Update app.py

app.py

@@ -1,4 +1,203 @@
 import streamlit as st
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit.components.v1 as components
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Set the page to wide mode and specify a title
+st.set_page_config(layout="wide", page_title="Stock Insight Explorer", page_icon=":chart_with_upwards_trend:")
+
+# Display the application title in the app itself
+st.title('Stock Insight Explorer: Visual Analytics & Dividend Overview')
+# Input field for stock symbol
+stock_symbol = st.text_input('Enter Stock Symbol', value='JEPI')
+
+# Fetch stock data
+stock_data = yf.Ticker(stock_symbol)
+
+# Get historical data for the past year
+end_date = pd.Timestamp.today() 
+start_date = end_date - pd.DateOffset(years=1)
+historical_data = stock_data.history(start=start_date, end=end_date)
+
+# Display company name and current stock price in bold
+company_name = stock_data.info['longName']
+current_price = historical_data['Close'].iloc[-1]
+# st.markdown(f"**Company Name:** {company_name}")
+# https://finance.yahoo.com/quote/JEPQ?.tsrc=fin-srch
+company_url = f"https://finance.yahoo.com/quote/{stock_symbol}?.tsrc=fin-srch"
+
+# Use HTML anchor tag to create a hyperlink
+st.markdown(f"**Company Name:** <a href='{company_url}' target='_blank'>{company_name}</a>", unsafe_allow_html=True)
+
+st.markdown(f"**Current Price:** ${current_price:.2f}")
+
+# Creating 3 columns for the charts 
+col1, col2, col3, col4 = st.columns(4)
+
+
+# Closing prices chart
+with col1:
+    st.subheader('Closing Prices Over The Past Year')
+    st.line_chart(historical_data['Close'])
+
+# Volume of stock trades chart
+with col2:
+    st.subheader('Volume of Stock Trades Over The Past Year')
+    st.bar_chart(historical_data['Volume'])
+
+# Calculate 20 day and 50 day moving averages
+historical_data['20 Day MA'] = historical_data['Close'].rolling(window=20).mean()
+historical_data['50 Day MA'] = historical_data['Close'].rolling(window=50).mean()
+
+# Line chart for closing price, 20 day MA, and 50 day MA
+with col3:
+    st.subheader('Closing Price and Moving Averages')
+    plt.figure(figsize=(10, 5))
+    plt.plot(historical_data.index, historical_data['Close'], label='Close Price')
+    plt.plot(historical_data.index, historical_data['20 Day MA'], label='20 Day MA')
+    plt.plot(historical_data.index, historical_data['50 Day MA'], label='50 Day MA')
+    plt.legend()
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.title('Closing Price, 20 Day MA, and 50 Day MA')
+    st.pyplot(plt)
+
+# Get the dividend data
+# Assuming stock_data is your Ticker object from yfinance
+dividends = stock_data.dividends
+
+# Check if there are any dividends
+if not dividends.empty:
+    last_dividend_value = dividends.iloc[-1]  # Get the last dividend value from the dividends history
+else:
+    # Fallback to the lastDividendValue from the .info attribute if no historical dividends are found
+    last_dividend_value = stock_data.info.get('lastDividendValue', 0)
+
+with col4:
+    st.subheader('Dividends')
+    
+    markdown_output = ""  # Initialize an empty string to accumulate messages
+    
+    # Ensure dividends.index is in the correct format
+    dividends.index = pd.to_datetime(dividends.index).date
+
+    if not dividends.empty:
+        plt.figure(figsize=(10, 5))
+        dividends.plot(kind='bar')
+        plt.title(f'Dividends of {stock_symbol} Over Time')
+        plt.ylabel('Dividend Amount')
+        plt.xlabel('Date')
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        st.pyplot(plt)
+
+        # Calculate total annual dividends
+        today = pd.to_datetime("today")
+        last_year_dividends = dividends.loc[dividends.index > (today - pd.DateOffset(years=1)).date()]
+        total_annual_dividends = last_year_dividends.sum()
+        
+        if current_price and current_price > 0:
+            annual_dividend_yield = (total_annual_dividends / current_price) * 100
+            markdown_output += f"**Annual Dividend Yield:** {annual_dividend_yield:.2f}%.\n"
+        else:
+            markdown_output += "Current price not available. Cannot calculate annual dividend yield.\n"
+    else:
+        markdown_output += "This stock does not have any dividend data available.\n"
+        total_annual_dividends = 0  # Define it as 0 if there are no dividends
+
+    if current_price and current_price > 0 and last_dividend_value:   
+        dividend_yield = (last_dividend_value / current_price) * 100
+        markdown_output += f"**Last Dividend Yield:** {dividend_yield:.2f}%, ${last_dividend_value:.2f} per share."
