Update app.py

app.py

@@ -16,11 +16,17 @@ st.set_page_config(
     layout="wide"
 )
 
-# Constants and configurations
+# Constants
 START = "2015-01-01"
 TODAY = date.today().strftime("%Y-%m-%d")
 
-# Custom CSS for better styling
+# Asset categories
+ASSETS = {
+    'Stocks': ['GOOG', 'AAPL', 'MSFT', 'GME'],
+    'Cryptocurrencies': ['BTC-USD', 'ETH-USD', 'DOGE-USD', 'ADA-USD']
+}
+
+# Custom CSS
 st.markdown("""
     <style>
     .stButton>button {
@@ -32,79 +38,116 @@ st.markdown("""
     </style>
     """, unsafe_allow_html=True)
 
-class AssetPredictor:
-    def __init__(self):
-        self.assets = {
-            'Stocks': ['GOOG', 'AAPL', 'MSFT', 'GME'],
-            'Cryptocurrencies': ['BTC-USD', 'ETH-USD', 'DOGE-USD', 'ADA-USD']
-        }
+@st.cache_data(ttl=3600)
+def load_data(ticker):
+    """Load and validate financial data."""
+    try:
+        data = yf.download(ticker, START, TODAY)
+        if data.empty:
+            raise ValueError(f"No data found for {ticker}")
         
-    @st.cache_data(ttl=3600)  # Cache data for 1 hour
-    def load_data(self, ticker):
-        """Load and validate financial data."""
-        try:
-            data = yf.download(ticker, START, TODAY)
-            if data.empty:
-                raise ValueError(f"No data found for {ticker}")
-            
-            data.reset_index(inplace=True)
-            # Ensure all required columns exist and are numeric
-            required_columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
-            for col in required_columns:
-                if col not in data.columns:
-                    raise ValueError(f"Missing required column: {col}")
-                if col != 'Date':
-                    data[col] = pd.to_numeric(data[col], errors='coerce')
-            
-            data.dropna(inplace=True)
-            return data
-        except Exception as e:
-            st.error(f"Error loading data: {str(e)}")
-            return None
-
-    def prepare_prophet_data(self, data):
-        """Prepare data for Prophet model."""
-        df_prophet = data[['Date', 'Close']].copy()
-        df_prophet.columns = ['ds', 'y']
-        return df_prophet
-
-    def train_prophet_model(self, data, period):
-        """Train and return Prophet model with customized parameters."""
-        model = Prophet(
-            yearly_seasonality=True,
-            weekly_seasonality=True,
-            daily_seasonality=True,
-            changepoint_prior_scale=0.05,
-            seasonality_prior_scale=10.0,
-            changepoint_range=0.9
-        )
+        data.reset_index(inplace=True)
+        required_columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
         
-        # Add custom seasonalities
-        model.add_seasonality(
-            name='monthly',
-            period=30.5,
-            fourier_order=5
-        )
+        for col in required_columns:
+            if col not in data.columns:
+                raise ValueError(f"Missing required column: {col}")
+            if col != 'Date':
+                data[col] = pd.to_numeric(data[col], errors='coerce')
         
-        model.fit(data)
-        future = model.make_future_dataframe(periods=period)
-        return model, future
+        data.dropna(inplace=True)
+        return data
+    except Exception as e:
+        st.error(f"Error loading data: {str(e)}")
+        return None
 
-def main():
-    predictor = AssetPredictor()
+def calculate_rsi(prices, period=14):
+    """Calculate Relative Strength Index."""
+    delta = prices.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+    rs = gain / loss
+    return 100 - (100 / (1 + rs))
+
+def prepare_prophet_data(data):
+    """Prepare data for Prophet model."""
+    df_prophet = data[['Date', 'Close']].copy()
+    df_prophet.columns = ['ds', 'y']
+    return df_prophet
+
+def train_prophet_model(data, period):
+    """Train and return Prophet model with customized parameters."""
+    model = Prophet(
+        yearly_seasonality=True,
+        weekly_seasonality=True,
+        daily_seasonality=True,
+        changepoint_prior_scale=0.05,
+        seasonality_prior_scale=10.0,
+        changepoint_range=0.9
+    )
     
-    # Sidebar for user inputs
-    st.sidebar.title("⚙️ Configuration")
-    asset_type = st.sidebar.radio("Select Asset Type", list(predictor.assets.keys()))
-    selected_asset = st.sidebar.selectbox(
-        'Select Asset',
-        predictor.assets[asset_type]
+    # Add custom seasonalities
+    model.add_seasonality(
+        name='monthly',
+        period=30.5,
+        fourier_order=5
+    )
+    
+    model.fit(data)
+    future = model.make_future_dataframe(periods=period)
+    return model, future
+
+def plot_technical_analysis(data, selected_asset):
+    """Create technical analysis plot."""
+    fig = go.Figure()
+    
+    # Add candlestick chart
+    fig.add_trace(go.Candlestick(
+        x=data['Date'],
+        open=data['Open'],
+        high=data['High'],
+        low=data['Low'],
+        close=data['Close'],
+        name='Price'
+    ))
+    
+    # Add moving averages
+    fig.add_trace(go.Scatter(
+        x=data['Date'],
+        y=data['SMA_20'],
+        name='SMA 20',
+        line=dict(color='orange')
+    ))
+    
+    fig.add_trace(go.Scatter(
+        x=data['Date'],
+        y=data['SMA_50'],
+        name='SMA 50',
+        line=dict(color='blue')
+    ))
+    
+    fig.update_layout(
+        title=f'{selected_asset} Technical Analysis',
+        yaxis_title='Price',
+        template='plotly_dark'
     )
     
