Python Engineer

Python and Machine Learning Tutorials

Build A Stock Prediction Web App In Python

07 Feb 2021

In this tutorial we build a stock prediction web app in Python using streamlit, Yahoo finance, and Facebook Prophet.

Resources:

Credits:

Installation

We need to install streamlit, Facebook prophet, yfinance, and plotly. Run this in your terminal:

$ pip install streamlit fbprophet yfinance plotly

The Code

Thanks to streamlit it does not require a lot of code to implement a nice looking web app. This is the whole code:

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 START = "2015-01-01" TODAY = date.today().strftime("%Y-%m-%d") st.title('Stock Forecast App') stocks = ('GOOG', 'AAPL', 'MSFT', 'GME') selected_stock = st.selectbox('Select dataset for prediction', stocks) n_years = st.slider('Years of prediction:', 1, 4) period = n_years * 365 @st.cache def load_data(ticker): data = yf.download(ticker, START, TODAY) data.reset_index(inplace=True) return data data_load_state = st.text('Loading data...') data = load_data(selected_stock) data_load_state.text('Loading data... done!') 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)

Run the application

To start the app, run

$ streamlit run main.py

where main.py should be the name of your file in this case.