Update app.py

app.py

@@ -4,23 +4,29 @@ import numpy as np
 import plotly.graph_objects as go
 import streamlit as st
 import altair as alt
-import matplotlib.pyplot as plt
-import seaborn as sns
 from bs4 import BeautifulSoup
 import requests
 from datetime import datetime
 from rich import print
-import jsonschema
 
 # Step 1: Define a function to fetch real-time market data up to the current date
 def fetch_data(ticker_symbol):
-    ticker = yf.Ticker(ticker_symbol)
-    end_date = datetime.now().strftime('%Y-%m-%d')  # Get the current date
-    data = ticker.history(start="2023-01-01", end=end_date)  # Fetches data from the start of this year to the current date
-    return data
+    try:
+        ticker = yf.Ticker(ticker_symbol)
+        end_date = datetime.now().strftime('%Y-%m-%d')  # Get the current date
+        data = ticker.history(start="2023-01-01", end=end_date)  # Fetches data from the start of this year to the current date
+        if data.empty:
+            raise ValueError("No data found for the given ticker. Please check the ticker symbol.")
+        return data
+    except Exception as e:
+        st.error(f"An error occurred while fetching data: {str(e)}")
+        return pd.DataFrame()
 
 # Step 2: Define a function to calculate indicators
 def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendLength=50):
+    if df.empty:
+        return df
+
     # Calculate Moving Averages
     df['MA20'] = df['Close'].rolling(window=20).mean()
     df['MA50'] = df['Close'].rolling(window=50).mean()
@@ -60,97 +66,98 @@ st.title("Advanced Indian Share Market Analysis")
 st.sidebar.header("Select Stock Ticker")
 
 # Add a stock selector input box
-ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., RELIANCE.NS, ^NSEI)", "^NSEI")
+ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., RELIANCE.NS for NSE or TCS.BO for BSE)", "^NSEI")
 
 # Step 4: Fetch Data and Calculate Indicators
 try:
     nifty_data = fetch_data(ticker_symbol)
     nifty_data = calculate_indicators(nifty_data)
 
