Market News Aggregator

Aggregate, classify, summarize, and analyze market news from Chinese financial portals (Sina Finance, EastMoney, Hexun, Jin10). Use for (1) pre-market briefing on overnight news, (2) intraday real-time alerts for market-moving events, (3) end-of-day news review. Supports sector classification, sentiment tagging, keyword extraction, and importance ranking.

Market News Aggregator — 市场新闻聚合

Overview

聚合国内财经网站新闻，按品种/板块分类，生成摘要、提取关键词、判断情绪（偏多/偏空/中性），并按重要度排序。适用于盘前/盘中/盘后三个场景。

Data Sources — 数据源

Source	URL	Coverage	Notes
新浪财经	https://finance.sina.com.cn/	要闻、宏观、行业、个股	资讯页可导航
东方财富	https://www.eastmoney.com/	快讯、研报、公告、宏观	滚动新闻量最大
和讯网	https://www.hexun.com/	期货、外汇、宏观	偏大宗商品
金十数据	https://www.jin10.com/	实时快讯、数据公布、事件预警	速度快、偏交易

News Categories — 分类体系

text

宏观宏观 — 宏观经济政策、GDP、CPI、PMI、利率、汇率
农产品 — 大豆、玉米、棉花、白糖、生猪、鸡蛋等
有色 — 铜、铝、锌、铅、镍、锡、黄金、白银
黑色 — 铁矿石、螺纹钢、焦煤、焦炭、热卷
能源化工 — 原油、甲醇、PTA、纯碱、尿素、橡胶、燃料油
股指 — IF、IC、IH、IM 及大盘指数

Workflow

Step 1: Determine Fetch Target

Based on the user's request, pick the right source(s) and category:

Scenario	Recommended Source(s)	Depth
盘前隔夜消息 (pre-market)	新浪财经 + 金十数据	综合
盘中异动 (intraday alert)	金十数据 快讯	实时
盘后回顾 (EOD review)	东方财富 + 新浪财经	深度
特定品种 (specific product)	和讯网	垂直

Step 2: Fetch Using web_fetch

Use the web_fetch tool with extractMode=text to get clean text content. Set maxChars appropriately:

yaml

web_fetch:
  url: "<source-url>"
  extractMode: text
  maxChars: 8000   # per page, enough for ~5-10 headlines + snippets

Example fetches:

• 新浪财经要闻: https://finance.sina.com.cn/
• 新浪财经期货: https://finance.sina.com.cn/futures/
• 东方财富快讯: https://www.eastmoney.com/
• 金十数据: https://www.jin10.com/

Respect robots.txt: Use moderate fetch intervals and reasonable maxChars. Do not scrape at high frequency.

Step 3: Classify News by Sector

Parse fetched text and classify each news item. Look for keywords in headline and body:

Sector	Trigger Keywords
宏观	央行、MLF、降准、降息、GDP、CPI、PMI、进出口、美联储、非农、加息、数据
农产品	豆粕、豆油、玉米、棉花、白糖、生猪、鸡蛋、棕榈、菜油、USDA
有色	铜、铝、锌、铅、镍、锡、黄金、白银、LME、库存、矿
黑色	螺纹、铁矿、焦煤、焦炭、热卷、限产、粗钢、地产、基建
能源化工	原油、甲醇、PTA、纯碱、尿素、橡胶、燃料油、OPEC、EIA、炼厂
股指	沪指、创业板、上证、深证、北向、涨停、成交量、A股

Output format — group news under their sector heading:

text

### 🏛️ 宏观宏观
1. [标题] — 摘要（来源，时间）
2. [标题] — 摘要（来源，时间）

Step 4: Generate Summary (AI)

For each news item (or at least the top 5-8 most important ones), produce a one-line summary:

• Length: 20-40 Chinese characters max
• Style: Concise, fact-only, no opinion
• Focus: What happened + why it matters to markets

Example:

text

❌ 不好: "央行宣布降准0.5个百分点，市场流动性得到改善"
✅ 好: "央行降准0.5%，释放长期资金约1万亿"

Step 5: Extract Keywords

Extract 2-5 key terms per news item. Prioritize actionable terms that traders care about:

