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