471 lines
17 KiB
Python
471 lines
17 KiB
Python
"""
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新闻 LLM 分析模块
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使用 LLM 分析新闻内容并生成交易建议
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"""
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import json
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from app.utils.logger import logger
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from app.news_agent.fetcher import NewsItem
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from app.config import get_settings
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from openai import AsyncOpenAI
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class NewsAnalyzer:
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"""新闻 LLM 分析器 (DeepSeek) - 异步版本"""
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def __init__(self):
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self.settings = get_settings()
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self.client = None
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try:
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# 使用 DeepSeek API (异步客户端)
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self.client = AsyncOpenAI(
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api_key=self.settings.deepseek_api_key,
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base_url="https://api.deepseek.com"
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)
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except Exception as e:
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logger.error(f"LLM 客户端初始化失败: {e}")
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# 批量分析配置
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self.batch_size = 10 # 每次最多分析 10 条新闻（只传标题，可以增加数量）
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self.max_retries = 2
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def _build_analysis_prompt(self, news_item: NewsItem) -> str:
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"""构建单条新闻的分析提示词"""
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prompt = f"""你是一名专业的金融新闻分析师。请分析以下新闻标题，并以 JSON 格式输出结果。
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**新闻标题**: {news_item.title}
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**新闻来源**: {news_item.source}
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**新闻分类**: {news_item.category}
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请按以下 JSON 格式输出（不要包含其他内容）:
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```json
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{{
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"market_impact": "high/medium/low",
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"impact_type": "bullish/bearish/neutral",
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"sentiment": "positive/negative/neutral",
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"summary": "简洁的新闻摘要（1句话，不超过50字）",
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"key_points": ["关键点1", "关键点2", "关键点3"],
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"trading_advice": "简洁的交易建议（1句话，不超过30字）",
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"relevant_symbols": ["相关的币种或股票代码"],
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"confidence": 85
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}}
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```
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**分析要求**:
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1. market_impact: 对市场的潜在影响（high/medium/low）
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⚠️ **high（重大影响）- 请严格判断，只有以下情况才标记为 high**：
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- 监管层面：ETF批准/拒绝、交易所封禁/解禁、央行政策重大变化
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- 企业层面：破产/退市/重大并购（>100亿美元）、财务造假
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- 技术层面：严重安全漏洞（被盗>1亿美元）、网络暂停
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- 宏观层面：重大地缘政治事件、经济数据远超预期
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❌ **以下情况不应该标记为 high**：
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- 普通价格波动（涨跌<10%）
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- 分析师观点/评级调整
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- CEO发表常规评论
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- 一般业务合作/投资
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- 常规财报发布（非意外业绩）
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- **medium**: 对价格有**短期影响**但不会改变长期趋势的事件
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* 财报业绩、管理层变动、一般并购、机构评级调整
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* 业务合作、技术升级、普通投资新闻
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- **low**: 常规信息，影响有限
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* 分析师观点、一般评论、价格波动、市场常规动态
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**判断原则**:
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1. 问自己"这条新闻会改变市场/公司的长期格局吗？"
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2. 如果会→high，如果只是短期波动→medium，如果无关紧要→low
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3. 宁可判断为 medium，也不要过度判断为 high
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4. 价格波动类新闻，除非涨跌>15%，否则不应是 high
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2. impact_type: 对价格的影响方向（bullish=利好, bearish=利空, neutral=中性）
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3. sentiment: 新闻情绪（positive=正面, negative=负面, neutral=中性）
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4. summary: 根据标题推断并总结新闻核心内容
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5. key_points: 基于标题推断3-5个关键信息点
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6. trading_advice: 给出简明的交易建议
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7. relevant_symbols: 根据标题列出相关的交易代码（如 BTC, ETH, NVDA, TSLA 等）
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8. confidence: 分析置信度（0-100）
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请只输出 JSON，不要包含其他解释。
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"""
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return prompt
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def _build_batch_analysis_prompt(self, news_items: List[NewsItem]) -> str:
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"""构建批量分析提示词"""
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news_text = ""
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for i, item in enumerate(news_items, 1):
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news_text += f"""
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--- 新闻 {i} ---
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标题: {item.title}
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来源: {item.source}
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分类: {item.category}
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---
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"""
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prompt = f"""你是一名专业的金融新闻分析师。请分析以下 {len(news_items)} 条新闻标题，并以 JSON 数组格式输出结果。
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{news_text}
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请按以下 JSON 格式输出（不要包含其他内容）:
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```json
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[
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{{
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"title": "新闻标题",
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"market_impact": "high/medium/low",
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"impact_type": "bullish/bearish/neutral",
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"sentiment": "positive/negative/neutral",
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"summary": "简洁的新闻摘要（1句话，不超过50字）",
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"key_points": ["关键点1", "关键点2"],
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"trading_advice": "简洁的交易建议（1句话，不超过30字）",
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"relevant_symbols": ["相关代码"],
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"confidence": 85
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}}
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]
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```
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**market_impact 判断标准（严格）**:
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⚠️ **high（重大影响）- 请严格判断**：
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- 监管：ETF批准/拒绝、交易所封禁/解禁、央行政策重大变化
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- 企业：破产/退市、重大并购（>100亿美元）、财务造假
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- 技术：严重安全漏洞（被盗>1亿美元）、网络暂停
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- 宏观：重大地缘政治事件、经济数据远超预期
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❌ **以下情况不应该标记为 high**：
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- 普通价格波动（涨跌<10%）
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- 分析师观点/评级调整
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- CEO发表常规评论
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- 一般业务合作/投资
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- 常规财报发布（非意外业绩）
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- **medium**: 对价格有**短期影响**但不会改变长期趋势
