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| # pip install streamlit fbprophet yfinance plotly | |
| # !pip install yfinance | |
| # !pip install prophet | |
| import streamlit as st | |
| from datetime import date, datetime, timedelta | |
| import pandas as pd | |
| import yfinance as yf | |
| from prophet import Prophet | |
| from prophet.plot import plot_plotly | |
| from plotly import graph_objs as go | |
| # 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" | |
| 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!') | |
| 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) | |