macmini可以直接连自己的局域网ip,不需要通过host.docker.internal

db_config.py

@@ -14,8 +14,8 @@ from typing import Optional
 class DatabaseConfig:
     """数据库配置类"""
 
-    def __init__(self, env: str = "online"):
+    def __init__(self):
-        self.host = "host.docker.internal" if env == "online" else "192.168.0.115"
+        self.host = "192.168.0.115"
         self.port = 5432
         self.database = "etf_db"
         self.username = "admin"

index_downloader.py

@@ -3,7 +3,7 @@ import re
 import akshare as ak
 import pandas as pd
 from loguru import logger
-from index_spot import stock_zh_index_spot_em
+
 
 # index_hist_df = ak.index_zh_a_hist(
 #     symbol="000001",  # 指数代码,如上证指数
@@ -18,7 +18,7 @@ def get_all_stock_index():
     index_df_list = []
     for source in index_choice:
         logger.info(f"正在获取 {source}...")
-        index_df = stock_zh_index_spot_em(symbol=source)
+        index_df = ak.stock_zh_index_spot_em(symbol=source)
         index_df["symbol"] = source
         index_df_list.append(index_df)
         logger.info(f"{source}: {index_df.shape[0]}")

index_spot.py

@@ -1,214 +0,0 @@
-import logging
-import pandas as pd
-import requests
-import math
-import time
-from typing import List, Dict
-from akshare.utils.tqdm import get_tqdm
-from loguru import logger
-def __stock_zh_main_spot_em() -> pd.DataFrame:
-    """
-    东方财富网-行情中心-沪深重要指数
-    https://quote.eastmoney.com/center/hszs.html
-    :return: 指数的实时行情数据
-    :rtype: pandas.DataFrame
-    """
-    url = "https://33.push2.eastmoney.com/api/qt/clist/get"
-    params = {
-        "pn": "1",
-        "pz": "100",
-        "po": "1",
-        "np": "1",
-        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
-        "fltt": "2",
-        "invt": "2",
-        "dect": "1",
-        "wbp2u": "|0|0|0|web",
-        "fid": "",
-        "fs": "b:MK0010",
-        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,"
-        "f23,f24,f25,f26,f22,f11,f62,f128,f136,f115,f152",
-    }
-    r = requests.get(url, params=params)
-    data_json = r.json()
-    temp_df = pd.DataFrame(data_json["data"]["diff"])
-    temp_df.reset_index(inplace=True)
-    temp_df["index"] = temp_df["index"].astype(int) + 1
-    temp_df.rename(
-        columns={
-            "index": "序号",
-            "f2": "最新价",
-            "f3": "涨跌幅",
-            "f4": "涨跌额",
-            "f5": "成交量",
-            "f6": "成交额",
-            "f7": "振幅",
-            "f10": "量比",
-            "f12": "代码",
-            "f14": "名称",
-            "f15": "最高",
-            "f16": "最低",
-            "f17": "今开",
-            "f18": "昨收",
-        },
-        inplace=True,
-    )
-    temp_df = temp_df[
-        [
-            "序号",
-            "代码",
-            "名称",
-            "最新价",
-            "涨跌幅",
-            "涨跌额",
-            "成交量",
-            "成交额",
-            "振幅",
-            "最高",
-            "最低",
-            "今开",
-            "昨收",
-            "量比",
-        ]
-    ]
-    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
-    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
-    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
-    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
-    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
-    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
-    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
-    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
-    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
-    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
-    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
-    return temp_df
-
-
-def fetch_paginated_data(url: str, base_params: Dict, timeout: int = 15*2):
-    """
-    东方财富-分页获取数据并合并结果
-    https://quote.eastmoney.com/f1.html?newcode=0.000001
-    :param url: 股票代码
-    :type url: str
-    :param base_params: 基础请求参数
-    :type base_params: dict
-    :param timeout: 请求超时时间
-    :type timeout: str
-    :return: 合并后的数据
