Measuring Reward and Risk of an Investment

Let’s load the msft.csv dataframe and make it chart:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:.4f}'.format
plt.style.use("seaborn")

msft = pd.read_csv("msft.csv", index_col = "Date", parse_dates = ["Date"])
msft

msft.Price.plot(figsize = (15, 8), fontsize = 13)
plt.legend(fontsize = 13)
plt.show()

we can get some summary statistics by using this commnad:

msft.describe()

Mean return is the performance metric for the reward so we have daily mean return of 11 percent. The standard deviation of returns is the metric for the risk (volatility) which is 1.72

Calculate daily returns for Bitcoin

import pandas as pd
pd.options.display.float_format = '{:.4f}'.format

close = pd.read_csv("close.csv", index_col = "Date", parse_dates = ["Date"])
btc = close["BTC-USD"].dropna().to_frame().copy()

Calculate the arithmetic mean return and the standard deviation of returns for Bitcoin.

btc["Returns"] = btc.pct_change(periods = 1) * 100
print(btc)
mu = btc.Returns.mean() 
sigma = btc.Returns.std()

print(mu)
print(sigma)

Compare Bitcoin with Microsoft (mu = 0.116, sigma = 1.726). Bitcoin is higher risk and reward.