--- name: a-share-thesis-79d81acbaa6e description: A股投资论点建立与追踪/投资逻辑梳理。当用户说"投资论点"、"thesis"、"为什么看好XX"、"为什么看空XX"、"投资逻辑"、"帮我梳理XX的逻辑"、"跟踪一下XX"、"催化剂"、"XX还能不能拿"、"投资论点追踪"、"thesis tracker"时触发。MUST USE when user asks to build/track an investment thesis, articulate bull/bear case, or review catalysts and risk points for a stock position. 帮助建立结构化投资论点（核心论点+支撑论据+风险+催化剂），并追踪论点状态变化。通过 cn-stock-data 获取最新数据验证论点，结合 web 搜索获取事件催化剂。支持机构投研风格（formal）和个人持仓笔记风格（brief）。 --- ## 数据源 ```bash SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts" # 最新行情（验证价格/估值变化） python "$SCRIPTS/cn_stock_data.py" quote --code [CODE] # 财务指标（验证基本面论据） python "$SCRIPTS/cn_stock_data.py" finance --code [CODE] # K 线（论点建立以来的股价走势） python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [论点建立日期] # 资金流向（市场认可度） python "$SCRIPTS/cn_stock_data.py" fund_flow --code [CODE] --days 30 ``` 补充：通过 web 搜索获取最新公告、行业新闻、分析师观点等催化剂信息。 ## Workflow ### Step 1: 建立/加载论点 **新建论点**时，收集以下信息： 1. 标的公司（代码 + 名称） 2. 方向：看多(Long) / 看空(Short) / 中性观察(Neutral) 3. 核心论点（1-2 句话，必须可证伪） 4. 支撑论据（3-5 条，每条附数据支撑） 5. 关键风险（3-5 条，每条附触发条件） 6. 催化剂（未来 3-6 个月内可能兑现的事件） 7. 目标价 / 估值区间 8. 止损条件（什么情况下放弃论点） 9. 信心等级：高/中/低 **加载已有论点**：用户提供之前的论点文档或口述核心逻辑。 ### Step 2: 数据验证 通过 cn-stock-data 获取最新数据，逐条验证论据： - 每条论据对应的财务/行情指标是否符合预期 - 标注：✅ 符合预期 / ⚠️ 需要关注 / ❌ 不符合预期 ### Step 3: 论点记分卡 构建记分卡表格： | 论据 | 原始预期 | 当前状态 | 趋势(↑/→/↓) | 信号强度 | |------|---------|---------|-------------|---------| 综合评估：论点强化 / 维持 / 弱化 / 已失效 ### Step 4: 催化剂日历 | 预计日期 | 事件 | 类型 | 预期影响 | 状态 | |---------|------|------|---------|------| | 2026-04-xx | Q1 业绩预告 | 财报 | 验证增长论据 | 待兑现 | | 2026-xx-xx | 行业政策发布 | 政策 | 正面催化 | 待兑现 | 催化剂类型：财报/政策/行业事件/公司公告/宏观经济/技术突破 ### Step 5: 输出 #### 风格说明 | 维度 | formal（机构投研） | brief（个人持仓笔记） | |------|-------------------|---------------------| | 篇幅 | 2-4 页 | 半页-1 页 | | 论点结构 | 完整（论据+风险+催化剂各 3-5 条） | 精简（各 2-3 条） | | 记分卡 | 完整表格 + 趋势分析 | 简化版（仅标注状态） | | 催化剂 | 完整日历 + 概率评估 | 关键 2-3 个事件 | | 数据引用 | 每条附具体数字和来源 | 关键数字即可 | | 结论 | 客观（论点强化/弱化） | 可加主观判断（继续持有/减仓/加仓） | | 免责声明 | 需要 | 不需要 | ## 关键规则 1. **可证伪性**：每条论据必须有明确的验证标准，模糊的论据（如"公司管理层很优秀"）必须转化为可量化指标 2. **动态追踪**：论点不是一次性的，每次用户提及"跟踪XX"时应加载已有论点并更新 3. **诚实面对反面证据**：当数据不支持论点时必须如实标注，不回避负面信号 4. **分离事实与观点**：数据验证部分纯客观，个人判断仅在 brief 模式的结论部分出现 5. **时间框架**：明确论点的时间框架（短线 1-3 月 / 中线 3-12 月 / 长线 1 年+） 6. **与其他 skill 联动**：建议用 a-share-earnings-analysis 做更深入的财报验证，用 a-share-comps 做估值对标 ## 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-thesis/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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