Market Research App With Python & Streamlit: A How-To Guide

Are you ready to dive into the world of market analysis? This guide will walk you through creating your very own market research and analytics app using Python and Streamlit. This project will harness the power of Yahoo Finance to gather real-time market data and present it in an interactive and insightful dashboard. We'll cover everything from fetching current market data to identifying top-performing stocks. Let's get started!

Setting Up Your Environment

Before we begin, ensure you have Python installed on your system. You'll also need to install the necessary libraries. Open your terminal or command prompt and run the following command:

pip install streamlit yfinance pandas plotly

This command installs Streamlit for creating the app interface, yfinance for fetching data from Yahoo Finance, pandas for data manipulation, and plotly for creating interactive charts. These tools are the foundation of our market research app, enabling us to efficiently collect, process, and visualize financial data. With these libraries installed, you're well-equipped to start building a powerful market analysis tool that can provide valuable insights into stock market trends and performance. Next, we'll start by collecting current market data. First, create a requirements.txt file to make it easier for your team to install all the necessary dependencies.

Collecting Current Market Data

Current market data is the lifeblood of any market research application. We'll use the yfinance library to fetch this data. Here's how you can do it:

import yfinance as yf
import streamlit as st
import pandas as pd

# Define the stock tickers you want to track
tickers = ['AAPL', 'MSFT', 'GOOG', 'AMZN', 'TSLA']

# Fetch the data
data = yf.download(tickers, period='1d')

# Display the data
st.write(data)

This code snippet fetches the latest daily data for Apple (AAPL), Microsoft (MSFT), Google (GOOG), Amazon (AMZN), and Tesla (TSLA). The yf.download() function retrieves the data, and st.write() displays it in your Streamlit app. You can modify the tickers list to include any stocks you're interested in. Ensure that the tickers you use are valid and actively traded on Yahoo Finance to avoid errors. The period parameter ('1d' in this case) specifies the time frame for the data. You can change it to '1wk', '1mo', '1y', or other valid periods to get data for different durations. Displaying this data in a user-friendly format enhances the app's usability. You can use Streamlit's st.dataframe() or st.table() functions to present the data in a more structured way, making it easier for users to read and analyze. Additionally, consider adding error handling to manage cases where data might be unavailable or the ticker symbol is invalid. This makes your app more robust and reliable for users.

Implementing Stock Search Functionality

A crucial feature of any market research app is the ability to find stocks quickly by their code or name. Here’s how you can implement this:

def get_stock_data(ticker):
 try:
 data = yf.Ticker(ticker).history(period='1d')
 return data
 except:
 return None

ticker = st.text_input('Enter stock ticker:', 'AAPL')
stock_data = get_stock_data(ticker)

if stock_data is not None:
 st.write(f'Data for {ticker}:')
 st.write(stock_data)
else:
 st.write('Stock not found.')

This code defines a function get_stock_data that fetches historical data for a given stock ticker. The st.text_input widget allows users to enter a stock ticker, and the app displays the corresponding data. Error handling ensures that the app gracefully handles invalid ticker symbols.Enhance this functionality by integrating a dropdown menu or autocomplete feature for stock tickers, which can improve the user experience by making it easier to select valid tickers. You can use a predefined list of stock tickers or fetch a list from an external source to populate the options. Additionally, consider adding real-time price updates to the displayed data to provide users with the most current information. Implement caching to avoid repeatedly fetching the same data, which can improve performance and reduce the load on the Yahoo Finance API. By incorporating these enhancements, you can create a more robust and user-friendly stock search feature that adds significant value to your market research app. This allows users to perform targeted searches and quickly access the information they need, making the app an indispensable tool for stock market analysis.

