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990e0e7209
* Move TA extension code * Move QA extension code * Move charting extension code * Exclude js and html assets from pre-commit secrets search * Move static html and js code for the plotting infrastructure * Remove security exclude rule that's not available on the CI
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
"""Helper functions for QA"""
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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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import pandas as pd
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# ruff: ignore=S310
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def get_fama_raw(start_date: str, end_date: str) -> pd.DataFrame:
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"""Gets base Fama French data to calculate risk
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Returns
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-------
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pd.DataFrame
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A data with fama french model information
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"""
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with urlopen( # nosec
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"https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip"
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) as url:
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# Download Zipfile and create pandas DataFrame
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with ZipFile(BytesIO(url.read())) as zipfile:
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with zipfile.open("F-F_Research_Data_Factors.CSV") as zip_open:
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df = pd.read_csv(
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zip_open,
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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 x: len(str(x).strip()) == 6)]
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df["Date"] = df["Date"].astype(str) + "01"
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df["Date"] = pd.to_datetime(df["Date"], format="%Y%m%d")
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df["MKT-RF"] = pd.to_numeric(df["MKT-RF"], downcast="float")
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df["SMB"] = pd.to_numeric(df["SMB"], downcast="float")
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df["HML"] = pd.to_numeric(df["HML"], downcast="float")
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df["RF"] = pd.to_numeric(df["RF"], downcast="float")
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df["MKT-RF"] = df["MKT-RF"] / 100
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df["SMB"] = df["SMB"] / 100
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df["HML"] = df["HML"] / 100
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df["RF"] = df["RF"] / 100
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df = df.set_index("Date")
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df = df.loc[start_date:end_date] # type: ignore
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return df
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