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ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
"""Beta model"""
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__docformat__ = "numpy"
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from typing import Tuple
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import pandas as pd
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from scipy import stats
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from openbb_terminal.stocks import stocks_helper
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def beta_model(
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symbol: str,
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ref_symbol: str,
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data: pd.DataFrame = None,
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ref_data: pd.DataFrame = None,
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interval: int = 1440,
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) -> Tuple[pd.Series, pd.Series, float, float]:
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"""Calculate beta for a ticker and a reference ticker.
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Parameters
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----------
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symbol: str
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A ticker to calculate beta for
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ref_symbol: str
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A reference ticker symbol for the beta calculation (default in terminal is SPY)
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data: pd.DataFrame
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The selected ticker symbols price data
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ref_data: pd.DataFrame
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The reference ticker symbols price data
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interval: int
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The interval of the ref_data. This will ONLY be used if ref_data is None
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Returns
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-------
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Tuple[pd.Series, pd.Series, float, float]
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Stock ticker symbols close-to-close returns, Reference ticker symbols close-to-close returns, beta, alpha
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"""
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if data is None:
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data = stocks_helper.load(symbol=symbol)
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else:
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# TODO: When loaded in the stocks menu, the stock df columns are all
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# lowercase but when loaded via stocks_helper.load(ticker) they start
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# with an uppercase char. This should be consistent.
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data = data.rename({"close": "Close"}, axis=1)
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if ref_data is None:
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ref_data = stocks_helper.load(
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symbol=ref_symbol,
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interval=interval,
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start_date=data.index[0],
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end_date=data.index[-1],
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verbose=False,
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)
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if ref_data.empty:
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raise Exception("Invalid ref_symbol ticker")
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else:
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ref_data = ref_data.rename({"close": "Close"}, axis=1)
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# join returns
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sr = 100 * data["Close"].pct_change().to_frame()
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sr = sr.rename({"Close": "Stock Pct Ret"}, axis=1)
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rr = 100 * ref_data["Close"].pct_change().to_frame()
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rr = rr.rename({"Close": "Ref Pct Ret"}, axis=1)
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df = sr.merge(rr, how="outer", left_index=True, right_index=True)
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df = df.dropna()
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sr = df["Stock Pct Ret"].tolist()
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rr = df["Ref Pct Ret"].tolist()
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# compute lin reg
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if not rr or not sr:
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return pd.Series(dtype="object"), pd.Series(dtype="object"), 0.0, 0.0
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model = stats.linregress(rr, sr)
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beta = model.slope
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alpha = model.intercept
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return sr, rr, beta, alpha
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