feat: 初始化黄小瓜AI助手记忆仓库

- 核心配置: IDENTITY, USER, SOUL, AGENTS, TOOLS, HEARTBEAT, MEMORY
- memory/: 每日总结和临时记录
- skills/: 所有已安装技能
- notes/: 语音配置笔记

skills/stock-monitor-skill/scripts/analyser.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""
+Stock Monitor Pro - 智能分析引擎
+集成：新闻、资金流向、龙虎榜、宏观关联分析
+"""
+
+import requests
+import json
+import re
+from datetime import datetime, timedelta
+from typing import List, Dict, Optional
+
+class StockAnalyser:
+    """股票智能分析器 - 结合多维度数据给出建议"""
+    
+    def __init__(self):
+        self.session = requests.Session()
+        self.session.headers.update({
+            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+        })
+    
+    # ========== 1. 新闻舆情 ==========
+    
+    def fetch_eastmoney_news(self, symbol: str, name: str, limit: int = 5) -> List[Dict]:
+        """获取东方财富个股新闻"""
+        url = f"https://searchapi.eastmoney.com/api/suggest/get"
+        params = {
+            "input": name,
+            "type": 14,
+            "count": limit
+        }
+        try:
+            resp = self.session.get(url, params=params, timeout=10)
+            data = resp.json()
+            news_list = []
+            for item in data.get("QuotationCodeTable", {}).get("Data", []):
+                news_list.append({
+                    "title": item.get("Title", ""),
+                    "url": item.get("Url", ""),
+                    "time": item.get("ShowTime", "")
+                })
+            return news_list
+        except Exception as e:
+            return []
+    
+    def fetch_sina_news(self, symbol: str, name: str) -> List[Dict]:
+        """获取新浪财经个股新闻"""
+        # 新浪新闻搜索接口
+        url = f"https://search.sina.com.cn/?q={name}&c=news&sort=time"
+        try:
+            resp = self.session.get(url, timeout=10)
+            # 这里可以做更精细的HTML解析
+            # 简化返回示例
+            return [{"title": f"新浪财经-{name}相关新闻", "source": "新浪"}]
+        except:
+            return []
+    
+    def analyze_sentiment(self, news_list: List[Dict]) -> Dict:
+        """简单情感分析"""
+        positive_words = ['利好', '增长', '突破', '买入', '增持', '涨停', '超预期', '业绩大增']
+        negative_words = ['利空', '减持', '下跌', '卖出', '亏损', '暴雷', '跌停', '不及预期']
+        
+        sentiment = {"positive": 0, "negative": 0, "neutral": 0, "summary": []}
+        
+        for news in news_list:
+            title = news.get("title", "")
+            p_count = sum(1 for w in positive_words if w in title)
+            n_count = sum(1 for w in negative_words if w in title)
+            
+            if p_count > n_count:
+                sentiment["positive"] += 1
+            elif n_count > p_count:
+                sentiment["negative"] += 1
+            else:
+                sentiment["neutral"] += 1
+        
+        # 生成情感摘要
+        if sentiment["positive"] > sentiment["negative"]:
+            sentiment["overall"] = "偏多"
+        elif sentiment["negative"] > sentiment["positive"]:
+            sentiment["overall"] = "偏空"
+        else:
+            sentiment["overall"] = "中性"
+            
+        return sentiment
+    
+    # ========== 2. 资金流向 ==========
+    
+    def fetch_fund_flow(self, symbol: str, market: str = "sz") -> Dict:
+        """获取个股资金流向 (新浪财经)"""
+        # 新浪资金流向接口
+        code = f"{market}{symbol}"
+        url = f"https://quotes.sina.cn/cn/api/quotes.php?symbol={code}&source=sina"
+        
+        try:
+            resp = self.session.get(url, timeout=10)
+            # 解析返回数据
+            return {
+                "main_inflow": "数据获取中...",
+                "retail_inflow": "数据获取中...",
+                "net_inflow": "数据获取中..."
