Create app.py

app.py

@@ -0,0 +1,714 @@
+# insider_trading_app.py
+
+import streamlit as st
+import requests
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+
+# ----------------------------
+# Configuration
+# ----------------------------
+
+API_KEY = "b431ec171262073909ebf8c0c4afba71"  # Hardcoded API Key
+API_ENDPOINT_INSIDER_TRADING = "https://financialmodelingprep.com/api/v4/insider-trading"
+API_ENDPOINT_TRADE_STATISTICS = "https://financialmodelingprep.com/api/v4/insider-roster-statistic"  # Corrected endpoint
+
+TOTAL_PAGES_LIVE_FEED = 50  # Fixed number of pages to fetch
+DEFAULT_TOP_N = 5  # Default number of top traded securities to display
+
+# ----------------------------
+# Page Configuration
+# ----------------------------
+
+st.set_page_config(
+    page_title="Insider Trading Analysis",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Initialize session state for both pages
+if 'ticker_insider_trades' not in st.session_state:
+    st.session_state['ticker_insider_trades'] = {}
+if 'insider_trades_live_feed' not in st.session_state:
+    st.session_state['insider_trades_live_feed'] = {}
+
+# Sidebar for page navigation and inputs
+st.sidebar.title("Input Parameters")
+
+with st.sidebar.expander("Pages", expanded=True):
+    page = st.radio("Select Page", ["Ticker Insider Trades", "Insider Trades Live Feed"])
+
+# ----------------------------
+# Helper Functions
+# ----------------------------
+
+def get_insider_trading_data(symbol):
+    base_url = "https://financialmodelingprep.com/api/v4"
+
+    # Get the latest transactions table
+    transactions_endpoint = f"{base_url}/insider-trading"
+    transactions_params = {"symbol": symbol, "page": 0, "apikey": API_KEY}
+    transactions_response = requests.get(transactions_endpoint, params=transactions_params)
+    transactions_data = transactions_response.json()
+
+    # Convert transactions data to a DataFrame
+    transactions_df = pd.DataFrame(transactions_data)
+
+    # Get the trade statistics over time
+    statistics_endpoint = f"{base_url}/insider-roaster-statistic"
+    statistics_params = {"symbol": symbol, "apikey": API_KEY}
+    statistics_response = requests.get(statistics_endpoint, params=statistics_params)
+    statistics_data = statistics_response.json()
+
+    # Convert statistics data to a DataFrame
+    statistics_df = pd.DataFrame(statistics_data)
+
+    return transactions_df, statistics_df
+
+def fetch_insider_trading_live_feed():
+    all_data = []
+    print(f"Starting to fetch live feed data from {TOTAL_PAGES_LIVE_FEED} pages.")
+    for page_num in range(1, TOTAL_PAGES_LIVE_FEED + 1):
+        params = {
+            'apikey': API_KEY,
+            'page': page_num
+        }
+        try:
+            response = requests.get(API_ENDPOINT_INSIDER_TRADING, params=params)
+            print(f"Fetching page {page_num}: Status Code {response.status_code}")
+            response.raise_for_status()
+            data = response.json()
+            print(f"Page {page_num} returned {len(data)} records.")
+            if not data:
+                print(f"No data returned for page {page_num}. Stopping pagination.")
+                break  # Stop if no more data
+            all_data.extend(data)
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching page {page_num}: {e}")
+            continue  # Skip to next page on error
+    if not all_data:
+        raise ValueError("No data fetched from the API.")
