每份报告都对 4 种 DCF 方法做周期感知计算,生成情绪缓冲后的评级,并渲染为含双语切换和 45+ 内联术语提示的自含 HTML 文件。由 awesome-stock-analysis Claude Code skill 构建。Each report runs cyclicality-aware DCF across 4 methods, computes a sentiment-buffered verdict, and renders as a self-contained HTML file with bilingual EN / 中 toggle and 45+ inline term tooltips. Built with the awesome-stock-analysis Claude Code skill.
传统 DCF 模板用历史 FCF 增长率,对周期股和加速增长股都会系统性失真。本报告默认主估值锚是 季度年度化(Q-annualized) — 最新季 EPS × 4 × 周期感知 P/E 倍数 — 再配合另外三种 DCF 镜头交叉验证。Most stock-DCF templates use historical FCF growth, which systematically misprices cyclicals and accelerating growers. The default primary anchor here is Q-annualized — latest quarterly EPS × 4 × cycle-aware P/E multiple — paired with three other DCF lenses for cross-check.
周期股从盈利数据判断,不从行业名称。市场情绪由价格动量代理生成,并对基本面评级做缓冲调整。Cyclicality is detected from income data, not industry name. Sentiment is computed from price-action proxies and buffered against the fundamental verdict.
查看完整方法论 →Read methodology →开源项目。生成这些报告的同一套流水线已打包为 Claude Code skill。克隆仓库,配置 FMP API key,10 秒内分析任意美股票代码。This is open-source. The same pipeline that generated these reports is packaged as a Claude Code skill. Clone the repo, set your FMP API key, and analyze any US ticker in 10 seconds.
./run.sh NVDA
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