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Today we will create a simple dashboard which contains information about a stock using Streamlit, Python and yfinance. The dashboard will allow a user to enter a stock ticker and then return a list of important metrics associated with that stock.
This is a relatively straightforward article but feel free to get creative with it and extend to suit your own needs. We will also not be deploying this dashboard today, I will save that for tomorrows article. Stay tuned for that!
The dashboard we are building will look like this:
Why Streamlit?
Streamlit is a great framework for delivering data analysis applications to users in only a few lines of code. Its great for quick solutions where devs want to get an mvp up and running quickly, without having to worry about things like complex deployments, hosting, VMs etc.
Streamlit is a great framework for beginners just dipping their toes into data analysis and AI/ML.
With that in mind lets jump into the code.
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Code Walkthrough
As explained, this is a simple solution with the intention of getting you started using streamlit in a non-intimidating way.
Create a new file called app.py in your IDE.
Then install the streamlit package using
pip install streamlitLets import the packages we will be using
import streamlit as st
import pandas as pd
import yfinance as yf
from datetime import dateCreate a title for our app and add a form with an input box where the user will enter the ticker and a submit button
st.title('Welcome to Streamlit app to get information about a stock')
with st.form(key='my_form_to_submit'):
ticker = st.text_input('Enter Ticker:')
submit_button = st.form_submit_button(label='Submit')Once the submit button is clicked we then run our code to get the stock information using yfinance. We need to structure the app like this as if we do not include the if statement, streamlit will run our all of our code together and our data will be missing, throwing errors instead!
if submit_button:
ticker = ticker.upper()
data = yf.download(ticker, period='5d')
dataClose = data['Close']
most_recent_price = dataClose.sort_values('Date', ascending=False).iloc[0][f'{ticker}']
most_recent_price = str(round(most_recent_price, 2))
company = yf.Ticker(ticker)
informationTicker = company.info
sector = informationTicker['sector']Next we need some different logic for dividend information as not every company offers dividends. We can set both dividendYield and exDividendDate to “N/A” and use a try except block to update the values if present else we just pass.
dividendYield = "N/A"
exDividendDate = "N/A"
try:
dividendYield = informationTicker['dividendYield']
exDividendDate = date.fromtimestamp(informationTicker['exDividendDate'])
except:
pass
volume = informationTicker['volume']
avgVolume = informationTicker['averageVolume']
marketCap = informationTicker['marketCap']
forwardPE = informationTicker['forwardPE']
enterpriseValue = informationTicker['enterpriseValue']
ebitda = informationTicker['ebitda']
EVDividedByEbitda = enterpriseValue / ebitdaNext, write all of our info so the user can see it.
st.write(f'Current Price for {ticker} is {most_recent_price}')
st.write(f'Sector: {sector}')
st.write(f'Dividend Yield: {dividendYield}')
st.write(f'Ex Dividend Date: {exDividendDate}')
st.write(f'Volume: {volume}')
st.write(f'Average Volume: {avgVolume}')
st.write(f'Market Cap: {marketCap}')
st.write(f'Forward PE: {forwardPE}')
st.write(f'Enterprise Value: {enterpriseValue}')
st.write(f'Ebitda: {ebitda}')
st.write(f'EV/Ebitda: {EVDividedByEbitda}')To start the app run the following command and a new window should open in your web browser with the dashboard running!
streamlit run app.pyFeel free to add styling to the dashboard as it is currently quite plain.
Tomorrow we will deploy the application so that you can get your first users!
Conclusion
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Thanks for reading.