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
442 lines
14 KiB
Python
442 lines
14 KiB
Python
# -*- coding:utf-8 -*-
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# !/usr/bin/env python
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"""
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Date: 2026/3/22 21:00
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Desc: 50 ETF 期权波动率指数 QVIX
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300 ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?50ETF
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http://1.optbbs.com/s/vix.shtml?300ETF
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"""
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import pandas as pd
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from functools import lru_cache
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@lru_cache
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def __get_optbbs_daily() -> pd.DataFrame:
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"""
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读取原始数据
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http://1.optbbs.com/d/csv/d/k.csv
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:return: 原始数据
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/k.csv"
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temp_df = pd.read_csv(url, encoding="gbk")
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return temp_df
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def index_option_50etf_qvix() -> pd.DataFrame:
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"""
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50ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?50ETF
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:return: 50ETF 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, :5]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_50etf_min_qvix() -> pd.DataFrame:
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"""
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50 ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?50ETF
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:return: 50 ETF 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vix50.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_300etf_qvix() -> pd.DataFrame:
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"""
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300 ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?300ETF
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:return: 300 ETF 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 9, 10, 11, 12]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_300etf_min_qvix() -> pd.DataFrame:
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"""
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300 ETF 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?300ETF
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:return: 300 ETF 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vix300.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_500etf_qvix() -> pd.DataFrame:
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"""
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500 ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?500ETF
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:return: 500 ETF 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 67, 68, 69, 70]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_500etf_min_qvix() -> pd.DataFrame:
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"""
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500 ETF 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?500ETF
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:return: 500 ETF 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vix500.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_cyb_qvix() -> pd.DataFrame:
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"""
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创业板 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?CYB
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:return: 创业板 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 71, 72, 73, 74]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_cyb_min_qvix() -> pd.DataFrame:
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"""
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创业板 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?CYB
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:return: 创业板 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vixcyb.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_kcb_qvix() -> pd.DataFrame:
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"""
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科创板 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?KCB
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:return: 科创板 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 83, 84, 85, 86]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_kcb_min_qvix() -> pd.DataFrame:
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"""
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科创板 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?KCB
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:return: 科创板 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vixkcb.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_100etf_qvix() -> pd.DataFrame:
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"""
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深证100ETF 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?100ETF
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:return: 深证100ETF 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 75, 76, 77, 78]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_100etf_min_qvix() -> pd.DataFrame:
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"""
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深证100ETF 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?100ETF
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:return: 深证100ETF 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vix100.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df.loc[:, "qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_300index_qvix() -> pd.DataFrame:
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"""
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中证300股指 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?Index
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:return: 中证300股指 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 17, 18, 19, 20]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_300index_min_qvix() -> pd.DataFrame:
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"""
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中证300股指 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?Index
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:return: 中证300股指 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vixindex.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df["qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_1000index_qvix() -> pd.DataFrame:
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"""
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中证1000股指 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?Index1000
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:return: 中证1000股指 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 25, 26, 27, 28]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_1000index_min_qvix() -> pd.DataFrame:
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"""
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中证1000股指 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?Index1000
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:return: 中证1000股指 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vixindex1000.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df["qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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def index_option_50index_qvix() -> pd.DataFrame:
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"""
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上证50股指 期权波动率指数 QVIX
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http://1.optbbs.com/s/vix.shtml?50index
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:return: 上证50股指 期权波动率指数 QVIX
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:rtype: pandas.DataFrame
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"""
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temp_df = __get_optbbs_daily().iloc[:, [0, 79, 80, 81, 82]]
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temp_df.columns = [
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"date",
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"open",
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"high",
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"low",
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"close",
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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return temp_df
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def index_option_50index_min_qvix() -> pd.DataFrame:
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"""
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上证50股指 期权波动率指数 QVIX-分时
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http://1.optbbs.com/s/vix.shtml?50index
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:return: 上证50股指 期权波动率指数 QVIX-分时
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:rtype: pandas.DataFrame
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"""
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url = "http://1.optbbs.com/d/csv/d/vix50index.csv"
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temp_df = pd.read_csv(url).iloc[:, :2]
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temp_df.columns = [
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"time",
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"qvix",
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]
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temp_df["qvix"] = pd.to_numeric(temp_df["qvix"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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index_option_50etf_qvix_df = index_option_50etf_qvix()
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print(index_option_50etf_qvix_df)
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index_option_50etf_min_qvix_df = index_option_50etf_min_qvix()
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print(index_option_50etf_min_qvix_df)
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index_option_300etf_qvix_df = index_option_300etf_qvix()
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print(index_option_300etf_qvix_df)
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index_option_300etf_min_qvix_df = index_option_300etf_min_qvix()
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print(index_option_300etf_min_qvix_df)
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index_option_500etf_qvix_df = index_option_500etf_qvix()
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print(index_option_500etf_qvix_df)
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index_option_500etf_min_qvix_df = index_option_500etf_min_qvix()
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print(index_option_500etf_min_qvix_df)
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index_option_cyb_qvix_df = index_option_cyb_qvix()
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print(index_option_cyb_qvix_df)
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index_option_cyb_min_qvix_df = index_option_cyb_min_qvix()
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print(index_option_cyb_min_qvix_df)
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index_option_kcb_qvix_df = index_option_kcb_qvix()
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print(index_option_kcb_qvix_df)
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index_option_kcb_min_qvix_df = index_option_kcb_min_qvix()
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print(index_option_kcb_min_qvix_df)
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index_option_100etf_qvix_df = index_option_100etf_qvix()
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print(index_option_100etf_qvix_df)
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index_option_100etf_min_qvix_df = index_option_100etf_min_qvix()
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print(index_option_100etf_min_qvix_df)
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index_option_300index_qvix_df = index_option_300index_qvix()
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print(index_option_300index_qvix_df)
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index_option_300index_min_qvix_df = index_option_300index_min_qvix()
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print(index_option_300index_min_qvix_df)
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index_option_1000index_qvix_df = index_option_1000index_qvix()
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print(index_option_1000index_qvix_df)
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index_option_1000index_min_qvix_df = index_option_1000index_min_qvix()
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print(index_option_1000index_min_qvix_df)
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index_option_50index_qvix_df = index_option_50index_qvix()
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print(index_option_50index_qvix_df)
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index_option_50index_min_qvix_df = index_option_50index_min_qvix()
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print(index_option_50index_min_qvix_df)
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