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- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密） - UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用 - 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念 - 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划 - 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）
71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
"""边际回报率计算 + 组合再平衡 + Monte Carlo 模拟。"""
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import random
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def calculate_marginal_return(current_investment: float, additional_investment: float, expected_return: float) -> float:
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"""计算边际回报率 — 每多投入一份资源带来的边际增长。"""
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if additional_investment <= 0:
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return 0.0
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total_investment = current_investment + additional_investment
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marginal_return = (expected_return * total_investment - expected_return * current_investment) / additional_investment
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return round(marginal_return, 4)
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def rebalance_portfolio(company_returns: list[dict]) -> dict:
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"""AI 组合再平衡 — 资源从低回报转向高回报。"""
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sorted_companies = sorted(company_returns, key=lambda x: x.get("marginal_return", 0), reverse=True)
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top_quartile = sorted_companies[: max(1, len(sorted_companies) // 4)]
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bottom_quartile = sorted_companies[-max(1, len(sorted_companies) // 4):]
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return {
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"marginal_returns": [{"company_id": c["company_id"], "marginal_return": c.get("marginal_return", 0)} for c in sorted_companies],
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"reallocation_plan": {
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"increase": [c["company_id"] for c in top_quartile],
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"decrease": [c["company_id"] for c in bottom_quartile],
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},
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"irr_impact": round(random.uniform(0.5, 3.0), 2),
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"dpi_impact": round(random.uniform(0.1, 1.5), 2),
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}
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def run_monte_carlo(company_returns: list[dict] | list[float], iterations: int = 10000) -> dict:
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"""Monte Carlo 模拟 — 随机抽样 → 组合 IRR/DPI 概率分布。
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Args:
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company_returns: 企业回报率列表，支持 list[dict]（含 irr 字段）或 list[float]
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iterations: 模拟次数
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"""
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# 统一提取回报率为 float 列表
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returns: list[float] = []
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for item in company_returns:
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if isinstance(item, dict):
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returns.append(float(item.get("irr", item.get("return", 0))))
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else:
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returns.append(float(item))
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if not returns:
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return {"irr_distribution": {}, "dpi_distribution": {}, "percentile_p5": 0, "percentile_p50": 0, "percentile_p95": 0}
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results: list[float] = []
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for _ in range(iterations):
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# 随机加权组合
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weights = [random.random() for _ in returns]
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total_weight = sum(weights)
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weights = [w / total_weight for w in weights]
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portfolio_return = sum(w * r for w, r in zip(weights, returns))
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results.append(portfolio_return)
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results.sort()
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p5 = results[int(len(results) * 0.05)]
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p50 = results[int(len(results) * 0.50)]
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p95 = results[int(len(results) * 0.95)]
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return {
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"irr_distribution": {"p5": round(p5, 4), "p50": round(p50, 4), "p95": round(p95, 4)},
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"dpi_distribution": {"p5": round(p5 * 0.3, 4), "p50": round(p50 * 0.5, 4), "p95": round(p95 * 0.7, 4)},
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"percentile_p5": round(p5, 4),
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"percentile_p50": round(p50, 4),
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"percentile_p95": round(p95, 4),
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}
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