
Rq Earnings Analysis
Generate structured Chinese earnings analysis reports from Ricequant JSON datasets via the earnings-analysis report template and generator script.
Install
npx skills add https://github.com/ricequant/ricequant-skills --skill rq-earnings-analysisWhat is this skill?
- Markdown report template with placeholders: executive summary, financial quality, thesis update, valuation, risks
- Data contract for generate_report.py: company_info, industry, historical_financials, roe_history, valuation factors, pri
- Derives YoY/QoQ metrics, margins, cash conversion, leverage, and post-earnings price reaction from JSON inputs
- Documents dividend_yield bps-to-percent conversion for display
- Bilingual finance workflow aimed at Ricequant / A-share style identifiers (order_book_id, quarters)
Adoption & trust: 1 installs on skills.sh; 26 GitHub stars; 1/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
Recommended Skills
Journey fit
Investment and company research is where earnings dissection happens before commitment sizing or product bets tied to public markets. Research is the shelf because the artifact is an analytical report with valuation, thesis, and risk sections—not shipping code.
Common Questions / FAQ
Is Rq Earnings Analysis safe to install?
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SKILL.md
READMESKILL.md - Rq Earnings Analysis
# 财报分析报告 - 报告日期:[[REPORT_DATE]] - 公司:[[COMPANY_NAME]]([[STOCK_CODE]]) - 最新财报期:[[LATEST_QUARTER]] - 披露日:[[EVENT_DATE]] ## 执行摘要 [[EXEC_SUMMARY]] ## 信息截面 [[INFO_PANEL]] ## 财报概览 [[EARNINGS_OVERVIEW]] ## 市场预期、卖方反馈与价格反应 [[EXPECTATION_AND_REACTION]] ## 公告原文与管理层表述 [[ANNOUNCEMENT_SECTION]] ## 财务质量与资产负债表 [[FINANCIAL_QUALITY]] ## 投资逻辑更新 [[THESIS_UPDATE]] ## 估值与定位 [[VALUATION_SECTION]] ## 风险提示 [[RISK_SECTION]] ## 附录:生成说明 [[APPENDIX]] # earnings-analysis 数据契约 `earnings-analysis/scripts/generate_report.py` 默认从 `--data-dir` 读取以下 JSON 文件。 ## 1. `company_info.json` 典型字段: - `order_book_id` - `symbol` - `abbrev_symbol` - `industry_name` - `listed_date` 用途: - 公司名称、简称、上市信息 ## 2. `industry.json` 典型字段: - `first_industry_name` - `second_industry_name` - `third_industry_name` 用途: - 行业分层描述 ## 3. `historical_financials.json` 典型字段: - `order_book_id` - `quarter` - `info_date` - `revenue` - `net_profit` - `gross_profit` - `cash_from_operating_activities` - `total_assets` - `total_liabilities` 用途: - 自动识别最新财报季度 - 计算同比、环比、毛利率、现金转化率、资产负债率 ## 4. `roe_history.json` 典型字段: - `order_book_id` - `date` - `return_on_equity_weighted_average` 用途: - 盈利质量趋势 ## 5. `market_cap.json` / `pe_ratio.json` / `pb_ratio.json` / `dividend_yield.json` 典型字段: - `order_book_id` - `date` - 对应 factor 字段 说明: - `dividend_yield` 原始值为 bps,生成报告时需要除以 `100` 后按百分比展示 用途: - 当前估值与股东回报定位 ## 6. `price_window.json` 典型字段: - `order_book_id` - `datetime` - `close` - `volume` - `total_turnover` 用途: - 计算财报前后价格反应 - 计算成交额放大 ## 7. `benchmark_window.json` 典型字段: - `order_book_id` - `datetime` - `close` 用途: - 计算相对沪深300的超额收益 ## 8. `consensus.json` 典型字段: - `date` - `create_tm` - `report_year_t` - `comp_con_operating_revenue_t / t1 / t2 / t3` - `comp_con_net_profit_t / t1 / t2 / t3` - `comp_con_eps_t / t1 / t2 / t3` - `con_targ_price` 用途: - 财报前一致预期 - 财报后预期变化 ## 9. `research_reports.json` 典型字段: - `create_tm` - `date` - `report_title` - `summary` - `institute` - `author` - `fiscal_year` - `net_profit_t / t1 / t2` - `eps_t / t1 / t2` - `targ_price` 用途: - 财报后卖方解读 - 目标价和年度利润口径的补充 说明: - `summary` 是财报后“文字解释层”的首选字段,用于补充卖方对业绩、预期修正和核心关注点的描述 - `report_title`、`summary`、`targ_price` 与 `net_profit_t / t1 / t2` 需要一起看,不能只保留数值预测 ## 10. `announcement_raw.json` 典型字段: - `info_date` - `title` - `info_type` - `media` - `file_type` - `announcement_link` 用途: - 识别正式财报、主要经营数据公告和业绩说明会公告 - 在正文中保留公告原文链接,供后续 PDF / HTML 读取 ## 11. `announcement_extracts.json` 该文件可选,可由具备 PDF / HTML 原文解析能力的流程生成。 典型字段: - `records[].title` - `records[].info_date` - `records[].announcement_link` - `records[].is_annual_or_interim_report` - `records[].fetch_status` - `records[].extract_status` - `records[].raw_sections.company_intro` - `records[].raw_sections.management_discussion` - `records[].raw_sections.risk_warning` - `records[].raw_sections.outlook` - `records[].summaries.company_intro` - `records[].summaries.management_discussion` - `records[].summaries.risk_warning` - `records[].summaries.outlook` - `records[].sections.company_intro` - `records[].sections.management_discussion` - `records[].sections.risk_warning` - `records[].sections.outlook` 用途: - `raw_sections` 保存较长原文段落,供当前 skill 内的 LLM 直接读取 - `summaries` 保存基于 `raw_sections` 回写的总结性文本;报告生成时只消费该层 - `summaries` 必须由当前 LLM 回写客户可读摘要,不能直接复制 `raw_sections` 原文或 PDF 抽取碎片 - `sections` 为兼容旧结构保留,当前可视为 `raw_sections` 的兼容镜像 - `company_intro` / `management_discussion` / `outlook` 主要面向年报、半年报正文;季度报告和临时公告默认不强制抽取这三类字段 - 若源站拦截或 PDF 不可读,也必须保留失败状态和原文链接,不能静默丢失 - 不额外创建 `announcement_summaries.json`;LLM 应直接在 `announcement_extracts.json` 的 `records[].summaries.*` 中回写结果 ## 12. `web_search_findings.json` 该文件可选,仅用于补充 `RQData CLI` 无法直接提供的实时外部语境。 每条记录至少包含: - `query` - `source_name` - `source_type` - `title` - `url` - `published_at` - `retrieved_at` - `summary` - `why_relevant` - `confidence` - `finding_type` 推荐附加字段: - `subject` - `stance` 允许的 `finding_type`: - `company_news` - `management_update` - `earnings_call` - `industry_context` - `policy_context` 说明: -