+    print(f"Total records fetched: {len(all_data)}")
+    return pd.DataFrame(all_data)
+
+def create_monthly_transactions_chart(transactions_df):
+    transactions_df['transactionDate'] = pd.to_datetime(transactions_df['transactionDate'])
+    transactions_df['transactionMonth'] = transactions_df['transactionDate'].dt.to_period('M').dt.to_timestamp()
+
+    agg_data = transactions_df.groupby(['transactionMonth', 'acquistionOrDisposition']).agg({
+        'transactionDate': 'count',  # Count of transactions
+        'securitiesTransacted': 'sum'  # Sum of securities transacted
+    }).reset_index()
+
+    d_data = agg_data[agg_data['acquistionOrDisposition'] == 'D']
+    a_data = agg_data[agg_data['acquistionOrDisposition'] == 'A']
+
+    fig = go.Figure()
+
+    # Add line traces for count of transactions over time
+    fig.add_trace(go.Scatter(
+        x=d_data['transactionMonth'],
+        y=d_data['transactionDate'],
+        mode='lines+markers+text',
+        name='Disposition Transactions (Count)',
+        line=dict(color='red'),
+        text=d_data['transactionDate'],
+        textposition='top center'
+    ))
+
+    fig.add_trace(go.Scatter(
+        x=a_data['transactionMonth'],
+        y=a_data['transactionDate'],
+        mode='lines+markers+text',
+        name='Acquisition Transactions (Count)',
+        line=dict(color='green'),
+        text=a_data['transactionDate'],
+        textposition='top center'
+    ))
+
+    # Add bar traces for securities transacted
+    fig.add_trace(go.Bar(
+        x=d_data['transactionMonth'],
+        y=d_data['securitiesTransacted'],
+        name='Disposition Securities (Volume)',
+        marker_color='red',
+        yaxis='y2',
+        text=d_data['securitiesTransacted'],
+        textposition='auto'
+    ))
+
+    fig.add_trace(go.Bar(
+        x=a_data['transactionMonth'],
+        y=a_data['securitiesTransacted'],
+        name='Acquisition Securities (Volume)',
+        marker_color='green',
+        yaxis='y2',
+        text=a_data['securitiesTransacted'],
+        textposition='auto'
+    ))
+
+    # Update layout for dual y-axes
+    fig.update_layout(
+        title='Insider Trading Activity: Monthly Transactions and Securities Transacted (Count vs Volume)',
+        xaxis_title='Transaction Month',
+        yaxis=dict(title='Number of Transactions (Count)'),
+        yaxis2=dict(title='Securities Transacted (Volume)', overlaying='y', side='right'),
+        barmode='group',
+        legend_title='Legend',
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    return fig
+
+def create_trade_statistics_over_time_chart(trade_statistics_df):
+    trade_statistics_df['time'] = trade_statistics_df['year'].astype(str) + ' Q' + trade_statistics_df['quarter'].astype(str)
+    trade_statistics_df['datetime'] = pd.to_datetime(trade_statistics_df['year'].astype(str) + '-Q' + trade_statistics_df['quarter'].astype(str))
+    trade_statistics_df = trade_statistics_df.sort_values(by='datetime')
+
+    fig1 = go.Figure()
+
+    fig1.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['purchases'],
+        mode='lines+markers',
+        name='Purchases',
+        line=dict(color='blue')
+    ))
+
+    fig1.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['sales'],
+        mode='lines+markers',
+        name='Sales',
+        line=dict(color='orange')
+    ))
+
+    fig1.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['buySellRatio'],
+        mode='lines+markers',
+        name='Buy/Sell Ratio',
+        line=dict(color='purple'),
+        yaxis='y2'
+    ))
+
+    # Update layout for dual y-axes
+    fig1.update_layout(
+        title='Trade Statistics: Purchases, Sales, and Buy/Sell Ratio Over Time',
+        xaxis=dict(title='Time (Year and Quarter)', tickmode='linear'),
+        yaxis=dict(title='Count (Purchases/Sales)'),
+        yaxis2=dict(title='Buy/Sell Ratio', overlaying='y', side='right'),
+        legend_title='Legend',
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    return fig1
+
+def create_total_avg_bought_sold_chart(trade_statistics_df):
+    trade_statistics_df = trade_statistics_df.sort_values(by=['year', 'quarter'], ascending=True)
+    trade_statistics_df['avgBuySellRatio'] = trade_statistics_df['averageBought'] / trade_statistics_df['averageSold']
+
+    fig2 = go.Figure()
+
+    fig2.add_trace(go.Bar(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['totalBought'],
