cyb50-quant/cyb50-pro/risk/position_sizing.py

"""
反脆弱仓位管理器

基于改进凯利公式计算最优仓位：
f* = (p×b - q) / b + γ×Convexity + δ×Regime_Adapt
"""

from dataclasses import dataclass
from typing import Dict, Optional, Any
import numpy as np

from core.ecosystem import MacroRegime, UnifiedEcosystem


@dataclass
class PositionSize:
    """仓位计算结果"""
    base_size: float          # 基础仓位
    adjusted_size: float      # 调整后的仓位
    kelly_fraction: float     # 凯利比例
    convexity_adjustment: float
    regime_adjustment: float
    max_position: float
    reasoning: str


class AntifragilePositionSizer:
    """
    反脆弱仓位管理器

    核心算法：
    1. 基础凯利公式计算
    2. 凸性调节（正凸性加分，负凸性减分）
    3. 生态适应系数调节
    """

    def __init__(
        self,
        kelly_fraction: float = 0.7,   # 激进凯利: 70%
        gamma_convexity: float = 0.15,
        max_position: float = 1.0,     # 允许满仓
        min_position: float = 0.0
    ):
        self.kelly_fraction = kelly_fraction
        self.gamma_convexity = gamma_convexity
        self.max_position = max_position
        self.min_position = min_position

        # 生态适应系数 - 激进配置
        self.regime_multipliers = {
            MacroRegime.SPRING: 0.9,
            MacroRegime.SUMMER: 1.2,   # 夏季超配120%
            MacroRegime.AUTUMN: 0.3,
            MacroRegime.WINTER: 0.0,
            MacroRegime.UNKNOWN: 0.0
        }

    def calculate_position(
        self,
        win_probability: float,
        win_loss_ratio: float,
        ecosystem: Optional[UnifiedEcosystem] = None,
        convexity: float = 0.0
    ) -> PositionSize:
        """
        计算建议仓位

        Args:
            win_probability: 胜率 (0-1)
            win_loss_ratio: 盈亏比
            ecosystem: 市场生态
            convexity: 凸性特征（正凸性策略为正，负凸性为负）

        Returns:
            PositionSize: 仓位计算结果
        """
        # 基础凯利公式
        q = 1 - win_probability
        if win_loss_ratio <= 0:
            kelly_f = 0.0
        else:
            kelly_f = (win_probability * win_loss_ratio - q) / win_loss_ratio
            kelly_f = max(0, min(1, kelly_f))

        # 应用半凯利
        base_size = kelly_f * self.kelly_fraction

        # 凸性调节
        convexity_adj = self.gamma_convexity * convexity

        # 生态适应调节
        regime_adj = 0.0
        if ecosystem and hasattr(ecosystem, 'macro'):
            regime = ecosystem.macro.regime
            regime_multiplier = self.regime_multipliers.get(regime, 1.0)
            regime_adj = (regime_multiplier - 1.0) * base_size

        # 计算最终仓位
        adjusted_size = base_size + convexity_adj + regime_adj
        adjusted_size = max(self.min_position, min(self.max_position, adjusted_size))

        return PositionSize(
            base_size=base_size,
            adjusted_size=adjusted_size,
            kelly_fraction=kelly_f,
            convexity_adjustment=convexity_adj,
            regime_adjustment=regime_adj,
            max_position=self.max_position,
            reasoning=f"Kelly={kelly_f:.2f}, Convexity={convexity_adj:+.2f}, Regime={regime_adj:+.2f}"
        )