In trading, the price we receive is a collection of data points at constant time intervals called time series. They are time dependent and can have increasing or decreasing trends and seasonality trends, in other words, variations specific to a particular time frame. Like any other retail products, financial products follow trends and seasonality during different seasons. There are multiple seasonality effects: weekend, monthly, and holidays.
In this section, we will use the GOOG data from 2001 to 2018 to study price variations based on the months.
- We will write the code to regroup the data by months, calculate and return the monthly returns, and then compare these returns in a histogram. We will observe that GOOG has a higher return in October:
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader...
