Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+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
+
+# Step 2: Define a function to calculate indicators
+def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendLength=50):
+    # Calculate Moving Averages
+    df['MA20'] = df['Close'].rolling(window=20).mean()
+    df['MA50'] = df['Close'].rolling(window=50).mean()
+
+    # Calculate EMA for Buy Signal
+    df['SignalEMA'] = df['Close'].ewm(span=lengthEMA, adjust=False).mean()
+
+    # Calculate RSI
+    delta = df['Close'].diff(1)
+    gain = (delta.where(delta > 0, 0)).rolling(window=lengthRSI).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=lengthRSI).mean()
+    rs = gain / loss
+    df['RSI'] = 100 - (100 / (1 + rs))
+
+    # Calculate Momentum
+    df['Momentum'] = df['Close'].diff(momentumLength)
+
+    # Composite SIGNAL Calculation
+    df['SignalComposite'] = (0.5 * df['SignalEMA']) + (0.3 * (df['RSI'] - 50) / 100) + (0.2 * (df['Momentum'] / df['Close'].rolling(window=lengthRSI).mean()))
+
+    # Smooth the Composite SIGNAL with EMA
+    df['SmoothedSignal'] = df['SignalComposite'].ewm(span=lengthEMA, adjust=False).mean()
+
+    # Trend Filter (SMA)
+    df['TrendSMA'] = df['Close'].rolling(window=trendLength).mean()
+
+    # Buy and Sell Signals
+    buyThreshold = 0.75
+    sellThreshold = -0.75
+    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA'])
+    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA'])
+
+    return df
+
+# Step 3: Streamlit UI Setup for Stock Selection
+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")
+
+# 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.head())
+
+    # 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)}")