-    # Step 5: Display Stock Data
-    st.subheader(f"Data Overview for {ticker_symbol}")
-    st.write(nifty_data.tail())
-
-    # Step 6: Interactive Visualization Using Plotly
-    st.subheader(f"Interactive Buy and Sell Signals for {ticker_symbol}")
-    fig = go.Figure()
-
-    # Add Close Price line
-    fig.add_trace(go.Scatter(
-        x=nifty_data.index,
-        y=nifty_data['Close'],
-        mode='lines',
-        name='Close Price',
-        line=dict(color='blue')
-    ))
-
-    # Add Trend SMA line
-    fig.add_trace(go.Scatter(
-        x=nifty_data.index,
-        y=nifty_data['TrendSMA'],
-        mode='lines',
-        name='Trend SMA',
-        line=dict(color='gray')
-    ))
-
-    # Plot Buy Signals
-    buy_signals = nifty_data[nifty_data['BuySignal']]
-    fig.add_trace(go.Scatter(
-        x=buy_signals.index,
-        y=buy_signals['Close'],
-        mode='markers',
-        name='Buy Signal',
-        marker=dict(symbol='triangle-up', color='green', size=10)
-    ))
-
-    # Plot Sell Signals
-    sell_signals = nifty_data[nifty_data['SellSignal']]
-    fig.add_trace(go.Scatter(
-        x=sell_signals.index,
-        y=sell_signals['Close'],
-        mode='markers',
-        name='Sell Signal',
-        marker=dict(symbol='triangle-down', color='red', size=10)
-    ))
-
-    # Update layout for better readability
-    fig.update_layout(
-        title=f"Buy and Sell Signals with Trend Filter for {ticker_symbol}",
-        xaxis_title="Date",
-        yaxis_title="Close Price",
-        legend_title="Legend",
-        template="plotly_dark"
-    )
-
-    st.plotly_chart(fig)
-
-    # Step 7: Altair Chart for Moving Averages
-    st.subheader(f"Moving Averages for {ticker_symbol}")
-    alt_chart = alt.Chart(nifty_data.reset_index()).mark_line().encode(
-        x='Date:T',
-        y=alt.Y('MA20:Q', title='Moving Average (20-day)'),
-        color=alt.value('orange')
-    ).properties(title="20-Day Moving Average")
-    st.altair_chart(alt_chart, use_container_width=True)
-
-    # Step 8: Market News Integration Using BeautifulSoup
-    st.sidebar.header("Latest Market News")
-    news_url = 'https://www.moneycontrol.com/news/'
-    response = requests.get(news_url)
-    soup = BeautifulSoup(response.content, 'html.parser')
-
-    headlines = [headline.text for headline in soup.find_all('h2')[:5]]
-    st.sidebar.subheader("Top 5 News Headlines")
-    for idx, headline in enumerate(headlines, 1):
-        st.sidebar.write(f"{idx}. {headline}")
-
-    # Step 9: Alerts Using Rich Library
-    st.sidebar.subheader("Set Alerts:")
-    alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
-    alert_value = st.sidebar.number_input('Enter Alert Value')
-
-    if alert_value:
-        print(f"[bold cyan]Alert Set for {alert_type} at Value:[/bold cyan] {alert_value}")
+    # Step 5: Display Stock Data if available
+    if not nifty_data.empty:
+        st.subheader(f"Data Overview for {ticker_symbol}")
+        st.write(nifty_data.tail())
+
+        # Step 6: Interactive Visualization Using Plotly
+        st.subheader(f"Interactive Buy and Sell Signals for {ticker_symbol}")
+        fig = go.Figure()
+
+        # Add Close Price line
+        fig.add_trace(go.Scatter(
+            x=nifty_data.index,
+            y=nifty_data['Close'],
+            mode='lines',
+            name='Close Price',
+            line=dict(color='blue')
+        ))
+
+        # Add Trend SMA line
+        fig.add_trace(go.Scatter(
+            x=nifty_data.index,
+            y=nifty_data['TrendSMA'],
+            mode='lines',
+            name='Trend SMA',
+            line=dict(color='gray')
+        ))
+
+        # Plot Buy Signals
+        buy_signals = nifty_data[nifty_data['BuySignal']]
+        fig.add_trace(go.Scatter(
+            x=buy_signals.index,
+            y=buy_signals['Close'],
+            mode='markers',
+            name='Buy Signal',
+            marker=dict(symbol='triangle-up', color='green', size=10)
+        ))
+
+        # Plot Sell Signals
+        sell_signals = nifty_data[nifty_data['SellSignal']]
+        fig.add_trace(go.Scatter(
+            x=sell_signals.index,
+            y=sell_signals['Close'],
+            mode='markers',
+            name='Sell Signal',
+            marker=dict(symbol='triangle-down', color='red', size=10)
+        ))
+
+        # Update layout for better readability
+        fig.update_layout(
+            title=f"Buy and Sell Signals with Trend Filter for {ticker_symbol}",
+            xaxis_title="Date",
+            yaxis_title="Close Price",
+            legend_title="Legend",
+            template="plotly_dark"
+        )
+
+        st.plotly_chart(fig)
+
+        # Step 7: Altair Chart for Moving Averages
+        st.subheader(f"Moving Averages for {ticker_symbol}")
+        alt_chart = alt.Chart(nifty_data.reset_index()).mark_line().encode(
+            x='Date:T',
+            y=alt.Y('MA20:Q', title='Moving Average (20-day)'),
+            color=alt.value('orange')
+        ).properties(title="20-Day Moving Average")
+        st.altair_chart(alt_chart, use_container_width=True)
+
+        # Step 8: Market News Integration Using BeautifulSoup
+        st.sidebar.header("Latest Market News")
+        news_url = 'https://www.moneycontrol.com/news/'
+        response = requests.get(news_url)
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        headlines = [headline.text for headline in soup.find_all('h2')[:5]]
+        st.sidebar.subheader("Top 5 News Headlines")
+        for idx, headline in enumerate(headlines, 1):
+            st.sidebar.write(f"{idx}. {headline}")
+
+        # Step 9: Alerts Using Rich Library
+        st.sidebar.subheader("Set Alerts:")
+        alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
+        alert_value = st.sidebar.number_input('Enter Alert Value')
+
+        if alert_value:
+            print(f"[bold cyan]Alert Set for {alert_type} at Value:[/bold cyan] {alert_value}")
 
 except Exception as e:
     st.error(f"An error occurred: {str(e)}")