Amazon Stock Price Analysis in Python

By Subhojit Jalal

Here we see the Amazon stock price last five year and we also calculate the last five year mean value and variance

Step:1

First, we import necessary librates

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

from sklearn.metrics import mean_squared_error

from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.tsa.ar_model import AR
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARMA

Step:2
Then we will upload the dataset of Amazon_Dataset

Amazon_Data = pd.read_csv('Amazon_Dataset.csv')
Amazon_Data

Step:3

Now we will do basic data exploration

head(): This helps to see a few sample rows of the data

Amazon_Data.head()

describe(): This provides the summarized information of the data

Amazon_Data.describe()

Step:4

Now we look at  Data and Close column together

t=Amazon_Data['Date']
t

t.head()

c=Amazon_Data['Close']
c.head()

new=pd.concat([t,c],axis=1)
new.head()

Y = Amazon.values
size = int(len(Y) * 0.70)  # 70 % 
# Training set
train = Y[:size]
# testing set
test  = Y[size:len(Y)]

print("Total Samples    : %d" % len(Y))
print("Training Samples : %d" % len(train))
print("Testing Samples  : %d" % len(test))

Step:5

Plotting Scatter v/s Density

def plotScatterMatrix(Amazon_Data, plotSize, textSize):
    Amazon_Data = Amazon_Data.select_dtypes(include =[np.number]) 
    Amazon_Data = Amazon_Data.dropna('columns')
    Amazon_Data = Amazon_Data[[col for col in Amazon_Data if Amazon_Data[col].nunique() > 1]]
    columnNames = list(Amazon_Data)
    if len(columnNames) > 10: 
        columnNames = columnNames[:10]
    Amazon_Data = Amazon_Data[columnNames]
    ax = pd.plotting.scatter_matrix(Amazon_Data, alpha=0.2, figsize=[plotSize, plotSize], diagonal='density')
    corrs = Amazon_Data.corr().values
    for p, q in zip(*plt.np.triu_indices_from(ax, k = 1)):
        ax[p, q].annotate('Corr. coef = %.2f' % corrs[p, q], (0.8, 0.2), xycoords='axes fraction', ha='center', va='bottom', size=textSize)
    plt.plot(Amazon.Date, marker= '+', markersize =2, markerfacecolor= 'green', markeredgecolor= 'black')
    plt.show()

plotScatterMatrix(Amazon_Data, 18, 10)

Step:6

Now we keep only the close column and see the last five-year closing stock price

# Keep only 'Close' column
Amazon = Amazon_Data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)

Amazon = Amazon[Amazon['Date'] >='2015-01-01']

plt.figure(figsize=(10, 5))
plt.title('Amazon stock closing prices for last 5 years', fontsize=14)
plt.plot(Amazon.Close, marker= '+', markersize =2, markerfacecolor= 'green', markeredgecolor= 'red')

So we calculate the last five year mean and variance price

Q1_2019_mean = Amazon[(Amazon['Date'] >= '2019-01-01') & (Amazon['Date'] < '2019-03-31')].mean()
Q1_2019_var  = Amazon[(Amazon['Date'] >= '2019-01-01') & (Amazon['Date'] < '2019-03-31')].var()
Q2_2017_mean = Amazon[(Amazon['Date'] >= '2017-01-01') & (Amazon['Date'] < '2017-03-31')].mean()
Q2_2017_var  = Amazon[(Amazon['Date'] >= '2017-01-01') & (Amazon['Date'] < '2017-03-31')].var()
Q3_2015_mean = Amazon[(Amazon['Date'] >= '2015-10-01') & (Amazon['Date'] < '2015-12-31')].mean()
Q3_2015_var  = Amazon[(Amazon['Date'] >= '2015-10-01') & (Amazon['Date']< '2015-12-31')].var()



print('2019 Quarter 1 closing price mean    : %.2f ' % (Q1_2019_mean))
print('2019 Quarter 1 closing price variance : %.2f ' % (Q1_2019_var))
print("---------------------------------------------- ")
print('2017 Quarter 2 closing price mean     : %.2f ' % (Q2_2017_mean))
print('2017 Quarter 2 closing price variance : %.2f ' % (Q2_2017_var))
print("---------------------------------------------- ")
print('2015 Quarter 3 closing price mean     : %.2f ' % (Q3_2015_mean))
print('2015 Quarter 3 closing price variance : %.2f ' % (Q3_2015_var))

Here we calculate the closing mean price and variance and see the stock price increases in the last five years