Multi-Seller Account Portfolio Management — 多店铺账号组合管理：跨账号资源协同与风险隔离

"""
Multi-Seller Account Portfolio Management
多账号组合优化 + 关联风险矩阵
"""
import numpy as np
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class SellerAccount:
    account_id: str
    monthly_roas: list   # 过去12个月ROAS历史
    current_health: float = 1.0   # 0-1，账号健康分
    shared_ip: Optional[str] = None
    shared_payment: Optional[str] = None
    shared_supplier: Optional[str] = None
    product_overlap: float = 0.0  # 与其他账号的产品重叠度（0-1）


def compute_linkage_risk(a1: SellerAccount, a2: SellerAccount) -> float:
    """计算两账号间的关联风险（0=独立，1=高度关联）"""
    risk = 0.0
    # 共享基础设施权重
    if a1.shared_ip and a1.shared_ip == a2.shared_ip:
        risk += 0.40
    if a1.shared_payment and a1.shared_payment == a2.shared_payment:
        risk += 0.30
    if a1.shared_supplier and a1.shared_supplier == a2.shared_supplier:
        risk += 0.20
    risk += 0.10 * a1.product_overlap
    return min(1.0, risk)


def portfolio_optimization(accounts: list, total_budget: float,
                           risk_aversion: float = 1.0) -> dict:
    """
    Markowitz 组合优化：最大化 ROAS - λ × 风险
    简化版（无二次规划求解器依赖）
    """
    n = len(accounts)
    expected_roas = np.array([np.mean(a.monthly_roas) for a in accounts])
    roas_std = np.array([np.std(a.monthly_roas) for a in accounts])

    # 协方差矩阵（用历史 ROAS 序列估计）
    roas_matrix = np.array([a.monthly_roas for a in accounts])
    min_len = min(len(r) for r in roas_matrix)
    roas_matrix = np.array([r[:min_len] for r in roas_matrix])
    cov_matrix = np.cov(roas_matrix)

    # 简化优化：风险调整后的 ROAS 排序分配
    # 真实生产用 scipy.optimize.minimize 或 cvxpy
    health_weights = np.array([a.current_health for a in accounts])
    adjusted_roas = expected_roas * health_weights - risk_aversion * roas_std

    # 按调整后 ROAS 比例分配（简化版）
    adjusted_roas_pos = np.maximum(adjusted_roas, 0.1)
    weights = adjusted_roas_pos / adjusted_roas_pos.sum()

    portfolio_roas = float(np.sum(weights * expected_roas))