279 lines
12 KiB
Python
279 lines
12 KiB
Python
"""
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公式元数据注册表
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用于将计算引擎中的每个公式节点（含子公式）映射到唯一的 code、名称、公式说明以及排序，
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以便在 valuation_calculation_steps 表中进行结构化记录，并最终生成可读的计算报告。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from decimal import Decimal
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from typing import Dict, List, Optional
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@dataclass(frozen=True)
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class FormulaMeta:
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code: str
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name: str
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formula: str
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order: Decimal
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parent_code: Optional[str]
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group_code: str
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FormulaTreeNode = Dict[str, object]
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def _node(
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code: str,
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name: str,
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formula: str,
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order: str,
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children: Optional[List[FormulaTreeNode]] = None,
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group: Optional[str] = None,
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) -> FormulaTreeNode:
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return {
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"code": code,
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"name": name,
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"formula": formula,
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"order": order,
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"group_code": group,
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"children": children or [],
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}
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FORMULA_TREE: List[FormulaTreeNode] = [
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_node(
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"FINAL_A",
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"最终估值A",
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"最终估值A = 模型估值B × 0.7 + 市场估值C × 0.3",
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"10",
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children=[
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_node(
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"MODEL_B",
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"模型估值B",
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"模型估值B = （经济价值B1 × 0.7 + 文化价值B2 × 0.3） × 风险调整系数B3",
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"20",
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group="MODEL_B",
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children=[
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_node(
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"MODEL_B_ECON_B1",
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"经济价值B1",
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"经济价值B1 = 基础价值B11 × (1 + 流量因子B12) × 政策乘数B13",
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"21",
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children=[
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_node(
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"MODEL_B_ECON_B11",
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"基础价值B11",
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"基础价值B11 = 财务价值F × (0.45 + 0.05 × 行业系数I) + 法律强度L × (0.35 + 0.05 × 行业系数I) + 发展潜力D × 0.2",
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"21.1",
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children=[
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_node(
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"MODEL_B_ECON_B11_FINANCIAL_VALUE",
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"财务价值F",
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"财务价值F = [3年内年均收益 × (1 + 增长率)^5] ÷ 5",
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"21.11",
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),
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_node(
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"MODEL_B_ECON_B11_LEGAL_STRENGTH",
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"法律强度L",
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"法律强度L = 专利分 × 0.4 + 普及分 × 0.3 + 侵权分 × 0.3",
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"21.12",
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),
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_node(
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"MODEL_B_ECON_B11_DEVELOPMENT_POTENTIAL",
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"发展潜力D",
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"发展潜力D = 专利分 × 0.5 + ESG分 × 0.2 + 创新投入比 × 0.3",
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"21.13",
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),
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_node(
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"MODEL_B_ECON_B11_INDUSTRY_COEFFICIENT",
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"行业系数I",
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"行业系数I = (目标行业平均ROE - 基准行业ROE) ÷ 基准行业ROE",
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"21.14",
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),
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],
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),
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_node(
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"MODEL_B_FLOW_B12",
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"流量因子B12",
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"流量因子B12 = ln(S1 ÷ S2) × 0.3 + 社交媒体传播度S3 × 0.7",
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"21.2",
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children=[
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_node(
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"MODEL_B_FLOW_B12_INTERACTION_INDEX",
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"互动量指数",
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"互动量指数 = (点赞 + 评论 + 分享) ÷ 1000",
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"21.21",
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),
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_node(
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"MODEL_B_FLOW_B12_COVERAGE_INDEX",
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"覆盖人群指数",
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"覆盖人群指数 = 粉丝数 ÷ 10000",
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"21.22",
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),
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_node(
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"MODEL_B_FLOW_B12_CONVERSION_EFFICIENCY",
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"转化效率",
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"转化效率 = 商品链接点击量 ÷ 内容浏览量",
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"21.23",
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),
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_node(
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"MODEL_B_FLOW_B12_SOCIAL_SPREAD",
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"社交媒体传播度S3",
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"社交媒体传播度S3 = 互动量指数 × 0.4 + 覆盖人群指数 × 0.3 + 转化效率 × 0.3",
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"21.24",
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),
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],
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),
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_node(
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"MODEL_B_POLICY_B13",
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"政策乘数B13",
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"政策乘数B13 = 1 + 政策契合度评分P × 0.15，其中 P = 政策匹配度 × 0.4 + 实施阶段评分 × 0.3 + 资金支持度 × 0.3",
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"21.3",
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),
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],
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),
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_node(
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"MODEL_B_CULTURAL_B2",
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"文化价值B2",
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"文化价值B2 = 活态传承系数B21 × 0.6 + (纹样基因值B22 ÷ 10) × 0.4",
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"22",
