Create app.py

app.py

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+import streamlit as st
+from datetime import date
+import yfinance as yf
+from fbprophet import Prophet
+from fbprophet.plot import plot_plotly
+from plotly import graph_objs as go
+
+# Constants for date range
+START = "2015-01-01"
+TODAY = date.today().strftime("%Y-%m-%d")
+
+# Streamlit app title
+st.title('Stock Forecast App')
+
+# Stock selection
+stocks = ('GOOG', 'AAPL', 'MSFT', 'GME')
+selected_stock = st.selectbox('Select dataset for prediction', stocks)
+
+# Years of prediction slider
+n_years = st.slider('Years of prediction:', 1, 4)
+period = n_years * 365
+
+@st.cache
+def load_data(ticker):
+    """Load stock data from Yahoo Finance."""
+    data = yf.download(ticker, START, TODAY)
+    data.reset_index(inplace=True)
+    return data
+
+# Load data and show loading state
+data_load_state = st.text('Loading data...')
+data = load_data(selected_stock)
+data_load_state.text('Loading data... done!')
+
+# Display raw data
+st.subheader('Raw data')
+st.write(data.tail())
+
+# Plot raw data function
+def plot_raw_data():
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Stock Open"))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Stock Close"))
+    fig.layout.update(title_text='Time Series Data with Rangeslider', xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+
+# Call the plotting function
+plot_raw_data()
+
+# Prepare data for Prophet model
+df_train = data[['Date', 'Close']]
+df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
+
+# Create and fit the Prophet model
+m = Prophet()
+m.fit(df_train)
+
+# Create future dataframe and make predictions
+future = m.make_future_dataframe(periods=period)
+forecast = m.predict(future)
+
+# Show forecast data and plot forecast
+st.subheader('Forecast data')
+st.write(forecast.tail())
+st.write(f'Forecast plot for {n_years} years')
+
+fig1 = plot_plotly(m, forecast)
+st.plotly_chart(fig1)
+
+# Show forecast components
+st.write("Forecast components")
+fig2 = m.plot_components(forecast)
+st.write(fig2)