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- **low**: 常规信息，影响有限
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**判断原则**: 问自己"这条新闻会改变市场/公司的长期格局吗？" 如果会→high，否则→medium/low
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请只输出 JSON 数组，不要包含其他解释。
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"""
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return prompt
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def _parse_llm_response(self, response: str) -> Optional[Dict[str, Any]]:
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"""解析 LLM 响应"""
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try:
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# 尝试提取 JSON
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response = response.strip()
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# 移除可能的 markdown 代码块标记
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if response.startswith("```json"):
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response = response[7:]
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if response.startswith("```"):
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response = response[3:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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# 解析 JSON
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return json.loads(response)
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except json.JSONDecodeError as e:
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# 尝试修复截断的 JSON
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logger.warning(f"JSON 解析失败，尝试修复: {e}")
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try:
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# 查找最后一个完整的对象
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response = response.strip()
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# 如果是数组，找到最后一个完整的对象
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if response.startswith('['):
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# 找到每个完整对象的结束位置
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brace_count = 0
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last_complete = 0
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for i, char in enumerate(response):
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if char == '{':
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brace_count += 1
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elif char == '}':
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brace_count -= 1
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if brace_count == 0:
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last_complete = i + 1
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break
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if last_complete > 0:
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# 提取完整的数组
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fixed = response[:last_complete]
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if not fixed.endswith(']'):
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fixed += ']'
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if not fixed.endswith('}'):
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fixed += '}'
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return json.loads(fixed)
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except:
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pass
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logger.error(f"JSON 解析失败: {e}, 响应: {response[:500]}")
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return None
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def _parse_llm_array_response(self, response: str) -> Optional[List[Dict[str, Any]]]:
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"""解析 LLM 数组响应"""
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try:
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# 尝试提取 JSON
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response = response.strip()
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# 移除可能的 markdown 代码块标记
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if response.startswith("```json"):
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response = response[7:]
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if response.startswith("```"):
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response = response[3:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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# 解析 JSON 数组
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result = json.loads(response)
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if isinstance(result, list):
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return result
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elif isinstance(result, dict) and 'title' in result:
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# 如果返回单个对象，包装成数组
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return [result]
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return None
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except json.JSONDecodeError as e:
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# 尝试修复截断的 JSON 数组
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logger.warning(f"JSON 数组解析失败，尝试修复: {e}")
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try:
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response = response.strip()
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if response.startswith('['):
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# 找到每个完整对象
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objects = []
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brace_count = 0
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obj_start = -1
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for i, char in enumerate(response):
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if char == '{':
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if obj_start == -1:
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obj_start = i
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brace_count += 1
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elif char == '}':
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brace_count -= 1
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if brace_count == 0 and obj_start >= 0:
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# 提取完整对象
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obj_str = response[obj_start:i + 1]
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try:
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obj = json.loads(obj_str)
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if isinstance(obj, dict) and 'title' in obj:
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objects.append(obj)
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except:
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pass
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obj_start = -1
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if objects:
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return objects
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except:
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pass
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logger.error(f"JSON 数组解析失败: {e}, 响应: {response[:500]}")
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return None
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async def analyze_single(self, news_item: NewsItem) -> Optional[Dict[str, Any]]:
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"""
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分析单条新闻 (异步)
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Args:
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news_item: 新闻项
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Returns:
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分析结果字典或 None
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"""
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if not self.client:
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logger.warning("LLM 客户端未初始化")
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return None
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try:
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prompt = self._build_analysis_prompt(news_item)
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for attempt in range(self.max_retries):
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try:
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response = await self.client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": "你是一名专业的金融新闻分析师，擅长分析新闻标题对市场的影响。"},
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{"role": "user", "content": prompt}
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],
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temperature=0.3,
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max_tokens=1000 # 只传标题，减少输出token
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)
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result = self._parse_llm_response(response.choices[0].message.content)
