Import 9 alphaear finance skills
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
This commit is contained in:
Kunthawat Greethong
526
skills/alphaear-reporter/scripts/tools/toolkits.py
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526
skills/alphaear-reporter/scripts/tools/toolkits.py
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"""
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AlphaEar 工具包层 - Agno Toolkit 适配器
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复用 utils 中的底层工具实现，提供 Agno Agent 兼容的 Toolkit 接口
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"""
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from datetime import datetime
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from typing import Optional
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from agno.tools import Toolkit
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from loguru import logger
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from ..utils.database_manager import DatabaseManager
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from ..utils.news_tools import NewsNowTools, PolymarketTools
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from ..utils.stock_tools import StockTools
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from ..utils.search_tools import SearchTools
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from ..utils.sentiment_tools import SentimentTools
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class NewsToolkit(Toolkit):
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"""
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新闻工具包 - 包装 NewsNowTools 为 Agno Toolkit
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提供热点新闻获取、内容提取等功能
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"""
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def __init__(self, db: DatabaseManager, **kwargs):
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self._news_tools = NewsNowTools(db)
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self._sources = self._news_tools.SOURCES
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tools = [
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self.fetch_hot_news,
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self.fetch_news_content,
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self.get_unified_trends,
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self.enrich_news_content,
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]
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super().__init__(name="news_toolkit", tools=tools, **kwargs)
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def fetch_hot_news(self, source_id: str, count: int = 10) -> str:
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"""
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从指定新闻源获取热点新闻列表。
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Args:
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source_id: 新闻源标识符。可选值按类别:
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**金融类**: "cls" (财联社), "wallstreetcn" (华尔街见闻), "xueqiu" (雪球)
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**综合类**: "weibo" (微博热搜), "zhihu" (知乎热榜), "baidu" (百度热搜),
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"toutiao" (今日头条), "douyin" (抖音), "thepaper" (澎湃新闻)
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**科技类**: "36kr" (36氪), "ithome" (IT之家), "v2ex", "juejin" (掘金),
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"hackernews" (Hacker News)
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推荐金融分析使用 "cls", "wallstreetcn", "xueqiu"。
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count: 获取的新闻数量，默认 10 条。
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Returns:
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热点新闻列表的文本描述，包含排名、标题和链接。如果源不可用则返回错误信息。
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"""
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logger.info(f"🔧 [TOOL CALLED] fetch_hot_news(source_id={source_id}, count={count})")
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items = self._news_tools.fetch_hot_news(source_id, count=count, fetch_content=False)
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if not items:
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return f"获取 {source_id} 热点失败"
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source_name = self._sources.get(source_id, source_id)
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result = f"## {source_name} 热点 (获取时间: {datetime.now().strftime('%Y-%m-%d %H:%M')})\n\n"
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for item in items:
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result += f"{item['rank']}. {item['title']}\n 链接: {item['url']}\n\n"
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logger.info(f"✅ [TOOL SUCCESS] Got {len(items)} news items from {source_id}")
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return result
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def fetch_news_content(self, url: str) -> str:
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"""
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使用 Jina Reader 抓取指定 URL 的网页正文内容。
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Args:
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url: 需要抓取内容的完整网页 URL，必须以 http:// 或 https:// 开头。
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Returns:
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提取的网页正文内容，如果失败则返回错误信息。
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"""
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content = self._news_tools.fetch_news_content(url)
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if content:
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return content[:5000] # 限制长度
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return "内容抓取失败"
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def get_unified_trends(self, sources: str = "wallstreetcn,cls") -> str:
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"""
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获取多平台综合热点报告。
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Args:
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sources: 要扫描的新闻源，用逗号分隔。
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可选值: weibo, zhihu, baidu, toutiao, wallstreetcn, cls
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默认: "wallstreetcn,cls" (金融资讯)
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Returns:
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格式化的热点汇总报告。
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"""
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source_list = [s.strip() for s in sources.split(",")]
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report = self._news_tools.get_unified_trends(source_list)
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return report
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def enrich_news_content(self, source: str = None, limit: int = 5) -> str:
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"""
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为数据库中缺少正文内容的新闻补充内容。
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Args:
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source: 筛选特定新闻源（如 "cls"），为空则处理所有。
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limit: 最多处理的新闻数量，默认 5 条。
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Returns:
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处理结果的描述。
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"""
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logger.info(f"🔧 [TOOL CALLED] enrich_news_content(source={source}, limit={limit})")
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# 获取需要补充内容的新闻
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news_items = self._news_tools.db.get_daily_news(source=source, limit=limit)
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items_without_content = [n for n in news_items if not n.get('content')]
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if not items_without_content:
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return "没有需要补充内容的新闻"
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updated_count = 0