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children=[
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_node(
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"MODEL_B_CULTURAL_B21",
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"活态传承系数B21",
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"活态传承系数B21 = 传承人等级系数 × 0.4 + 教学传播频次 × 0.3 + 跨界合作深度 × 0.3",
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"22.1",
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children=[
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_node(
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"MODEL_B_CULTURAL_B21_TEACHING_FREQ",
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"教学传播频次",
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"教学传播频次 = 线下传习次数 × 0.6 + 线上课程点击量（万） × 0.4",
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"22.11",
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),
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],
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),
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_node(
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"MODEL_B_CULTURAL_B22",
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"纹样基因值B22",
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"纹样基因值B22 = (结构复杂度SC × 0.6 + 归一化信息熵H × 0.4) × 历史传承度HI × 10",
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"22.2",
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),
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],
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),
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_node(
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"MODEL_B_RISK_B3",
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"风险调整系数B3",
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"风险调整系数B3 = 0.8 + 风险评分总和R × 0.4，其中 R = 市场风险 × 0.3 + 法律风险 × 0.4 + 传承风险 × 0.3",
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"23",
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children=[
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_node(
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"MODEL_B_RISK_B3_MARKET",
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"市场风险",
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"市场风险依据价格波动率：波动率 ≤5% 计10分，5-15%计5分，>15%计0分",
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"23.1",
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),
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_node(
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"MODEL_B_RISK_B3_LEGAL",
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"法律风险",
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"法律风险根据诉讼状态评分（无诉讼/已解决/未解决）",
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"23.2",
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),
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_node(
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"MODEL_B_RISK_B3_INHERITANCE",
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"传承风险",
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"传承风险依据传承人年龄：≤50岁10分，50-70岁5分，>70岁0分，取最高分",
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"23.3",
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),
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],
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),
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],
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),
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_node(
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"MARKET_C",
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"市场估值C",
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"市场估值C = 市场竞价C1 × 热度系数C2 × 稀缺性乘数C3 × 时效性衰减C4",
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"30",
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group="MARKET_C",
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children=[
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_node(
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"MARKET_C_C1",
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"市场竞价C1",
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"市场竞价C1 结合历史交易价格、人工竞价与专家估值的加权结果",
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"30.1",
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),
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_node(
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"MARKET_C_C2",
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"热度系数C2",
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"热度系数C2 = 1 + 浏览热度分（依据日均浏览量与收藏数量）",
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"30.2",
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),
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_node(
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"MARKET_C_C3",
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"稀缺性乘数C3",
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"稀缺性乘数C3 = 1 + 稀缺等级分",
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"30.3",
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),
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_node(
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"MARKET_C_C4",
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"时效性衰减C4",
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"时效性衰减C4 依据距最近市场活动天数的衰减系数",
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"30.4",
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),
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],
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),
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_node(
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"DYNAMIC_PLEDGE_RATE",
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"动态质押率DPR",
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"动态质押率DPR = 基础质押率 × (1 + 流量修正系数) + 政策加成系数 - 流动性调节因子",
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"40",
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group="DYNAMIC_PLEDGE",
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),
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],
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)
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]
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def _build_index() -> Dict[str, FormulaMeta]:
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index: Dict[str, FormulaMeta] = {}
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def dfs(nodes: List[FormulaTreeNode], parent_code: Optional[str], group_code: Optional[str]):
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for node in nodes:
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code = node["code"]
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name = node["name"]
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formula = node["formula"]
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order = Decimal(str(node["order"]))
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explicit_group = node.get("group_code")
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if explicit_group:
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current_group = explicit_group
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elif parent_code is None:
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current_group = code
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else:
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current_group = group_code or parent_code
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meta = FormulaMeta(
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code=code,
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name=name,
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formula=formula,
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order=order,
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parent_code=parent_code,
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group_code=current_group,
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)
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index[code] = meta
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dfs(node.get("children", []), code, current_group)
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dfs(FORMULA_TREE, None, None)
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return index
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FORMULA_INDEX: Dict[str, FormulaMeta] = _build_index()
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def get_formula_meta(code: str) -> FormulaMeta:
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meta = FORMULA_INDEX.get(code)
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if not meta:
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raise KeyError(f"公式编码未注册: {code}")
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return meta
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