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if result:
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logger.info(f"新闻分析成功: {news_item.title[:50]}... -> {result.get('market_impact')}")
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return result
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except Exception as e:
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logger.warning(f"分析失败 (尝试 {attempt + 1}/{self.max_retries}): {e}")
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logger.error(f"新闻分析失败，已达最大重试次数: {news_item.title[:50]}")
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return None
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except Exception as e:
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logger.error(f"分析新闻时出错: {e}")
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return None
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async def analyze_batch(self, news_items: List[NewsItem]) -> List[Optional[Dict[str, Any]]]:
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"""
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批量分析新闻 (异步)
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Args:
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news_items: 新闻项列表
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Returns:
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分析结果列表（与输入顺序一致）
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"""
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if not self.client:
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logger.warning("LLM 客户端未初始化")
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return [None] * len(news_items)
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results = []
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# 分批处理
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for i in range(0, len(news_items), self.batch_size):
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batch = news_items[i:i + self.batch_size]
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try:
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prompt = self._build_batch_analysis_prompt(batch)
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response = await self.client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": "你是一名专业的金融新闻分析师，擅长分析新闻标题对市场的影响。"},
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{"role": "user", "content": prompt}
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],
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temperature=0.3,
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max_tokens=2000 # 批量分析需要更多 token
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)
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batch_results = self._parse_llm_array_response(response.choices[0].message.content)
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if batch_results:
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# 按标题匹配结果
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title_to_result = {r.get('title'): r for r in batch_results if r and isinstance(r, dict)}
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for item in batch:
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result = title_to_result.get(item.title)
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results.append(result)
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if result:
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logger.info(f"新闻分析成功: {item.title[:50]}... -> {result.get('market_impact')}")
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else:
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results.extend([None] * len(batch))
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except Exception as e:
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logger.error(f"批量分析失败: {e}")
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results.extend([None] * len(batch))
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return results
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def calculate_priority(self, analysis: Dict[str, Any], quality_score: float = 0.5) -> float:
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"""
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根据分析结果计算优先级
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Args:
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analysis: LLM 分析结果
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quality_score: 质量分数
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Returns:
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优先级分数
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"""
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score = 0.0
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# 市场影响（更严格的权重）
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impact_weights = {'high': 50, 'medium': 25, 'low': 5} # 降低 low 和 medium 的权重
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score += impact_weights.get(analysis.get('market_impact', 'low'), 5)
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# 方向性（利空利好比中性重要）
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if analysis.get('impact_type') in ['bullish', 'bearish']:
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score += 10 # 从 15 降低到 10
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# 置信度（降低权重）
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score += (analysis.get('confidence', 50) / 100) * 8 # 从 10 降低到 8
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# 质量分数（保持）
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score += quality_score * 15 # 从 20 降低到 15
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# 是否有相关代码（提高重要性）
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if analysis.get('relevant_symbols'):
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score += 12 # 从 5 提高到 12
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return score
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class NewsAnalyzerSimple:
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"""简化版新闻分析器（仅关键词规则，不使用 LLM）"""
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def __init__(self):
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pass
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def analyze_single(self, news_item: NewsItem) -> Dict[str, Any]:
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"""
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基于规则分析新闻
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Args:
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news_item: 新闻项
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Returns:
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分析结果字典
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"""
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# 使用已有的影响评分
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impact_score = getattr(news_item, 'impact_score', 0.0)
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# 根据 impact_score 确定市场影响
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if impact_score >= 1.0:
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market_impact = 'high'
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elif impact_score >= 0.7:
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market_impact = 'medium'
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else:
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market_impact = 'low'
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# 检查关键词确定方向
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text = f"{news_item.title} {news_item.content}".lower()
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bullish_keywords = ['上涨', '增长', '突破', '新高', 'bullish', 'surge', 'rally', 'gain', '批准', '合作']
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bearish_keywords = ['下跌', '暴跌', '崩盘', 'ban', 'bearish', 'crash', 'plunge', 'fall', '禁令', '风险']
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bullish_count = sum(1 for k in bullish_keywords if k in text)
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bearish_count = sum(1 for k in bearish_keywords if k in text)
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if bullish_count > bearish_count:
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impact_type = 'bullish'
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sentiment = 'positive'
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elif bearish_count > bullish_count:
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impact_type = 'bearish'
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sentiment = 'negative'
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else:
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impact_type = 'neutral'
|
||
sentiment = 'neutral'
|
||
|
||
# 获取相关代码
|
||
relevant_symbols = list(set(getattr(news_item, 'relevant_symbols', [])))
|
||
|
||
return {
|
||
'market_impact': market_impact,
|
||
'impact_type': impact_type,
|
||
'sentiment': sentiment,
|
||
'summary': news_item.title,
|
||
'key_points': [news_item.title[:100]],
|
||
'trading_advice': getattr(news_item, 'impact_reason', '关注市场动态'),
|
||
'relevant_symbols': relevant_symbols,
|
||
'confidence': 70,
|
||
'analyzed_by': 'rules'
|
||
}
|