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cursor = self._news_tools.db.conn.cursor()
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for item in items_without_content[:limit]:
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url = item.get('url')
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if url:
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content = self._news_tools.fetch_news_content(url)
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if content:
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cursor.execute(
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"UPDATE daily_news SET content = ? WHERE id = ?",
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(content[:10000], item['id'])
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)
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updated_count += 1
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self._news_tools.db.conn.commit()
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logger.info(f"✅ [TOOL SUCCESS] Enriched {updated_count} news items with content")
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return f"✅ 已为 {updated_count} 条新闻补充正文内容"
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class PolymarketToolkit(Toolkit):
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"""
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Polymarket 预测市场工具包 - 获取热门预测市场数据
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预测市场数据可反映公众情绪、预期和关注度
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"""
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def __init__(self, db: DatabaseManager, **kwargs):
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self._poly_tools = PolymarketTools(db)
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tools = [
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self.get_prediction_markets,
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self.get_market_summary,
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]
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super().__init__(name="polymarket_toolkit", tools=tools, **kwargs)
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def get_prediction_markets(self, limit: int = 20) -> str:
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"""
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获取 Polymarket 活跃预测市场的关键数据。
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预测市场反映公众对重大事件的概率预期，可用于:
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- 分析市场情绪和风险偏好
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- 了解热门话题的关注度
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- 获取重大事件的概率预期
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Args:
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limit: 获取的市场数量，默认 20 个。
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Returns:
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预测市场数据列表，包含问题、结果概率和交易量。
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如果获取失败返回错误信息。
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"""
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logger.info(f"🔧 [TOOL CALLED] get_prediction_markets(limit={limit})")
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markets = self._poly_tools.get_active_markets(limit)
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if not markets:
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return "❌ 无法获取 Polymarket 数据（可能是网络问题）"
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result = f"## 🔮 Polymarket 热门预测 (共 {len(markets)} 个)\n\n"
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for i, m in enumerate(markets[:limit], 1):
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question = m.get("question", "Unknown")
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prices = m.get("outcomePrices", [])
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volume = m.get("volume", 0)
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result += f"{i}. **{question}**\n"
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if prices:
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result += f" 概率: {prices}\n"
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if volume:
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try:
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result += f" 交易量: ${float(volume):,.0f}\n"
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except:
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result += f" 交易量: {volume}\n"
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result += "\n"
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logger.info(f"✅ [TOOL SUCCESS] Got {len(markets)} prediction markets")
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return result
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def get_market_summary(self, limit: int = 10) -> str:
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"""
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获取预测市场摘要报告，了解当前热门话题和公众预期。
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Args:
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limit: 获取的市场数量，默认 10 个。
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Returns:
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格式化的预测市场报告。
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"""
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return self._poly_tools.get_market_summary(limit)
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class StockToolkit(Toolkit):
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"""
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股票工具包 - 包装 StockTools 为 Agno Toolkit
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提供股票搜索、价格查询等功能
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"""
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def __init__(self, db: DatabaseManager, **kwargs):
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self._stock_tools = StockTools(db)
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tools = [
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self.search_ticker,
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self.get_stock_price,
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]
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super().__init__(name="stock_toolkit", tools=tools, **kwargs)
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def search_ticker(self, query: str) -> str:
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"""
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模糊搜索 A 股股票代码或名称。
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Args:
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query: 搜索关键词，可以是股票代码（如 "600519"）或名称关键词（如 "茅台"、"宁德"、"比亚迪"）。
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Returns:
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匹配的股票列表，包含代码和名称。
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"""
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q = (query or "").strip()
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# Guardrails: prevent overly generic queries that tend to return arbitrary "...股份" matches.
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generic_terms = {
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"股份",
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"有限公司",
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"概念股",
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"受益股",
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"龙头",
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"标的",
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"相关股票",
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"合作概念股",
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}
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if not q:
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return "查询为空，无法搜索股票"
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if q in generic_terms:
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return f"查询过于泛化（{q}），为避免误匹配已拒绝。请提供更具体的公司名或6位代码。"
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# If it's not a numeric code, require at least 2 non-space chars.
