123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
from __future__ import annotations
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import io
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import warnings
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import zipfile
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from pathlib import Path
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from urllib.error import URLError
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from urllib.request import Request, urlopen
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import pandas as pd
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KEN_FRENCH_DAILY_FF5_ZIP_URL = (
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"https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/"
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"F-F_Research_Data_5_Factors_2x3_daily_CSV.zip"
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)
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EXPECTED_FACTOR_COLUMNS = ["MKT_RF", "SMB", "HML", "RMW", "CMA", "RF"]
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class ExternalFactorFormatError(ValueError):
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pass
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def _download_kf_zip_bytes() -> bytes:
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request = Request(
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KEN_FRENCH_DAILY_FF5_ZIP_URL,
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headers={"User-Agent": "quant-factor-attribution/0.1"},
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)
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with urlopen(request, timeout=30) as response:
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return response.read()
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def _parse_kf_daily_csv(raw_bytes: bytes) -> pd.DataFrame:
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with zipfile.ZipFile(io.BytesIO(raw_bytes)) as archive:
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member_names = [
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name
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for name in archive.namelist()
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if not name.endswith("/") and name.lower().endswith((".csv", ".txt"))
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]
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if not member_names:
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raise ExternalFactorFormatError("Ken French archive did not contain a CSV or TXT file")
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try:
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text = archive.read(member_names[0]).decode("utf-8-sig")
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except UnicodeDecodeError as exc:
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raise ExternalFactorFormatError("Ken French factor file was not valid UTF-8 text") from exc
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lines = [line for line in text.splitlines() if line.strip()]
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try:
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header_index = next(i for i, line in enumerate(lines) if "Mkt-RF" in line)
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except StopIteration as exc:
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raise ExternalFactorFormatError("Ken French factor file was missing the daily factor header") from exc
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table = "\n".join(lines[header_index:])
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try:
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factors = pd.read_csv(io.StringIO(table))
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except pd.errors.ParserError as exc:
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raise ExternalFactorFormatError("Ken French factor table could not be parsed") from exc
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factors = factors.rename(columns={"Mkt-RF": "MKT_RF"})
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date_column = factors.columns[0]
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missing_columns = [column for column in EXPECTED_FACTOR_COLUMNS if column not in factors.columns]
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if missing_columns:
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raise ExternalFactorFormatError(
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f"Ken French factor table was missing columns: {', '.join(missing_columns)}"
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)
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factors = factors[factors[date_column].astype(str).str.fullmatch(r"\d{8}")]
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if factors.empty:
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raise ExternalFactorFormatError("Ken French factor table did not contain daily rows")
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try:
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factors[date_column] = pd.to_datetime(factors[date_column], format="%Y%m%d")
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except ValueError as exc:
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raise ExternalFactorFormatError("Ken French factor table contained invalid dates") from exc
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factors = factors.set_index(date_column)
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factors.index.name = None
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try:
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factors = factors[EXPECTED_FACTOR_COLUMNS].astype(float) / 100.0
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except ValueError as exc:
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raise ExternalFactorFormatError("Ken French factor table contained non-numeric values") from exc
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return factors
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def _warn_and_load_cached_factors(cache_path: Path, reason: str) -> pd.DataFrame:
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warnings.warn(
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f"Using cached data from {cache_path} because {reason}.",
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UserWarning,
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stacklevel=2,
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)
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return pd.read_csv(cache_path, index_col=0, parse_dates=True)
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def load_external_us_factors(cache_dir: Path | str = "data/factors") -> pd.DataFrame:
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cache_path = Path(cache_dir) / "ff5_us_daily.csv"
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cache_path.parent.mkdir(parents=True, exist_ok=True)
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try:
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raw_bytes = _download_kf_zip_bytes()
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except (URLError, TimeoutError, ConnectionError) as exc:
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if cache_path.exists():
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return _warn_and_load_cached_factors(cache_path, f"download failed: {exc}")
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raise
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try:
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factors = _parse_kf_daily_csv(raw_bytes)
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except zipfile.BadZipFile as exc:
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if cache_path.exists():
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return _warn_and_load_cached_factors(cache_path, f"the upstream ZIP was invalid: {exc}")
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raise
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except ExternalFactorFormatError as exc:
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if cache_path.exists():
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return _warn_and_load_cached_factors(
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cache_path,
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f"the upstream factor format was invalid: {exc}",
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)
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raise
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factors.to_csv(cache_path)
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return factors
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