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102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
"""Factors model"""
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__docformat__ = "numpy"
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from urllib.request import urlopen
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from zipfile import ZipFile
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from io import BytesIO
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from typing import Tuple
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import statsmodels.api as sm
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import yfinance as yf
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import pandas as pd
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def get_fama_raw() -> 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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fama : pd.DataFrame
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A data with fama french model information
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"""
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with urlopen(
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"http://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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return df
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def get_historical_5(ticker: str):
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"""Get 5 year monthly historical performance for a ticker with dividends filtered
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Parameters
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----------
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ticker : str
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A ticker in string form
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Returns
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----------
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data : pd.DataFrame
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A dataframe with historical information
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"""
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tick = yf.Ticker(ticker)
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df = tick.history(period="5y", interval="1mo")
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df = df[df.index.to_series().apply(lambda x: x.day == 1)]
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df = df.drop(["Dividends", "Stock Splits"], axis=1)
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df = df.dropna()
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return df
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def capm_information(ticker) -> Tuple[float, float]:
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"""Provides information that relates to the CAPM model
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Parameters
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----------
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ticker : str
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A ticker in string form
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Returns
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----------
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beta : float
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The beta for a stock
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sys : float
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The systematic risk for a stock
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"""
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df_f = get_fama_raw()
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df_h = get_historical_5(ticker)
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df = df_h.join(df_f)
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df = df.dropna()
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df["Monthly Return"] = df["Close"].pct_change()
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df["Excess Monthly Return"] = df["Monthly Return"] - df["RF"]
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df["Excess MKT-RF"] = df["MKT-RF"] - df["RF"]
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df = df.dropna()
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y = df[["Excess Monthly Return"]]
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x = df["Excess MKT-RF"]
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x = sm.add_constant(x)
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model = sm.OLS(y, x).fit()
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beta = model.params["Excess MKT-RF"]
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sy = model.rsquared
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return beta, sy
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