通达信TdxQuant获取分钟级别数据及实时数据

import sys
import os
import pandas as pd
import numpy as np
from datetime import datetime
tdx_install_path = r"D:\lwj\new_tdx"  # 你的通达信安装根目录
pyplugins_user_path = os.path.join(tdx_install_path, "PYPlugins", "user")
sys.path.insert(0, pyplugins_user_path)
# 【重要】切换工作目录到通达信根目录，防止初始化找不到日志文件或报错
os.chdir(tdx_install_path)
from tqcenter import tq 
# 初始化tq
tq.initialize(__file__)
def raw_data_to_dataframe_complete(raw_data):
    """
    将tq.get_market_data返回的原始数据完整转换为DataFrame
    支持多股票、多字段的完整转换
    """
    # 检查数据是否为空
    if not raw_data:
        print("原始数据为空")
        return pd.DataFrame()
    
    # 获取所有字段
    fields = list(raw_data.keys())
 
   
    # 获取第一个字段的DataFrame来提取股票代码信息
    first_field = fields[0]
    first_df = raw_data[first_field]
    
    
    if isinstance(first_df, pd.DataFrame) and hasattr(first_df, 'columns'):
        # 获取所有股票代码
        stock_codes = first_df.columns.tolist()
        
        # 创建合并后的DataFrame
        df_list = []
        
        for stock_code in stock_codes:
            stock_data = {}
            
            # 提取每个字段下该股票的数据
            for field in fields:
                field_data = raw_data[field]
                
                if isinstance(field_data, pd.DataFrame) and stock_code in field_data.columns:
                    stock_data[field] = field_data[stock_code].values
                elif isinstance(field_data, pd.Series):
                    # 如果是Series，尝试通过索引获取
                    stock_data[field] = field_data.values
                else:
                    stock_data[field] = np.nan  # 填充缺失值
            
            # 创建该股票的DataFrame
            if stock_data[first_field] is not np.nan:
                stock_df = pd.DataFrame(stock_data)
                stock_df['股票代码'] = stock_code
                
                # 添加日期索引
                if hasattr(first_df, 'index'):
                    stock_df['日期'] = first_df.index
                else:
                    stock_df['日期'] = range(len(stock_df))
                
                df_list.append(stock_df)
        
        # 合并所有股票的数据
        if df_list:
            final_df = pd.concat(df_list, ignore_index=True)
            # 重新排序列
            col_order = ['股票代码', '日期'] + fields
            final_df = final_df[col_order]
            return final_df
    
    
    return pd.DataFrame()
def main():
    stock_list = ["600519.SH",'000001.SZ']   # 贵州茅台，平安银行
    start_time = "20260127"                                  # 数据起始日期
    end_time = datetime.now().strftime("%Y%m%d")           # 数据结束日期（当前日期）
# 2. 获取并整理收盘价数据
    raw_data = tq.get_market_data(
                stock_list=stock_list,
                start_time=start_time,
                end_time=end_time,
                period='1m',
                dividend_type='front',
                field_list=['Open', 'High', 'Low', 'Close', 'Amount'],
                fill_data=True
            )
    print(raw_data)
        
        
            # 转换为标准DataFrame格式
    df_standard = raw_data_to_dataframe_complete(raw_data)
    print("\n标准格式DataFrame:")
    print(df_standard)
if __name__ == "__main__":
    main()

market_snapshot= tq.get_market_snapshot(stock_code='600519.SH')
print(market_snapshot)
print(market_snapshot['Amount'])

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