Imagine that you want to have all the important historical financial information of yahoo finance on your PC. Well, in that case you would probably go there and check it. Anyway, if you are a person, who wants to create a customized dashboard with it, then you can use the streamlit library in python 🙂
In this article, we would show the code from the YouTube video, used for the creation of the Stock Dashboard with Python. Generally speaking, it is this one:
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import streamlit as st import yfinance as yf import pandas as pd import matplotlib.pyplot as plt # Page title st.title("Stock Data Dashboard 📈") # Sidebar inputs st.sidebar.header("VitoshAcademy LiveCoding") ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., AAPL, MSFT)", value="MSFT") start_date = st.sidebar.date_input("Start Date", value=pd.to_datetime("2020-01-01")) end_date = st.sidebar.date_input("End Date", value=pd.to_datetime("2024-12-31")) ma_window = st.sidebar.slider("Moving Average Window", min_value=5, max_value=50, value=20) # Fetching data st.write(f"Fetching data for **{ticker_symbol}** from {start_date} to {end_date}...") data = yf.download(ticker_symbol, start=start_date, end=end_date) if data.empty: st.error("No data found. Please check the ticker symbol or date range.") st.stop() data['MA'] = data['Close'].rolling(window=ma_window).mean() # Initialize Tabs tabs = st.tabs(["📋 Raw Data", "📈 Price Chart", "📊 Volume Chart", "📏 Moving Averages", "💰 Dividends & Splits"]) # Tab 1: Raw Data with tabs[0]: st.subheader(f"Raw Data for {ticker_symbol}") st.write(data.tail()) st.download_button("Download Data as CSV", data.to_csv(), file_name=f"{ticker_symbol}_data.csv") # Tab 2: Closing Price Chart with tabs[1]: if "Close" in data: st.subheader("Closing Price Over Time") st.line_chart(data['Close']) else: st.warning("Closing price data is not available for this stock.") # Tab 3: Volume Chart with tabs[2]: if "Volume" in data: st.subheader("Volume Over Time") st.bar_chart(data['Volume']) else: st.warning("Volume data is not available for this stock.") # Tab 4: Moving Averages with tabs[3]: st.subheader(f"Closing Price with {ma_window}-Day Moving Average") fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(data.index, data['Close'], label="Closing Price", color='blue') ax.plot(data.index, data['MA'], label=f"{ma_window}-Day Moving Average", color='orange') ax.set_title(f"Closing Price with {ma_window}-Day Moving Average") ax.set_xlabel("Date") ax.set_ylabel("Price") ax.legend() st.pyplot(fig) # Tab 5: Dividends & Splits with tabs[4]: st.subheader("Dividends & Splits") ticker = yf.Ticker(ticker_symbol) dividends = ticker.dividends splits = ticker.splits st.write("**Dividends:**") st.write(dividends if not dividends.empty else "No dividends found during this period.") st.write("**Splits:**") st.write(splits if not splits.empty else "No splits found during this period.") |
The way to call it is this one liner into the terminal – streamlit run stock_dashboard_streamlit.py
The beautiful dashboard looks like that:
Information about dividends and stock splits of MSFT for a 5 year period.
Stock price of AAPL chart.
MSFT price with 50-day moving average.
The turning this python tool into “live” and some additional interesting features of the yfinance library are presented in the video below:
Enjoy it! 🙂