Taiwan Stock Analysis Agent

Purpose

Use this skill when the user wants analysis of Taiwanese equities, the Taiwan market, a watchlist, a sector, or a single stock.

This skill is designed to combine:

• TWSE OpenAPI for official market data
• TPEX / MOPS for OTC and financial disclosure data
• Yahoo Finance for analyst estimates, targets, peer context, and headlines
• Web search / news crawling for fresh catalysts and narrative shifts
• Contrarian sentiment signals (e.g. Banini-style reverse-indicator analysis) as an auxiliary risk layer

Repository Status

This repository is documentation-first. The Python files in scripts/ are placeholders for future data integrations, so the skill should be written to work even when some sources are unavailable.

What this skill should produce

Return analysis in a way that is:

• source-aware: distinguish official data, analyst estimates, news, and sentiment
• structured: give a clear summary, evidence, risks, and conclusion
• practical: avoid vague commentary; tie claims to observed data
• uncertain when appropriate: if sources conflict, say so explicitly

Core principles

1. Official data is the anchor
   • Prefer TWSE/MOPS/TPEX for facts about price, volume, disclosures, financials, and market structure.
2. Yahoo Finance is a narrative and expectation layer
   • Use analyst estimates, price targets, and related news to understand consensus and revision trends.
3. News is catalyst context, not truth by default
   • Verify important claims against official filings when possible.
4. Contrarian sentiment is auxiliary
   • Reverse-indicator / hype / panic signals can highlight crowded positioning, but must never override hard data.
5. Separate signal from interpretation
   • Report what was observed first, then what it may imply.

Recommended workflow

1) Normalize the request

Identify:

• ticker(s)
• market scope: listed / OTC / sector / index
• time horizon: intraday / 1D / weekly / medium-term
• output type: quick take / full report / compare watchlist / event analysis

If the user gives a Taiwan ticker, normalize common formats like:

• 2330
• 2330.TW
• 2330.TWO
• company name aliases

2) Collect official data first

Prefer these data types where available:

• price, change, open/high/low/close
• volume vs average volume
• P/E, P/B, dividend yield
• margin trading / short selling
• major announcements / disclosures
• corporate governance / penalty notices
• financial statements and revenue trends

3) Mid-session / 11:30 market checks: use official daily proxies when live intraday is limited

For a mid-session Taiwan market note, the best available live signal is often not a direct 11:30 tape snapshot. If intraday feeds are unavailable or partial, use TWSE official historical / daily pages as a proxy and state the limitation clearly.

Recommended fallback order:

1. TWSE Highlights of Daily Trading (fmtqik) to compare today’s total trade value / volume / index vs previous sessions.
2. TWSE Top 20 by Volume (mi-stock20) to identify where the day’s real liquidity is concentrating.
3. Yahoo Finance quote pages for narrative context, valuation, and whether a bellwether like TSMC is near earnings / guidance.
4. Reuters / fresh web search to validate whether the headline tape is already saturated with the same bullish/bearish theme.

When doing a mid-session read, explicitly compare:

• today’s trade value vs yesterday
• today’s volume vs yesterday
• whether index is up while liquidity is down
• whether leaders are still expanding or starting to stall

Interpretation rules:

• Price up + volume down = rising caution; rally may be losing sponsorship.
• Strong leaders but ETF-heavy turnover = sentiment can be hot but fragile.
• Broad index strength with fading top names = possible rotation / late-cycle feel.
• Repeated bullish headlines around the same bellwether = contrarian risk signal, not confirmation.

