quick-event-etf-study

name

quick-event-etf-study

description

Event-driven concept ETF research: start from a concept or event, identify related stocks, build a market-cap-weighted ETF index, analyze market-cap changes around the event window, and produce an interactive HTML dashboard. Use when the user asks about concept stocks, concept ETFs, event-driven analysis, or event studies. Triggers on: mentions of hot topics, policies, or events impacting China A-share concept sectors; requests to build thematic ETFs or concept indices; analysis of stock performance before and after specific events.

IMPORTANT: Output-Language Lock

• The final conversation reply and every deliverable (dashboard / charts / tables / custom_html) must follow the language of the user's latest query, not the market
• If the prompt is in English and the symbols are China / Hong Kong stocks, both the reply and the deliverables must stay in English; stock references should default to ticker code such as 600519.SH / 0700.HK
• If the prompt is in Chinese, both the reply and the deliverables must stay in Chinese; when a Chinese stock name is known, prefer the Chinese name
• Do not make this mistake: the HTML is in English but the actual conversation reply switches back to Chinese
• If the English stock name is uncertain, use the ticker code instead of a Chinese stock name

Event Study ETF

Workflow

1. Read the pitfalls: read references/common_pitfalls.md in full, then self-check against the checklist at the end before delivery.
2. Freeze reproducibility metadata: hard-code query, language, event_date_source, generated_at, price_adjustment, market, data_source, and constituent_snapshot in the code configuration block. Resolve language to a concrete "zh" or "en" string from the query text (CJK detection) before hard-coding it. Do not let reruns of the same study update these values automatically.
3. Identify concept stocks: search concept stocks across Tonghuashun (10jqka), Xueqiu, and East Money -> save a source snapshot CSV -> take the union as constituent candidates -> validate with mshtools/ifind -> assign T1/T2/T3 tiers by relevance. See references/concept_research.md for methodology.
4. Fetch data: use MCP ifind to fetch forward-adjusted daily prices plus total shares -> save raw returns/previews under raw/ -> compute daily market cap.
   • Set the window length exactly to the user's request: if the user asks for "buy after the event and hold for one week", use 3-5 trading days before the event plus 1-2 weeks after the event (about 10-15 trading days).
   • General rule: start_date = 3-5 trading days before the reference date; end_date = 2-3 trading days after the user's focus window.
5. Build the ETF: use market cap on the pre-event reference date to calculate weights, then generate both market-cap-weighted NAV and equal-weighted NAV.
6. Export standard files: call references/export_event_results.py to produce 3 standard data files plus 1 reproducibility manifest. Always pass market ("china_a" or "us") and generated_at.
7. Generate the dashboard: call references/render_event_dashboard.py to read the standard files and produce an HTML dashboard. Use assets/dashboard_template.html as the shell template. See "Dashboard Chart Selection" below for choosing modules.
8. Static charts: use Matplotlib to generate standalone PNG files in the cwd.
9. Report: write report.md; it must include ## Assumptions and ## Known Limitations.
10. Self-check: trial run -> 4 standard files written -> run references/validate_event_outputs.py -> reconcile numbers -> complete the pitfalls checklist.
11. Deliver: runnable code + 4 standard files + report.md + PNG files + HTML dashboard.

Load On Demand

File	When to read it
references/common_pitfalls.md	Required reading, first step for every task
references/concept_research.md	When identifying concept stocks or searching for related companies
references/dashboard_schema.md	When generating or customizing the HTML dashboard
references/export_event_results.py	Call when exporting standard files
references/render_event_dashboard.py	Call when generating the dashboard
references/validate_event_outputs.py	Validate before delivery
references/event_study_template.py	Skeleton for writing analysis code

Standard Output Files

Write 4 files to the cwd, using the concept name as the prefix (e.g. ai_chip):

File	Content
<prefix>_prices.csv	Daily constituent prices and market caps:date, ticker, name, close, market_cap, tier
<prefix>_portfolio.csv	Daily ETF NAV and total market cap:date, mcap_weighted_nav, equal_weighted_nav, total_market_cap
<prefix>_summary.json	Summary metadata + statistics + constituent list
<prefix>_run_manifest.json	Reproducibility manifest: input hashes, parameters, dependency versions, output hashes

Key Reproducibility Rules

• generated_at must be passed explicitly and reused for reproducible reruns.
• language must be resolved to "zh" or "en" and hard-coded in the configuration block.
• Weights based on market cap from the trading day before the event.
• NAV base date is pre_event_date, anchored at 100.
• Missing-price handling: ffill_before_pct_change.
• Every ifind call must record actual parameters in the manifest.
• Save constituent source snapshots as <prefix>_constituents_sources.csv.

HTML Dashboard

• Use assets/dashboard_template.html as the shell template.
• Output one standalone HTML file: <prefix>_dashboard.html.
• Module selection via include_modules parameter. Available modules:

Module ID	Chart Content	Suggested Scenario
overview	KPI cards + main NAV curve + drawdown	Required
nav	Market-cap-weighted vs equal-weighted NAV dual-line	When comparing weighting methods
weight	Tier-colored weight donut	When many constituents or uneven weights
impact	Per-stock event-day/peak/latest return bars	When analyzing stock-level reactions
mcap	Sector total market-cap trend area	When focusing on sector value changes
table	Constituent detail table	Required

Selection guidance:

• Full: ["overview", "nav", "weight", "impact", "mcap", "table"]
• Concise: ["overview", "nav", "table"]
• Stock-focused: ["overview", "weight", "impact", "table"]
• Trend-focused: ["overview", "nav", "mcap", "table"]

Color Scheme

Market-aware colors: China A-shares (china_a) use red up/green down; US equities (us) use green up/red down.

Market	Up	Down
china_a	#ef5350	#26a69a
us	#26a69a	#ef5350

• Main chart NAV line color follows the sign of total ETF return.
• KPI cards involving gains/losses pass raw for market-aware coloring.
• Regular comparison charts (nav, mcap, weight) use fixed data colors: blue #3b82f6, orange #f97316, purple #8b5cf6.
• Tier coloring: T1 #3b82f6, T2 #60a5fa, T3 #93c5fd.
• Event-date marker: red dashed line #ef4444 with white label on red background.

custom_html Constraints

• DOM ids and CSS classes must use the es-custom- prefix.
• echarts is already loaded globally in the template.
• Titles, labels, and tooltips must use the same language as dashboard language.

Matplotlib Charts

• Dark theme: dark background plus light text.
• Use red/green on the main chart to match the dashboard color scheme; blue tones for other charts.
• macOS Unicode font: FontProperties(fname="/System/Library/Fonts/Supplemental/Arial Unicode.ttf").
• File name: <prefix>_<name>.png, dpi=150.

Required Report Sections

report.md must include:

• ## Assumptions: event-date source, reference-date choice, constituent criteria, weighting method, share basis, window length, price-adjustment method.
• ## Known Limitations: survivorship bias, data coverage, excessive single-stock weight, market-cap calculation basis, and event expectations priced in before the official event date.

Core Rules

• Use mshtools/ifind for data; do not hard-code prices.
• Proactively warn when a single-stock weight exceeds 30%.
• Always compute both market-cap-weighted and equal-weighted versions.
• Keep all output artifacts in one consistent language matching the user's query.
• The event date must be evidence-backed.

Out Of Scope

Options/derivatives pricing, live trading, deep single-stock fundamental analysis, and cross-market arbitrage.