app.py

import streamlit as st
from datetime import date
import yfinance as yf
import pandas as pd  # Importing pandas to avoid NameError
from prophet import Prophet
from prophet.plot import plot_plotly
from plotly import graph_objs as go

# Constants for date range
START = "2015-01-01"
TODAY = date.today().strftime("%Y-%m-%d")

# Streamlit app title
st.title('Stock & Cryptocurrency Forecast App')

# Stock and cryptocurrency selection
assets = ('GOOG', 'AAPL', 'MSFT', 'GME', 'BTC-USD', 'ETH-USD')  # Added BTC and ETH
selected_asset = st.selectbox('Select dataset for prediction', assets)

# Years of prediction slider
n_years = st.slider('Years of prediction:', 1, 4)
period = n_years * 365

@st.cache_data
def load_data(ticker):
    """Load stock or cryptocurrency data from Yahoo Finance."""
    data = yf.download(ticker, START, TODAY)
    data.reset_index(inplace=True)
    return data

# Load data and show loading state
data_load_state = st.text('Loading data...')
data = load_data(selected_asset)
data_load_state.text('Loading data... done!')

# Display raw data
st.subheader('Raw Data')
st.write(data.tail())

# Check if 'Close' column exists before converting
if 'Close' in data.columns:
    # Ensure 'Close' prices are numeric and handle any missing values
    data['Close'] = pd.to_numeric(data['Close'], errors='coerce')
    data.dropna(subset=['Close'], inplace=True)
else:
    st.error("The 'Close' column is missing from the data. Please check the selected asset.")

# Plot raw data function
def plot_raw_data():
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Open Price", line=dict(color='blue')))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close Price", line=dict(color='orange')))
    fig.layout.update(title_text='Time Series Data with Rangeslider', xaxis_rangeslider_visible=True)
    st.plotly_chart(fig)

# Call the plotting function if there's enough data
if not data.empty:
    plot_raw_data()

    # Prepare data for Prophet model
    df_train = data[['Date', 'Close']]
    df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})

    # Create and fit the Prophet model
    m = Prophet()
    m.fit(df_train)

    # Create future dataframe and make predictions
    future = m.make_future_dataframe(periods=period)
    forecast = m.predict(future)

    # Show forecast data and plot forecast
    st.subheader('Forecast Data')
    st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
    st.write(f'Forecast plot for the next {n_years} years')

    fig1 = plot_plotly(m, forecast)
    st.plotly_chart(fig1)

    # Show forecast components
    st.subheader("Forecast Components")
    fig2 = m.plot_components(forecast)
    st.plotly_chart(fig2)

    # Additional Insights: Displaying key metrics
    st.subheader("Key Metrics")
    latest_data = forecast.iloc[-1]
    st.write(f"Predicted Price: ${latest_data['yhat']:.2f}")
    st.write(f"Lower Bound: ${latest_data['yhat_lower']:.2f}")
    st.write(f"Upper Bound: ${latest_data['yhat_upper']:.2f}")

else:
    st.error("No valid data available to make predictions.")

# User Guidance Section
st.sidebar.header("User Guidance")
st.sidebar.write("""
This application allows you to predict stock prices or cryptocurrency values based on historical data.
- Select a stock or cryptocurrency from the dropdown menu.
- Use the slider to choose how many years into the future you want to predict.
- View the forecasted prices along with confidence intervals.
""")