怎么获取昨日涨停指数今日表现分时数据附代码

import requests
import json
import pandas as pd
def get_and_process_stock_trend():
    """
    获取股票分时数据，并将其转换为Pandas DataFrame进行分析。
    """
    # 1. 定义请求的URL和参数
    url = "https://quota-h.10jqka.com.cn/fuyao/common_hq_aggr/quote/v1/single_trend"
    payload = {
        "code_list": [{"codes": ["883900"], "market": "48"}],
        "trade_date": 0,
        "gpid": 1,
        "time_zone": "Asia/Shanghai",
        "trade_class": "intraday"
    }
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
        'Content-Type': 'application/json'
    }
    try:
        # 2. 发送POST请求
        print("正在发送POST请求...")
        response = requests.post(url, headers=headers, json=payload, timeout=10)
        response.raise_for_status()
        data = response.json()
        # 3. 检查API返回的状态
        if data.get("status_code") != 0:
            print(f"API返回错误: {data.get('status_msg')}")
            return
        # 4. 提取核心数据
        quote_data_item = data["data"]["quote_data"][0]
        base_price = quote_data_item["base_price"]
        value_list = quote_data_item["value"]
        print(f"成功获取数据，代码: {quote_data_item['code']}, 基准价格: {base_price}")
        print("-" * 50)
        # 5. 创建Pandas DataFrame
        df = pd.DataFrame(value_list, columns=['timestamp', 'price', 'volume', 'amount'])
        # 6. 数据处理与转换（处理时区）
        df['datetime'] = pd.to_datetime(df['timestamp'], unit='ms').dt.tz_localize('Asia/Shanghai')
        # 将datetime列设为索引
        df.set_index('datetime', inplace=True)
        # 删除原始的时间戳列
        df.drop(columns=['timestamp'], inplace=True)
        # 7. 计算涨跌幅
        df['change_pct'] = ((df['price'] - base_price) / base_price) * 100
        df['change_pct'] = df['change_pct'].round(2)
        # 8. 打印处理后的DataFrame
        print("--- 处理后的DataFrame---")
        print(df)
        # 9. (可选) 打印数据摘要
        print("\n--- 数据摘要 ---")
        print(f"最高价: {df['price'].max():.4f}")
        print(f"最低价: {df['price'].min():.4f}")
        print(f"最新价: {df['price'].iloc[-1]:.4f}")
        print(f"最大涨幅: {df['change_pct'].max():.2f}%")
        print(f"最大跌幅: {df['change_pct'].min():.2f}%")
    except requests.exceptions.RequestException as e:
        print(f"\n请求失败，发生错误: {e}")
    except json.JSONDecodeError:
        print("\n请求成功，但返回的不是有效的JSON格式。")
    except KeyError as e:
        print(f"\n处理数据时发生错误，缺少预期的键: {e}")
    except Exception as e:
        print(f"\n发生未知错误: {e}")
# --- 主程序入口 ---
if __name__ == "__main__":
    get_and_process_stock_trend()