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
167 lines
4.5 KiB
Python
167 lines
4.5 KiB
Python
#!/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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