-    :rtype: pandas.DataFrame
-    """
-    # 复制参数以避免修改原始参数
-    params = base_params.copy()
-    # 获取第一页数据，用于确定分页信息
-    r = requests.get(url, params=params, timeout=timeout)
-    data_json = r.json()
-    # 计算分页信息
-    per_page_num = len(data_json["data"]["diff"])
-    total_page = math.ceil(data_json["data"]["total"] / per_page_num)
-    # 存储所有页面数据
-    temp_list = []
-    # 添加第一页数据
-    temp_list.append(pd.DataFrame(data_json["data"]["diff"]))
-    # 获取进度条
-    tqdm = get_tqdm()
-    # 获取剩余页面数据
-    for page in range(2, total_page + 1):
-        logger.info(f"获取第 {page}/{total_page} 页数据")
-        params.update({"pn": page})
-        r = requests.get(url, params=params, timeout=timeout)
-        data_json = r.json()
-        inner_temp_df = pd.DataFrame(data_json["data"]["diff"])
-        temp_list.append(inner_temp_df)
-        time.sleep(300)
-    # 合并所有数据
-    temp_df = pd.concat(temp_list, ignore_index=True)
-    temp_df["f3"] = pd.to_numeric(temp_df["f3"], errors="coerce")
-    temp_df.sort_values(by=["f3"], ascending=False, inplace=True, ignore_index=True)
-    temp_df.reset_index(inplace=True)
-    temp_df["index"] = temp_df["index"].astype(int) + 1
-    return temp_df
-
-def stock_zh_index_spot_em(symbol: str = "上证系列指数") -> pd.DataFrame:
-    """
-    东方财富网-行情中心-沪深京指数
-    https://quote.eastmoney.com/center/gridlist.html#index_sz
-    :param symbol: "上证系列指数"; choice of {"沪深重要指数", "上证系列指数", "深证系列指数", "指数成份", "中证系列指数"}
-    :type symbol: str
-    :return: 指数的实时行情数据
-    :rtype: pandas.DataFrame
-    """
-    if symbol == "沪深重要指数":
-        return __stock_zh_main_spot_em()
-
-    url = "https://48.push2.eastmoney.com/api/qt/clist/get"
-    symbol_map = {
-        "上证系列指数": "m:1+t:1",
-        "深证系列指数": "m:0 t:5",
-        "指数成份": "m:1+s:3,m:0+t:5",
-        "中证系列指数": "m:2",
-    }
-    params = {
-        "pn": "1",
-        "pz": "100",
-        "po": "1",
-        "np": "1",
-        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
-        "fltt": "2",
-        "invt": "2",
-        "wbp2u": "|0|0|0|web",
-        "fid": "f12",
-        "fs": symbol_map[symbol],
-        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,"
-        "f26,f22,f33,f11,f62,f128,f136,f115,f152",
-    }
-    temp_df = fetch_paginated_data(url, params)
-    temp_df.rename(
-        columns={
-            "index": "序号",
-            "f2": "最新价",
-            "f3": "涨跌幅",
-            "f4": "涨跌额",
-            "f5": "成交量",
-            "f6": "成交额",
-            "f7": "振幅",
-            "f10": "量比",
-            "f12": "代码",
-            "f14": "名称",
-            "f15": "最高",
-            "f16": "最低",
-            "f17": "今开",
-            "f18": "昨收",
-        },
-        inplace=True,
-    )
-    temp_df = temp_df[
-        [
-            "序号",
-            "代码",
-            "名称",
-            "最新价",
-            "涨跌幅",
-            "涨跌额",
-            "成交量",
-            "成交额",
-            "振幅",
-            "最高",
-            "最低",
-            "今开",
-            "昨收",
-            "量比",
-        ]
-    ]
-    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
-    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
-    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
-    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
-    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
-    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
-    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
-    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
-    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
-    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
-    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
-    return temp_df

update_data.py

@@ -76,7 +76,7 @@ def main():
         return
 
     try:
-        db_config = DatabaseConfig(env="daily")
+        db_config = DatabaseConfig()
         logger.info(f"数据库连接: {db_config.connection_string}")
 
         # 如果只是测试连接