60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
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"""
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Action model — compact menu for simultaneous-move tree search.
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Bad: enumerate every price tick x every quantity x every TTL x every TIF.
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Good: 8-24 meaningful actions per player, 3-12 per counterparty role.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Mapping, Optional, Tuple
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from malkhut.state import ActionKind, AgentRole, OrderType, Side
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@dataclass(frozen=True, slots=True)
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class FulfilmentAction:
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"""One atomic action candidate."""
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kind: ActionKind
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side: Optional[Side]
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order_type: Optional[OrderType]
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price_ticks_from_best: int
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qty_fraction: float
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ttl_ms: int
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cancel_order_id: Optional[str] = None
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reduce_only: bool = False
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post_only: bool = False
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metadata: Mapping[str, Any] = field(default_factory=dict)
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@dataclass(frozen=True, slots=True)
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class CounterpartyAction:
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"""Adversarially useful aggregate actions that alter book/fill outcomes."""
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role: AgentRole
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kind: ActionKind
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side: Optional[Side]
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price_ticks_from_best: int
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qty_fraction_of_top: float
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toxicity: float = 0.0
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metadata: Mapping[str, Any] = field(default_factory=dict)
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JointAction = Tuple[Any, ...] # (our_action, cp_action_1, cp_action_2, ...)
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@dataclass(frozen=True, slots=True)
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class PlannedPolicy:
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"""Output of the planner: distribution over actions + selected action."""
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actions: Tuple[FulfilmentAction, ...]
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probabilities: Tuple[float, ...]
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selected_action: FulfilmentAction
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diagnostics: Mapping[str, Any]
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@dataclass(frozen=True, slots=True)
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class RiskDecision:
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approved: bool
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action: Optional[FulfilmentAction]
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reason: str
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adjusted: bool = False
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