fund-sector-ai-scorer

name	fund-sector-ai-scorer
description	Use when analyzing Chinese fund, ETF, index, Tushare, Eastmoney, Tiantian Fund, holdings, or financial-news data to produce 77-sector scores and evidence-linked holding observations. Trigger for 板块购买建议评分, 建议评分分布饼图, 当前持仓分析, 加仓减仓观察, 标签评分, 近一周资讯AI分析, sector scoring, total-100 allocation, or up/down magnitude pie charts.

Fund Sector AI Scorer

Overview

Use this skill to turn recent Chinese market news plus fund/index行情 into a disciplined 77-tag sector analysis. The main output is a non-personalized purchase-observation allocation where all sector scores sum to exactly 100 points.

This skill borrows three operating principles:

• From Tushare workflows: verify data source, token/base URL, date range, and missing-field fallbacks before analysis.
• From Eastmoney fund monitoring: separate evidence, conclusions, and risk notes; avoid individualized buy orders.
• From quantitative research: treat news as one factor, confirm with market/fund behavior, and penalize crowding, stale data, and one-source hype.

Core Workflow

1. Validate inputs
   Record each source, endpoint, fetch time, row count, date range, and failure. Do not silently mix stale data with fresh data. If Tushare/Eastmoney/Tiantian data is unavailable, label the fallback explicitly.

2. Normalize evidence
   Convert every item into compact records: source, datetime, title/content, matchedTags, sentimentHint, riskHint, recencyWeight, and optional market fields such as pct_chg, amount, fund_name, index_code.

3. Tag against the fixed taxonomy
   Use the 77-sector taxonomy in references/scoring-rubric.md. A single fund or news item may map to multiple tags when holdings, index theme, fund name, or keywords support it. Prefer specific tags such as PCB, CPO, 半导体材料设备, 港创新药 over broad parents such as 大科技 or 消费 when both match.

4. Compress before AI analysis
   Feed the AI summarized evidence, not a raw dump. Include per-tag aggregates: news count, high-quality source count, catalyst examples, risk examples, latest timestamps, matched fund count, average涨跌幅, positive/negative幅度贡献, and representative headlines.

5. Ask AI for a constrained result
   Require JSON with all 77 tags represented in scores, total score exactly 100, and concise reasoning for the leading tags. The score is a relative purchase-observation weight, not a probability of profit and not personal investment advice.

6. Validate and normalize
   Check that every sector id is known, scores are numeric and non-negative, total equals 100 after rounding, and risk-heavy tags are not promoted without a risk note. If AI output fails validation, repair by normalizing valid scores or use a local factor fallback.

7. Present clearly
   Show the top allocation tags, risk/watch labels, source coverage, and evidence snippets. For the recommendation-score pie, show only the 15 leading positive tags and never add an 其他 slice. For daily market pies, up/down pies must be based on relative涨跌幅幅度 contribution, not fund counts.

AI Prompt Contract

When calling an LLM, include this contract or equivalent:

{
  "task": "Analyze recent Chinese fund/news/index evidence and allocate exactly 100 points across the fixed 77 sector tags.",
  "rules": [
    "Scores are non-personalized purchase-observation weights.",
    "Use news catalysts, source diversity, recency, fund/index confirmation, and risk penalties.",
    "Do not score by news count alone.",
    "Prefer specific tags over broad parent tags when evidence supports both.",
    "Return every tag with a numeric score; all scores must sum to 100."
  ],
  "output": {
    "scores": {"sector_id": 0},
    "actions": {"sector_id": "积极关注|分批关注|中性观察|暂缓"},
    "notes": {"top_sector_id": "short evidence and risk note for leading sectors"}
  }
}

Scoring Guardrails

• Concentrate only when evidence quality supports it. Weak or generic evidence can receive 0 to 0.5 points.
• For this dashboard, prefer exactly 15 leading tags with positive scores and set weak-evidence tags to 0; avoid spreading 1-3 points across most of the taxonomy.
• A hot tag with clear crowding, policy risk, sharp reversal, or single-source hype should be capped or downgraded.
• Defensive and bond/cash tags may score highly when risk-off evidence dominates, even if news heat is low.
• Do not present the result as guaranteed returns, personalized financial advice, or a command to buy.
• Always state the evidence window, usually "近一周资讯" plus "今日行情".

Market Pie Rule

For 今日标签行情:

• Prefer Eastmoney's batch valuation ranking endpoint FundGuZhi/GetFundGZList for the full-market snapshot. One batch snapshot should be cached and reused by sector aggregation and holding views.
• Do not scan the full fund catalog through one fundgz request per fund by default. Keep per-fund fundgz requests only as a fallback for codes missing from the batch snapshot.
• Record the valuation source, request count, returned row count, valuation date, update date, cache age, and fallback reason.
• 涨幅饼图 = each tag's positive magnitude contribution divided by total positive magnitude.
• 跌幅饼图 = absolute value of each tag's negative magnitude contribution divided by total negative magnitude.
• Fund count may be displayed as context, but it must not determine pie area.

Recommendation Score Pie Rule

For 建议评分分布:

• Keep the underlying 77-tag allocation normalized to exactly 100 points.
• Select at most the 15 highest positive scores, sorted descending, for the visible pie and legend.
• Do not create, return, or display an 其他 slice for the unshown remainder.
• Normalize pie angles over the selected scores so the visible slices fill the circle; keep legend values as the original allocation scores, not the normalized angle percentages.
• Filter legacy cached 其他 items in the renderer so old analysis files cannot restore the oversized slice.

Current Holding Analysis

When the user provides current fund holdings, map every fund to one or more of the fixed 77 tags using the fund code/name, catalog type, index theme, and holdings composition when available. Then inherit the matched tags' news evidence, AI allocation scores, risk heat, and same-day market confirmation.

For each holding output:

• code, name, matchedTags, and current estimated change;
• supportScore derived primarily from the strongest matched sector scores;
• risk, representative headlines, and same-day tag average;
• one observation label: 考虑加仓, 继续持有, 考虑减仓, or 考虑清仓;
• a short evidence-linked reason and confidence level.

Use these guardrails:

• 考虑加仓 requires strong sector support, acceptable risk, and no material same-day contradiction.
• 继续持有 is the default when evidence is mixed, moderate, or incomplete.
• 考虑减仓 requires weak support, elevated risk, or a material negative market confirmation.
• 考虑清仓 requires multiple simultaneous negatives: near-zero support, high risk, and a sharp adverse move. Never trigger it from one headline or one weak tag match.
• Do not infer personal suitability. State that the output excludes the user's horizon, liquidity needs, total asset allocation, and risk tolerance.
• These labels are research observations, not automatic orders or guaranteed individualized advice.

Detailed Rubric

Read references/scoring-rubric.md when implementing the scoring logic, prompt, local fallback, or UI explanation.