Bachelor of Math, Statistics, and Education

3 minute read

Apple Stock

Introduction:

We are going to use Apple’s stock price.

Step 1. Import the necessary libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Step 2. Import the dataset from this address

pd.read_csv("https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv")
Date Open High Low Close Volume Adj Close
0 2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35
1 2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97
2 2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03
3 2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48
4 2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52
... ... ... ... ... ... ... ...
8460 1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41
8461 1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40
8462 1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39
8463 1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42
8464 1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45

8465 rows × 7 columns

Step 3. Assign it to a variable apple

apple = pd.read_csv("https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv")

Step 4. Check out the type of the columns

apple.dtypes

Date          object
Open         float64
High         float64
Low          float64
Close        float64
Volume         int64
Adj Close    float64
dtype: object

Step 5. Transform the Date column as a datetime type

apple.Date = pd.to_datetime(apple.Date)

apple.dtypes

Date         datetime64[ns]
Open                float64
High                float64
Low                 float64
Close               float64
Volume                int64
Adj Close           float64
dtype: object

Step 6. Set the date as the index

apple = apple.set_index("Date")
apple
Open High Low Close Volume Adj Close
Date
2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35
2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97
2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03
2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48
2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52
... ... ... ... ... ... ...
1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41
1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40
1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39
1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42
1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45

8465 rows × 6 columns

Step 7. Is there any duplicate dates?

dup = apple[apple.index.duplicated]
if len(dup) != 0:
    print("Yes, there are duplicate dates.")
else:
    print("No, there are no duplicate dates.")

No, there are no duplicate dates.

Step 8. Ops…it seems the index is from the most recent date. Make the first entry the oldest date.

apple = apple.sort_values(by="Date", ascending=True)
apple
Open High Low Close Volume Adj Close
Date
1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45
1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42
1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39
1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40
1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41
... ... ... ... ... ... ...
2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52
2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48
2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03
2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97
2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35

8465 rows × 6 columns

Step 9. Get the last business day of each month

apple_month = apple.resample("BM").mean()
apple_month
Open High Low Close Volume Adj Close
Date
1980-12-31 30.481538 30.567692 30.443077 30.443077 2.586252e+07 0.473077
1981-01-30 31.754762 31.826667 31.654762 31.654762 7.249867e+06 0.493810
1981-02-27 26.480000 26.572105 26.407895 26.407895 4.231832e+06 0.411053
1981-03-31 24.937727 25.016818 24.836364 24.836364 7.962691e+06 0.387727
1981-04-30 27.286667 27.368095 27.227143 27.227143 6.392000e+06 0.423333
... ... ... ... ... ... ...
2014-03-31 533.593333 536.453810 530.070952 533.214286 5.954403e+07 75.750000
2014-04-30 540.081905 544.349048 536.262381 541.074286 7.660787e+07 76.867143
2014-05-30 601.301905 606.372857 598.332857 603.195714 6.828177e+07 86.058571
2014-06-30 222.360000 224.084286 220.735714 222.658095 5.745506e+07 91.885714
2014-07-31 94.294000 95.004000 93.488000 94.470000 4.218366e+07 94.470000

404 rows × 6 columns

Step 10. What is the difference in days between the first day and the oldest

diff = apple.index.max() - apple.index.min() 
diff.days

12261

Step 11. How many months in the data we have?

apple_month = apple.resample("BM").mean()
len(apple_month.index)

404

Step 12. Plot the ‘Adj Close’ value. Set the size of the figure to 13.5 x 9 inches

data = apple['Adj Close'].plot()
fig = data.get_figure()
fig.set_size_inches(13.5, 9)

png

BONUS: Create your own question and answer it.

Plot the total ‘volume’ of Apple Stock being traded each day. Set the size of the figure to 13.5 x 9 inches

data2 = apple['Volume'].plot()
fig = data2.get_figure()
fig.set_size_inches(13.5, 9)

png

Plot the price-earnings ratio each day. Set the size of the figure to 13.5 x 9 inches

apple['Price_lag_1'] = apple['Adj Close'].shift(-1)
apple['Price_diff_1'] = apple['Price_lag_1'] - apple['Adj Close']
apple['Daily_return'] = apple['Price_diff_1'] / apple['Adj Close']
apple['Updown'] = [1 if apple['Daily_return'].loc[Date] > 0 else 0 for Date in apple.index]

apple_chart = apple[['Adj Close','Price_lag_1','Price_diff_1','Daily_return','Updown']]
apple_chart.head()
Adj Close Price_lag_1 Price_diff_1 Daily_return Updown
Date
1980-12-12 0.45 0.42 -0.03 -0.066667 0
1980-12-15 0.42 0.39 -0.03 -0.071429 0
1980-12-16 0.39 0.40 0.01 0.025641 1
1980-12-17 0.40 0.41 0.01 0.025000 1
1980-12-18 0.41 0.44 0.03 0.073171 1 
apple['Updown'].value_counts() #주가 상승일(1), 하락일(0)의 수

0    4744
1    3721
Name: Updown, dtype: int64

plt.figure(figsize=(13.5, 9))
plt.plot(apple.index, apple['Daily_return'])
plt.axhline(y=0, color='red', ls='--')
plt.show() #일일 주가 수익률 그래프

png

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