stock-tracker
This commit is contained in:
C菌
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2019/11/12 14:51
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Desc:
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"""
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@@ -0,0 +1,12 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2019/11/14 20:32
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Desc: 学术板块配置文件
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"""
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# EPU
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epu_home_url = "http://www.policyuncertainty.com/index.html"
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# FF-Factor
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ff_home_url = "http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html"
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@@ -0,0 +1,60 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/20 22:00
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Desc: 经济政策不确定性指数
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https://www.policyuncertainty.com/index.html
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"""
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import pandas as pd
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def article_epu_index(symbol: str = "China") -> pd.DataFrame:
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"""
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经济政策不确定性指数
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https://www.policyuncertainty.com/index.html
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:param symbol: 指定的国家名称, e.g. “China”
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:type symbol: str
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:return: 经济政策不确定性指数数据
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:rtype: pandas.DataFrame
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"""
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# 切勿修改 http 否则会读取不到 csv 文件
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if symbol == "China New":
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symbol = "SCMP_China"
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if symbol == "China":
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symbol = "SCMP_China"
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if symbol == "USA":
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symbol = "US"
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if symbol == "Hong Kong":
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symbol = "HK"
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epu_df = pd.read_excel(
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io=f"http://www.policyuncertainty.com/media/{symbol}_EPU_Data_Annotated.xlsx",
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engine="openpyxl",
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)
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return epu_df
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if symbol in ["Germany", "France", "Italy"]: # 欧洲
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symbol = "Europe"
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if symbol == "South Korea":
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symbol = "Korea"
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if symbol == "Spain New":
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symbol = "Spain"
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if symbol in ["Ireland", "Chile", "Colombia", "Netherlands", "Singapore", "Sweden"]:
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epu_df = pd.read_excel(
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io=f"http://www.policyuncertainty.com/media/{symbol}_Policy_Uncertainty_Data.xlsx",
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engine="openpyxl",
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)
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return epu_df
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if symbol == "Greece":
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epu_df = pd.read_excel(
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io=f"http://www.policyuncertainty.com/media/FKT_{symbol}_Policy_Uncertainty_Data.xlsx",
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engine="openpyxl",
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)
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return epu_df
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url = f"http://www.policyuncertainty.com/media/{symbol}_Policy_Uncertainty_Data.csv"
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epu_df = pd.read_csv(url)
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return epu_df
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if __name__ == "__main__":
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article_epu_index_df = article_epu_index(symbol="China")
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print(article_epu_index_df)
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@@ -0,0 +1,166 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/20 22:30
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Desc: FF-data-library
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https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
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"""
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from io import StringIO
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import pandas as pd
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import requests
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from akshare.article.cons import ff_home_url
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def article_ff_crr() -> pd.DataFrame:
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"""
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FF多因子模型
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https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
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:return: FF多因子模型单一表格
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:rtype: pandas.DataFrame
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"""
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res = requests.get(ff_home_url)
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# first table
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list_index = (
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pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[2, :]
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.index.tolist()
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)
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list_0 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[0, :]
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.iloc[0]
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.split(" ")
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if item != ""
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]
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list_1 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[0, :]
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.iloc[1]
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.split(" ")
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if item != ""
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]
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list_2 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[0, :]
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.iloc[2]
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.split(" ")
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if item != ""
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]
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list_0.insert(0, "-")
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list_1.insert(0, "-")
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list_2.insert(0, "-")
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temp_columns = (
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pd.read_html(StringIO(res.text), header=0)[4]
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.iloc[:, 0]
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.str.split(" ", expand=True)
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.T[0]
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.dropna()
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.tolist()
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)
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table_one = pd.DataFrame(
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[list_0, list_1, list_2], index=list_index, columns=temp_columns
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).T
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# second table
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list_index = (
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pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[1, :]
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.index.tolist()
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)
