stock-expectation-repricing-analysis

---

name: stock_expectation_repricing_analysis description: Use this standalone stock strategy skill for 预期重估 (expectation_repricing) analysis. Accept one or more A-share stock codes, Chinese stock names, US stock tickers, or Hong Kong stock codes, collect market/news data, and produce a Codex-readable Markdown report focused on the 预期重估 strategy. Source strategy: ~/daily_stock_analysis/strategies/expectation_repricing.yaml.

预期重估 Stock Analysis Skill

This skill is a strategy-specific variant of stock_ai_analysis. It uses the same bundled analyzer structure. User config, generated prompts, reports, and final reports for this strategy live under:

~/.stock_analysis/expectation_repricing/

Downloaded market-data caches are shared across strategies under ~/.stock_analysis/cache/ so users do not need to fetch the same data repeatedly.

Strategy Source

• Source file: ~/daily_stock_analysis/strategies/expectation_repricing.yaml
• Strategy id: expectation_repricing
• Display name: 预期重估
• Category: framework
• Aliases: 预期, 预期差, 预期重估
• Required source tools: search_stock_news, get_stock_info, get_realtime_quote, analyze_trend
• Summary: 分析业绩预期、政策预期和估值预期的变化，寻找预期差修复或预期过热后的回落风险。

Workflow

1. Work only inside this skill directory. Do not import, inspect, or depend on files outside it during normal analysis.
2. The script creates and reads user config at ~/.stock_analysis/expectation_repricing/.env. Always run config check first:

python scripts/analyze.py --check-config

3. First-run hard rule: if --check-config shows CONFIG_CREATED: yes, explicitly tell the user initialization config is required before any analysis. Do not run --stocks in the same turn.
4. Incomplete-config hard rule: if required capability keys are missing for the user request, stop and ask the user to complete configuration using references/configuration.md before running analysis. Never run partial/incomplete analysis.
5. Ask for stock codes, names, or tickers if not supplied. Accept 600487, 北方华创, AAPL, 00700.HK, or mixed input like 北方华创,600487,AAPL,00700.HK.
6. Only after configuration is complete, run analysis:

python scripts/analyze.py --stocks <codes>

Use --no-llm for data-only smoke runs. Use --send-email only after the user confirms email delivery. Use --no-email for validation runs when EMAIL_ENABLED=true.

7. Open the latest prompt files under ~/.stock_analysis/expectation_repricing/prompts/, use the current Codex agent to generate the final analysis, and write that analysis to a Markdown file under ~/.stock_analysis/expectation_repricing/final_reports/.
8. The final analysis must apply the strategy rules below. Do not return a generic stock report when this skill is invoked.
9. Send email only from a finalized current-agent report:

python scripts/analyze.py --send-final-report ~/.stock_analysis/expectation_repricing/final_reports/<report>.md

If a generated base report exists, merge the final analysis back into the original report before sending:

python scripts/analyze.py --send-final-report ~/.stock_analysis/expectation_repricing/final_reports/<report>.md --base-report ~/.stock_analysis/expectation_repricing/reports/<base_report>.md

Strategy Rules

预期重估策略（Expectation Repricing Strategy）

适用场景：

• 市场对公司业绩、政策、行业景气、估值中枢或竞争格局的预期正在变化。
• 需要判断当前价格反映的是“预期修复”“预期落空”还是“预期过热”。

分析框架：

1. 预期来源

   • 使用 search_stock_news 识别近期改变市场预期的信息：业绩预告、机构观点、订单、政策、产品进展、行业数据。
   • 区分硬信息（公告、财报、订单）和软信息（传闻、观点、情绪）。

2. 预期差方向

   • 正向预期差：市场原本悲观，新增信息显示业务或业绩好于预期。
   • 负向预期差：市场原本乐观，新增信息低于预期或验证失败。
   • 若信息已经被连续大涨充分反映，需要提示预期兑现风险。

3. 估值重估

   • 使用 PE/PB、市值、ROE、现金流等字段判断估值重估是否有基本面支撑。
   • 估值提升需要匹配盈利质量、增长持续性和行业空间。
   • 估值回落时，观察是否由一次性扰动还是长期逻辑变化导致。

4. 价格确认

   • 使用 analyze_trend 判断预期变化是否已经转化为趋势。
   • 放量突破可视为预期被资金确认；缩量反弹则更偏修复观察。
   • 高位放量滞涨、利好不涨或跌破关键支撑，可能意味着预期转弱。

输出要求：

• 明确当前是：正向预期差 / 预期兑现 / 负向预期差 / 预期不明。
• 说明哪些信息改变了市场预期，哪些仍待验证。
• 给出观察点：下一份财报、订单兑现、政策落地、估值回落或技术确认。

评分调整建议：

• 正向预期差且价格尚未充分反映：sentiment_score +15
• 正向预期差已被连续大涨兑现：sentiment_score -5
• 负向预期差或核心假设被证伪：sentiment_score -15
• 信息不充分但存在潜在修复：维持中性并降低置信度

Guardrails

• Never print real API keys, passwords, webhook URLs, or tokens.
• Ask before overwriting an existing ~/.stock_analysis/expectation_repricing/.env value.
• Ask before sending email. Do not enable EMAIL_ENABLED=true unless the user explicitly wants automatic delivery.
• Do not block the user on optional keys. Offer a skip path and explain the consequence.
• Required-capability keys are not optional. If missing, stop and ask for config instead of running degraded analysis.
• First run always requires an explicit initialization prompt to the user before analysis.
• Never run partial or incomplete functionality; this is mandatory.
• Explain failures by impact: blocking, fallback succeeded, or data-quality degradation.
• Do not call local Hermes or other local agent CLIs from this skill. The current Codex agent is responsible for reading generated prompt files and writing the final analysis.
• Reports and emails must contain the strategy analysis result, not raw prompt context.

Bundled Resources

• scripts/analyze.py: deterministic entry point.
• scripts/stock_agent/: self-contained helper functions for config, data, search, LLM, and report rendering.
• scripts/stock_agent/data.py: strategy-local data extraction code that reuses the shared ~/.stock_analysis/cache/ directory to avoid duplicate downloads across strategies.
• assets/env.example: template copied to ~/.stock_analysis/expectation_repricing/.env on first run.
• requirements.txt: Python dependency list.
• references/configuration.md: API-key setup details for this strategy folder.
• references/reporting.md: report and interface-status interpretation.