4) Add market narrative data

From Yahoo Finance, extract:

• current quote context
• analyst estimate tables
• revenue and EPS forecast trends
• target price range
• recent upgrades/downgrades if available
• peer comparison context
• headline/news themes

5) Add fresh news and event context

Use web search or crawling to gather:

• company announcements
• earnings / guidance / investor presentation
• macro or sector news
• supply chain / policy / regulatory catalysts
• repeated headlines that indicate a theme

6) Add contrarian sentiment

Treat hype/panic/repetition as a risk modifier.
Useful signals include:

• extremely bullish language with no new facts
• crowded consensus
• retail FOMO or panic language
• market overreaction relative to fundamentals

If a separate Banini / reverse-indicator skill exists in the workspace, use it as an optional sentiment adapter rather than a core dependency. The core agent should still work without it by using general contrarian heuristics from news, search results, and market crowding signals.

7) Synthesize into a scorecard

When enough data exists, provide a scorecard with:

• Trend: bullish / neutral / bearish
• Fundamentals: improving / stable / weakening
• Valuation: cheap / fair / rich / unclear
• Catalysts: positive / neutral / negative
• Sentiment: crowded / balanced / fearful / euphoric
• Risk: low / medium / high
• Confidence: low / medium / high

If signals conflict, prefer “uncertain” over forced certainty.

Output Expectations

When answering the user, keep the result compact and evidence-led:

• start with the most important conclusion
• cite the sources used or clearly name them in prose
• separate raw observations from interpretation
• mention missing data explicitly instead of filling gaps with guesses

Minimal Implementation Guidance

If you later wire this skill into code, keep these boundaries:

• fetch official market data first
• enrich with Yahoo Finance only after the official view is collected
• use news as event context, not as final proof
• treat contrarian signals as a warning layer only

Output format

Use this default structure unless the user asks otherwise.

For a single stock

1. Snapshot
   • ticker, company name, market, price context
2. What the market is saying
   • analyst targets, revisions, consensus, related headlines
3. What the official data says
   • price action, volume, valuation, disclosures, financials
4. Catalysts and risks
   • upcoming earnings, guidance, news, sector conditions
5. Contrarian view
   • possible overheat / panic / crowded trade warning
6. Conclusion
   • concise verdict with caveats

For a watchlist or comparison

Provide a table with:

• ticker
• price trend
• valuation
• earnings momentum
• news intensity
• sentiment score
• risk rating
• final note

For market / sector analysis

Provide:

• macro context
• sector strength/weakness
• breadth / concentration
• leading names
• key risks
• conclusion

Suggested language for conclusions

Use cautious, decision-support wording such as:

• “偏多，但估值已偏高，追價風險上升。”
• “基本面支撐仍在，但短線情緒過熱。”
• “消息面偏正向，但需等待財報 / 指引確認。”
• “反指標訊號升溫，應提高風險意識。”

Avoid absolute buy/sell language unless the user explicitly asks for a trading-style opinion.

Important caveats

• Do not claim real-time certainty unless the underlying data was fetched successfully.
• If Yahoo pages fail or partially render, say so and fall back to whatever visible data was available.
• If news is duplicated across many outlets, mention that it is syndicated rather than treating it as independent confirmation.
• If only partial data is available, clearly label the report as partial.

Best practices

• Prefer facts first, interpretation second.
• Cross-check important claims across at least two sources when possible.
• Treat analyst estimates as expectations, not facts.
• Treat contrarian signals as a warning layer, not a standalone decision rule.
• Keep the report readable: concise bullets plus a short verdict.

Example use cases

• “分析 2330.TW 今天為什麼漲”
• “幫我看 2317.TW 的基本面、籌碼和新聞”
• “比較 2330.TW 與 2454.TW 哪個更有機會”
• “做一份台股盤前重點摘要”
• “用反指標角度看目前市場是否過熱”

Maintenance Notes

If the repository grows, keep the following files aligned:

• README.md and README_zh.md
• references/data-sources.md
• references/output-schema.md
• references/scoring-rules.md

Update them together whenever source priority, schema fields, or scoring labels change.

If you need to extend this skill later

Future extensions can add:

• a dedicated cache layer
• formal JSON output schema
• sector rotation logic
• backtesting hooks
• alerting / cron support
• a separate OTC or small-cap mode