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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
"""Helper functions for the quantitative extension."""
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from pandas import DataFrame, Series
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_FAMA_FRENCH_FACTORS_URL = (
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"https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/"
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"F-F_Research_Data_Factors_CSV.zip"
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)
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def get_fama_raw(start_date: str, end_date: str) -> "DataFrame":
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"""Download the Fama-French research factors for a date range.
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Returns a DataFrame indexed by date with the excess market return ('mkt_rf'),
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size ('smb'), value ('hml'), and risk-free ('rf') factors as decimal fractions.
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"""
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from io import BytesIO
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from urllib.request import urlopen
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from zipfile import ZipFile
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from pandas import read_csv, to_datetime, to_numeric
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with (
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urlopen(_FAMA_FRENCH_FACTORS_URL) as response, # noqa: S310
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ZipFile(BytesIO(response.read())) as archive,
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archive.open("F-F_Research_Data_Factors.csv") as handle,
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):
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df = read_csv(
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handle,
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header=0,
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names=["date", "mkt_rf", "smb", "hml", "rf"],
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skiprows=3,
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)
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df = df[df["date"].apply(lambda value: len(str(value).strip()) == 6)]
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df["date"] = to_datetime(df["date"].astype(str) + "01", format="%Y%m%d")
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for column in ("mkt_rf", "smb", "hml", "rf"):
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df[column] = to_numeric(df[column], downcast="float") / 100
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df = df.set_index("date")
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if to_datetime(start_date) > df.index.max():
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raise ValueError(
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f"Start date '{start_date}' is after the last available Fama-French"
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f" observation '{df.index.max().date()}'."
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)
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return df.loc[start_date:end_date]
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def validate_window(input_data: "Series | DataFrame", window: int) -> None:
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"""Raise a ValueError when the rolling window exceeds the data length."""
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if window > len(input_data):
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raise ValueError(
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f"Window '{window}' is larger than the data length '{len(input_data)}'."
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)
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