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 for debugging purposes
st.subheader('Raw Data')
st.write(data.tail())

# Check if 'Close' column exists before converting
if 'Close' in data.columns:
    # Check data types of the DataFrame
    st.write("Data Types:")
    st.write(data.dtypes)

    # Display unique values in the 'Close' column for debugging
    st.write("Unique values in 'Close' column:")
    st.write(data['Close'].unique())

    # Ensure 'Close' prices are numeric and handle any missing values
    try:
        # Convert to numeric with coercion for invalid parsing
        data['Close'] = pd.to_numeric(data['Close'], errors='coerce')
        # Drop rows with NaN values in 'Close'
        data.dropna(subset=['Close'], inplace=True)
    except Exception as e:
        st.error(f"Error converting 'Close' prices to numeric: {e}")
else:
    st.error("The 'Close' column is missing from the data. Please check the selected asset.")

# Check if there is valid data after processing
if not data.empty and 'Close' in data.columns:
    # 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
    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()
    
    try:
        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}")

    except Exception as e:
        st.error(f"Error fitting model: {e}")

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.
""")