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130 lines
3.3 KiB
Python
130 lines
3.3 KiB
Python
"""可插拔滑点模型。"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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import numpy as np
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class SlippageModel(ABC):
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"""滑点模型基类。
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所有滑点模型必须实现 compute() 方法，返回总滑点成本（金额）。
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"""
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@abstractmethod
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def compute(
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self,
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price: float,
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size: float,
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volume: float,
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volatility: float,
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direction: str,
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) -> float:
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"""计算滑点成本。
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Args:
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price: 成交价格
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size: 订单数量（股）
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volume: 当日成交量（股），0 表示无数据
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volatility: 近期年化波动率，0 表示无数据
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direction: 交易方向 BUY / SELL
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Returns:
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总滑点成本（金额，非比率）
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"""
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...
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class FixedSlippage(SlippageModel):
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"""固定每股滑点（向后兼容）。"""
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def __init__(self, per_share: float = 0.01) -> None:
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self._per_share = per_share
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def compute(
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self,
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price: float,
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size: float,
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volume: float,
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volatility: float,
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direction: str,
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) -> float:
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return size * self._per_share
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class PercentSlippage(SlippageModel):
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"""按成交金额百分比滑点。"""
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def __init__(self, rate: float = 0.001) -> None:
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self._rate = rate
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def compute(
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self,
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price: float,
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size: float,
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volume: float,
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volatility: float,
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direction: str,
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) -> float:
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return price * size * self._rate
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class SquareRootSlippage(SlippageModel):
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"""方根市场冲击模型（Almgren-Chriss 简化版）。
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impact = σ × √(participation_rate) × price × size × impact_coeff
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当 volume=0 或 volatility=0 时退化为 PercentSlippage(rate=0.001)。
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"""
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def __init__(self, impact_coeff: float = 0.1) -> None:
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self._impact_coeff = impact_coeff
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self._fallback = PercentSlippage(rate=0.001)
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def compute(
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self,
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price: float,
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size: float,
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volume: float,
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volatility: float,
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direction: str,
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) -> float:
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if size <= 0:
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return 0.0
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if volume <= 0 or volatility <= 0:
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return self._fallback.compute(price, size, volume, volatility, direction)
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participation_rate = min(size / volume, 1.0)
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impact = volatility * np.sqrt(participation_rate) * price * size * self._impact_coeff
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return float(impact)
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class VolumeSlippage(SlippageModel):
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"""成交量比例滑点。
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cost = (base_bps / 10000) × (size / volume) × price × size
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当 volume=0 时退化为 PercentSlippage(rate=base_bps/10000)。
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"""
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def __init__(self, base_bps: float = 10.0) -> None:
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self._base_bps = base_bps
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self._fallback = PercentSlippage(rate=base_bps / 10000.0)
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def compute(
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self,
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price: float,
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size: float,
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volume: float,
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volatility: float,
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direction: str,
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) -> float:
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if size <= 0:
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return 0.0
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if volume <= 0:
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return self._fallback.compute(price, size, volume, volatility, direction)
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rate = self._base_bps / 10000.0
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participation = min(size / volume, 1.0)
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return rate * participation * price * size
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