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list_0 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[1, :]
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.iloc[0]
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.split(" ")
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if item != ""
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]
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list_1 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[1, :]
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.iloc[1]
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.split(" ")
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if item != ""
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]
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list_2 = [
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item
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for item in pd.read_html(StringIO(res.text), header=0, index_col=0)[4]
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.iloc[1, :]
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.iloc[2]
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.split(" ")
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if item != ""
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]
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list_0.insert(0, "-")
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list_1.insert(0, "-")
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list_2.insert(0, "-")
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temp_columns = (
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pd.read_html(StringIO(res.text), header=0)[4]
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.iloc[:, 0]
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.str.split(" ", expand=True)
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.T[1]
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.dropna()
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.tolist()
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)
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table_two = pd.DataFrame(
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[list_0, list_1, list_2], index=list_index, columns=temp_columns
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).T
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# third table
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df = pd.read_html(StringIO(res.text), header=0, index_col=0)[4].iloc[2, :]
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name_list = (
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pd.read_html(StringIO(res.text), header=0)[4]
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.iloc[:, 0]
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.str.split(r" ", expand=True)
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.iloc[2, :]
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.tolist()
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)
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value_list_0 = df.iloc[0].split(" ")
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value_list_0.insert(0, "-")
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value_list_0.insert(1, "-")
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value_list_0.insert(8, "-")
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value_list_0.insert(15, "-")
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value_list_1 = df.iloc[1].split(" ")
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value_list_1.insert(0, "-")
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value_list_1.insert(1, "-")
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value_list_1.insert(8, "-")
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value_list_1.insert(15, "-")
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value_list_2 = df.iloc[2].split(" ")
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value_list_2.insert(0, "-")
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value_list_2.insert(1, "-")
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value_list_2.insert(8, "-")
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value_list_2.insert(15, "-")
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name_list.remove("Small Growth Big Value")
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name_list.insert(5, "Small Growth")
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name_list.insert(6, "Big Value")
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temp_list = [item for item in name_list if "Portfolios" not in item]
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temp_list.insert(0, "Fama/French Research Portfolios")
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temp_list.insert(1, "Size and Book-to-Market Portfolios")
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temp_list.insert(8, "Size and Operating Profitability Portfolios")
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temp_list.insert(15, "Size and Investment Portfolios")
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temp_df = pd.DataFrame([temp_list, value_list_0, value_list_1, value_list_2]).T
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temp_df.index = temp_df.iloc[:, 0]
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temp_df = temp_df.iloc[:, 1:]
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# concat
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all_df = pd.DataFrame()
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all_df = pd.concat([all_df, table_one])
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all_df = pd.concat([all_df, table_two])
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temp_df.columns = table_two.columns
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all_df = pd.concat([all_df, temp_df])
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all_df.reset_index(inplace=True)
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all_df.rename(columns={"index": "item"}, inplace=True)
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return all_df
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if __name__ == "__main__":
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article_ff_crr_df = article_ff_crr()
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print(article_ff_crr_df)
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@@ -0,0 +1,46 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2020/4/10 19:58
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Desc: Economic Research from Federal Reserve Bank of St. Louis
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https://research.stlouisfed.org/econ/mccracken/fred-databases/
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FRED-MD and FRED-QD are large macroeconomic databases designed for the empirical analysis of “big data.” The datasets of monthly and quarterly observations mimic the coverage of datasets already used in the literature, but they add three appealing features. They are updated in real-time through the FRED database. They are publicly accessible, facilitating the replication of empirical work. And they relieve the researcher of the task of incorporating data changes and revisions (a task accomplished by the data desk at the Federal Reserve Bank of St. Louis).
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"""
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import pandas as pd
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def fred_md(date: str = "2020-01") -> pd.DataFrame:
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"""
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The accompanying paper shows that factors extracted from the FRED-MD dataset share the same predictive content as those based on the various vintages of the so-called Stock-Watson data. In addition, it suggests that diffusion indexes constructed as the partial sum of the factor estimates can potentially be useful for the study of business cycle chronology.
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:param date: e.g., "2020-03"; from "2015-01" to now
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:type date: str
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:return: Monthly Data
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:rtype: pandas.DataFrame
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"""
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url = (
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f"https://s3.amazonaws.com/files.fred.stlouisfed.org/fred-md/monthly/{date}.csv"
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)
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temp_df = pd.read_csv(url)
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return temp_df
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def fred_qd(date: str = "2020-01") -> pd.DataFrame:
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"""
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FRED-QD is a quarterly frequency companion to FRED-MD. It is designed to emulate the dataset used in "Disentangling the Channels of the 2007-2009 Recession" by Stock and Watson (2012, NBER WP No. 18094) but also contains several additional series. Comments or suggestions are welcome.
