Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import yfinance as yf
+
+# Importing forecasting algorithms from the algo directory
+from algo.sarima import sarima_forecast
+from algo.linear_regression import linear_regression_forecast
+from algo.tbats import tbats_forecast
+from algo.random_forest import random_forest_forecast
+
+# Function to fetch stock data
+def fetch_stock_data(ticker, start_date, end_date):
+    data = yf.download(ticker, start=start_date, end=end_date)
+    return data['Close']
+
+# Function to plot forecasts
+def plot_forecasts(data, forecasts, title='Stock Price Forecast'):
+    plt.figure(figsize=(10, 6))
+    plt.plot(data.index, data, label='Historical Prices', color='black', alpha=0.75)
+    
+    for name, forecast in forecasts.items():
+        plt.plot(forecast.index, forecast, label=name)
+    
+    if len(forecasts) > 1:
+        combined_forecast = pd.concat(forecasts.values()).groupby(level=0).mean()
+        plt.plot(combined_forecast.index, combined_forecast, label='Combined Forecast', color='red', linestyle='--')
+    
+    plt.title(title)
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    st.pyplot(plt)
+
+# Streamlit UI in Sidebar
+st.sidebar.title("Input Parameters")
+ticker = st.sidebar.text_input('Enter Ticker Symbol', 'AAPL')
+start_date = st.sidebar.date_input('Select Start Date', value=pd.to_datetime('2020-01-01'))
+end_date = st.sidebar.date_input('Select End Date', value=pd.to_datetime('2023-01-01'))
+forecast_horizon = st.sidebar.number_input('Forecast Horizon (days)', min_value=1, value=180)
+forecast_date = st.sidebar.date_input('Forecast Date', min_value=end_date, value=end_date + pd.Timedelta(days=180))
+
+# User selects which forecasting models to use in Sidebar
+options = st.sidebar.multiselect('Select forecasting models to use',
+                                 ['SARIMA', 'Linear Regression', 'TBATS', 'Random Forest'],
+                                 ['SARIMA', 'Linear Regression'])
+
+if st.sidebar.button('Analyze'):
+    data = fetch_stock_data(ticker, start_date, end_date)
+    forecasts = {}
+    
+    if 'SARIMA' in options:
+        forecasts['SARIMA'] = sarima_forecast(data, forecast_horizon)
+    if 'Linear Regression' in options:
+        forecasts['Linear Regression'] = linear_regression_forecast(data, forecast_horizon)
+    if 'TBATS' in options:
+        forecasts['TBATS'] = tbats_forecast(data, forecast_horizon)
+    if 'Random Forest' in options:
+        forecasts['Random Forest'] = random_forest_forecast(data, forecast_horizon)
+    
+    plot_forecasts(data, forecasts, f"Forecasted Stock Prices for {ticker}")
+    
+    # Output the forecasted price for the selected date, if available
+    forecast_date_str = forecast_date.strftime('%Y-%m-%d')
+    for model_name, forecast in forecasts.items():
+        if forecast_date_str in forecast.index:
+            st.write(f"Forecasted price by {model_name} on {forecast_date_str}: {forecast.loc[forecast_date_str]:.2f}")