Install
openclaw skills install market-news-aggregatorMarket 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
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:
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:
### 🏛️ 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:
❌ 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:
📰 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）
- Fetch from 新浪财经 + 金十数据
- Focus on overnight (隔夜) — US markets, EU markets, macro data releases
- Special attention to: USDA reports, EIA data, Fed speeches, OPEC statements
- Goal: Identify what moved overnight and prepare for 09:00 open
Quick check command:
web_fetch:
url: "https://www.jin10.com/"
extractMode: text
maxChars: 6000
盘中（Intraday, 09:00-15:00）
- Check 金十数据 快讯 every 30-60 minutes
- Key triggers: sudden price spikes, volume surges, government announcements
- Filter for: 品种关键词 that match the user's current positions
- Goal: Alert on real-time catalysts affecting open positions
盘后（EOD, 15:00-17:00）
- Fetch from 东方财富 + 新浪财经 — deeper coverage
- Summarize all market-moving news from the day
- Include: data calendar (what came out, actual vs expected), policy announcements
- 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).
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.