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