songzhuoyuan
346 lines
12 KiB
Python
346 lines
12 KiB
Python
"""
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新闻公告数据获取模块
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使用pywencai获取股票的最新新闻和公告信息
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"""
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import pandas as pd
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import pywencai
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import sys
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import io
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import warnings
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from datetime import datetime
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from http_timeout import call_with_timeout
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warnings.filterwarnings('ignore')
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# 设置标准输出编码为UTF-8（仅在命令行环境，避免streamlit冲突）
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def _setup_stdout_encoding():
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"""仅在命令行环境设置标准输出编码"""
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if sys.platform == 'win32' and not hasattr(sys.stdout, '_original_stream'):
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try:
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# 检测是否在streamlit环境中
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import streamlit
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# 在streamlit中不修改stdout
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return
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except ImportError:
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# 不在streamlit环境，可以安全修改
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try:
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='ignore')
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except:
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pass
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_setup_stdout_encoding()
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class NewsAnnouncementDataFetcher:
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"""新闻公告数据获取类"""
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def __init__(self):
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self.max_items = 20 # 最多获取的新闻/公告数量
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def get_news_and_announcements(self, symbol):
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"""
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获取股票的新闻和公告数据
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Args:
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symbol: 股票代码（6位数字）
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Returns:
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dict: 包含新闻和公告数据的字典
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"""
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data = {
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"symbol": symbol,
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"news_data": None,
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"announcement_data": None,
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"data_success": False
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}
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# 只支持中国股票
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if not self._is_chinese_stock(symbol):
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data["error"] = "新闻公告数据仅支持中国A股股票"
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return data
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try:
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# 获取新闻数据
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print("📰 正在获取最新新闻数据...")
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news_data = self._get_news_data(symbol)
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if news_data:
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data["news_data"] = news_data
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print(f" ✓ 成功获取 {len(news_data.get('items', []))} 条新闻")
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# 获取公告数据
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print("📢 正在获取最新公告数据...")
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announcement_data = self._get_announcement_data(symbol)
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if announcement_data:
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data["announcement_data"] = announcement_data
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print(f" ✓ 成功获取 {len(announcement_data.get('items', []))} 条公告")
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# 如果至少有一个成功，则标记为成功
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if news_data or announcement_data:
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data["data_success"] = True
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print("✅ 新闻公告数据获取完成")
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else:
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print("⚠️ 未能获取到新闻公告数据")
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except Exception as e:
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print(f"❌ 获取新闻公告数据失败: {e}")
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data["error"] = str(e)
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return data
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def _is_chinese_stock(self, symbol):
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"""判断是否为中国股票"""
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return symbol.isdigit() and len(symbol) == 6
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def _get_news_data(self, symbol):
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"""获取新闻数据"""
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try:
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# 构建问句
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query = f"{symbol}新闻"
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print(f" 使用问财查询: {query}")
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# 使用pywencai查询
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result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
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if result is None:
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print(f" 问财查询返回None")
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return None
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# 处理不同类型的返回结果
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df_result = None
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if isinstance(result, dict):
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try:
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df_result = pd.DataFrame([result])
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except Exception as e:
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print(f" 无法转换为DataFrame: {e}")
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return None
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elif isinstance(result, pd.DataFrame):
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df_result = result
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else:
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print(f" 问财返回未知类型: {type(result)}")
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return None
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if df_result is None or df_result.empty:
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print(f" 查询结果为空")
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return None
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# 检查是否是嵌套结构
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if 'tableV1' in df_result.columns and len(df_result.columns) == 1:
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table_v1_data = df_result.iloc[0]['tableV1']
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if isinstance(table_v1_data, pd.DataFrame):
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df_result = table_v1_data
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elif isinstance(table_v1_data, list) and len(table_v1_data) > 0:
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df_result = pd.DataFrame(table_v1_data)
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else:
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print(f" tableV1数据类型不支持: {type(table_v1_data)}")
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return None
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if df_result is None or df_result.empty:
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return None
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# 提取新闻数据
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news_items = []
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# 限制数量
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df_result = df_result.head(self.max_items)
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for idx, row in df_result.iterrows():
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item = {}
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# 尝试提取常见的新闻字段
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for col in df_result.columns:
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col_lower = str(col).lower()
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value = row.get(col)
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# 跳过空值和DataFrame类型
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if value is None or (isinstance(value, float) and pd.isna(value)):
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continue
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if isinstance(value, pd.DataFrame):
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continue
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# 保存字段
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try:
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item[col] = str(value)
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except:
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item[col] = "无法解析"
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if item: # 如果有数据才添加
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news_items.append(item)
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if not news_items:
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return None
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return {
