Professional Financial Charts in Plotly with Python

Professional Financial Charts in Plotly with Python

I’m going to walk you through the process of creating interactive and professional financial charts in Plotly with Python. Plotly is built on top of Python and enables data scientists to produce professional and great-looking plots with less-code. The categories of plots include basic charts, statistical charts, ML and AI charts, scientific charts, and financial charts.

Plotly for Visualization

One of the most popular packages used for interactive visualizations is Plotly. Plotly is built on top of python and enables data scientists to produce professional and great-looking plots with less-code. It became popular because of its extensive category of plots which can be produced in no-time. The categories of plots include basic charts, statistical charts, ML and AI charts, scientific charts, and financial charts.

In this article, I’m going to walk you through the process of creating interactive and professional financial charts in Plotly with python. We will also explore yahoo’s API for pulling historical stock data which we will be using for visualizations. Let’s get started!

Importing packages

Our primary packages include pandas for data processing, pandas DataReader for pulling the historical stock data, Datetime to deal with dates, finally, Plotly and its dependencies for interactive visualizations. Follow the code to import the primary packages into our python environment.

Python Implementation:

## Importing packages

import pandas as pd
import datetime as dt
import pandas_datareader.data as web
import plotly.express as px
import plotly.graph_objects as go

Our next process is going to be pulling the historical stock data for visualizations using the pandas DataReader package.

Pulling Stock Data

For our visualizations, we are going to pull six companies’ historical data namely Facebook, Amazon, Apple, Netflix, Google, and Microsoft using yahoo’s API. Let’s pull the data in python!

Python Implementation:

## Data

start = dt.datetime(2019,1,1)
end = dt.datetime.now()

stocks = web.DataReader(['FB','AMZN', 'AAPL', 'NFLX', 'GOOGL', 'MSFT'], 'yahoo', start, end)
stocks_close = pd.DataFrame(web.DataReader(['FB','AMZN', 'AAPL', 'NFLX', 'GOOGL', 'MSFT'], 'yahoo', start, end)['Close'])

Firstly, we have defined two variables specifying the start and end date of our data. Next, using the pandas DataReader package, we have pulled the historical data of the companies. Finally, we stored only the close price data of companies in the ‘_stocks_close_’ variable. Now, we are ready to do visualizations on our stock data.

Area Chart

Often called ‘Mountain’ charts, area charts are a more simplified interpretation of standard line charts. They plot closing prices over a given period, and the area beneath the line is shaded. Follow the code to create an area chart with Plotly in python.

Python Implementation:

## Area chart

area_chart = px.area(stocks_close.FB, title = 'FACEBOOK SHARE PRICE (2013-2020)')

area_chart.update_xaxes(title_text = 'Date')
area_chart.update_yaxes(title_text = 'FB Close Price', tickprefix = '$')
area_chart.update_layout(showlegend = False)

area_chart.show()

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