Import Raw Packages

#Install datareader for Data visualizaton !pip install pandas_datareader==0.9.0 #Import Pandas, Numpy and PlotLy for Data visualization. import numpy as np import pandas as pd import plotly.graph_objs as go

I. Import Data

API and DateTime package

#Import the right API library in order to import data from Yahoo finance. #Furthermore don't forget datetime package to get the dates in the right format !! #PDR package from pandas_datareader import data #Datetime package from datetime import date

Daily returns

df[pct_daily_return] = (1 + df[closing_price]).pct_change(1)

df[pct_daily_return] = (df[closing_price] / df[closing_price].shift(1) ) - 1

b. Daily cumulative return

The formulae for a daily cumulative return is the following:

$ ii = (1+r_t) * i{t-1} $

We can notice that we are only multiplying our previous investment i at t-1 by 1 + our percentage return. Pandas simplify the way to calculate with its cumprod () method. Using the following command:

df[daily_cumulative_return] = (1 + df[pct_daily_return]).cumprod()

#Define start and end date start = pd.to_datetime('2019-02-01') end = pd.to_datetime('2019-12-22')

#Set your tickers tickers = ['SPY'] # Getting length of list length = len(tickers) i = 0 # Iterating using while loop while i < length: print(tickers[i] + " is uploading data") #Create Variable in which each dataframe will be stored locals()[str(tickers[i])+"_data_2019"] = data.DataReader(tickers[i],'yahoo' ,start=start, end=end) #Create a CSV file in which you are going to store your dataframe in order to keep track of the change overtime. locals()[str(tickers[i])+"_data_2019"].to_csv(str(tickers[i])+"_data.csv") i += 1

#Define start and end date start = pd.to_datetime('2020-02-01') end = pd.to_datetime('2020-12-22')

#Set your tickers tickers = ['SPY'] # Getting length of list length = len(tickers) i = 0 # Iterating using while loop while i < length: print(tickers[i] + " is uploading data") #Create Variable in which each dataframe will be stored locals()[str(tickers[i])+"_data_2020"] = data.DataReader(tickers[i],'yahoo' ,start=start, end=end) #Create a CSV file in which you are going to store your dataframe in order to keep track of the change overtime. locals()[str(tickers[i])+"_data_2020"].to_csv(str(tickers[i])+"_data.csv") i += 1

Data imported check

SPY_data_2020

SPY_data_2019

II. Generate calculated fields

#Set a list grouping the different dataframes which will be impacted. df_list = [SPY_data_2020, SPY_data_2019] tickers = ['CY', 'PY'] # Getting length of list length = len(df_list) #Setting i=0 i = 0 # Iterating using while loop while i < length: print("Dataframe " + str(i) + " is calculating the daily return") #Generating a "Returns" column using PCT methodology. df_list[i]['Returns'] = df_list[i]['Close'].pct_change(1) #Generating a "Cumulative Returns" column using Cum Prod methodology. df_list[i]['Cumulative Return'] = (1 + df_list[i]['Returns']).cumprod() print(tickers[i] + " is setting Returns and Cumulative Returns variables") #Create Variable in which each dataframe will be stored locals()[str(tickers[i])+"_Returns"] = df_list[i]['Returns'] locals()[str(tickers[i])+"_Cum_Returns"] = df_list[i]['Cumulative Return'] i += 1

III. Plot data

import plotly.figure_factory as ff # Add histogram data x1 = CY_Returns.fillna(0) x2 = PY_Returns.fillna(0) # Group data together hist_data = [x1, x2] group_labels = ['Covid19', 'PY'] # Create distplot with custom bin_size (set bin size = 0.01) fig = ff.create_distplot(hist_data, group_labels, bin_size=.01) fig.show()

Import Raw Packages

I. Import Data

API and DateTime package

Daily returns

b. Daily cumulative return

Data imported check

II. Generate calculated fields

III. Plot data

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