mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-06 17:31:53 +00:00
6dea3a931a
* Fixes * Added better handling * Adds more handling * Adds more handling * Added screener handling * Added fixes * Added fixes
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
"""Beta model"""
|
|
__docformat__ = "numpy"
|
|
|
|
from typing import Tuple
|
|
|
|
import pandas as pd
|
|
from scipy import stats
|
|
from openbb_terminal.stocks import stocks_helper
|
|
|
|
|
|
def beta_model(
|
|
symbol: str,
|
|
ref_symbol: str,
|
|
data: pd.DataFrame = None,
|
|
ref_data: pd.DataFrame = None,
|
|
interval: int = 1440,
|
|
) -> Tuple[pd.Series, pd.Series, float, float]:
|
|
"""Calculate beta for a ticker and a reference ticker.
|
|
|
|
Parameters
|
|
----------
|
|
symbol: str
|
|
A ticker to calculate beta for
|
|
ref_symbol: str
|
|
A reference ticker symbol for the beta calculation (default in terminal is SPY)
|
|
data: pd.DataFrame
|
|
The selected ticker symbols price data
|
|
ref_data: pd.DataFrame
|
|
The reference ticker symbols price data
|
|
interval: int
|
|
The interval of the ref_data. This will ONLY be used if ref_data is None
|
|
|
|
Returns
|
|
-------
|
|
sr: pd.Series
|
|
Stock ticker symbols close-to-close returns
|
|
rr: pd.Series
|
|
Reference ticker symbols close-to-close returns
|
|
beta: float
|
|
alpha: float
|
|
"""
|
|
if data is None:
|
|
data = stocks_helper.load(symbol)
|
|
else:
|
|
# TODO: When loaded in the stocks menu, the stock df columns are all
|
|
# lowercase but when loaded via stocks_helper.load(ticker) they start
|
|
# with an uppercase char. This should be consistent.
|
|
data = data.rename({"close": "Close"}, axis=1)
|
|
if ref_data is None:
|
|
ref_data = stocks_helper.load(ref_symbol, interval=interval)
|
|
if ref_data.empty:
|
|
raise Exception("Invalid ref_symbol ticker")
|
|
else:
|
|
ref_data = ref_data.rename({"close": "Close"}, axis=1)
|
|
|
|
# join returns
|
|
sr = 100 * data["Close"].pct_change().to_frame()
|
|
sr = sr.rename({"Close": "Stock Pct Ret"}, axis=1)
|
|
rr = 100 * ref_data["Close"].pct_change().to_frame()
|
|
rr = rr.rename({"Close": "Ref Pct Ret"}, axis=1)
|
|
df = sr.merge(rr, how="outer", left_index=True, right_index=True)
|
|
df = df.dropna()
|
|
sr = df["Stock Pct Ret"].tolist()
|
|
rr = df["Ref Pct Ret"].tolist()
|
|
|
|
# compute lin reg
|
|
if not rr or not sr:
|
|
return pd.Series(dtype="object"), pd.Series(dtype="object"), 0.0, 0.0
|
|
model = stats.linregress(rr, sr)
|
|
beta = model.slope
|
|
alpha = model.intercept
|
|
|
|
return sr, rr, beta, alpha
|