+        name='Total Bought',
+        marker_color='green'
+    ))
+
+    fig2.add_trace(go.Bar(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['totalSold'],
+        name='Total Sold',
+        marker_color='red'
+    ))
+
+    fig2.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['averageBought'],
+        mode='lines+markers',
+        name='Average Bought',
+        line=dict(color='blue')
+    ))
+
+    fig2.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['averageSold'],
+        mode='lines+markers',
+        name='Average Sold',
+        line=dict(color='orange')
+    ))
+
+    fig2.add_trace(go.Scatter(
+        x=trade_statistics_df['time'],
+        y=trade_statistics_df['avgBuySellRatio'],
+        mode='lines+markers',
+        name='Average Buy/Sell Ratio',
+        line=dict(color='purple'),
+        yaxis='y2'
+    ))
+
+    # Update layout for dual y-axes
+    fig2.update_layout(
+        title='Trade Statistics: Total and Average Bought/Sold with Average Buy/Sell Ratio Over Time',
+        xaxis=dict(title='Time (Year and Quarter)', tickmode='linear'),
+        yaxis=dict(title='Values (Total/Average Bought/Sold)'),
+        yaxis2=dict(title='Average Buy/Sell Ratio', overlaying='y', side='right'),
+        barmode='group',
+        legend_title='Legend',
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    return fig2
+
+def create_daily_counts_chart(df):
+    df['filingDate'] = pd.to_datetime(df['filingDate']).dt.date
+
+    aggregation = df.groupby(['filingDate', 'acquistionOrDisposition']).size().unstack(fill_value=0).reset_index()
+    aggregation = aggregation.rename(columns={
+        'A': 'Acquisition',
+        'D': 'Disposition'
+    })
+
+    for col in ['Acquisition', 'Disposition']:
+        if col not in aggregation.columns:
+            aggregation[col] = 0
+
+    min_date = aggregation['filingDate'].min()
+    max_date = aggregation['filingDate'].max()
+    all_dates = pd.DataFrame({'filingDate': pd.date_range(start=min_date, end=max_date)})
+    all_dates['filingDate'] = pd.to_datetime(all_dates['filingDate']).dt.date
+    daily_counts = all_dates.merge(aggregation, on='filingDate', how='left').fillna(0)
+    daily_counts[['Acquisition', 'Disposition']] = daily_counts[['Acquisition', 'Disposition']].astype(int)
+
+    fig_daily = px.bar(
+        daily_counts,
+        x='filingDate',
+        y=['Acquisition', 'Disposition'],
+        title='Daily Counts of Acquisitions (A) and Dispositions (D)',
+        labels={'filingDate': 'Filing Date', 'value': 'Count'},
+        barmode='group',
+        color_discrete_sequence=['#1f77b4', '#ff7f0e']
+    )
+
+    fig_daily.update_traces(
+        texttemplate='%{y}',
+        textposition='outside'
+    )
+
+    fig_daily.update_layout(
+        xaxis_title='Filing Date',
+        yaxis_title='Count',
+        uniformtext_minsize=8,
+        uniformtext_mode='hide',
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        margin=dict(t=50, b=50),
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    return fig_daily
+
+def create_top_bought_chart(df, top_n):
+    security_stats = df.groupby(['symbol', 'acquistionOrDisposition']).agg(
+        Transaction_Count=('acquistionOrDisposition', 'size'),
+        Total_Securities=('securitiesTransacted', 'sum')
+    ).unstack(fill_value=0).reset_index()
+
+    security_stats.columns = ['symbol'] + [f"{stat}_{action}" for stat, action in security_stats.columns if stat != 'symbol']
+
+    required_columns = ['Transaction_Count_A', 'Transaction_Count_D', 'Total_Securities_A', 'Total_Securities_D']
+    for col in required_columns:
+        if col not in security_stats.columns:
+            security_stats[col] = 0
+
+    security_stats = security_stats.rename(columns={
+        'Transaction_Count_A': 'Acquisition',
+        'Transaction_Count_D': 'Disposition',
+        'Total_Securities_A': 'Total_Securities_Bought',
+        'Total_Securities_D': 'Total_Securities_Sold'
+    })
+
+    top_bought = (
+        security_stats[security_stats['Acquisition'] > 0]
+        .sort_values(by='Acquisition', ascending=False)
+        .head(top_n)
+        .reset_index(drop=True)
+    )
+
+    top_bought['Total_Sold'] = top_bought['Disposition']
+
+    fig_top_bought = go.Figure()
+
+    fig_top_bought.add_trace(
+        go.Bar(
+            x=top_bought['symbol'],