• Policy actions: "降准", "加息", "限产", "收储"
• Price levels: "突破 XXXX", "创 X 年新高"
• Supply/demand: "减产", "累库", "检修", "开工率"
• Events: "交割", "移仓", "基差", "价差"

Step 6: Sentiment Analysis

Tag each news item with a sentiment direction:

Tag	Meaning	When to Use
🟢 偏多	利好/利多	Positive for price (supply cut, policy support, strong demand)
🔴 偏空	利空/利淡	Negative for price (supply surge, policy tightening, weak data)
⚪ 中性	中性/无明确方向	Informational, mixed signals, or unclear impact

Apply sentiment relative to the sector/contract, not the general economy. E.g., "原油增产" = 🔴 偏空 for oil.

Step 7: Importance Ranking

Rank news items by market impact (high → low):

Level	Score	Criteria
🔴 重大	★★★	Policy shifts, unexpected data, major supply disruption
🟡 重要	★★	Routine data release, moderate supply change, meeting results
⚪ 一般	★	Commentary, ordinary operations, non-market-moving

Step 8: Format Final Output

Use this output format as the final presentation:

text

📰 市场新闻聚合 — [日期/时段]

━━━ 🔴 重大关注 ━━━

1. [标题]（来源）
   摘要：XXX
   关键词：XXX, XXX
   情绪：🟢 偏多 / 🔴 偏空 / ⚪ 中性

2. ...

━━━ 🟡 重要信息 ━━━

...

━━━ ⚪ 一般资讯 ━━━

...

━━━ 📊 分板块汇总 ━━━

🏛️ 宏观宏观：[N] 条 | 🟢X  🔴X  ⚪X
🌾 农产品： [N] 条 | 🟢X  🔴X  ⚪X
🏭 有色：   [N] 条 | 🟢X  🔴X  ⚪X
⛏️ 黑色：   [N] 条 | 🟢X  🔴X  ⚪X
🛢️ 能源化工：[N] 条 | 🟢X  🔴X  ⚪X
📈 股指：   [N] 条 | 🟢X  🔴X  ⚪X

Usage Scenarios

盘前（Pre-market, 08:00-08:30）

1. Fetch from 新浪财经 + 金十数据
2. Focus on overnight (隔夜) — US markets, EU markets, macro data releases
3. Special attention to: USDA reports, EIA data, Fed speeches, OPEC statements
4. Goal: Identify what moved overnight and prepare for 09:00 open

Quick check command:

yaml

web_fetch:
  url: "https://www.jin10.com/"
  extractMode: text
  maxChars: 6000

盘中（Intraday, 09:00-15:00）

1. Check 金十数据 快讯 every 30-60 minutes
2. Key triggers: sudden price spikes, volume surges, government announcements
3. Filter for: 品种关键词 that match the user's current positions
4. Goal: Alert on real-time catalysts affecting open positions

盘后（EOD, 15:00-17:00）

1. Fetch from 东方财富 + 新浪财经 — deeper coverage
2. Summarize all market-moving news from the day
3. Include: data calendar (what came out, actual vs expected), policy announcements
4. Goal: Provide a complete picture for end-of-day position review

Scripts

scripts/fetch_news.py

A Python script for batch fetching from multiple sources. Use when the user needs a consolidated multi-source fetch (not individual page fetches via web_fetch).

bash

python scripts/fetch_news.py --sources sina,jin10 --sectors macro,agri --max-items 20

Parameters:

• --sources: Comma-separated source list (sina, eastmoney, hexun, jin10)
• --sectors: Filter by sector keywords
• --max-items: Max items to return (default 20)
• --output: Output format (text, json)

Important Notes

• Don't over-fetch: One fetch per source per scenario is enough. ~5-8k chars per fetch.
• Source attribution: Always include the source name for each news item — credibility matters.
• Timestamps: Include approximate time if available (e.g., "10:23", "昨夜").
• Cross-reference: When a key event is reported differently across sources, note discrepancies.
• No forward-looking statements: Summarize what happened, don't predict outcomes.
• Respect robots.txt: Do not scrape at high frequency; reasonable intervals only.