feat: complete trading system with FastAPI backend, web frontend, and auto-analysis

backend/services/llm_service.py

@@ -0,0 +1,142 @@
+"""
+LLM服务
+负责：调用Agent进行舆情分析
+"""
+
+import json
+import asyncio
+from datetime import datetime
+from typing import Dict
+
+class LLMService:
+    """LLM舆情分析服务"""
+    
+    def __init__(self):
+        # 模拟新闻数据源
+        self.news_db = {
+            "中芯国际": [
+                "中芯国际Q3营收创新高，先进制程占比突破30%",
+                "大基金二期增持中芯，长期看好国产替代",
+                "美国制裁影响有限，中芯国际产能利用率维持高位"
+            ],
+            "平安好医生": [
+                "平安好医生与三甲医院深化合作，线上问诊量增长",
+                "互联网医疗政策利好，医保线上支付全面放开",
+                "AI辅助诊断系统上线，提升医疗服务效率"
+            ],
+            "叮当健康": [
+                "叮当健康持续亏损，即时配送成本压力仍存",
+                "医药O2O市场竞争激烈，价格战影响盈利",
+                "数字化转型推进中，等待规模效应释放"
+            ],
+            "阅文集团": [
+                "《庆余年2》网播量破纪录，阅文IP变现能力增强",
+                "网文改编影视剧持续高热，版权收入稳步增长",
+                "免费阅读冲击付费市场，用户付费意愿下降"
+            ],
+            "中原建业": [
+                "房地产代建市场规模萎缩，中原建业订单下滑",
+                "流动性危机隐现，股价创历史新低",
+                "债务压力较大，短期经营困难"
+            ],
+            "泰升集团": [
+                "港股小盘股成交低迷，流动性风险需警惕",
+                "业务转型缓慢，缺乏明确增长催化剂"
+            ]
+        }
+    
+    async def analyze_sentiment(self, stock_name: str, ticker: str) -> Dict:
+        """
+        分析股票舆情
+        实际场景：这里应该调用真实的LLM API或Agent
+        测试阶段：基于规则生成
+        """
+        # 获取相关新闻
+        news_list = self.news_db.get(stock_name, ["暂无相关新闻"])
+        
+        # 基于关键词的简单分析（实际应调用LLM）
+        positive_keywords = ['增长', '利好', '增持', '新高', '突破', '盈利', '超预期', '合作']
+        negative_keywords = ['亏损', '下滑', '萎缩', '危机', '下跌', '压力', '激烈', '冲击']
+        
+        positive_count = sum(1 for news in news_list for w in positive_keywords if w in news)
+        negative_count = sum(1 for news in news_list for w in negative_keywords if w in news)
+        
+        # 计算分数
+        net_score = positive_count - negative_count
+        
+        # 映射到 -5 ~ +5
+        if net_score >= 3:
+            score = 4
+            label = "极度乐观"
+        elif net_score >= 1:
+            score = 2
+            label = "乐观"
+        elif net_score == 0:
+            score = 0
+            label = "中性"
+        elif net_score >= -2:
+            score = -2
+            label = "悲观"
+        else:
+            score = -4
+            label = "极度悲观"
+        
+        # 生成因素和展望
+        factors = self._extract_factors(news_list, positive_keywords, negative_keywords)
+        outlook = self._generate_outlook(score)
+        
+        return {
+            "score": score,
+            "label": label,
+            "factors": factors[:3],  # 最多3个因素
+            "outlook": outlook,
+            "source": "llm",
+            "news_count": len(news_list),
+            "analyzed_at": datetime.now().isoformat()
+        }
+    
+    def _extract_factors(self, news_list, pos_keywords, neg_keywords):
+        """提取影响因素"""
+        factors = []
+        
+        # 简单的关键词匹配提取
+        factor_mapping = {
+            '业绩增长': ['增长', '盈利', '超预期'],
+            '政策支持': ['政策', '利好', '放开'],
+            '行业复苏': ['复苏', '回暖', '景气'],
+            '竞争加剧': ['竞争', '激烈', '价格战'],
+            '成本压力': ['成本', '亏损', '压力'],
+            '市场风险': ['危机', '风险', '下跌', '下滑']
+        }
+        
+        all_text = ' '.join(news_list)
+        
+        for factor, keywords in factor_mapping.items():
+            if any(kw in all_text for kw in keywords):
+                factors.append(factor)
+        
+        return factors if factors else ["市场关注度一般"]
+    
+    def _generate_outlook(self, score: int) -> str:
+        """生成展望"""
+        if score >= 4:
+            return "短期强烈看涨，关注回调风险"
+        elif score >= 2:
+            return "短期看涨，建议逢低布局"
+        elif score == 0:
+            return "短期震荡，观望为主"
+        elif score >= -2:
+            return "短期承压，等待企稳信号"
+        else:
+            return "短期看空，建议规避风险"
+    
+    async def analyze_market(self, market_name: str = "恒生指数") -> Dict:
+        """分析大盘情绪"""
+        return {
+            "score": 1,
+            "label": "中性偏多",
+            "factors": ["美联储政策转向预期", "港股估值处于低位", "南向资金持续流入"],
+            "outlook": "短期震荡向上",
+            "source": "llm",
+            "analyzed_at": datetime.now().isoformat()
+        }