--- name: a-share-earnings-preview-9470f31536bc description: A股业绩前瞻/季报预览/财报预期情景分析。当用户提到"业绩前瞻"或"earnings preview"就触发。关键词："业绩前瞻"、"季报预览"、"预期怎么样"、"earnings preview"、"XX下周发财报"、"财报前怎么看"、"业绩预告分析"、"预计业绩"、"业绩展望"、"财报前瞻"、"预计这个季度业绩"、"业绩预期"、"情景分析"、"业绩预测"。MUST USE when user asks about earnings preview, pre-earnings scenario analysis, earnings expectations/forecast, or quarterly outlook before earnings release. This is the ONLY skill for pre-earnings forward-looking analysis. 在财报发布前，基于历史数据和市场预期构建牛/熊/基准三种情景分析。通过 cn-stock-data 获取历史财务趋势，结合 web 搜索获取市场一致预期和业绩预告。支持机构前瞻风格（formal）和个人预判风格（brief）。 --- # A股业绩前瞻 (A-Share Earnings Preview) ## 数据源 ```bash SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts" # 历史财务指标（分析趋势） python "$SCRIPTS/cn_stock_data.py" finance --code [CODE] # 最新行情（当前估值水平） python "$SCRIPTS/cn_stock_data.py" quote --code [CODE] # 近 6 个月 K 线（股价已 price-in 多少预期） python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [6个月前日期] # 资金流向（机构是否提前布局） python "$SCRIPTS/cn_stock_data.py" fund_flow --code [CODE] --days 30 ``` 补充：通过 web 搜索获取： - 市场一致预期（券商预测均值） - 业绩预告/快报（如已发布） - 同行业已发布财报的参考信号 - 公司近期公告、调研纪要 ## Workflow ### Step 1: 背景梳理 1. 确认即将发布的报告期（Q1/半年报/Q3/年报） 2. 通过 cn-stock-data 获取过去 4-8 个季度的财务数据，建立趋势基线 3. Web 搜索： - 是否已发布业绩预告/快报？（有则直接用预告数据） - 市场一致预期（Wind/东方财富/券商研报的营收/净利润预测） - 同行业已公布业绩的公司表现（作为参考信号） - 公司近期重大事件（收购/产能投放/政策影响） ### Step 2: 关键指标框架 **财务指标预期**（基于历史趋势 + 市场预期）： | 指标 | 上季度实际 | 去年同期 | 市场预期 | 个人预判 | |------|-----------|---------|---------|---------| | 营业收入(亿) | | | | | | 同比增速(%) | | | | | | 归母净利润(亿) | | | | | | 同比增速(%) | | | | | | 毛利率(%) | | | | | | 净利率(%) | | | | | **行业特定运营指标**（通过 web 搜索获取）： - 白酒：批价走势、渠道库存、预收款 - 银行：净息差趋势、不良率、拨备覆盖率 - 科技：订单/合同额、研发进度 - 新能源：出货量、价格趋势、产能利用率 - 消费：同店增长、线上占比、库存 - 医药：管线进度、集采影响、销售额 - 地产：销售面积/金额、回款率、新开工 ### Step 3: 情景分析 构建三种情景： | | 牛市情景(Bull) | 基准情景(Base) | 熊市情景(Bear) | |--|--------------|--------------|--------------| | 营收 | | | | | 净利润 | | | | | 关键驱动 | [什么因素超预期] | [符合市场预期] | [什么因素低于预期] | | 概率 | XX% | XX% | XX% | | 股价反应 | +X% to +X% | -X% to +X% | -X% to -X% | 每个情景的逻辑必须自洽，概率合计 = 100% ### Step 4: 催化剂检查清单 列出决定股价反应的 3-5 个关键观察点： 1. [指标1]：预期 XX，若超过/低于 XX 则表明... 2. [指标2]：... 3. [管理层指引]：关注前瞻指引的措辞变化 4. [特定事件]：... ### Step 5: 输出 根据用户需求选择 formal 或 brief 风格，参考 `references/preview-template.md` 中的模板。 ## 风格说明 | 维度 | formal（机构前瞻） | brief（个人预判） | |------|-------------------|-----------------| | 篇幅 | 2-3 页 | 半页 | | 历史趋势 | 4-8 季度完整表格 | 简述趋势方向 | | 情景分析 | 完整三情景 + 概率 + 股价反应估计 | 简化版（重点写偏离预期的情景） | | 运营指标 | 行业特定的 3-5 个 | 1-2 个最关键的 | | 催化剂 | 完整检查清单 | 1-2 个最重要观察点 | | 交易建议 | 不给（只分析） | 可加个人操作思路 | | 免责声明 | 需要 | 不需要 | ## A 股财报日历特殊性 - **年报**: 次年 1-4 月披露（4/30 截止） - **Q1 季报**: 4/30 前披露 - **半年报**: 7-8 月披露（8/31 截止） - **Q3 季报**: 10/31 前披露 - **业绩预告**: 通常提前 1-2 个月，创业板强制预告 - **业绩快报**: 部分公司在正式报告前发布简要数据 ## 关键规则 1. **区分预告和预测**：如果公司已发布业绩预告/快报，那是"已知信息"而非预测；真正的前瞻是分析正式报告可能的超预期/低于预期之处 2. **预期差是核心**：股价反应取决于实际 vs 预期的差值，而非绝对好坏 3. **避免过度精确**：给出合理区间比精确数字更诚实 4. **季节性调整**：注意 A 股的季度盈利不均匀分布（Q4 常有减值/一次性项目） 5. **同比 vs 环比**：高基数/低基数效应会扭曲同比增速，环比更能反映边际变化 6. **与 a-share-earnings-analysis 联动**：前瞻报告发布后，实际财报出来时用 a-share-earnings-analysis 做验证 ## 使用示例 ### 示例 1: 基本使用 ```python # 调用 skill result = run_skill({ "param1": "value1", "param2": "value2" }) ``` ### 示例 2: 命令行使用 ```bash python scripts/run_skill.py --input data.json ``` --- Source attribution: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/a-share-earnings-preview/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## 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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You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/a-share-earnings-preview/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Finance & Accounting, Marketing Roles: Financial Analyst, Market Research Analyst, Product Marketer Specialties: API Tools: Cn Stock Data, Web Search Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 117cffeca97a82397376700bf06597129bef65168d59d10547a6fba39158cf68