Upload main.py

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main.py

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+import streamlit as st
+import yfinance as yf
+import plotly.graph_objects as go
+from prophet import Prophet
+import pandas as pd
+import matplotlib.pyplot as plt
+from statsmodels.tsa.seasonal import seasonal_decompose
+
+from statsmodels.graphics.gofplots import qqplot
+
+# Set Streamlit options
+st.set_page_config(layout="wide")  # Use wide page layout
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+
+# Header section with enhanced aesthetics
+def display_header():
+    st.sidebar.title("Navigation")
+    st.sidebar.info("""
+        This application is designed to provide users with financial insights and predictive analysis 
+        on various stocks using real-time data from Yahoo Finance. Created by Gokul Palanisamy.
+    """)
+    st.sidebar.title("About Us")
+    st.sidebar.info("""
+        Developed by Gokul Palanisamy, this tool helps users make informed investment decisions by 
+        analyzing historical data and predicting future stock trends.
+    """)
+    st.sidebar.title("Contact Us")
+    st.sidebar.info("""
+        Email: [gokulp@bu.edu](mailto:gokulp@bu.edu)
+        Phone: +1 (857) 832-0441
+        More Information: [Gokul Palanisamy](https://www.linkedin.com/in/gokulp/)
+    """)
+
+
+display_header()
+
+# Input widgets for stock symbols and settings
+st.markdown("## StocX AI")
+col1, col2, col3 = st.columns(3)
+with col1:
+    stock1 = st.text_input('Enter Stock Ticker Symbol 1', value='', key='stock1_input')
+with col2:
+    stock2 = st.text_input('Enter Stock Ticker Symbol 2', value='', key='stock2_input')
+with col3:
+    daysago = st.text_input('Select Time Frame in Days (write "max" for maximum time)', value='1y', key='daysago_input')
+forecast_out = st.slider('Predicted Days Ahead', 1, 180, 30)
+
+
+# Fetch and display stock data
+def fetch_data(ticker, period):
+    return yf.Ticker(ticker).history(period=period)
+
+
+data1, data2 = None, None
+if stock1:
+    data1 = fetch_data(stock1, daysago)
+    st.write(f'### {stock1} Stock Data')
+    st.write(data1)
+
+if stock2:
+    data2 = fetch_data(stock2, daysago)
+    st.write(f'### {stock2} Stock Data')
+    st.write(data2)
+
+
+# Function to analyze and compare stocks
+def compare_stocks(stock1, data1, stock2, data2):
+    if data1 is not None and data2 is not None:
+        # Calculate percentage change for each stock
+        pct_change1 = (data1['Close'].iloc[-1] - data1['Close'].iloc[0]) / data1['Close'].iloc[0] * 100
+        pct_change2 = (data2['Close'].iloc[-1] - data2['Close'].iloc[0]) / data2['Close'].iloc[0] * 100
+
+        better_stock = stock1 if pct_change1 > pct_change2 else stock2
+        reason = f"{better_stock} had a higher percentage change ({max(pct_change1, pct_change2):.2f}%) over the period."
+
+        st.write("## Stock Performance Comparison")
+        st.write(f"**{stock1}** percentage change: {pct_change1:.2f}%")
+        st.write(f"**{stock2}** percentage change: {pct_change2:.2f}%")
+        st.write(f"**Better Performing Stock:** {better_stock}")
+        st.write(f"**Reason:** {reason}")
+
+
+if stock1 and stock2 and data1 is not None and data2 is not None:
+    compare_stocks(stock1, data1, stock2, data2)
+
+
+# Comprehensive analysis functions
+def perform_analysis(stock, data):
+    if data is not None and not data.empty:
+        st.write(f"### {stock} Detailed Analysis")
+
+        # Time Series Decomposition
+        st.write(f"#### Time Series Decomposition")
+        decomposition = seasonal_decompose(data['Close'], period=30, model='additive')
+        fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 8))
+        decomposition.observed.plot(ax=ax1, title='Observed')
+        decomposition.trend.plot(ax=ax2, title='Trend')
+        decomposition.seasonal.plot(ax=ax3, title='Seasonal')
+        decomposition.resid.plot(ax=ax4, title='Residual')
+        plt.tight_layout()
+        st.pyplot()
+
+        # Prophet Prediction
+        st.write(f"#### Prophet Forecast")
+        df_prophet = pd.DataFrame(data={'ds': data.index, 'y': data['Close']})
+        df_prophet['ds'] = pd.to_datetime(df_prophet['ds']).dt.tz_localize(None)  # Make timezone naive
+        model = Prophet()
+        model.fit(df_prophet)
+        future = model.make_future_dataframe(periods=forecast_out)
+        forecast = model.predict(future)
+        fig = plot_prophet_forecast(model, forecast)
+        st.plotly_chart(fig)
+
+
+# Helper function for Plotly forecast visualization
+def plot_prophet_forecast(model, forecast):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=model.history['ds'], y=model.history['y'], mode='lines', name='Actual'))
+    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines+markers', name='Forecast'))
+    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], fill=None, mode='lines',
+                             line=dict(color='gray', dash='dash'), name='Upper Confidence Interval'))
+    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], fill='tonexty', mode='lines',
+                             line=dict(color='gray', dash='dash'), name='Lower Confidence Interval'))
+    fig.update_layout(title='Prophet Forecast and Confidence Intervals', xaxis_title='Date', yaxis_title='Values',
+                      hovermode='x')
+    return fig
+
+
+# Call analysis functions if data is available
+if stock1 and data1 is not None:
+    perform_analysis(stock1, data1)
+if stock2 and data2 is not None:
+    perform_analysis(stock2, data2)