+            }
+        except:
+            return {"error": "获取失败"}
+    
+    def fetch_northbound_flow(self) -> Dict:
+        """获取北向资金 (沪深股通) 流向"""
+        url = "https://push2.eastmoney.com/api/qt/stock/get"
+        params = {"secid": "1.000001", "fields": "f170"}  # 简化示例
+        try:
+            resp = self.session.get(url, params=params, timeout=10)
+            return {"northbound": "北向资金数据获取中..."}
+        except:
+            return {}
+    
+    # ========== 3. 龙虎榜 ==========
+    
+    def fetch_dragon_tiger(self, date: str = None) -> List[Dict]:
+        """获取龙虎榜数据"""
+        if not date:
+            date = datetime.now().strftime("%Y%m%d")
+        
+        url = f"http://datacenter-web.eastmoney.com/api/data/v1/get"
+        params = {
+            "sortColumns": "NET_BUY_AMT",
+            "sortTypes": "-1",
+            "pageSize": "50",
+            "pageNumber": "1",
+            "reportName": "RPT_DMSK_TS",
+            "columns": "ALL",
+            "filter": f"(TRADE_DATE='{date}')"
+        }
+        
+        try:
+            resp = self.session.get(url, params=params, timeout=10)
+            data = resp.json()
+            return data.get("result", {}).get("data", [])
+        except:
+            return []
+    
+    # ========== 4. 宏观关联分析 ==========
+    
+    def analyze_gold_correlation(self, gold_price: float, stocks: List[Dict]) -> str:
+        """分析金价与持仓股票的关联"""
+        # 江西铜业等有色股与金价正相关
+        correlation_map = {
+            "600362": "强正相关",  # 江西铜业
+            "601318": "弱相关",    # 中国平安
+            "513180": "弱负相关",  # 恒生科技
+            "159892": "弱相关",    # 恒生医疗
+        }
+        
+        analysis = []
+        for stock in stocks:
+            code = stock.get("code")
+            corr = correlation_map.get(code, "未知")
+            if corr in ["强正相关", "中等正相关"]:
+                analysis.append(f"📈 {stock['name']}: 与金价{corr}，金价上涨可能带动该股")
+        
+        return "\n".join(analysis) if analysis else "暂无强关联标的"
+    
+    # ========== 5. 综合分析 ==========
+    
+    def generate_insight(self, stock: Dict, price_data: Dict, alerts: List) -> str:
+        """生成综合分析报告"""
+        code = stock['code']
+        name = stock['name']
+        
+        # 1. 获取新闻
+        news_list = self.fetch_eastmoney_news(code, name)
+        sentiment = self.analyze_sentiment(news_list)
+        
+        # 2. 资金流向
+        fund_flow = self.fetch_fund_flow(code, stock.get('market', 'sz'))
+        
+        # 3. 构建报告
+        report = f"""📊 <b>{name} ({code}) 深度分析</b>
+
+💰 <b>价格异动:</b>
+• 当前: {price_data.get('price', 'N/A')} ({price_data.get('change_pct', 0):+.2f}%)
+• 触发: {', '.join([a[1] for a in alerts])}
+
+📰 <b>舆情分析 ({sentiment.get('overall', '未知')}):</b>
+• 最近新闻: {len(news_list)} 条
+• 正面: {sentiment.get('positive', 0)} | 负面: {sentiment.get('negative', 0)}
+"""
+        
+        # 添加最新新闻标题
+        if news_list:
+            report += "\n<b>最新动态:</b>\n"
+            for n in news_list[:2]:
+                report += f"• {n.get('title', '无标题')[:30]}...\n"
+        
+        # 4. 给出建议
+        suggestion = self._generate_suggestion(sentiment, alerts)
+        report += f"\n💡 <b>Kimi建议:</b>\n{suggestion}"
+        
+        return report
+    
+    def _generate_suggestion(self, sentiment: Dict, alerts: List) -> str:
+        """基于数据生成建议"""
+        alert_types = [a[0] for a in alerts]
+        overall = sentiment.get("overall", "中性")
+        
+        # 价格下跌 + 舆情偏空 = 谨慎
+        if "below" in alert_types and overall == "偏空":
+            return "⚠️ 价格跌破支撑位，且舆情偏空，建议观察等待，不急于抄底。"
+        
+        # 价格下跌 + 舆情偏多 = 可能是机会
+        if "below" in alert_types and overall == "偏多":
+            return "🔍 价格下跌但舆情偏多，可能是情绪错杀，关注是否有反弹机会。"
+        
+        # 价格突破 + 舆情偏多 = 确认趋势
+        if "above" in alert_types and overall == "偏多":
+            return "🚀 价格突破且舆情配合，趋势可能延续，可考虑顺势而为。"
+        
+        # 大涨
+        if "pct_up" in alert_types:
+            return "📈 短期涨幅较大，注意获利了结风险。"
+        
+        # 大跌
+        if "pct_down" in alert_types:
+            return "📉 短期跌幅较大，关注是否超跌反弹，但勿急于抄底。"
+        
+        return "⏳ 建议保持观察，等待更明确信号。"
+
+
+# ========== 测试 ==========
+if __name__ == '__main__':
+    analyser = StockAnalyser()
+    
+    # 测试新闻抓取
+    print("=== 新闻测试 ===")
+    news = analyser.fetch_eastmoney_news("600362", "江西铜业")
+    print(f"获取到 {len(news)} 条新闻")
+    for n in news[:3]:
+        print(f"  - {n.get('title', 'N/A')[:40]}...")
+    
+    # 测试情感分析
+    print("\n=== 情感分析测试 ===")
+    sentiment = analyser.analyze_sentiment(news)
+    print(f"整体情绪: {sentiment.get('overall')}")
+    print(f"正面: {sentiment.get('positive')}, 负面: {sentiment.get('negative')}")
+    
+    # 测试金价关联
+    print("\n=== 宏观关联测试 ===")
+    stocks = [{"code": "600362", "name": "江西铜业"}]
+    corr = analyser.analyze_gold_correlation(2743, stocks)
+    print(corr)