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:param date: e.g., "2020-03"; from "2015-01" to now
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:type date: str
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:return: Quarterly Data
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:rtype: pandas.DataFrame
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"""
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url = f"https://s3.amazonaws.com/files.fred.stlouisfed.org/fred-md/quarterly/{date}.csv"
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temp_df = pd.read_csv(url)
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return temp_df
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if __name__ == "__main__":
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fred_md_df = fred_md(date="2023-03")
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print(fred_md_df)
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fred_qd_df = fred_qd(date="2023-03")
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print(fred_qd_df)
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@@ -0,0 +1,191 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/20 20:51
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Desc: 修大成主页-Risk Lab-Realized Volatility; Oxford-Man Institute of Quantitative Finance Realized Library
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"""
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import json
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import pandas as pd
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import requests
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import urllib3
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from bs4 import BeautifulSoup
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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|
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def article_oman_rv(symbol: str = "FTSE", index: str = "rk_th2") -> pd.DataFrame:
|
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"""
|
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Oxford-Man Institute of Quantitative Finance Realized Library 的数据
|
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:param symbol: str ['AEX', 'AORD', 'BFX', 'BSESN', 'BVLG', 'BVSP', 'DJI', 'FCHI', 'FTMIB', 'FTSE', 'GDAXI', 'GSPTSE', 'HSI', 'IBEX', 'IXIC', 'KS11', 'KSE', 'MXX', 'N225', 'NSEI', 'OMXC20', 'OMXHPI', 'OMXSPI', 'OSEAX', 'RUT', 'SMSI', 'SPX', 'SSEC', 'SSMI', 'STI', 'STOXX50E']
|
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:param index: str 指标 ['medrv', 'rk_twoscale', 'bv', 'rv10', 'rv5', 'rk_th2', 'rv10_ss', 'rsv', 'rv5_ss', 'bv_ss', 'rk_parzen', 'rsv_ss']
|
||||
:return: pandas.DataFrame
|
||||
|
||||
The Oxford-Man Institute's "realised library" contains daily non-parametric measures of how volatility financial assets or indexes were in the past. Each day's volatility measure depends solely on financial data from that day. They are driven by the use of the latest innovations in econometric modelling and theory to design them, while we draw our high frequency data from the Thomson Reuters DataScope Tick History database. Realised measures are not volatility forecasts. However, some researchers use these measures as an input into forecasting models. The aim of this line of research is to make financial markets more transparent by exposing how volatility changes through time.
|
||||
|
||||
This Library is used as the basis of some of our own research, which effects its scope, and is made available here to encourage the more widespread exploitation of these methods. It is given 'as is' and solely for informational purposes, please read the disclaimer.
|
||||
|
||||
The volatility data can be visually explored. We make the complete up-to-date dataset available for download. Lists of assets covered and realized measures available are also available.
|
||||
| Symbol | Name | Earliest Available | Latest Available |
|
||||
|-----------|-------------------------------------------|--------------------|-------------------|
|
||||
| .AEX | AEX index | January 03, 2000 | November 28, 2019 |
|
||||
| .AORD | All Ordinaries | January 04, 2000 | November 28, 2019 |
|
||||
| .BFX | Bell 20 Index | January 03, 2000 | November 28, 2019 |
|
||||
| .BSESN | S&P BSE Sensex | January 03, 2000 | November 28, 2019 |
|
||||
| .BVLG | PSI All-Share Index | October 15, 2012 | November 28, 2019 |
|
||||
| .BVSP | BVSP BOVESPA Index | January 03, 2000 | November 28, 2019 |
|
||||
| .DJI | Dow Jones Industrial Average | January 03, 2000 | November 27, 2019 |
|
||||
| .FCHI | CAC 40 | January 03, 2000 | November 28, 2019 |
|
||||
| .FTMIB | FTSE MIB | June 01, 2009 | November 28, 2019 |
|
||||
| .FTSE | FTSE 100 | January 04, 2000 | November 28, 2019 |
|
||||
| .GDAXI | DAX | January 03, 2000 | November 28, 2019 |
|
||||
| .GSPTSE | S&P/TSX Composite index | May 02, 2002 | November 28, 2019 |
|
||||