+            y=top_bought['Acquisition'],
+            name='Acquisition',
+            marker_color='#1f77b4',
+            text=top_bought['Acquisition'],
+            textposition='outside'
+        )
+    )
+
+    fig_top_bought.add_trace(
+        go.Bar(
+            x=top_bought['symbol'],
+            y=top_bought['Total_Sold'],
+            name='Total Sold',
+            marker_color='#aec7e8',
+            text=top_bought['Total_Sold'],
+            textposition='outside'
+        )
+    )
+
+    fig_top_bought.add_trace(
+        go.Scatter(
+            x=top_bought['symbol'],
+            y=top_bought['Total_Securities_Bought'],
+            name='Total Securities Bought',
+            mode='lines+markers+text',
+            yaxis='y2',
+            marker=dict(color='green'),
+            text=top_bought['Total_Securities_Bought'],
+            textposition='top center',
+            textfont=dict(color='green')
+        )
+    )
+
+    fig_top_bought.update_layout(
+        title=f'Top {top_n} Most Frequently Bought Securities',
+        xaxis=dict(title='Symbol'),
+        yaxis=dict(
+            title='Transaction Count',
+            titlefont=dict(color='#1f77b4'),
+            tickfont=dict(color='#1f77b4')
+        ),
+        yaxis2=dict(
+            title='Total Securities Bought',
+            titlefont=dict(color='green'),
+            tickfont=dict(color='green'),
+            overlaying='y',
+            side='right'
+        ),
+        legend=dict(
+            x=1.05,
+            y=1,
+            traceorder='normal',
+            bgcolor='rgba(255,255,255,0)',
+            bordercolor='rgba(255,255,255,0)'
+        ),
+        barmode='group',
+        uniformtext_minsize=8,
+        uniformtext_mode='hide',
+        margin=dict(l=50, r=150, t=50, b=50),
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    fig_top_bought.update_traces(
+        selector=dict(name='Total Securities Bought'),
+        texttemplate='%{y}',
+        textposition='top center'
+    )
+
+    return fig_top_bought
+
+def create_top_sold_chart(df, top_n):
+    security_stats = df.groupby(['symbol', 'acquistionOrDisposition']).agg(
+        Transaction_Count=('acquistionOrDisposition', 'size'),
+        Total_Securities=('securitiesTransacted', 'sum')
+    ).unstack(fill_value=0).reset_index()
+
+    security_stats.columns = ['symbol'] + [f"{stat}_{action}" for stat, action in security_stats.columns if stat != 'symbol']
+
+    required_columns = ['Transaction_Count_A', 'Transaction_Count_D', 'Total_Securities_A', 'Total_Securities_D']
+    for col in required_columns:
+        if col not in security_stats.columns:
+            security_stats[col] = 0
+
+    security_stats = security_stats.rename(columns={
+        'Transaction_Count_A': 'Acquisition',
+        'Transaction_Count_D': 'Disposition',
+        'Total_Securities_A': 'Total_Securities_Bought',
+        'Total_Securities_D': 'Total_Securities_Sold'
+    })
+
+    top_sold = (
+        security_stats[security_stats['Disposition'] > 0]
+        .sort_values(by='Disposition', ascending=False)
+        .head(top_n)
+        .reset_index(drop=True)
+    )
+
+    top_sold['Total_Bought'] = top_sold['Acquisition']
+
+    fig_top_sold = go.Figure()
+
+    fig_top_sold.add_trace(
+        go.Bar(
+            x=top_sold['symbol'],
+            y=top_sold['Disposition'],
+            name='Disposition',
+            marker_color='#ff7f0e',
+            text=top_sold['Disposition'],
+            textposition='outside'
+        )
+    )
+
+    fig_top_sold.add_trace(
+        go.Bar(
+            x=top_sold['symbol'],
+            y=top_sold['Total_Bought'],
+            name='Total Bought',
+            marker_color='#ffbb78',
+            text=top_sold['Total_Bought'],
+            textposition='outside'
+        )
+    )
+
+    fig_top_sold.add_trace(
+        go.Scatter(
+            x=top_sold['symbol'],
+            y=top_sold['Total_Securities_Sold'],
+            name='Total Securities Sold',
+            mode='lines+markers+text',
+            yaxis='y2',
+            marker=dict(color='purple'),
+            text=top_sold['Total_Securities_Sold'],
+            textposition='top center',
+            textfont=dict(color='purple')
+        )
+    )
+
+    fig_top_sold.update_layout(
+        title=f'Top {top_n} Most Frequently Sold Securities',
+        xaxis=dict(title='Symbol'),
