I'm currently trying to gain market betas from tickers gained through yahoo finance datasreader. I was wondering if there is a way to calculate each stocks market beta, and put it in a dataframe?
This is what I have for my code so far:
import pandas_datareader.data as pdr
Tickers=['SBUX','TLRY']
SD='2005-01-31'
ED='2018-12-31'
TickerW=pdr.datareader(Tickers,'yahoo',SD,ED)
TickerW.head()
Okay, to make sure we're on the same page, we use the formula and definition of market beta from here: https://www.investopedia.com/terms/b/beta.asp
Beta = Covariance(Stock Returns, Market Returns) / Variance(Market Returns)
So first of all, we need the tickers for the market as well as the tickers for the stock. Which ticker you use here depends a lot on what market you want to compare against: Total stock market? Just the S&P 500? Maybe some other international equity index? There's no 100% right answer here, but a good way to pick is think about who the "movers" of your stock are, and what other stocks they hold. (Check out Damodaran's course on valuation, free on the interwebs if you google it).
So now your question becomes: How do I compute the covariance and variance of stock returns?
First, the pandas tickers have a bunch of information. The one we want is the "Adjusted Close". That's the daily closing price of the stock, retroactively adjusted for any "special" events like stock splits, reverse splits, and dividends. Because let's say a stock trades for $1000 a pop one day, but then undergoes a 1 for 2 stock split, so now instead of 1 share for $1000, you have 2 shares for $500 each. In a "raw" price chart, it would appear as if your stock just lost 50% value in a single day when in reality nothing happened. The Adjusted Close time series takes care of that to make sure that only "real" changes to the stock's value are reflected.
You can get that by calling prices = TickerW['Adj. Close'] or whatever key yahoo finance uses these days. By just looking at the TickerW dataframe you should be able to figure that out on your own :)
Next, we'd be changing prices into returns. That's just prices.shift(1) / prices (or maybe the other way round :D consult the documentation and try it out yourself). (Nerd note: Instead of these returns, it is mathematically more sound to use the logarithmic returns, because they have certain reasonable properties. If you want, throw a "log" around the returns.
Finally, we now have a series of returns (or log returns). One series for the stock returns, one for the market returns (e.g. from SPY, for the S&P 500). Now we just need to use them in the formula for beta.
Well, the way to go here is to do what I just did: Hit up google for "pandas covariance between two series" and that gets us to https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.cov.html
So basically, cov = stock_returns.cov(market_returns) and var = market_returns.var and then beta = cov / var.
I'd say that should be enough info to send you on your way. Good luck.