| .HSI | HANG SENG Index | January 03, 2000 | November 28, 2019 |
|
||||
| .IBEX | IBEX 35 Index | January 03, 2000 | November 28, 2019 |
|
||||
| .IXIC | Nasdaq 100 | January 03, 2000 | November 27, 2019 |
|
||||
| .KS11 | Korea Composite Stock Price Index (KOSPI) | January 04, 2000 | November 28, 2019 |
|
||||
| .KSE | Karachi SE 100 Index | January 03, 2000 | November 28, 2019 |
|
||||
| .MXX | IPC Mexico | January 03, 2000 | November 28, 2019 |
|
||||
| .N225 | Nikkei 225 | February 02, 2000 | November 28, 2019 |
|
||||
| .NSEI | NIFTY 50 | January 03, 2000 | November 28, 2019 |
|
||||
| .OMXC20 | OMX Copenhagen 20 Index | October 03, 2005 | November 28, 2019 |
|
||||
| .OMXHPI | OMX Helsinki All Share Index | October 03, 2005 | November 28, 2019 |
|
||||
| .OMXSPI | OMX Stockholm All Share Index | October 03, 2005 | November 28, 2019 |
|
||||
| .OSEAX | Oslo Exchange All-share Index | September 03, 2001 | November 28, 2019 |
|
||||
| .RUT | Russel 2000 | January 03, 2000 | November 27, 2019 |
|
||||
| .SMSI | Madrid General Index | July 04, 2005 | November 28, 2019 |
|
||||
| .SPX | S&P 500 Index | January 03, 2000 | November 27, 2019 |
|
||||
| .SSEC | Shanghai Composite Index | January 04, 2000 | November 28, 2019 |
|
||||
| .SSMI | Swiss Stock Market Index | January 04, 2000 | November 28, 2019 |
|
||||
| .STI | Straits Times Index | January 03, 2000 | November 28, 2019 |
|
||||
| .STOXX50E | EURO STOXX 50 | January 03, 2000 | November 28, 2019 |
|
||||
"""
|
||||
url = "https://realized.oxford-man.ox.ac.uk/theme/js/visualization-data.js?20191111113154"
|
||||
res = requests.get(url)
|
||||
soup = BeautifulSoup(res.text, "lxml")
|
||||
soup_text = soup.find("p").get_text()
|
||||
data_json = json.loads(soup_text[soup_text.find("{") : soup_text.rfind("};") + 1])
|
||||
date_list = data_json[f".{symbol}"]["dates"]
|
||||
temp_df = pd.DataFrame([date_list, data_json[f".{symbol}"][index]["data"]]).T
|
||||
temp_df.index = pd.to_datetime(temp_df.iloc[:, 0], unit="ms")
|
||||
temp_df = temp_df.iloc[:, 1]
|
||||
temp_df.index.name = "date"
|
||||
temp_df.name = f"{symbol}-{index}"
|
||||
return temp_df
|
||||
|
||||
|
||||
def article_oman_rv_short(symbol: str = "FTSE") -> pd.DataFrame:
|
||||
"""
|
||||
Oxford-Man Institute of Quantitative Finance Realized Library 的数据
|
||||
:param symbol: str FTSE: FTSE 100, GDAXI: DAX, RUT: Russel 2000, SPX: S&P 500 Index, STOXX50E: EURO STOXX 50, SSEC: Shanghai Composite Index, N225: Nikkei 225
|
||||
:return: pandas.DataFrame
|
||||
|
||||
The Oxford-Man Institute's "realised library" contains daily non-parametric measures of how volatility financial assets or indexes were in the past. Each day's volatility measure depends solely on financial data from that day. They are driven by the use of the latest innovations in econometric modelling and theory to design them, while we draw our high frequency data from the Thomson Reuters DataScope Tick History database. Realised measures are not volatility forecasts. However, some researchers use these measures as an input into forecasting models. The aim of this line of research is to make financial markets more transparent by exposing how volatility changes through time.
|
||||
|
||||
This Library is used as the basis of some of our own research, which effects its scope, and is made available here to encourage the more widespread exploitation of these methods. It is given 'as is' and solely for informational purposes, please read the disclaimer.
|
||||
|
||||
The volatility data can be visually explored. We make the complete up-to-date dataset available for download. Lists of assets covered and realized measures available are also available.
|
||||
"""
|
||||
url = "https://realized.oxford-man.ox.ac.uk/theme/js/front-page-chart.js"
|
||||
headers = {
|
||||
"Accept": "*/*",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Host": "realized.oxford-man.ox.ac.uk",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "https://realized.oxford-man.ox.ac.uk/?from=groupmessage&isappinstalled=0",
|
||||
"Sec-Fetch-Mode": "no-cors",
|
||||
"Sec-Fetch-Site": "same-origin",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36",
|
||||
}
|
||||
|
||||
res = requests.get(url, headers=headers, verify=False)
|
||||
soup = BeautifulSoup(res.text, "lxml")
|
||||
soup_text = soup.find("p").get_text()
|
||||
data_json = json.loads(soup_text[soup_text.find("{") : soup_text.rfind("}") + 1])
|
||||
temp_df = pd.DataFrame(data_json[f".{symbol}"]["data"])
|
||||
temp_df.index = pd.to_datetime(temp_df.iloc[:, 0], unit="ms")
|
||||
temp_df = temp_df.iloc[:, 1]
|
||||
temp_df.index.name = "date"
|
||||
temp_df.name = f"{symbol}"
|
||||
return temp_df
|
||||
|
||||
|
||||
def article_rlab_rv(symbol: str = "39693") -> pd.DataFrame:
|
||||
"""
|
||||
修大成主页-Risk Lab-Realized Volatility
|
||||
:param symbol: str 股票代码
|
||||
:return: pandas.DataFrame
|
||||
1996-01-02 0.000000
|
||||
1996-01-04 0.000000
|
||||
1996-01-05 0.000000
|
||||
1996-01-09 0.000000
|
||||
1996-01-10 0.000000
|
||||
...
