Twitter is one of the popular social media applications where people share what they feel in a limited number of words. Twitter is popular but not in the stock market. As Twitter is being delisted from the New York stock exchange, let’s analyze the complete timeline of Twitter in the stock market. In this article, I will take you through the task of Twitter Stock Market analysis using Python.
Twitter Stock Market Analysis using Python
Twitter started its journey in the stock market in 2013. So to analyze the complete timeline of Twitter in the stock market, we need the stock price data of Twitter from 2013 to 2022. I found a dataset that contains the data we need for this task. You can download the dataset from here.
Now let’s start with the task of Twitter Stock Market Analysis by importing the necessary Python libraries and the dataset:
import pandas as pd
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"
data = pd.read_csv("TWTR.csv")
print(data.head()) Date Open High Low Close Adj Close \
0 2013-11-07 45.099998 50.090000 44.000000 44.900002 44.900002
1 2013-11-08 45.930000 46.939999 40.685001 41.650002 41.650002
2 2013-11-11 40.500000 43.000000 39.400002 42.900002 42.900002
3 2013-11-12 43.660000 43.779999 41.830002 41.900002 41.900002
4 2013-11-13 41.029999 42.869999 40.759998 42.599998 42.599998
Volume
0 117701670.0
1 27925307.0
2 16113941.0
3 6316755.0
4 8688325.0
The dataset contains data about:
- Date
- The opening Price of the day
- The highest price of the day
- The lowest price of the day
- The closing price of the day
- The adjusted closing price of the day
- The total number of shares traded in the day (volume)
Let’s have a look at the column insights:
print(data.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2264 entries, 0 to 2263 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 2264 non-null object 1 Open 2259 non-null float64 2 High 2259 non-null float64 3 Low 2259 non-null float64 4 Close 2259 non-null float64 5 Adj Close 2259 non-null float64 6 Volume 2259 non-null float64 dtypes: float64(6), object(1) memory usage: 123.9+ KB None
The Date column is an object in this dataset. We will convert it into a datetime data type later. Now, let’s have a look at whether this dataset contains any null values or not:
print(data.isnull().sum())
Date 0 Open 5 High 5 Low 5 Close 5 Adj Close 5 Volume 5 dtype: int64
There are five null values in each column except the date column. Let’s remove the rows with null values and move further:
data = data.dropna()
Now let’s have a look at the stock prices of Twitter over the years:
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
open=data["Open"],
high=data["High"],
low=data["Low"],
close=data["Close"])])
figure.update_layout(title = "Twitter Stock Prices Over the Years",
xaxis_rangeslider_visible=False)
figure.show()
So since the introduction of Twitter in the stock market, it was only profitable at the beginning and 2021. Let’s visualize a bar chart to analyze the stock prices of Twitter in detail:
figure = px.bar(data,
x = "Date",
y= "Close",
color="Close")
figure.update_xaxes(rangeslider_visible=True)
figure.show()
The above graph shows the stock prices of Twitter over the years. You can use the range slider below to zoom into a particular time period. Look at the image as an example.
Using the range slider, we can see that the first quarter of 2021 was the best time period for Twitter over the years in the stock market. We can also assign buttons to control time periods. Let’s add buttons to analyze the stock prices of Twitter in different time periods:
figure = px.bar(data, x = "Date", y= "Close", color="Close")
figure.update_xaxes(rangeslider_visible=True)
figure.update_layout(title = "Twitter Stock Prices Over the Years",
xaxis_rangeslider_visible=False)
figure.update_xaxes(
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=3, label="3m", step="month", stepmode="backward"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(count=2, label="2y", step="year", stepmode="backward"),
dict(step="all")
])
)
)
figure.show()
The buttons in the visualization above will help you understand the stock prices of Twitter at different time periods. Look at the image below as an example.
When we clicked the button “1y”, the graph shows the performance of Twitter in the stock market from the past year.
Now let’s have a look at the complete timeline of Twitter in the stock market:
data["Date"] = pd.to_datetime(data["Date"],
format = '%Y-%m-%d')
data['Year'] = data['Date'].dt.year
data["Month"] = data["Date"].dt.month
fig = px.line(data,
x="Month",
y="Close",
color='Year',
title="Complete Timeline of Twitter")
fig.show()
So since the introduction of Twitter in the stock market, 2014 went well for Twitter in the first four years. 2016 and 2017 were the worst for Twitter in the stock market. Its stock prices went up in 2018, 2019, and 2020. And then came 2021, the best year for Twitter in the stock market. Twitter reached its highest-ever stock price in the year 2021. But the stock prices of Twitter went down again in 2022.
Summary
So this is how you can analyze the complete timeline of Twitter in the stock market from 2013 to 2022. Twitter is a popular social media application and is still getting more popular after Elon Musk took over Twitter. But it never was among the best-performing companies in the stock market. I hope you liked this article on Twitter Stock Market Analysis using Python. Feel free to ask valuable questions in the comments section below.
