add the application file

app.py

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+# pip install streamlit fbprophet yfinance plotly
+import streamlit as st
+from datetime import date, datetime, timedelta
+
+import yfinance as yf
+from prophet import Prophet
+from prophet.plot import plot_plotly
+from plotly import graph_objs as go
+import pandas as pd
+
+
+# TODAY = date.today().strftime("%Y-%m-%d")
+TODAY = datetime.today()
+
+st.title('Stock Forecast')
+
+st.markdown('This app is built to predict the stock market performance')
+
+stocks = ('TSLA', 'FB', 'NVDA', 'BABA', 'GOOG', 'AAPL', 'MSFT', 'GME', 'AMZN', 'XIACF')
+selected_stock = st.selectbox('Select dataset for prediction', stocks)
+
+n_years = st.slider('Years of prediction:', 1, 4)
+period = n_years * 365
+
+new_resolution = st.radio(
+	"Do you want to get the higher resolution or short time interval, please choose one:",
+	('In 1 day', 'In 1 hour', 'In 5 minutes'))
+
+if new_resolution == 'In 5 minutes':
+	new_interval = "5m"
+	START = TODAY - timedelta(days=30)
+elif new_resolution == 'In 1 hour':
+	new_interval = "1h"
+	START = TODAY - timedelta(days=365)
+else:	
+	new_interval = "1d"
+	START = "2018-01-01"
+
+@st.cache
+def load_data(ticker):
+    data = yf.download(ticker, START, TODAY, interval = new_interval)
+    data.reset_index(inplace=True)
+    return data
+
+	
+data_load_state = st.text('Loading data...')
+data = load_data(selected_stock)
+data_load_state.text('... Data loaded, well done!')
+
+@st.cache
+def convert_df(df):
+     # IMPORTANT: Cache the conversion to prevent computation on every rerun
+     return df.to_csv().encode('utf-8')
+
+csv = convert_df(data)
+
+st.download_button(
+    label="Download data as CSV",
+    data=csv,
+    file_name='stock_data.csv',
+    mime='text/csv',
+ )
+
+
+st.subheader('Raw data')
+st.write(data.tail())
+
+# Plot raw data
+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)
+	
+plot_raw_data()
+
+# Predict forecast with Prophet.
+df_train = data[['Date','Close']]
+df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
+
+m = Prophet()
+m.fit(df_train)
+future = m.make_future_dataframe(periods=period)
+forecast = m.predict(future)
+
+# Show 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)
+
+st.write("Forecast components")
+fig2 = m.plot_components(forecast)
+st.write(fig2)
+