oficcejo
470 lines
18 KiB
Python
470 lines
18 KiB
Python
"""
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风险数据获取模块
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使用pywencai获取股票风险相关信息：
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1. 限售解禁数据
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2. 大股东减持公告
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3. 近期重要事件
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"""
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import pywencai
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import pandas as pd
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from typing import Dict, Any
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import time
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import warnings
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import os
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# 屏蔽pywencai的Node.js警告信息（不影响功能）
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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os.environ['PYTHONWARNINGS'] = 'ignore::DeprecationWarning'
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os.environ['NODE_NO_WARNINGS'] = '1' # 屏蔽Node.js警告
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class RiskDataFetcher:
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"""风险数据获取类"""
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def __init__(self):
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"""初始化"""
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pass
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def get_risk_data(self, symbol: str) -> Dict[str, Any]:
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"""
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获取股票风险相关数据
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Args:
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symbol: 股票代码（如：600000）
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Returns:
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包含风险数据的字典
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"""
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print(f"\n正在获取 {symbol} 的风险数据...")
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risk_data = {
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'symbol': symbol,
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'data_success': False,
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'lifting_ban': None, # 限售解禁数据
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'shareholder_reduction': None, # 大股东减持数据
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'important_events': None, # 重要事件数据
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'error': None
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}
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try:
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# 1. 获取限售解禁数据
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print(" 查询限售解禁数据...")
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lifting_ban = self._get_lifting_ban_data(symbol)
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risk_data['lifting_ban'] = lifting_ban
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if lifting_ban and lifting_ban.get('has_data'):
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print(f" 获取到限售解禁数据")
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else:
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print(f" 暂无限售解禁数据")
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time.sleep(1) # 避免请求过快
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# 2. 获取大股东减持公告
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print(" 查询大股东减持公告...")
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reduction = self._get_shareholder_reduction_data(symbol)
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risk_data['shareholder_reduction'] = reduction
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if reduction and reduction.get('has_data'):
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print(f" 获取到大股东减持数据")
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else:
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print(f" 暂无大股东减持数据")
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time.sleep(1) # 避免请求过快
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# 3. 获取近期重要事件
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print(" 查询近期重要事件...")
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events = self._get_important_events_data(symbol)
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risk_data['important_events'] = events
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if events and events.get('has_data'):
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print(f" 获取到重要事件数据")
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else:
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print(f" 暂无重要事件数据")
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# 如果至少有一个数据源成功，则认为获取成功
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if (lifting_ban and lifting_ban.get('has_data')) or \
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(reduction and reduction.get('has_data')) or \
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(events and events.get('has_data')):
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risk_data['data_success'] = True
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print(f"风险数据获取完成")
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else:
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print(f"未获取到风险相关数据")
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except Exception as e:
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print(f"风险数据获取失败: {str(e)}")
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risk_data['error'] = str(e)
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return risk_data
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def _get_lifting_ban_data(self, symbol: str) -> Dict[str, Any]:
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"""获取限售解禁数据"""
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result = {
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'has_data': False,
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'query': f"{symbol}限售解禁",
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'data': None,
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'summary': None
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}
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try:
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# 构建问句
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query = f"{symbol}限售解禁"
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# 使用pywencai查询
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response = pywencai.get(query=query, loop=True)
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if response is None:
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return result
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# 处理返回结果
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df_result = self._convert_to_dataframe(response)
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if df_result is None or df_result.empty:
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return result
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# 提取有用的信息
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result['has_data'] = True
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result['data'] = df_result
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# 生成摘要
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summary = []
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# 尝试提取关键字段
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if '解禁时间' in df_result.columns or '限售解禁日' in df_result.columns:
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time_col = '解禁时间' if '解禁时间' in df_result.columns else '限售解禁日'
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summary.append(f"发现 {len(df_result)} 条解禁记录")
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# 提取最近的解禁记录
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recent_records = df_result.head(5)
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for idx, row in recent_records.iterrows():
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record_info = []
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if time_col in row.index:
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record_info.append(f"日期: {row[time_col]}")
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if '解禁股数' in row.index:
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record_info.append(f"解禁股数: {row['解禁股数']}")
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if '解禁市值' in row.index:
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record_info.append(f"解禁市值: {row['解禁市值']}")
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if '股东名称' in row.index:
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record_info.append(f"股东: {row['股东名称']}")
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if record_info:
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summary.append(" | ".join(record_info))
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else:
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# 如果没有标准字段，只记录有数据
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summary.append(f"获取到 {len(df_result)} 条相关记录")
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result['summary'] = "\n".join(summary) if summary else "有限售解禁数据"
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except Exception as e:
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result['error'] = str(e)
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return result
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def _get_shareholder_reduction_data(self, symbol: str) -> Dict[str, Any]:
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"""获取大股东减持公告数据"""
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result = {
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'has_data': False,
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'query': f"{symbol}大股东减持公告",
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'data': None,
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'summary': None
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}
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try:
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# 构建问句
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query = f"{symbol}大股东减持公告"
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# 使用pywencai查询
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response = pywencai.get(query=query, loop=True)
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if response is None:
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return result
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# 处理返回结果
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df_result = self._convert_to_dataframe(response)
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if df_result is None or df_result.empty:
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return result
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# 提取有用的信息
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result['has_data'] = True
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result['data'] = df_result
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# 生成摘要
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summary = []
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# 尝试提取关键字段
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if '公告日期' in df_result.columns or '减持日期' in df_result.columns:
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date_col = '公告日期' if '公告日期' in df_result.columns else '减持日期'
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summary.append(f"发现 {len(df_result)} 条减持公告")
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# 提取最近的减持记录
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recent_records = df_result.head(5)
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for idx, row in recent_records.iterrows():
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record_info = []
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if date_col in row.index:
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record_info.append(f"日期: {row[date_col]}")
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if '股东名称' in row.index:
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record_info.append(f"股东: {row['股东名称']}")
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if '减持股数' in row.index:
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record_info.append(f"减持股数: {row['减持股数']}")
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if '减持比例' in row.index:
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record_info.append(f"减持比例: {row['减持比例']}")
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if record_info:
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summary.append(" | ".join(record_info))
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else:
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# 如果没有标准字段，只记录有数据
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summary.append(f"获取到 {len(df_result)} 条相关记录")
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result['summary'] = "\n".join(summary) if summary else "有大股东减持数据"
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except Exception as e:
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result['error'] = str(e)
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return result
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def _get_important_events_data(self, symbol: str) -> Dict[str, Any]:
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"""获取近期重要事件数据"""
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result = {
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'has_data': False,
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'query': f"{symbol}近期重要事件",
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'data': None,
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'summary': None
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}
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try:
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# 构建问句
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query = f"{symbol}近期重要事件"
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# 使用pywencai查询
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response = pywencai.get(query=query, loop=True)
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if response is None:
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return result
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# 处理返回结果
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df_result = self._convert_to_dataframe(response)
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if df_result is None or df_result.empty:
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return result
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# 提取有用的信息
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result['has_data'] = True
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result['data'] = df_result
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# 生成摘要
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summary = []
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# 尝试提取关键字段
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if '事件时间' in df_result.columns or '公告日期' in df_result.columns:
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time_col = '事件时间' if '事件时间' in df_result.columns else '公告日期'
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summary.append(f"发现 {len(df_result)} 条重要事件")
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# 提取最近的事件
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recent_events = df_result.head(10)
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for idx, row in recent_events.iterrows():
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event_info = []
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if time_col in row.index:
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event_info.append(f"时间: {row[time_col]}")
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if '事件类型' in row.index:
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event_info.append(f"类型: {row['事件类型']}")
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if '事件内容' in row.index:
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content = str(row['事件内容'])[:100] # 限制长度
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event_info.append(f"内容: {content}")
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elif '标题' in row.index:
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title = str(row['标题'])[:100]
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event_info.append(f"标题: {title}")
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if event_info:
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summary.append(" | ".join(event_info))
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else:
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# 如果没有标准字段，只记录有数据
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summary.append(f"获取到 {len(df_result)} 条相关记录")
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result['summary'] = "\n".join(summary) if summary else "有重要事件数据"
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except Exception as e:
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result['error'] = str(e)
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return result
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def _convert_to_dataframe(self, result) -> pd.DataFrame:
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"""将pywencai返回结果转换为DataFrame"""
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try:
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if result is None:
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return None
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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:
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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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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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# 处理嵌套结构（tableV1）
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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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return None
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# 处理嵌套结构（title_content等单列嵌套）
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# 如果只有一列，且该列的值是DataFrame，则展开
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if len(df_result.columns) == 1:
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col_name = df_result.columns[0]
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first_value = df_result.iloc[0][col_name]
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if isinstance(first_value, pd.DataFrame):
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print(f" 检测到嵌套DataFrame（列名: {col_name}），正在展开...")
