main.py

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import streamlit as st
import yfinance as yf
from datetime import date
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
from plotly import graph_objects as go

st.title("Facebook Prophet - Stock Price Prediction")
st.write("This is a demonstrator app created on Streamlit to show a use case of the Facebook Prophet "
         "timeseries model to forecast stock market data.")

START = "2015-01-01"
TODAY = date.today().strftime("%Y-%m-%d")

@st.experimental_memo
def load_stock_data(ticker:str):
    data = yf.download(ticker, START, TODAY)
    data.reset_index(inplace=True)
    return data

tickers = ["VOO", "AAPL", "GOOG", "TSLA", "MSFT", "GME"]

ticker = st.selectbox("Select tickers", tickers)

if "years_slider"not in st.session_state:
    st.session_state.years_slider = 1

n_years = st.slider("Years of prediction:", 1, 5, key="years_slider")
period = n_years * 365

data_load_state = st.text("Loading... {} data".format(ticker))
data = load_stock_data(ticker)
data_load_state.text("Loading... Complete!")
st.write(data.tail())

def plot_data():
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Open"))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close"))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['High'], name="High"))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Low'], name="Low"))
    fig_title = "{} data from {} to {}".format(ticker, START, TODAY)
    fig.update_layout(title_text=fig_title, xaxis_rangeslider_visible=True)
    st.plotly_chart(fig)

plot_data()

def make_prediction():
    df_train = data[["Date", "Close"]]
    df_train = df_train.rename(columns={"Date":"ds", "Close":"y"})
    prediction_progress = st.progress(0)
    prediction_progress.progress(10)

    model = Prophet()
    model.fit(df_train)

    prediction_progress.progress(20)

    future = model.make_future_dataframe(periods=period)

    prediction_progress.progress(40)

    forecast = model.predict(future)

    prediction_progress.progress(80)

    st.subheader("Forecast")

    st.write("Forecast timeseries")
    fig1 = plot_plotly(model, forecast)
    st.plotly_chart(fig1)

    st.write("Forecast components")
    fig2 = model.plot_components(forecast)
    st.write(fig2)

    prediction_progress.progress(100)

    st.success('Successfully forecasted {} stock close price'.format(ticker))

make_prediction()

if __name__ == '__main__':
    print("Run `streamlit run main.py` to run this app")