Table of Contents

• 1  Introduction
  • 1.1  Preliminary Actions
    • 1.1.1  Install Libraries
    • 1.1.2  Import Required Libraries
  • 1.2  Download Stock Data of Apple
  • 1.3  Perform EDA
    • 1.3.1  Checking for Null Value
  • 1.4  View the Distribution
  • 1.5  View the Box Plot
  • 1.6  Summary of our data
• 2  Moving Average
  • 2.1  Simple Moving Average (SMA)
    • 2.1.1  Plotting SMA of All
  • 2.2  Weighted Moving Average (WMA)
    • 2.2.1  Plotting WMA of All
  • 2.3  Exponential Moving Average (WMA)
    • 2.3.1  Plotting EMA of All
  • 2.4  Plotting Candlestick
• 3  Moving Median
• 4  Moving Variance
• 5  Conclusion

Introduction

This blog is a part of our series Python for Stock Market Analysis.

Disclaimer: This blog is for educational purpose only and we do not recommend taking the knowledge gained from this blog to implement in real financial exercises.

This blog tries to implement preliminary metrics that are used in the stock market analysis. The dataset we will be using is available via yahoofinance.

Preliminary Actions

Install Libraries

Please install:

• YahooFinance as pip install yfinance for downloading data of stock's history.
• Pandas as pip install pandas for data analysis.
• Plotly as pip install plotly for interactive visualizations.
• Cufflinks as pip install cufflinks for using interactive plots in pandas DataFrame.

You might need to install pip install -U kaleido if you need to save plots as png image.

If you are new into plotly, then we have an awesome blog about it where we have done plots based on COVID 19 dataset.

!pip install yfinance

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Import Required Libraries

import pandas as pd
import plotly.express as px
import cufflinks
import plotly.io as pio 
import yfinance as yf
cufflinks.go_offline()
cufflinks.set_config_file(world_readable=True, theme='pearl')
pio.renderers.default = "notebook" # should change by looking into pio.renderers

pd.options.display.max_columns = None

Download Stock Data of Apple

By default, we are allowed to download data from 1900-01-01

symbols = ["AAPL"]

df = yf.download(tickers=symbols)
df.head()

[*********************100%***********************]  1 of 1 completed

	Open	High	Low	Close	Adj Close	Volume
Date
1980-12-12	0.128348	0.128906	0.128348	0.128348	0.100326	469033600
1980-12-15	0.122210	0.122210	0.121652	0.121652	0.095092	175884800
1980-12-16	0.113281	0.113281	0.112723	0.112723	0.088112	105728000
1980-12-17	0.115513	0.116071	0.115513	0.115513	0.090293	86441600
1980-12-18	0.118862	0.119420	0.118862	0.118862	0.092911	73449600

It seems that data is only available from 1980-12-12. The column names in the above fields are:

• Open: It is the price of the stock at the start of the day of that date.
• Close: It is the price of the stock at the end of that date.
• High: It is the highest price of the stock on that date.
• Low: It is the lowest price of the stock on that date.
• Volume: It is the number of shares traded on that date.

Perform EDA

EDA or Exploratory Data Analysis is the first step in any Data Analysis and lets do that in our Stock Data too. We have blogs about doing EDA, Statistical and Inferential Analysis please check them out for more about EDAs.

Checking for Null Value

# convert column names into lowercase
df.columns = [c.lower() for c in df.columns]
df.rename(columns={"adj close":"adj_close"},inplace=True)

ndf = pd.DataFrame()
for c in df.columns:
    nc = df[c].isna().sum()
    tr = len(df[c])
    rate = nc/tr
    ndf = ndf.append({"col_name":c,"total_rows": tr, 
                "null_rows": nc,
                "rate": rate},ignore_index=True)
ndf

	col_name	null_rows	rate	total_rows
0	open	0.0	0.0	10390.0
1	high	0.0	0.0	10390.0
2	low	0.0	0.0	10390.0
3	close	0.0	0.0	10390.0
4	adj_close	0.0	0.0	10390.0
5	volume	0.0	0.0	10390.0

It seems that we do not have any null rows present on the data.

View the Distribution

It gives us the frequency of value's some range. It is simply a histogram.

fig = df.iplot(kind="hist",subplots=True, title="Distribution of All Variables", asFigure=True)
fig.write_image("stock_analysis/dist.png")
fig.show()