Looking to dip your toe into stock market forecasting? This project will walk you through building a web app using the Python Streamlit framework!
In this project, you’ll create a user-friendly interface to predict stock prices using Facebook Prophet, a powerful forecasting tool.
Key Features:
- Select stocks from the drop-down menu.
- Select a forecast period (up to 4 years).
- Visualize historical pricing data with networks.
- Make stock price predictions with Facebook Prophet.
- Explore aspects of prophecy for deeper insights.
Setting Up:
This project requires a few Python libraries: Streamlit, Facebook Prophet, yfinance, and Plotly. You can install them using pip:
Python
$ pip install streamlit fbprophet yfinance plotlyThe Code:
The beauty of Streamlit lies in its simplicity. Here’s the complete code for your web app:
Python
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)Running the App:
Save the code as main.py and execute it using Streamlit:
Python
streamlit run main.pyVoila! Your stock prediction web app will be live at http://localhost:8501.