File size: 4,236 Bytes
67eaf73 9e62a5b f0b24d3 9e62a5b f0b24d3 9e62a5b f0b24d3 9e62a5b f0b24d3 9e62a5b e5c214d e4dc510 9e62a5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import streamlit as st
import pandas as pd
import numpy as np
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']].head())
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='white'))
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='white'))
st.plotly_chart(fig2)
st.subheader('Performance Metrics')
st.write('The metrics below provide a quantitative measure of the model’s accuracy.')
st.write('Mean Absolute Error (MAE): A lower value indicates better performance.')
st.write('Mean Squared Error (MSE): A lower value indicates better performance, and it penalizes larger errors more than MAE.')
st.write('Root Mean Squared Error (RMSE): A lower value indicates better performance, similar to MSE, but 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() |