这是对我 股票Beta的理解误区 的补充
Anaconda installation Jupyter notebook
# *********************
import yfinance as yf # pip install yfinance
import pandas as pd
import datetime
import dateutil
import xlwings as xw
# **********************
def get_adj_close_data(
tickers: list[str],
start_date: datetime.date, end_date: datetime.date,
interval: str='1d'
) -> pd.DataFrame:
return yf.download(tickers=tickers, start=start_date, end=end_date, interval=interval)['Close']
def get_monthly_return_data(
tickers: list[str],
start_date: datetime.date, end_date: datetime.date
) -> pd.DataFrame:
p = get_adj_close_data(tickers=tickers, start_date=start_date, end_date=end_date)
# pick the last day's price of each month, not necessarily month end
p = p.groupby(p.index.to_period('M')).tail(1)
return (p/p.shift(1) - 1)[tickers]
def calc_beta_etc(monthly_return_data: pd.DataFrame, benchmark: str) -> pd.DataFrame:
"""
Input:
monthly_return_data: DataFrame with timestamp index and stock/index ticker columns
including the benchmark
benchmark: str of benchmark ticker
Output:
DataFrame with
"""
r_m = monthly_return_data[benchmark]
def calc_one_beta_etc(r_s, r_m):
"""
r_s: monthly stock returns
r_m: monthly benchmark returns
"""
rho = r_s.corr(r_m) # correlation
sigma_s = r_s.std() * (12**0.5) # annualized volatility
sigma_m = r_m.std() * (12**0.5) # annualized volatility
beta = rho * sigma_s / sigma_m
return pd.Series(
data = {'beta': beta,
'rho': rho,
'sigma_s': sigma_s,
'sigma_m': sigma_m,
'vol ratio': sigma_s/sigma_m,
}
)
return monthly_return_data.apply(lambda c: calc_one_beta_etc(r_s=c, r_m=r_m), axis=0).T
# *********************************
end_date = datetime.date(2025, 7, 31)
start_date = end_date - dateutil.relativedelta.relativedelta(years=5, days=15)
tickers = [
'AAPL',
'AMZN',
'GOOG',
'GOOGL',
'META',
'MSFT',
'NVDA',
'TSLA',
'T',
'^GSPC' # S&P500 Index, unfortunately the total return index isn't available from yfinance
]
monthly_return_data = get_monthly_return_data(tickers=tickers, start_date=start_date, end_date=end_date)
beta_etc = calc_beta_etc(monthly_return_data=monthly_return_data, benchmark='^GSPC')
# *********************************
wb = xw.Book()
ws1 = wb.sheets.add('monthly returns')
ws1.range('A1').options(index=True, header=True).value = monthly_return_data
ws2 = wb.sheets.add('beta etc')
ws2.range('A1').options(index=True, header=True).value = beta_etc
ws2.range('B:C,F:F').number_format = '0.0000'
ws2.range('D:E').number_format = '0.0000%'
