A super beginner who does not know the basics of Python tried to graph the stock price of GAFA

Please check the features of each library from the following articles.

Data analysis library! What is Pandas [For beginners] | Commentary by active engineers Introducing the data analysis (data science) library Pandas, which displays and graphs data statistics.

Graph drawing library! How to use matplotlib [for beginners] Explains in detail how to use matplotlib, a library that can draw graphs in Python.

What is the Python extension module "NumPy"? Install… ｜ Udemy Media NumPy is an extension module that makes machine learning calculations in Python faster and more efficient.

Environment

Google Colaboratory is used as the execution environment for Python.

• Google Colaboratory A service that allows you to use Jupyter Notebook with the minimum effort and cost, and to start a machine learning project right away with a browser and the Internet.

• Jupyter Notebook A tool for running Python on a browser and proceeding with programming while recording the execution results.

It is frequently used in data analysis sites and research institutes. By using these services, you can immediately check the execution result of Python on the browser.

Please refer to the following article for details.

• Google Colaboratory Colaboratory (abbreviation: Colab) is a service that allows you to write and execute Python from a browser.

• Indispensable for data analysis! How to use Jupyter Notebook [For beginners] It explains in detail how to use Jupyter Notebook, which is convenient for data analysis using Python for beginners.

I will summarize the flow up to this point.

• A Python library is required to analyze data with Python.
• You can easily proceed with the project using a service called Google Colaboratory.
• With a tool called Jupyter Notebook, you can run Python on a browser and proceed with programming while recording the execution results.

It is good to recognize that. I appologize if I am mistaken.

Get stock price and create graph

Now, let's actually write the code.

For the code, I referred to TF's article. TF has written many articles on asset management using Python. It would be fun if you could use Python so far.

• Check the performance and performance of US stocks quickly and neatly with Python [Python US stocks with pyfolio code]

And here is the completed code.

!pip install fix_yahoo_finance

import datetime
import fix_yahoo_finance as yf
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

start = datetime.date(2020,1,1)
end = datetime.date.today()

codelist = ["GOOGL","AAPL","FB","AMZN","^N225"]
data2 = yf.download(codelist, start=start, end=end)["Adj Close"]

display(data2.head(2).append(data2.tail(2)))
df_all=100*data2.pct_change()
display(df_all[codelist].head(2).append(df_all[codelist].tail(2)))

df_all[codelist].cumsum().plot(figsize=(8,6),fontsize=18)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=18)

plt.grid(True)
plt.show()

Code description

Install stock price information

!pip install fix_yahoo_finance

We use a library called pandas for data analysis. Use that pandas to install stock price information from Yahoo Finance.

Import current date

import datetime

import fix_yahoo_finance as yf

import matplotlib.pyplot as plt

matplotlib : A library used to draw graphs and display images in Python. pyplot : Modules in the matplotlib package.

An interface that implicitly and automatically creates shapes and axes to create the plot you want. Basically, the function of matplotlib is utilized through this module.

%matplotlib inline

import warnings
warnings.filterwarnings('ignore')

Substitute the first date of the period you want to get in start

start = datetime.date(2020,1,1)

Substitute today's date for end

end = datetime.date.today()

Assign GAFA name as an array to codelist

codelist = ["GOOGL","AAPL","FB","AMZN","^N225"]

I couldn't figure out the rules for name abbreviations, but it was okay to include the names that appear in tradingview. Also, substitute the Nikkei 225 data for comparison.

Download the stock price information (adjusted closing price) from start to end of the above codelist company in data2

data2 = yf.download(codelist, start=start, end=end)["Adj Close"]

Adj Close : Adjusted closing price

Display the top 2 and bottom 2 of data2 with display

display(data2.head(2).append(data2.tail(2)))

display : Preserve and display the layout of the table in dataframe format head (2) : Top 2 tail (2) : Bottom 2 append : Add element at the end

Calculate the ratio of data change of data2 for 100 times and substitute it for df_all

df_all=100*data2.pct_change()

pct_change : A function that calculates the rate of change (Value of that row-value of previous row) / value of previous row

Example) Today's stock price: 1,100 yen / share Yesterday's stock price: 1,000 yen / share (1,100-1,000) / 1,000 = 10 This means that the stock price has risen by 10%.

Display the top 2 and bottom 2 changes in the codelist (GAFA and Nikkei 225)

display(df_all[codelist].head(2).append(df_all[codelist].tail(2)))

df_all[codelist].cumsum().plot(figsize=(8,6),fontsize=18)

cumsum () function : NumPy function that outputs the sum of elements as an array plot : matplotlib methods. It is used when drawing a line graph. figsize : Horizontal size and vertical size fontsize : Font size of font case law

The legend is displayed in the vertical center on the right side of the graph

plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=18)

plt.legend : Display case law loc: Specify the location of case law center : Vertical position of case law left : Position next to the case bbox_to_anchor : Specify the position of the frame of the graph 1,0.5 : Right edge, vertical center

The execution results up to this point are as follows.

Display the grid on the graph

plt.grid(True)

Display the created graph on the screen

plt.show()

I've heard that there is nothing that Excel can do that Python can't do, but even though I'm a beginner, it made me feel that possibility.

GAFA is too strong

Now that we have a graph of the stock price, let's take a look at the contents.

GAFA temporarily dropped due to the corona shock, but quickly recovered to pre-corona levels and continues to hit record highs.

I think it's safe to buy one of GAFA without thinking about anything.

The Nikkei average is considerably weaker than GAFA.

However, thanks to all government policies, pre-Corona levels have almost recovered.

Even the weakest Nikkei average has risen nearly 30% from the lowest corona shock. I wanted to prepare a lot during the corona shock.

Summary

• Even a super beginner was able to acquire the stock price of GAFA.
• As I learned while doing it, I naturally gained knowledge.
• US stocks are better than Japanese stocks.
• Even if a crash comes, the government will do something about it, so you can do it.

This time I just got the stock price of GAFA, but I was able to experience the greatness of Python by itself.

It would be fun if you could create your own asset management portfolio with Python.