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df_result = first_value
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return df_result if not df_result.empty else None
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except Exception as e:
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print(f" 转换DataFrame时出错: {str(e)}")
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return None
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def format_risk_data_for_ai(self, risk_data: Dict[str, Any]) -> str:
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"""格式化风险数据供AI分析使用 - 直接转换DataFrame为字符串"""
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if not risk_data or not risk_data.get('data_success'):
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return "未获取到风险数据"
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formatted_text = []
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try:
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# 1. 限售解禁数据
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lifting_ban = risk_data.get('lifting_ban')
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if lifting_ban and lifting_ban.get('has_data') and lifting_ban.get('data') is not None:
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formatted_text.append("=" * 80)
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formatted_text.append("【限售解禁数据】")
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formatted_text.append("=" * 80)
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formatted_text.append(f"查询语句: {lifting_ban.get('query', '')}")
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formatted_text.append("")
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# 直接将DataFrame转换为字符串（最多50行）
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df = lifting_ban.get('data')
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try:
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df_str = df.head(50).to_string(index=False, max_rows=50, max_cols=20)
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formatted_text.append(f"共 {len(df)} 条记录，显示前50条：")
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formatted_text.append(df_str)
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except Exception as e:
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formatted_text.append(f"数据转换失败: {str(e)}")
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formatted_text.append("")
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# 2. 大股东减持数据
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reduction = risk_data.get('shareholder_reduction')
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if reduction and reduction.get('has_data') and reduction.get('data') is not None:
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formatted_text.append("=" * 80)
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formatted_text.append("【大股东减持数据】")
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formatted_text.append("=" * 80)
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formatted_text.append(f"查询语句: {reduction.get('query', '')}")
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formatted_text.append("")
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# 直接将DataFrame转换为字符串（最多50行）
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df = reduction.get('data')
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try:
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df_str = df.head(50).to_string(index=False, max_rows=50, max_cols=20)
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formatted_text.append(f"共 {len(df)} 条记录，显示前50条：")
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formatted_text.append(df_str)
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except Exception as e:
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formatted_text.append(f"数据转换失败: {str(e)}")
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formatted_text.append("")
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# 3. 重要事件数据
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events = risk_data.get('important_events')
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if events and events.get('has_data') and events.get('data') is not None:
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formatted_text.append("=" * 80)
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formatted_text.append("【重要事件数据】")
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formatted_text.append("=" * 80)
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formatted_text.append(f"查询语句: {events.get('query', '')}")
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formatted_text.append("")
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# 直接将DataFrame转换为字符串（最多50行）
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df = events.get('data')
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try:
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df_str = df.head(50).to_string(index=False, max_rows=50, max_cols=20)
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formatted_text.append(f"共 {len(df)} 条记录，显示前50条：")
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formatted_text.append(df_str)
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except Exception as e:
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formatted_text.append(f"数据转换失败: {str(e)}")
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formatted_text.append("")
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return "\n".join(formatted_text) if formatted_text else "暂无风险数据"
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except Exception as e:
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print(f"格式化风险数据时出错: {str(e)}")
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import traceback
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traceback.print_exc()
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return f"格式化风险数据时出错: {str(e)}"
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def _format_dataframe_for_ai(self, df: pd.DataFrame, data_type: str) -> str:
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"""将DataFrame格式化为AI易读的文本格式"""
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lines = []
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# 显示数据总数
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lines.append(f"共 {len(df)} 条{data_type}记录")
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lines.append("")
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# 显示列名
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lines.append(f"数据字段：{', '.join(df.columns.tolist())}")
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lines.append("")
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# 逐行显示数据（最多显示50条，避免数据过大）
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max_rows = min(50, len(df))
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for idx, row in df.head(max_rows).iterrows():
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lines.append(f"【记录 {idx + 1}】")
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# 显示每个字段的值
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for col in df.columns:
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value = row[col]
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# 处理不同类型的值
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if pd.isna(value):
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value_str = "无数据"
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elif isinstance(value, (int, float)):
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value_str = str(value)
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else:
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value_str = str(value)
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# 限制过长的字符串
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if len(value_str) > 200:
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value_str = value_str[:200] + "..."
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lines.append(f" {col}: {value_str}")
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lines.append("")
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if len(df) > max_rows:
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lines.append(f"... 还有 {len(df) - max_rows} 条记录（已省略）")
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lines.append("")
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return "\n".join(lines)
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# 测试代码
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if __name__ == "__main__":
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fetcher = RiskDataFetcher()
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# 测试获取风险数据
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test_symbol = "600000"
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print(f"测试获取 {test_symbol} 的风险数据...")
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risk_data = fetcher.get_risk_data(test_symbol)
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print("\n" + "=" * 60)
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print("获取结果:")
|
||
print("=" * 60)
|
||
print(f"数据获取成功: {risk_data['data_success']}")
|
||
|
||
if risk_data['data_success']:
|
||
print("\n格式化的风险数据:")
|
||
print(fetcher.format_risk_data_for_ai(risk_data))
|
||
|