app.py

import streamlit as st
import pandas as pd
import numpy as np
from prophet import Prophet
import yfinance as yf
from sklearn.metrics import mean_absolute_error, mean_squared_error
from prophet.plot import plot_plotly, plot_components_plotly

# Function to fetch stock data from Yahoo Finance
def fetch_stock_data(ticker_symbol, start_date, end_date):
    stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
    df = stock_data[['Adj Close']].reset_index()
    df = df.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
    return df

# Function to train the Prophet model
def train_prophet_model(df):
    model = Prophet()
    model.fit(df)
    return model

# Function to make the forecast
def make_forecast(model, periods):
    future = model.make_future_dataframe(periods=periods)
    forecast = model.predict(future)
    return forecast

# Function to calculate performance metrics
def calculate_performance_metrics(actual, predicted):
    mae = mean_absolute_error(actual, predicted)
    mse = mean_squared_error(actual, predicted)
    rmse = np.sqrt(mse)
    return {'MAE': mae, 'MSE': mse, 'RMSE': rmse}

# Streamlit app
def main():
    st.title('Stock Forecasting with Prophet')

    # Set up the layout
    st.sidebar.header('User Input Parameters')
    ticker_symbol = st.sidebar.text_input('Enter Ticker Symbol', 'RACE')
    start_date = st.sidebar.date_input('Start Date', value=pd.to_datetime('2015-01-01'))
    end_date = st.sidebar.date_input('End Date', value=pd.to_datetime('today'))

    # Dropdown for forecast horizon selection
    forecast_horizon = st.sidebar.selectbox('Forecast Horizon', 
                                            options=['1 year', '2 years', '3 years', '5 years'],
                                            format_func=lambda x: x.capitalize())
    
    # Convert the selected horizon to days
    horizon_mapping = {'1 year': 365, '2 years': 730, '3 years': 1095, '5 years': 1825}
    forecast_days = horizon_mapping[forecast_horizon]

    if st.sidebar.button('Forecast Stock Prices'):
        with st.spinner('Fetching data...'):
            df = fetch_stock_data(ticker_symbol, start_date, end_date)

        with st.spinner('Training model...'):
            model = train_prophet_model(df)
            forecast = make_forecast(model, forecast_days)

        st.subheader('Forecast Data')
        st.write('The table below shows the forecasted stock prices along with the lower and upper bounds of the predictions.')
        st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head())

        st.subheader('Forecast Plot')
        st.write('The plot below visualizes the predicted stock prices with their confidence intervals.')
        fig1 = plot_plotly(model, forecast)
        fig1.update_traces(marker=dict(color='red'), line=dict(color='white'))
        st.plotly_chart(fig1)

        st.subheader('Forecast Components')
        st.write('This plot breaks down the forecast into trend, weekly, and yearly components.')
        fig2 = plot_components_plotly(model, forecast)
        fig2.update_traces(line=dict(color='white'))
        st.plotly_chart(fig2)

        st.subheader('Performance Metrics')

        st.write('The metrics below provide a quantitative measure of the model’s accuracy.')
        st.write('Mean Absolute Error (MAE): A lower value indicates better performance.')
        st.write('Mean Squared Error (MSE): A lower value indicates better performance, and it penalizes larger errors more than MAE.')
        st.write('Root Mean Squared Error (RMSE): A lower value indicates better performance, similar to MSE, but in the same units as the target variable.')
        
        actual = df['y']
        predicted = forecast['yhat'][:len(df)]
        metrics = calculate_performance_metrics(actual, predicted)
        st.metric(label="Mean Absolute Error (MAE)", value="{:.2f}".format(metrics['MAE']), delta="Lower is better")
        st.metric(label="Mean Squared Error (MSE)", value="{:.2f}".format(metrics['MSE']), delta="Lower is better")
        st.metric(label="Root Mean Squared Error (RMSE)", value="{:.2f}".format(metrics['RMSE']), delta="Lower is better")

# Run the main function
if __name__ == "__main__":
    main()