知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/5/15 18:20
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Desc: 华尔街见闻-日历-宏观
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https://wallstreetcn.com/calendar
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"""
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from datetime import datetime, timedelta
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import numpy as np
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import pandas as pd
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import requests
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def __convert_date_format(date: str) -> str:
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"""
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将日期字符串从格式'%Y%m%d'转换为格式'%Y-%m-%d %H:%M:%S'。
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:param date: 日期字符串,格式为'%Y%m%d'
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:return: 转换后的日期字符串,格式为'%Y-%m-%d %H:%M:%S'
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"""
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datetime_obj = datetime.strptime(date, "%Y%m%d")
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return datetime_obj.strftime("%Y-%m-%d %H:%M:%S")
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def __format_date(date: str) -> int:
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"""
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将日期字符串转换为Unix时间戳。
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:param date: 日期字符串,格式为'%Y-%m-%d %H:%M:%S'
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:return: Unix时间戳
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"""
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datetime_obj = datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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return int(datetime_obj.timestamp())
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def macro_info_ws(date: str = "20240514") -> pd.DataFrame:
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"""
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华尔街见闻-日历-宏观
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https://wallstreetcn.com/calendar
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:param date: 日期
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:type date: str
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:return: 日历-宏观
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:rtype: pandas.DataFrame
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"""
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date = __convert_date_format(date)
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url = "https://api-one-wscn.awtmt.com/apiv1/finance/macrodatas"
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datetime_obj = datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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one_day = timedelta(days=1)
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new_datetime = datetime_obj + one_day
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date_str = new_datetime.strftime("%Y-%m-%d %H:%M:%S")
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params = {"start": __format_date(date), "end": __format_date(date_str)}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["items"])
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temp_df["public_date"] = pd.to_datetime(
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temp_df["public_date"], errors="coerce", unit="s", utc=True
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).dt.tz_convert("Asia/Shanghai")
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temp_df["public_date"] = temp_df["public_date"].dt.strftime("%Y-%m-%d %H:%M:%S")
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temp_df = temp_df.rename(
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columns={
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"public_date": "时间",
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"country": "地区",
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"title": "事件",
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"importance": "重要性",
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"actual": "今值",
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"forecast": "预期",
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"previous": "前值",
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"revised": "修正",
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"uri": "链接",
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}
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)
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temp_df = temp_df[
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[
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"时间",
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"地区",
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"事件",
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"重要性",
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"今值",
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"预期",
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"前值",
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"修正",
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"链接",
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]
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]
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temp_df["今值"] = pd.to_numeric(temp_df["今值"], errors="coerce")
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temp_df["预期"] = pd.to_numeric(temp_df["预期"], errors="coerce")
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temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce")
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temp_df["修正"] = pd.to_numeric(temp_df["修正"], errors="coerce")
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temp_df["前值"] = np.where(
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temp_df["修正"].notnull(), temp_df["修正"], temp_df["前值"]
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)
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del temp_df["修正"]
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return temp_df
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if __name__ == "__main__":
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macro_info_ws_df = macro_info_ws(date="20240514")
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print(macro_info_ws_df)
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