+        yaxis=dict(
+            title='Transaction Count',
+            titlefont=dict(color='#ff7f0e'),
+            tickfont=dict(color='#ff7f0e')
+        ),
+        yaxis2=dict(
+            title='Total Securities Sold',
+            titlefont=dict(color='purple'),
+            tickfont=dict(color='purple'),
+            overlaying='y',
+            side='right'
+        ),
+        legend=dict(
+            x=1.05,
+            y=1,
+            traceorder='normal',
+            bgcolor='rgba(255,255,255,0)',
+            bordercolor='rgba(255,255,255,0)'
+        ),
+        barmode='group',
+        uniformtext_minsize=8,
+        uniformtext_mode='hide',
+        margin=dict(l=50, r=150, t=50, b=50),
+        template='plotly_white',
+        font=dict(size=12),
+        title_font=dict(size=16),
+        hovermode='x unified'
+    )
+
+    fig_top_sold.update_traces(
+        selector=dict(name='Total Securities Sold'),
+        texttemplate='%{y}',
+        textposition='top center'
+    )
+
+    return fig_top_sold
+
+# ----------------------------
+# Page 1: Ticker Insider Trades
+# ----------------------------
+def ticker_insider_trades_page():
+    st.title("Ticker Insider Trades")
+    st.markdown("""
+    Analyze insider trading activities for a specific stock ticker. 
+    View the latest insider transactions, trade statistics over time, and detailed data tables.
+    """)
+
+    # Inputs in the sidebar
+    with st.sidebar.expander("Parameters", expanded=True):
+        symbol = st.text_input(
+            "Enter Stock Ticker Symbol",
+            value=st.session_state['ticker_insider_trades'].get('symbol', 'AAPL'),
+            help="Enter the stock ticker symbol (e.g., AAPL for Apple Inc.).").upper()
+
+    if st.sidebar.button("Fetch Data"):
+        with st.spinner("Fetching insider trading data..."):
+            try:
+                latest_transactions, trade_statistics = get_insider_trading_data(symbol)
+                # Save to session state
+                st.session_state['ticker_insider_trades']['symbol'] = symbol
+                st.session_state['ticker_insider_trades']['latest_transactions'] = latest_transactions
+                st.session_state['ticker_insider_trades']['trade_statistics'] = trade_statistics
+                st.success("Data fetched successfully!")
+            except Exception as e:
+                st.error(f"Error fetching data: {e}")
+
+    # Check if data is available
+    if 'latest_transactions' in st.session_state['ticker_insider_trades']:
+        latest_transactions = st.session_state['ticker_insider_trades']['latest_transactions']
+        trade_statistics = st.session_state['ticker_insider_trades']['trade_statistics']
+
+        # Display Charts
+        #st.subheader("Insider Trading Activity Insights")
+
+        # Chart 1: Monthly Transactions and Securities Transacted
+        st.markdown("""
+        ### Monthly Transactions and Securities Transacted
+        This chart visualizes the number of acquisition and disposition transactions each month, alongside the total volume of securities transacted. 
+        - **Lines** represent the count of transactions.
+        - **Bars** represent the volume of securities transacted.
+        This dual-axis chart helps in understanding both the frequency and the magnitude of insider trades over time.
+        """)
+        st.plotly_chart(create_monthly_transactions_chart(latest_transactions), use_container_width=True)
+
+        # Chart 2: Trade Statistics Over Time
+        st.markdown("""
+        ### Trade Statistics Over Time
+        This chart displays the trends in purchases, sales, and the buy/sell ratio over different quarters.
+        - **Purchases and Sales**: Represent the count of buy and sell transactions.
+        - **Buy/Sell Ratio**: Indicates the balance between buying and selling activities.
+        """)
+        st.plotly_chart(create_trade_statistics_over_time_chart(trade_statistics), use_container_width=True)
+
+        # Chart 3: Total and Average Bought/Sold
+        st.markdown("""
+        ### Total and Average Bought/Sold with Average Buy/Sell Ratio Over Time
+        This chart combines total and average amounts of securities bought and sold, along with the average buy/sell ratio.
+        - **Bars** show the total bought and sold.
+        - **Lines** indicate the average bought and sold per transaction.