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"items": news_items,
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"count": len(news_items),
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"columns": df_result.columns.tolist(),
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"query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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except Exception as e:
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print(f" 获取新闻数据异常: {e}")
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return None
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def _get_announcement_data(self, symbol):
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"""获取公告数据"""
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try:
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# 构建问句
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query = f"{symbol}公告"
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print(f" 使用问财查询: {query}")
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# 使用pywencai查询
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result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
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if result is None:
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print(f" 问财查询返回None")
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return None
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# 处理不同类型的返回结果
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df_result = None
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if isinstance(result, dict):
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try:
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df_result = pd.DataFrame([result])
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except Exception as e:
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print(f" 无法转换为DataFrame: {e}")
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return None
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elif isinstance(result, pd.DataFrame):
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df_result = result
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else:
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print(f" 问财返回未知类型: {type(result)}")
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return None
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if df_result is None or df_result.empty:
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print(f" 查询结果为空")
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return None
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# 检查是否是嵌套结构
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if 'tableV1' in df_result.columns and len(df_result.columns) == 1:
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table_v1_data = df_result.iloc[0]['tableV1']
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if isinstance(table_v1_data, pd.DataFrame):
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df_result = table_v1_data
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elif isinstance(table_v1_data, list) and len(table_v1_data) > 0:
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df_result = pd.DataFrame(table_v1_data)
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else:
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print(f" tableV1数据类型不支持: {type(table_v1_data)}")
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return None
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if df_result is None or df_result.empty:
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return None
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# 提取公告数据
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announcement_items = []
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# 限制数量
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df_result = df_result.head(self.max_items)
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for idx, row in df_result.iterrows():
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item = {}
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# 尝试提取常见的公告字段
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for col in df_result.columns:
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value = row.get(col)
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# 跳过空值和DataFrame类型
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if value is None or (isinstance(value, float) and pd.isna(value)):
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continue
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if isinstance(value, pd.DataFrame):
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continue
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# 保存字段
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try:
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item[col] = str(value)
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except:
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item[col] = "无法解析"
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if item: # 如果有数据才添加
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announcement_items.append(item)
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if not announcement_items:
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return None
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return {
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"items": announcement_items,
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"count": len(announcement_items),
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"columns": df_result.columns.tolist(),
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"query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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except Exception as e:
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print(f" 获取公告数据异常: {e}")
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return None
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def format_news_announcements_for_ai(self, data):
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"""
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将新闻公告数据格式化为适合AI阅读的文本
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"""
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if not data or not data.get("data_success"):
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return "未能获取新闻公告数据"
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text_parts = []
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# 新闻数据
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if data.get("news_data"):
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news_data = data["news_data"]
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text_parts.append(f"""
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【最新新闻】
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查询时间：{news_data.get('query_time', 'N/A')}
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新闻数量：{news_data.get('count', 0)}条
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""")
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for idx, item in enumerate(news_data.get('items', []), 1):
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text_parts.append(f"新闻 {idx}:")
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for key, value in item.items():
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# 跳过过长的字段
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if len(str(value)) > 500:
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value = str(value)[:500] + "..."
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text_parts.append(f" {key}: {value}")
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text_parts.append("") # 空行分隔
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# 公告数据
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if data.get("announcement_data"):
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announcement_data = data["announcement_data"]
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text_parts.append(f"""
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【最新公告】
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查询时间：{announcement_data.get('query_time', 'N/A')}
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公告数量：{announcement_data.get('count', 0)}条
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""")
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for idx, item in enumerate(announcement_data.get('items', []), 1):
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text_parts.append(f"公告 {idx}:")
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for key, value in item.items():
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# 跳过过长的字段
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if len(str(value)) > 500:
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value = str(value)[:500] + "..."
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text_parts.append(f" {key}: {value}")
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text_parts.append("") # 空行分隔
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return "\n".join(text_parts)
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# 测试函数
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if __name__ == "__main__":
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print("测试新闻公告数据获取...")
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fetcher = NewsAnnouncementDataFetcher()
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# 测试平安银行
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symbol = "000001"
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print(f"\n正在获取 {symbol} 的新闻公告数据...\n")
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data = fetcher.get_news_and_announcements(symbol)
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if data.get("data_success"):
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print("\n" + "="*60)
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print("新闻公告数据获取成功！")
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print("="*60)
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formatted_text = fetcher.format_news_announcements_for_ai(data)
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print(formatted_text)
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else:
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print(f"\n获取失败: {data.get('error', '未知错误')}")
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