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import streamlit as st
import pandas as pd
from prophet import Prophet
import yfinance as yf
from sklearn.metrics import mean_absolute_error, mean_squared_error
from prophet.plot import plot_plotly, plot_components_plotly
# Function to fetch stock data from Yahoo Finance
def fetch_stock_data(ticker_symbol, start_date, end_date):
stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
df = stock_data[['Adj Close']].reset_index()
df = df.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
return df
# Function to train the Prophet model
def train_prophet_model(df):
model = Prophet()
model.fit(df)
return model
# Function to make the forecast
def make_forecast(model, periods):
future = model.make_future_dataframe(periods=periods)
forecast = model.predict(future)
return forecast
# Function to calculate performance metrics
def calculate_performance_metrics(actual, predicted):
mae = mean_absolute_error(actual, predicted)
mse = mean_squared_error(actual, predicted)
rmse = np.sqrt(mse)
return {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
# Streamlit app
def main():
st.title('Stock Forecasting with Prophet')
# Set up the layout
st.sidebar.header('User Input Parameters')
ticker_symbol = st.sidebar.text_input('Enter Ticker Symbol', 'RACE')
start_date = st.sidebar.date_input('Start Date', value=pd.to_datetime('2015-01-01'))
end_date = st.sidebar.date_input('End Date', value=pd.to_datetime('today'))
# Dropdown for forecast horizon selection
forecast_horizon = st.sidebar.selectbox('Forecast Horizon',
options=['1 year', '2 years', '3 years', '5 years'],
format_func=lambda x: x.capitalize())
# Convert the selected horizon to days
horizon_mapping = {'1 year': 365, '2 years': 730, '3 years': 1095, '5 years': 1825}
forecast_days = horizon_mapping[forecast_horizon]
if st.sidebar.button('Forecast Stock Prices'):
with st.spinner('Fetching data...'):
df = fetch_stock_data(ticker_symbol, start_date, end_date)
with st.spinner('Training model...'):
model = train_prophet_model(df)
forecast = make_forecast(model, forecast_days)
st.subheader('Forecast Data')
st.write('The table below shows the forecasted stock prices along with the lower and upper bounds of the predictions.')
st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
st.subheader('Forecast Plot')
st.write('The plot below visualizes the predicted stock prices with their confidence intervals.')
fig1 = plot_plotly(model, forecast)
fig1.update_traces(marker=dict(color='red'), line=dict(color='black'))
st.plotly_chart(fig1)
st.subheader('Forecast Components')
st.write('This plot breaks down the forecast into trend, weekly, and yearly components.')
fig2 = plot_components_plotly(model, forecast)
fig2.update_traces(line=dict(color='black'))
st.plotly_chart(fig2)
st.subheader('Performance Metrics')
st.write('The metrics below provide a quantitative measure of the model’s accuracy. The Mean Absolute Error (MAE) is the average absolute difference between predicted and actual values, Mean Squared Error (MSE) is the average squared difference, and Root Mean Squared Error (RMSE) is the square root of MSE, which is more interpretable in the same units as the target variable.')
actual = df['y']
predicted = forecast['yhat'][:len(df)]
metrics = calculate_performance_metrics(actual, predicted)
st.metric(label="Mean Absolute Error (MAE)", value="{:.2f}".format(metrics['MAE']), delta="Lower is better")
st.metric(label="Mean Squared Error (MSE)", value="{:.2f}".format(metrics['MSE']), delta="Lower is better")
st.metric(label="Root Mean Squared Error (RMSE)", value="{:.2f}".format(metrics['RMSE']), delta="Lower is better")
# Run the main function
if __name__ == "__main__":
main() |