增强资金面mcp数据支撑
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oficcejo
+161
@@ -6,6 +6,7 @@ import ta
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from datetime import datetime, timedelta
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import requests
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import json
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import pywencai
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class StockDataFetcher:
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"""股票数据获取类"""
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@@ -545,3 +546,163 @@ class StockDataFetcher:
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except Exception as e:
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print(f"获取美股财务数据失败: {e}")
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return financial_data
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def get_fund_flow_data(self, symbol):
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"""使用问财获取资金流向数据
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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: 包含问财原始数据的字典
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"""
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fund_flow_data = {
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"symbol": symbol,
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"query_success": False,
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"raw_data": None, # 存储原始数据
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"data_source": "pywencai"
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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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fund_flow_data["error"] = "问财数据仅支持中国A股股票"
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return fund_flow_data
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try:
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# 构建问句，查询近20个交易日的资金流向数据
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query = f"{symbol}近20个交易日区间资金流向、区间主力资金流向、区间涨跌幅"
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print(f"正在使用问财查询资金流向数据: {query}")
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# 使用pywencai查询
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result = pywencai.get(query=query, loop=True)
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# 调试：打印result的类型
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print(f"问财返回的数据类型: {type(result)}")
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# 处理不同类型的返回结果，统一转换为DataFrame
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df_result = None
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if result is None:
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fund_flow_data["error"] = "问财查询返回None"
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print(f"问财查询返回None")
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elif isinstance(result, dict):
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# 如果返回的是字典，转换为DataFrame
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print(f"问财返回字典，转换为DataFrame")
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try:
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df_result = pd.DataFrame([result])
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print(f"成功转换，形状: {df_result.shape}")
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except Exception as e:
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fund_flow_data["error"] = f"无法转换为DataFrame: {str(e)}"
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print(f"转换失败: {e}")
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elif isinstance(result, pd.DataFrame):
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# 如果已经是DataFrame
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df_result = result
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print(f"问财返回DataFrame，形状: {df_result.shape}")
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else:
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fund_flow_data["error"] = f"问财返回了未知类型: {type(result)}"
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print(f"问财返回未知类型: {type(result)}")
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# 如果成功获取到DataFrame
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if df_result is not None and not df_result.empty and len(df_result) > 0:
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# 打印列名以便调试
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print(f"问财返回的列名: {df_result.columns.tolist()}")
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# 检查是否是嵌套结构（tableV1字段包含实际数据）
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if 'tableV1' in df_result.columns and len(df_result.columns) == 1:
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print(f"检测到嵌套结构，提取tableV1中的数据")
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table_v1_data = df_result.iloc[0]['tableV1']
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# 检查tableV1的类型
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print(f"tableV1的类型: {type(table_v1_data)}")
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if isinstance(table_v1_data, pd.DataFrame):
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# 如果是DataFrame，直接使用
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df_result = table_v1_data
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print(f"提取后的DataFrame形状: {df_result.shape}")
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print(f"提取后的列名: {df_result.columns.tolist()}")
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elif isinstance(table_v1_data, list) and len(table_v1_data) > 0:
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# 如果是列表，转换为DataFrame
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df_result = pd.DataFrame(table_v1_data)
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print(f"从列表转换的DataFrame形状: {df_result.shape}")
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print(f"从列表转换的列名: {df_result.columns.tolist()}")
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else:
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fund_flow_data["error"] = f"tableV1数据类型不支持: {type(table_v1_data)}"
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print(f"tableV1数据类型不支持: {type(table_v1_data)}")
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return fund_flow_data
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# 再次检查是否有数据
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if df_result is None or df_result.empty or len(df_result) == 0:
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fund_flow_data["error"] = "提取后的数据为空"
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print(f"提取后的数据为空")
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return fund_flow_data
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# 获取第一条记录
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data = df_result.iloc[0]
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# 标记查询成功
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fund_flow_data["query_success"] = True
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fund_flow_data["stock_name"] = str(data.get('股票简称', data.get('name', 'N/A')))
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fund_flow_data["stock_code"] = str(data.get('股票代码', data.get('code', symbol)))
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# 将所有数据转换为字典格式（方便AI阅读）
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raw_data_dict = {}
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for col in df_result.columns:
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value = data.get(col)
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# 转换为易读的格式
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try:
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if value is None or (isinstance(value, float) and pd.isna(value)):
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raw_data_dict[col] = "N/A"
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elif isinstance(value, (int, float)):
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# 如果是大数字（可能是金额），转换为亿元
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if abs(value) > 100000000:
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raw_data_dict[col] = f"{value} ({value/100000000:.2f}亿元)"
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else:
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raw_data_dict[col] = value
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elif isinstance(value, pd.DataFrame):
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# 如果值本身是DataFrame，跳过
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continue
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else:
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raw_data_dict[col] = str(value)
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except Exception as e:
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print(f"处理字段 {col} 时出错: {e}")
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raw_data_dict[col] = str(value)
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fund_flow_data["raw_data"] = raw_data_dict
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fund_flow_data["columns"] = df_result.columns.tolist()
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print(f"成功获取 {symbol} 的问财数据，共 {len(raw_data_dict)} 个字段")
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else:
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fund_flow_data["error"] = "问财查询返回空数据"
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print(f"问财查询返回空数据")
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except Exception as e:
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fund_flow_data["error"] = f"获取资金流向数据失败: {str(e)}"
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print(f"获取资金流向数据异常: {e}")
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import traceback
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traceback.print_exc()
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return fund_flow_data
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def _safe_convert(self, value):
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"""安全地转换数值"""
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if value is None or value == '' or (isinstance(value, float) and np.isnan(value)):
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return 'N/A'
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try:
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if isinstance(value, str):
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# 移除百分号和逗号
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value = value.replace('%', '').replace(',', '')
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return float(value)
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return value
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except:
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return value
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def _calculate_main_fund_ratio(self, main_fund, total_fund):
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"""计算主力资金占比"""
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try:
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if main_fund != 'N/A' and total_fund != 'N/A' and total_fund != 0:
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ratio = (main_fund / total_fund) * 100
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return f"{ratio:.2f}%"
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except:
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pass
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return 'N/A'
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