Displaying Top 5 Movers

Identifying the top 5 increase and decrease in stock values, as well as volume leaders, can provide valuable insights. Here’s how you can implement this feature:

def top_movers(tickers):
 data = yf.download(tickers, period='1d')
 if 'Close' not in data:
 return None
 close_prices = data['Close'].iloc[-1]
 open_prices = data['Open'].iloc[-1]

 changes = (close_prices - open_prices) / open_prices * 100
 volume = data['Volume'].iloc[-1]

 changes = changes.sort_values(ascending=False)
 volume = volume.sort_values(ascending=False)

 return changes.head(5), changes.tail(5), volume.head(5)

changes_up, changes_down, volume_top = top_movers(tickers)

st.write('Top 5 Gainers:')
st.write(changes_up)
st.write('Top 5 Losers:')
st.write(changes_down)
st.write('Top 5 Volume:')
st.write(volume_top)

This code calculates the percentage change in stock prices and identifies the top 5 gainers and losers. It also identifies the top 5 stocks by volume. This information is then displayed in the Streamlit app. Implement this by choosing the right stocks is very important, make sure you choose the stocks that move the most and have the highest volume.

To enhance this feature, consider adding visualizations such as bar charts or heatmaps to represent the top movers and volume leaders. This will make the data more visually appealing and easier to interpret. Additionally, provide users with the option to select different time periods for analysis, allowing them to see top movers over various durations. Implement filtering options to focus on specific sectors or industries, enabling users to narrow down their analysis to relevant areas of interest. Displaying key metrics such as the percentage change, volume, and market capitalization alongside the stock tickers can provide additional context and insights. By incorporating these enhancements, you can create a more comprehensive and informative top movers feature that empowers users to quickly identify and analyze significant market trends. This functionality is essential for any market research app, providing users with valuable information to make informed investment decisions. This can be a very useful tool for analysts.

Enhancing the User Interface with Streamlit

Streamlit makes it incredibly easy to create a user-friendly interface. Here are some tips to enhance your app's UI:

• Use Streamlit’s layout options: st.sidebar, st.columns, and st.expander can help you organize your content effectively.
• Add interactive widgets: Use st.slider, st.selectbox, and st.button to allow users to interact with the data.
• Incorporate charts and graphs: Use libraries like plotly or matplotlib to visualize the data. Clear and concise data visualization is key to conveying meaningful insights. Charts can help turn raw data into easily digestible information.

For example:

import plotly.graph_objects as go

def plot_stock_data(ticker):
 data = yf.Ticker(ticker).history(period='1y')
 fig = go.Figure(data=[go.Candlestick(x=data.index,
 open=data['Open'],
 high=data['High'],
 low=data['Low'],
 close=data['Close'])])
 st.plotly_chart(fig)

ticker = st.selectbox('Select a stock:', tickers)
plot_stock_data(ticker)

This code snippet creates an interactive candlestick chart for a selected stock. Candlestick charts are a popular tool in financial analysis, as they provide a comprehensive view of a stock's price movement over a specific period. They display the open, high, low, and close prices for each period, making it easy to identify trends and patterns. By integrating interactive charts into your Streamlit app, you can provide users with a powerful tool for visualizing and analyzing stock market data. This not only enhances the user experience but also enables users to gain deeper insights into market trends and make more informed investment decisions. Additionally, consider adding features like zooming and panning to allow users to explore the charts in more detail. You can also add annotations and trendlines to highlight key price levels and patterns. With these enhancements, your Streamlit app can become a valuable resource for both novice and experienced investors.

Conclusion

Building a market research and analytics app with Python and Streamlit is a fantastic way to learn about financial data and create a useful tool for yourself or others. By leveraging libraries like yfinance, pandas, and plotly, you can create a powerful and interactive dashboard that provides valuable insights into the stock market. Experiment with different features and visualizations to create an app that meets your specific needs. The possibilities are endless! Remember to continuously refine your app by adding more features, improving the user interface, and optimizing performance. This iterative approach will ensure that your app remains relevant and valuable to its users. Share your app with others and gather feedback to further enhance its functionality and usability. With dedication and creativity, you can create a market research app that empowers users to make informed investment decisions and stay ahead in the ever-changing world of finance. Happy coding!

Check out Yahoo Finance for additional information.