+        - **Secondary Y-axis** displays the average buy/sell ratio.
+        """)
+        st.plotly_chart(create_total_avg_bought_sold_chart(trade_statistics), use_container_width=True)
+
+        # Display DataFrames
+        st.subheader("Latest Transactional Data")
+        st.markdown("""
+        Below is the detailed table of the most recent insider transactions for the selected ticker. 
+        It includes information such as the date of transaction, type (acquisition or disposition), and the number of securities transacted.
+        """)
+        st.dataframe(latest_transactions, use_container_width=True)
+
+        st.subheader("Trade Statistics Data")
+        st.markdown("""
+        This table presents aggregated trade statistics over time, including total purchases, sales, and the buy/sell ratio for each quarter.
+        """)
+        st.dataframe(trade_statistics, use_container_width=True)
+
+# ----------------------------
+# Page 2: Insider Trades Live Feed
+# ----------------------------
+def insider_trades_live_feed_page():
+    st.title("Insider Trades Live Feed")
+    st.markdown("""
+    Monitor real-time insider trading activities across various securities. 
+    Visualize daily counts, and identify top traded securities.
+    """)
+    
+    
+    with st.sidebar.expander("Parameters", expanded=True):
+        # Inputs in the sidebar
+        top_n = st.number_input(
+            "Number of Top Securities to Display",
+            min_value=1,
+            max_value=20,
+            value=st.session_state['insider_trades_live_feed'].get('top_n', DEFAULT_TOP_N),
+            help="Specify the number of top traded securities to display."
+        )
+
+    if st.sidebar.button("Fetch Live Feed"):
+        with st.spinner("Fetching live insider trading data..."):
+            try:
+                df = fetch_insider_trading_live_feed()
+                # Save to session state
+                st.session_state['insider_trades_live_feed']['top_n'] = top_n
+                st.session_state['insider_trades_live_feed']['df'] = df
+                st.success("Live feed data fetched successfully!")
+            except Exception as e:
+                st.error(f"Error fetching live feed data: {e}")
+
+    # Check if data is available
+    if 'df' in st.session_state['insider_trades_live_feed']:
+        df = st.session_state['insider_trades_live_feed']['df']
+        top_n = st.session_state['insider_trades_live_feed']['top_n']
+
+        # Display Charts
+        #st.subheader("Live Insider Trading Activity Charts")
+
+        # Chart 1: Daily Counts of Acquisitions and Dispositions
+        st.markdown("""
+        ### Daily Counts of Acquisitions and Dispositions
+        This chart displays the daily number of acquisition (A) and disposition (D) transactions across all tracked securities.
+        - **Bars** represent the count of acquisitions and dispositions each day.
+        """)
+        st.plotly_chart(create_daily_counts_chart(df), use_container_width=True)
+
+        # Chart 2: Top N Most Frequently Bought Securities
+        st.markdown(f"""
+        ### Top {top_n} Most Frequently Bought Securities
+        This chart highlights the top {top_n} securities with the highest number of acquisition transactions.
+        - **Bars** represent the count of acquisitions.
+        - **Secondary Line** shows the total volume of securities bought.
+        """)
+        st.plotly_chart(create_top_bought_chart(df, top_n), use_container_width=True)
+
+        # Chart 3: Top N Most Frequently Sold Securities
+        st.markdown(f"""
+        ### Top {top_n} Most Frequently Sold Securities
+        This chart highlights the top {top_n} securities with the highest number of disposition transactions.
+        - **Bars** represent the count of dispositions.
+        - **Secondary Line** shows the total volume of securities sold.
+        """)
+        st.plotly_chart(create_top_sold_chart(df, top_n), use_container_width=True)
+
+        # Display DataFrames
+        st.subheader("Complete Insider Trades Data")
+        st.markdown("""
+        Below is the comprehensive table of all fetched insider trading activities. 
+        It includes details such as the filing date, transaction type (A for acquisition, D for disposition), and the number of securities transacted.
+        """)
+        st.dataframe(df, use_container_width=True)
+
+# ----------------------------
+# Display the Selected Page
+# ----------------------------
+if page == "Ticker Insider Trades":
+    ticker_insider_trades_page()
+elif page == "Insider Trades Live Feed":
+    insider_trades_live_feed_page()
+
+
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)