|
||||
2019-11-04 0.175107
|
||||
2019-11-05 0.185112
|
||||
2019-11-06 0.210373
|
||||
2019-11-07 0.240808
|
||||
2019-11-08 0.199549
|
||||
Name: RV, Length: 5810, dtype: float64
|
||||
|
||||
Website
|
||||
https://dachxiu.chicagobooth.edu/
|
||||
|
||||
Objective
|
||||
We provide up-to-date daily annualized realized volatilities for individual stocks, ETFs, and future contracts, which are estimated from high-frequency data. We are in the process of incorporating equities from global markets.
|
||||
|
||||
Data
|
||||
We collect trades at their highest frequencies available (up to every millisecond for US equities after 2007), and clean them using the prevalent national best bid and offer (NBBO) that are available up to every second. The mid-quotes are calculated based on the NBBOs, so their highest sampling frequencies are also up to every second.
|
||||
|
||||
Methodology
|
||||
We provide quasi-maximum likelihood estimates of volatility (QMLE) based on moving-average models MA(q), using non-zero returns of transaction prices (or mid-quotes if available) sampled up to their highest frequency available, for days with at least 12 observations. We select the best model (q) using Akaike Information Criterion (AIC). For comparison, we report realized volatility (RV) estimates using 5-minute and 15-minute subsampled returns.
|
||||
|
||||
References
|
||||
1. “When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility”, by Rui Da and Dacheng Xiu. 2017.
|
||||
2. “Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data”, by Dacheng Xiu. Journal of Econometrics, 159 (2010), 235-250.
|
||||
3. “How Often to Sample A Continuous-time Process in the Presence of Market Microstructure Noise”, by Yacine Aït-Sahalia, Per Mykland, and Lan Zhang. Review of Financial Studies, 18 (2005), 351–416.
|
||||
4. “The Distribution of Exchange Rate Volatility”, by Torben Andersen, Tim Bollerslev, Francis X. Diebold, and Paul Labys. Journal of the American Statistical Association, 96 (2001), 42-55.
|
||||
5. “Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models”, by Ole E Barndorff‐Nielsen and Neil Shephard. Journal of the Royal Statistical Society: Series B, 64 (2002), 253-280.
|
||||
"""
|
||||
print("由于服务器在国外, 请稍后, 如果访问失败, 请使用代理工具")
|
||||
url = "https://dachxiu.chicagobooth.edu/data.php"
|
||||
payload = {"ticker": symbol}
|
||||
res = requests.get(url, params=payload, verify=False)
|
||||
soup = BeautifulSoup(res.text, "lxml")
|
||||
title_fore = (
|
||||
pd.DataFrame(soup.find("p").get_text().split(symbol)).iloc[0, 0].strip()
|
||||
)
|
||||
title_list = (
|
||||
pd.DataFrame(soup.find("p").get_text().split(symbol))
|
||||
.iloc[1, 0]
|
||||
.strip()
|
||||
.split("\n")
|
||||
)
|
||||
title_list.insert(0, title_fore)
|
||||
temp_df = pd.DataFrame(soup.find("p").get_text().split(symbol)).iloc[2:, :]
|
||||
temp_df = temp_df.iloc[:, 0].str.split(" ", expand=True)
|
||||
temp_df = temp_df.iloc[:, 1:]
|
||||
temp_df.iloc[:, -1] = temp_df.iloc[:, -1].str.replace(r"\n", "")
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df.index = pd.to_datetime(temp_df.iloc[:, 1], format="%Y%m%d", errors="coerce")
|
||||
temp_df = temp_df.iloc[:, 1:]
|
||||
data_se = temp_df.iloc[:, 1]
|
||||
data_se.name = "RV"
|
||||
temp_df = data_se.astype("float", errors="ignore")
|
||||
temp_df.index.name = "date"
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
article_rlab_rv_df = article_rlab_rv(symbol="39693")
|
||||
print(article_rlab_rv_df)
|
||||
|
||||
article_oman_rv_short_df = article_oman_rv_short(symbol="FTSE")
|
||||
print(article_oman_rv_short_df)
|
||||
|
||||
article_oman_rv_df = article_oman_rv(symbol="FTSE", index="rk_th2")
|
||||
print(article_oman_rv_df)
|
||||
Reference in New Issue
Block a user