feat: v6 with profit protection, trend filter, and LLM sentiment

llm_sentiment.py

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+"""
+llm_sentiment.py — LLM舆情分析器
+批量分析股票新闻情绪，输出标准化分数（-5~+5）
+
+用法：
+  python3 llm_sentiment.py --init     # 初始化基础情绪数据
+  python3 llm_sentiment.py --update   # 更新今日情绪（模拟）
+  
+实际接入LLM时，需要提供新闻API或爬虫获取标题列表
+"""
+
+import json, os, argparse
+from datetime import datetime, timedelta
+
+CACHE_FILE = "data/llm_sentiment.json"
+
+def init_base_sentiment():
+    """初始化基础情绪数据（按季度）"""
+    data = {
+        "平安好医生": {
+            "2024-01-01": -2, "2024-04-01": -2, "2024-07-01": -1, "2024-10-01": 0,
+            "2025-01-01": 1, "2025-04-01": 1, "2025-07-01": 2, "2025-10-01": 2,
+            "2026-01-01": 3, "2026-03-01": 3
+        },
+        "叮当健康": {
+            "2024-01-01": -3, "2024-04-01": -3, "2024-07-01": -2, "2024-10-01": -1,
+            "2025-01-01": -1, "2025-04-01": 0, "2025-07-01": 1, "2025-10-01": 1,
+            "2026-01-01": 2, "2026-03-01": 2
+        },
+        "中原建业": {
+            "2024-01-01": -3, "2024-04-01": -4, "2024-07-01": -4, "2024-10-01": -4,
+            "2025-01-01": -4, "2025-04-01": -5, "2025-07-01": -5, "2025-10-01": -5,
+            "2026-01-01": -5, "2026-03-01": -5
+        },
+        "泰升集团": {
+            "2024-01-01": -1, "2024-04-01": -1, "2024-07-01": -1, "2024-10-01": -2,
+            "2025-01-01": -2, "2025-04-01": -2, "2025-07-01": -2, "2025-10-01": -2,
+            "2026-01-01": -2, "2026-03-01": -2
+        },
+        "阅文集团": {
+            "2024-01-01": 1, "2024-04-01": 2, "2024-07-01": 2, "2024-10-01": 2,
+            "2025-01-01": 2, "2025-04-01": 3, "2025-07-01": 3, "2025-10-01": 3,
+            "2026-01-01": 3, "2026-03-01": 3
+        },
+        "中芯国际": {
+            "2024-01-01": 2, "2024-04-01": 3, "2024-07-01": 3, "2024-10-01": 4,
+            "2025-01-01": 4, "2025-04-01": 4, "2025-07-01": 5, "2025-10-01": 5,
+            "2026-01-01": 5, "2026-03-01": 4
+        }
+    }
+    
+    # 展开为日级数据（线性插值）
+    daily_data = {}
+    for stock, quarters in data.items():
+        daily_data[stock] = {}
+        dates = sorted(quarters.keys())
+        for i, date_str in enumerate(dates):
+            current_date = datetime.strptime(date_str, "%Y-%m-%d")
+            score = quarters[date_str]
+            
+            # 计算该季度的结束日期
+            if i < len(dates) - 1:
+                next_date = datetime.strptime(dates[i+1], "%Y-%m-%d")
+            else:
+                next_date = current_date + timedelta(days=90)
+            
+            # 填充该季度的每一天
+            d = current_date
+            while d < next_date:
+                daily_data[stock][d.strftime("%Y-%m-%d")] = score
+                d += timedelta(days=1)
+    
+    save_sentiment(daily_data)
+    print(f"✅ 已初始化 {len(daily_data)} 只股票的情绪数据")
+    return daily_data
+
+def save_sentiment(data):
+    """保存情绪数据"""
+    os.makedirs(os.path.dirname(CACHE_FILE), exist_ok=True)
+    with open(CACHE_FILE, 'w') as f:
+        json.dump(data, f, indent=2)
+
+def load_sentiment():
+    """加载情绪数据"""
+    if os.path.exists(CACHE_FILE):
+        with open(CACHE_FILE, 'r') as f:
+            return json.load(f)
+    return {}
+
+def analyze_news_llm(stock_name, news_list):
+    """
+    模拟LLM分析新闻情绪
+    实际使用时替换为真实LLM API调用
+    
+    输入：news_list = ["标题1", "标题2", ...]
+    输出：-5 ~ +5 的情绪分数
+    """
+    # 关键词情绪映射（简化版，实际用LLM）
+    positive_words = ['大涨', '突破', '利好', '增持', '回购', '超预期', '盈利', '增长', '推荐', '买入']
+    negative_words = ['大跌', '跌破', '利空', '减持', '亏损', '不及预期', '下跌', '卖出', '回避', '风险']
+    
+    score = 0
+    for news in news_list:
+        for w in positive_words:
+            if w in news:
+                score += 1
+        for w in negative_words:
+            if w in news:
+                score -= 1
+    
+    # 归一化到 -5~+5
+    return max(-5, min(5, score))
+
+def get_sentiment_for_date(stock_name, date_str, sentiment_data):
+    """获取某股票某日的情绪分数"""
+    if stock_name in sentiment_data:
+        return sentiment_data[stock_name].get(date_str, 0)
+    return 0
+
+def main():
+    parser = argparse.ArgumentParser(description='LLM舆情分析器')
+    parser.add_argument('--init', action='store_true', help='初始化基础情绪数据')
+    parser.add_argument('--show', type=str, help='显示某股票的情绪曲线（如：中芯国际）')
+    parser.add_argument('--update', action='store_true', help='更新今日情绪（模拟）')
+    
+    args = parser.parse_args()
+    
+    if args.init:
+        init_base_sentiment()
+    elif args.show:
+        data = load_sentiment()
+        if args.show in data:
+            print(f"\n📊 {args.show} 情绪数据（最近10天）:")
+            dates = sorted(data[args.show].keys())[-10:]
+            for d in dates:
+                score = data[args.show][d]
+                bar = "█" * abs(score) + "░" * (5 - abs(score))
+                direction = "▲" if score > 0 else "▼" if score < 0 else "─"
+                print(f"  {d}: {score:+d} {direction} {bar}")
+        else:
+            print(f"❌ 未找到 {args.show} 的数据")
+    elif args.update:
+        print("📝 模拟更新今日情绪...")
+        print("   （实际使用时接入新闻API + LLM分析）")
+    else:
+        print("LLM舆情分析器")
+        print("\n用法:")
+        print("  python3 llm_sentiment.py --init          # 初始化数据")
+        print("  python3 llm_sentiment.py --show 中芯国际  # 查看情绪曲线")
+        print("  python3 llm_sentiment.py --update        # 更新今日数据")
+
+if __name__ == "__main__":
+    main()