000, "expected": 17.116002, "actual": 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], [ 450498, 2, 0.0 ], [ 450499, 4, 0.0 ] ] }, "worst_seeds": [ [ 450052, 1, -0.0028965400000000585 ], [ 450389, 1, -0.0008367840000000071 ], [ 450076, 1, -0.0005824972999981526 ], [ 450335, 1, -0.0003108084999998706 ], [ 450384, 1, -0.00019605509999998105 ] ], "notes": "All three tuning variants tied exactly; first-listed half selected. MA source was saved and hash-checked by script, never displayed or inspected. Pinned simulator unmodified; default 2000 steps; serial SimulationEngine runs, no sandbox or workers. All three variants ran on every tuning seed; only the frozen winner ran holdout. Statistics use paired differences; retail_diff and arb_diff are mean differences. Spread groups use sampled config, matching the initial-quote inference. Cancel/re-place preserves existing order IDs and prices; quantities floor to 0.01. Retreatment requires the opposite quote unchanged, and missing quotes do not trigger. No forbidden or finalist seeds touched. No promotion claimed." }