Blogs

First, import libraries:

import pandas_datareader.data as pdr
import pandas as pd
import numpy as np

Get at least 3 different historical stock prices:

aapl = pdr.DataReader('AAPL', 'yahoo', start='2018-07-12', end='2022-06-30')
tsla = pdr.DataReader('TSLA', 'yahoo', start='2018-07-12', end='2022-06-30')
amzn = pdr.DataReader('AMZN', 'yahoo', start='2018-07-12', end='2022-06-30')

Combine prices into a single dataframe:

prices = pd.concat([aapl.Close, tsla.Close, amzn.Close],axis=1)
prices.columns = ['AAPL','TSLA','AMZN']

Calculate neccecary metrics:

dly_rets = prices.pct_change()
riskfree_r = 0.04
excess_ret = dly_rets - (riskfree_r / 252)
avg_excess_rets = excess_ret.mean() * 252
cov_ret = excess_ret.cov() * 252

Calculate Kelly and other metrics:

kelly = np.linalg.inv(cov_ret) @ avg_excess_rets  
cagr_max = (np.dot(kelly.T, np.dot(cov_ret, kelly)) / 2) + riskfree_r
sharpe = np.sqrt(np.dot(kelly.T, np.dot(cov_ret,kelly)))

The result looks like this:

Kelly Optimum Leverages: [2.6, 1.6, -2.5]
Maximum Compouned Annual Growth Rate: 0.97
Sharpe Ratio: 1.36

Kelly Optimumum Leverages for Multiple Stock Porfolio