+    else:
+        markdown_output += "Cannot calculate last dividend yield."
+    
+    # Display all collected information in one markdown output
+
+    st.markdown(markdown_output, unsafe_allow_html=True)
+
+
+
+
+    
+# components.iframe("https://duckduckgo.com/?q=ZIM&va=i&t=hd&iar=news&df=w&ia=news")
+
+# components.iframe("https://news.google.com/rss/search?q=ZIM&hl=en-US&gl=US&ceid=US%3Aen", height=800, scrolling=True)
+# components.iframe("https://truevis.com", height=800, scrolling=True)
+
+
+from unicodedata import normalize
+from bs4 import BeautifulSoup
+from feedparser import parse as parse_feed
+
+def flatten_unicode_keys(entry_properties):
+    """Ensures passing unicode keywords to **kwargs."""
+    new_properties = {}
+    for key, value in entry_properties.items():
+        key_str = key.decode('utf-8') if isinstance(key, bytes) else key
+        new_key = normalize('NFKD', key_str).encode('ascii', 'ignore').decode('ascii')  # Decode the bytes object to string
+        new_properties[new_key] = value
+    return new_properties
+
+TEMPLATE = """
+<h2 class='title'>{title}</h2>
+<a class='link' href='{link}'>{title}</a>
+<span class='description'>{description}</span>
+"""
+
+def entry_to_html(entry_id, **kwargs):
+    new_kwargs = flatten_unicode_keys(kwargs)
+    new_kwargs.setdefault('title', 'No title')
+    new_kwargs.setdefault('link', 'No link')
+    new_kwargs.setdefault('description', '')
+    accordion_item = f"""
+    <div class="card">
+        <div class="card-header" id="heading{entry_id}">
+        <h5 class="mb-0">
+            <button class="btn btn-link" data-toggle="collapse" data-target="#collapse{entry_id}" aria-expanded="true" aria-controls="collapse{entry_id}">
+            {new_kwargs['title']}
+            </button>
+        </h5>
+        </div>
+        <div id="collapse{entry_id}" class="collapse {'show' if entry_id == 0 else ''}" aria-labelledby="heading{entry_id}" data-parent="#accordion">
+        <div class="card-body">
+            <a href='{new_kwargs['link']}' target='_blank'>{new_kwargs['title']}</a>
+            <p>{new_kwargs['description']}</p>
+        </div>
+        </div>
+    </div>
+    """
+    return accordion_item
+
+def convert_feed_to_accordion(url):
+    feed = parse_feed(url)
+    accordion_html = '<div id="accordion">'
+    for i, entry in enumerate(feed.entries):
+        accordion_html += entry_to_html(i, **entry)
+    accordion_html += '</div>'
+    return accordion_html
+
+# Bootstrap CSS and JS must be included in the output HTML to style the accordion correctly
+bootstrap_includes = """
+<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css">
+<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script>
+<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"></script>
+"""
+
+# Fetch and display the accordion
+url = f"https://news.google.com/rss/search?q={stock_symbol}&hl=en-US&gl=US&ceid=US%3Aen"
+# print(url)
+feed_accordion_html = bootstrap_includes + convert_feed_to_accordion(url)
+st.markdown(f"Latest news on **{stock_symbol}**")
+components.html(feed_accordion_html, height=800, scrolling=True)
+
+# def convert_feed_to_raw_data(url):
+#     feed = parse_feed(url)
+#     raw_data = [entry for entry in feed.entries]
+#     return raw_data
+
+# Fetch and display the raw RSS data
+# url = f"https://news.google.com/rss/search?q={stock_symbol}&hl=en-US&gl=US&ceid=US%3Aen"
+# raw_data = convert_feed_to_raw_data(url)
+# print(raw_data)
+# st.write(raw_data)