In programming, you will often come across situations where you will need to evaluate multiple conditions. For example, you may want to perform an action when the price of a stock is above 10 but below 12. Logical operators come into play in such situations. These logical operators allow a program to make a decision based on multiple conditions.

There are three logical operators: and, or and not.

and (&)

This is called the logical 'and' operator. The logical 'and' operator (&) returns the boolean value true if both operands are true and returns false otherwise. The following examples illustrate this:

#Assign values to Stock A and Stock B
stock_A = 10
stock_B = 12
#Test if Stock A is equal to 10 and Stock B is equal to 12.
(stock_A==10) & (stock_B==12)

This will return True if both the conditions evaluate to True. The result will be True as shown below:

> (stock_A==10) & (stock_B==12)
[1] True

However, how will this work if we apply this to Numpy arrays.

# The following are two arrays containing returns from two stocks over the past five days.
stock_A = np.array([10, 8, 9, 11, 12])
stock_B = np.array([8,11,10,10,12])