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if not any(ch.isdigit() for ch in q) and len(q.replace(" ", "")) < 2:
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return "查询过短，无法搜索股票。请提供更具体的公司名或6位代码。"
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results = self._stock_tools.search_ticker(query)
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if not results:
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return f"未找到匹配 '{query}' 的股票"
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output = f"## 股票搜索结果 (关键词: {query})\n\n"
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for r in results:
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output += f"- {r['code']} - {r['name']}\n"
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return output
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def get_stock_price(self, ticker: str, days: int = 30) -> str:
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"""
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获取指定股票的近期价格走势。
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Args:
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ticker: 股票代码，如 "600519"（贵州茅台）或 "000001"（平安银行）。
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days: 查询天数，默认 30 天。
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Returns:
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价格走势的文本摘要。
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"""
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from datetime import timedelta
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end_date = datetime.now().strftime('%Y-%m-%d')
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start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
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df = self._stock_tools.get_stock_price(ticker, start_date, end_date)
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if df.empty:
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return f"未能获取 {ticker} 的股价数据"
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latest = df.iloc[-1]
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change = ((latest['close'] - df.iloc[0]['close']) / df.iloc[0]['close']) * 100
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# 格式化历史数据供 LLM 分析 (取最近 15 天)
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history_df = df.tail(15).copy()
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history_df['date'] = history_df['date'].astype(str)
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# 简化列名以节省 token
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history_cols = ['date', 'open', 'close', 'high', 'low', 'volume']
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# 尝试使用 markdown 格式，如果失败退回到 string
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try:
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history_str = history_df[history_cols].to_markdown(index=False, numalign="left", stralign="left")
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except ImportError:
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history_str = history_df[history_cols].to_string(index=False)
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except Exception:
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history_str = history_df[history_cols].to_string(index=False)
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return f"""## {ticker} 价格走势 ({days}天)
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- 当前价: ¥{latest['close']:.2f}
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- 期间涨跌: {change:+.2f}%
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- 最高/最低: ¥{df['high'].max():.2f} / ¥{df['low'].min():.2f}
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- 数据范围: {df.iloc[0]['date']} -> {latest['date']}
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### 最近 15 个交易日详细数据 (OHLCV):
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{history_str}
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"""
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class SentimentToolkit(Toolkit):
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"""
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情绪分析工具包 - 包装 SentimentTools 为 Agno Toolkit
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提供文本情绪分析功能（支持 BERT 和 LLM 模式）
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"""
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def __init__(self, db: DatabaseManager, mode: str = "auto", **kwargs):
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self._sentiment_tools = SentimentTools(db, mode=mode)
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self._db = db
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tools = [
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self.analyze_sentiment,
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self.batch_update_sentiment,
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]
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super().__init__(name="sentiment_toolkit", tools=tools, **kwargs)
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def analyze_sentiment(self, text: str) -> str:
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"""
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分析文本的情绪极性。
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Args:
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text: 需要分析的文本内容，如新闻标题或摘要。
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Returns:
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情绪分析结果，包含分值(-1.0到1.0)和标签(positive/negative/neutral)。
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"""
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result = self._sentiment_tools.analyze_sentiment(text)
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score = result.get('score', 0.0)
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label = result.get('label', 'neutral')
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reason = result.get('reason', '')
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return f"""情绪分析结果:
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- 文本: {text[:100]}{'...' if len(text) > 100 else ''}
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- 分值: {score:.2f}
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- 标签: {label}
