deploy #10

src/streamlit_app.py

@@ -18,6 +18,8 @@ from indicators.macd import macd
 
 from strategy.rule_based_strategy import generate_signals_sma, generate_signals_ema
 from utils.backtester import backtest_signals
+from utils.logger import setup_logger
+import logging
  
 from indicators.enhanced_features import (
     create_volatility_features, create_enhanced_lag_features,
@@ -34,6 +36,12 @@ st.set_page_config(
     layout="wide"
 )
 
+# Initialize logging once
+if 'logging_initialized' not in st.session_state:
+    setup_logger(log_dir="logs", log_level=logging.INFO)
+    st.session_state.logging_initialized = True
+    logging.info("=== Streamlit Trading App Started ===")
+
 # Stock symbols
 STOCK_SYMBOLS = [
     'ADANIENT.NS', 'ADANIPORTS.NS', 'APOLLOHOSP.NS', 'ASIANPAINT.NS',
@@ -72,13 +80,16 @@ FEATURES = [
 @st.cache_data
 def load_stock_data(symbol, start_date, end_date):
     """Load stock data from Yahoo Finance"""
+    logging.info(f"Loading stock data for {symbol} from {start_date} to {end_date}")
     try:
         data = yf.download(symbol, start=start_date, end=end_date, session=session)
         # Flatten the MultiIndex columns
         if data.columns.nlevels > 1:
             data.columns = [col[0] for col in data.columns]
+        logging.info(f"Successfully loaded {len(data)} records for {symbol}")
         return data
     except Exception as e:
+        logging.error(f"Error loading data for {symbol}: {str(e)}")
         st.error(f"Error loading data: {e}")
         return None
 
@@ -133,11 +144,15 @@ selected_stock = st.sidebar.selectbox("Select Stock Symbol", STOCK_SYMBOLS, inde
 start_date = st.sidebar.date_input("Start Date", value=datetime(2023, 1, 1))
 end_date = st.sidebar.date_input("End Date", value=datetime.now())
 
+logging.info(f"User selected stock: {selected_stock}, date range: {start_date} to {end_date}")
+
 st.sidebar.subheader("📈 Technical Indicators")
 rsi_period = st.sidebar.slider("RSI Period", min_value=5, max_value=30, value=14, step=1)
 short_period = st.sidebar.slider("Short-term Period", min_value=5, max_value=50, value=20, step=1)
 long_period = st.sidebar.slider("Long-term Period", min_value=50, max_value=200, value=50, step=1)
 
+logging.info(f"RSI Period: {rsi_period}, Short-term Period: {short_period}, Long-term Period: {long_period}")
+
 # Strategy selection (for trading dashboard)
 strategy_type = st.sidebar.selectbox("Strategy Type", ["SMA-based", "EMA-based", "Both"])
 
@@ -148,6 +163,9 @@ stop_loss = st.sidebar.slider("Stop Loss (%)", 0.0, 20.0, 5.0, step=1.0) / 100
 take_profit = st.sidebar.slider("Take Profit (%)", 0.0, 50.0, 15.0, step=5.0) / 100
 use_risk_mgmt = st.sidebar.checkbox("Enable Risk Management", value=True)
 
+logging.info(f"Initial Cash: ₹{initial_cash}, Transaction Cost: {transaction_cost*100}%, "
+             f"Stop Loss: {stop_loss*100}%, Take Profit: {take_profit*100}%, Risk Management: {use_risk_mgmt}")
+
 # ========================= PRICE PREDICTION TAB =========================
 
 with tab1:
@@ -188,10 +206,14 @@ with tab1:
                 # Create feature vector
                 feature_vector = latest_data[FEATURES].values.reshape(1, -1)
                 feature_vector_scaled = scaler.transform(feature_vector)
+
+                logging.info(f"Making price prediction for {selected_stock}")
                     
                 # Make prediction
                 prediction = model.predict(feature_vector_scaled)[0]
                 probability = model.predict_proba(feature_vector_scaled)[0].max()
+
+                logging.info(f"Prediction: {'UP' if prediction == 1 else 'DOWN'} with {probability:.1%} confidence")
             
                 
                 # Display prediction
@@ -325,10 +347,12 @@ with tab2:
             
             with tab_sma:
                 st.subheader("📊 SMA Strategy Results")
+                logging.info(f"Starting backtest for {selected_stock} with {strategy_type} strategy")
                 sma_results, sma_metrics = backtest_signals(
                     df, signal_col='SMA_Signal', price_col='Close', 
                     initial_cash=initial_cash, transaction_cost=transaction_cost if use_risk_mgmt else 0
                 )
+                logging.info(f"Backtest completed for {selected_stock} with {strategy_type} strategy")
                 
                 # Set variables for common sections
                 results = sma_results
@@ -389,10 +413,12 @@ with tab2:
             
             with tab_ema:
                 st.subheader("📊 EMA Strategy Results")
+                logging.info(f"Starting backtest for {selected_stock} with {strategy_type} strategy")
                 ema_results, ema_metrics = backtest_signals(
                     df, signal_col='EMA_Signal', price_col='Close', 
                     initial_cash=initial_cash, transaction_cost=transaction_cost if use_risk_mgmt else 0
                 )
+                logging.info(f"Backtest completed for {selected_stock} with {strategy_type} strategy")
                 
                 # Set variables for common sections  
                 results = ema_results
@@ -456,10 +482,12 @@ with tab2:
             signal_col = 'SMA_Signal' if strategy_type == "SMA-based" else 'EMA_Signal'
             strategy_name = strategy_type.split('-')[0]
             
+            logging.info(f"Starting backtest for {selected_stock} with {strategy_type} strategy")
             results, metrics = backtest_signals(
                 df, signal_col=signal_col, price_col='Close', 
                 initial_cash=initial_cash, transaction_cost=transaction_cost if use_risk_mgmt else 0
             )
+            logging.info(f"Backtest completed. Final return: {metrics['Total Return']}")
             
             # Display metrics
             col1, col2, col3, col4 = st.columns(4)