--- name: style-factor-calculation-f4a2362a71bf description: 用于因子暴露的风格因子计算原子 skill，适用于通用行业金融计算场景。 --- # 风格因子计算 Skill ## 数据来源 本 Skill 支持多种金融数据输入格式，核心数据来源包括： ### 1. 价格序列数据 - **股票价格数据**：日收盘价、周收盘价、月收盘价 - **指数价格数据**：各类风格指数、行业指数 - **基金净值数据**：单位净值、累计净值 - **组合净值数据**：组合累计净值序列 ### 2. 财务数据 - **财务报表数据**：利润表、资产负债表、现金流量表 - **财务指标**：ROE、ROA、净利润率、营收增长率等 - **估值指标**：PE、PB、PS、PEG等 ### 3. 市场数据 - **市值数据**：总市值、流通市值 - **交易数据**：成交量、换手率 - **价格动量**：价格动量、收益率动量 ### 4. 数据格式要求 - **CSV格式**：标准时间序列数据，包含日期和指标列 - **Excel格式**：支持多工作表数据 - **JSON格式**：结构化时间序列数据 - **数据库连接**：支持SQL数据库直接查询 > 说明：本 Skill 不包含数据采集功能，需要用户提供清洗后的价格和财务数据。建议数据时间跨度不少于1年，以便进行准确的风格因子计算。 --- ## 功能 本 Skill 提供全面的风格因子计算能力，涵盖多种风格因子： ### 1. 价值风格因子 - **PE因子**：市盈率及其分位数 - **PB因子**：市净率及其分位数 - **PS因子**：市销率及其分位数 - **PEG因子**：PEG比率及其分位数 - **EV/EBITDA因子**：企业价值倍数及其分位数 ### 2. 成长风格因子 - **营收增长率**：营收增长率及其分位数 - **净利润增长率**：净利润增长率及其分位数 - **ROE增长率**：ROE增长率及其分位数 - **盈利质量**：盈利质量指标 ### 3. 规模风格因子 - **市值因子**：总市值、流通市值及其分位数 - **市值分组**：大盘、中盘、小盘分类 - **市值权重**：市值加权因子 ### 4. 动量风格因子 - **价格动量**：价格动量及其分位数 - **收益率动量**：收益率动量及其分位数 - **相对强度**：相对强度指标 ### 5. 质量风格因子 - **盈利质量**：盈利质量指标 - **财务质量**：财务质量指标 - **治理质量**：治理质量指标 ### 6. 因子暴露计算 - **因子暴露度**：计算组合对各类风格因子的暴露度 - **因子贡献度**：计算各因子对组合收益的贡献 - **因子相关性**：计算因子之间的相关性 ### 7. 数据处理能力 - **缺失值处理**：支持前向填充、插值等方法 - **异常值检测**：基于统计方法识别和处理异常值 - **数据标准化**：支持不同数据源的格式统一 - **因子标准化**：支持因子的标准化处理 --- ## 使用示例 ### 输出示例 ```json { "symbol": "000001.SZ", "date": "2024-12-31", "style_factors": { "value_factors": { "pe": 15.2, "pe_percentile": 45.3, "pb": 2.1, "pb_percentile": 52.8, "ps": 3.5, "ps_percentile": 48.2 }, "growth_factors": { "revenue_growth": 12.5, "revenue_growth_percentile": 65.8, "net_income_growth": 18.3, "net_income_growth_percentile": 72.5 }, "size_factors": { "market_cap": 50000000000, "market_cap_percentile": 78.5, "size_category": "large_cap" }, "momentum_factors": { "price_momentum": 8.5, "price_momentum_percentile": 58.2, "return_momentum": 7.2, "return_momentum_percentile": 55.8 } }, "factor_exposure": { "value_exposure": 0.45, "growth_exposure": 0.65, "size_exposure": 0.78, "momentum_exposure": 0.58 } } ``` --- ## 注意事项与限制 ### 1. 数据质量要求 - 价格和财务数据需要经过清洗和验证 - 建议使用足够长的历史数据（至少1年） - 异常数据会影响因子计算结果 ### 2. 计算方法选择 - 不同计算方法的结果可能不同 - 因子标准化方法需要统一 - 分位数计算需要足够的样本量 ### 3. 时间序列特性 - 风格因子具有时间依赖性 - 不同时间点的因子值不可直接比较 - 市场环境变化会影响因子表现 ### 4. 综合判断原则 - 单一风格因子不能全面反映投资风格 - 需要结合多个因子进行综合分析 - 应结合市场环境和投资策略进行判断 ### 5. 使用限制 - 本 Skill 输出为技术分析结果，不构成投资建议 - 使用者应结合专业判断和具体业务场景 - 对于重大决策，建议咨询专业投资顾问 --- ## 参考资料 - 见 references/ 目录中的相关文档，包括： - 风格因子计算公式手册 - 因子暴露计算方法 - 因子分析最佳实践指南 - 数据处理方法说明文档 ## License - 本 skill 代码部分采用 MIT License，详见 `LICENSE` 文件 - 依赖与运行环境以 `requirements.txt` 为准 - 文档内容采用 CC BY 4.0 许可 --- Source attribution: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/archive/style-factor-calculation/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/archive/style-factor-calculation/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Finance & Accounting, Marketing Roles: Data Analyst, Financial Analyst, Growth Marketer Specialties: API, Database Tools: Csv, Excel, Json, Sql Database Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 05a4227a7a4da18efc89a55a41c08525a2811652d3180ffc8f87cefb3079c71a