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- 分析: {reason}"""
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def batch_update_sentiment(self, source: str = None, limit: int = 20) -> str:
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"""
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批量更新数据库中新闻的情绪分数。
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Args:
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source: 筛选特定新闻源（如 "cls", "wallstreetcn"），为空则处理所有。
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limit: 最多处理的新闻数量，默认 20 条。
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Returns:
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更新结果的描述。
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"""
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logger.info(f"🔧 [TOOL CALLED] batch_update_sentiment(source={source}, limit={limit})")
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count = self._sentiment_tools.batch_update_news_sentiment(source=source, limit=limit)
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return f"✅ 已更新 {count} 条新闻的情绪分数"
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class SearchToolkit(Toolkit):
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"""
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搜索工具包 - 包装 SearchTools 为 Agno Toolkit
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提供网络搜索功能（支持 Jina、DuckDuckGo 和百度）
|
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当环境变量 JINA_API_KEY 设置时，默认使用 Jina Search，
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提供 LLM 友好的搜索结果。
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"""
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def __init__(self, db: DatabaseManager, **kwargs):
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self._search_tools = SearchTools(db)
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tools = [
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self.web_search,
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self.aggregate_search,
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]
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super().__init__(name="search_toolkit", tools=tools, **kwargs)
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def web_search(self, query: str, engine: str = None, max_results: int = 5) -> str:
|
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"""
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使用指定搜索引擎执行网络搜索。
|
||||
|
||||
Args:
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query: 搜索关键词，如 "英伟达财报" 或 "光伏行业政策"。
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engine: 搜索引擎选择。可选值:
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"jina" (Jina Search，需配置 JINA_API_KEY，LLM友好输出),
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"ddg" (DuckDuckGo，推荐英文/国际搜索),
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"baidu" (百度，推荐中文/国内搜索)。
|
||||
默认: 若配置了 JINA_API_KEY 则使用 "jina"，否则 "ddg"。
|
||||
max_results: 返回结果数量。默认 5。
|
||||
|
||||
Returns:
|
||||
搜索结果的文本描述。
|
||||
"""
|
||||
return self._search_tools.search(query, engine=engine, max_results=max_results)
|
||||
|
||||
def aggregate_search(self, query: str, max_results: int = 5) -> str:
|
||||
"""
|
||||
同时使用多个搜索引擎搜索并聚合结果。
|
||||
|
||||
Args:
|
||||
query: 搜索关键词。
|
||||
max_results: 每个引擎返回的最大结果数。默认 5。
|
||||
|
||||
Returns:
|
||||
聚合后的搜索结果。
|
||||
"""
|
||||
return self._search_tools.aggregate_search(query, max_results=max_results)
|
||||
|
||||
|
||||
class ContextSearchToolkit(Toolkit):
|
||||
"""
|
||||
上下文搜索工具包 - 用于 RAG 场景的文档片段检索
|
||||
|
||||
支持在内存中存储文档片段，并通过关键词搜索相关内容。
|
||||
适用于 ReportAgent 的分段编辑场景。
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self._store = {} # {doc_id: {"title": str, "content": str, "summary": str}}
|
||||
|
||||
tools = [
|
||||
self.search_context,
|
||||
self.get_toc,
|
||||
]
|
||||
super().__init__(name="context_search_toolkit", tools=tools, **kwargs)
|
||||
|
||||
def add_document(self, doc_id: str, title: str, content: str, summary: str = ""):
|
||||
"""添加文档到存储（供外部调用，非 LLM 工具）"""
|
||||
self._store[doc_id] = {
|
||||
"title": title,
|
||||
"content": content,
|
||||
"summary": summary or content[:200] + "..."
|
||||
}
|
||||
logger.info(f"📄 Added document to context store: {doc_id} - {title[:30]}...")
|
||||
|
||||
def clear(self):
|
||||
"""清空文档存储"""
|
||||
self._store.clear()
|
||||
logger.info("🗑️ Context store cleared")
|
||||
|
||||
def search_context(self, query: str, max_results: int = 3) -> str:
|
||||
"""
|
||||
在已存储的文档中搜索与查询相关的内容片段。
|
||||
|
||||
Args:
|
||||
query: 搜索关键词，如 "消费板块" 或 "茅台 预测"。
|
||||
max_results: 返回的最大结果数，默认 3。
|
||||
|
||||
Returns:
|
||||
匹配的文档片段，按相关性排序。
|
||||
"""
|
||||
logger.info(f"🔍 [TOOL CALLED] search_context(query={query}, max_results={max_results})")
|
||||
|
||||
if not self._store:
|
||||
return "⚠️ 上下文存储为空，无可搜索内容。"
|
||||
|
||||
# 简单的关键词匹配 + 计分
|
||||
query_terms = query.lower().split()
|
||||
results = []
|
||||
|
||||
for doc_id, doc in self._store.items():
|
||||
score = 0
|
||||
content_lower = doc["content"].lower()
|
||||
title_lower = doc["title"].lower()
|
||||
|
||||
for term in query_terms:
|
||||
# 标题匹配权重更高
|
||||
if term in title_lower:
|
||||
score += 3
|
||||
if term in content_lower:
|
||||
score += content_lower.count(term)
|
||||
|
||||
if score > 0:
|
||||
results.append((score, doc_id, doc))
|
||||
|
||||
# 按分数排序
|
||||
results.sort(key=lambda x: x[0], reverse=True)
|
||||
results = results[:max_results]
|
||||
|
||||
if not results:
|
||||
return f"未找到与 '{query}' 相关的内容。"
|
||||
|
||||
output = f"## 搜索结果 (查询: {query})\n\n"
|
||||
for score, doc_id, doc in results:
|
||||
output += f"### [{doc_id}] {doc['title']}\n"
|
||||
# 返回摘要而非全文，节省 token
|
||||
output += f"{doc['summary']}\n\n"
|
||||
|
||||
logger.info(f"✅ [TOOL SUCCESS] Found {len(results)} matching documents")
|
||||
return output
|
||||
|
||||
def get_toc(self) -> str:
|
||||
"""
|
||||
获取当前存储的所有文档的目录（TOC）。
|
||||
|
||||
Returns:
|
||||
文档目录列表，包含 ID 和标题。
|
||||
"""
|
||||
logger.info("🔍 [TOOL CALLED] get_toc()")
|
||||
|
||||
if not self._store:
|
||||
return "⚠️ 上下文存储为空。"
|
||||
|
||||
output = "## 文档目录 (TOC)\n\n"
|
||||
for doc_id, doc in self._store.items():
|
||||
output += f"- **[{doc_id}]** {doc['title']}\n"
|
||||
|
||||
return output
|
||||
|
||||
Reference in New Issue
Block a user