-    # Main content
+    return fig
+
+@st.cache_data
+def convert_df_to_csv(df):
+    """Convert dataframe to CSV for download."""
+    return df.to_csv(index=False).encode('utf-8')
+
+def main():
     st.title('📈 Advanced Stock & Cryptocurrency Forecast')
     
-    # Time range selection
+    # Sidebar configuration
+    st.sidebar.title("⚙️ Configuration")
+    asset_type = st.sidebar.radio("Select Asset Type", list(ASSETS.keys()))
+    selected_asset = st.sidebar.selectbox('Select Asset', ASSETS[asset_type])
+    
+    # Main content layout
     col1, col2 = st.columns(2)
     with col1:
         n_years = st.slider('Forecast Period (Years):', 1, 4)
@@ -115,79 +158,51 @@ def main():
     
     # Load and process data
     with st.spinner('Loading data...'):
-        data = predictor.load_data(selected_asset)
+        data = load_data(selected_asset)
         
     if data is not None:
-        # Display technical indicators
-        st.subheader('📊 Technical Analysis')
-        
         # Calculate technical indicators
         data['SMA_20'] = data['Close'].rolling(window=20).mean()
         data['SMA_50'] = data['Close'].rolling(window=50).mean()
         data['RSI'] = calculate_rsi(data['Close'])
         
-        # Create technical analysis plot
-        fig_technical = go.Figure()
-        fig_technical.add_trace(go.Candlestick(
-            x=data['Date'],
-            open=data['Open'],
-            high=data['High'],
-            low=data['Low'],
-            close=data['Close'],
-            name='Price'
-        ))
-        fig_technical.add_trace(go.Scatter(
-            x=data['Date'],
-            y=data['SMA_20'],
-            name='SMA 20',
-            line=dict(color='orange')
-        ))
-        fig_technical.add_trace(go.Scatter(
-            x=data['Date'],
-            y=data['SMA_50'],
-            name='SMA 50',
-            line=dict(color='blue')
-        ))
-        
-        fig_technical.update_layout(
-            title=f'{selected_asset} Technical Analysis',
-            yaxis_title='Price',
-            template='plotly_dark'
-        )
+        # Display technical analysis
+        st.subheader('📊 Technical Analysis')
+        fig_technical = plot_technical_analysis(data, selected_asset)
         st.plotly_chart(fig_technical, use_container_width=True)
         
         # Prepare and train Prophet model
-        df_prophet = predictor.prepare_prophet_data(data)
+        df_prophet = prepare_prophet_data(data)
         
         try:
-            model, future = predictor.train_prophet_model(df_prophet, period)
+            model, future = train_prophet_model(df_prophet, period)
             forecast = model.predict(future)
             
-            # Calculate performance metrics
+            # Calculate metrics
             historical_predictions = forecast[forecast['ds'].isin(df_prophet['ds'])]
             mae = mean_absolute_error(df_prophet['y'], historical_predictions['yhat'])
             rmse = np.sqrt(mean_squared_error(df_prophet['y'], historical_predictions['yhat']))
             mape = np.mean(np.abs((df_prophet['y'] - historical_predictions['yhat']) / df_prophet['y'])) * 100
             
-            # Display metrics in columns
+            # Display metrics
             st.subheader('📉 Model Performance Metrics')
             col1, col2, col3 = st.columns(3)
             col1.metric("MAE", f"${mae:.2f}")
             col2.metric("RMSE", f"${rmse:.2f}")
             col3.metric("MAPE", f"{mape:.2f}%")
             
-            # Forecast visualization
+            # Display forecast
             st.subheader('🔮 Price Forecast')
             fig_forecast = plot_plotly(model, forecast)
             fig_forecast.update_layout(template='plotly_dark')
             st.plotly_chart(fig_forecast, use_container_width=True)
             
-            # Show forecast components
+            # Display components
             st.subheader("📊 Forecast Components")
             fig_components = model.plot_components(forecast)
             st.plotly_chart(fig_components, use_container_width=True)
             
-            # Download forecast data
+            # Add download button
             csv = convert_df_to_csv(forecast)
             st.download_button(
                 label="Download Forecast Data",
@@ -198,19 +213,7 @@ def main():
             
         except Exception as e:
             st.error(f"Error in prediction: {str(e)}")
-            
-def calculate_rsi(prices, period=14):
-    """Calculate Relative Strength Index."""
-    delta = prices.diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-    rs = gain / loss
-    return 100 - (100 / (1 + rs))
-
-@st.cache_data
-def convert_df_to_csv(df):
-    """Convert dataframe to CSV for download."""
-    return df.to_csv(index=False).encode('utf-8')
+            st.exception(e)  # This will show the full traceback
 
 if __name__ == "__main__":
     main()