background trading on sentiment updating in UI

app.py

@@ -147,7 +147,7 @@ class NewsSentiment:
                                                        sort_by='publishedAt',  # <-- fixed argument name
                                                        page=1)
                 compound_score = 0
-                for article in articles['articles'][:5]:  # Check first 5 articles
+                for article in articles['articles'][:5]  # Check first 5 articles
                     # print(f'article= {article}')
                     score = self.sia.polarity_scores(article['title'])['compound']
                     compound_score += score
@@ -182,6 +182,10 @@ class StockAnalyzer:
         bars_data = {}
         try:
             bars = alp_api.get_bars(list(symbols), timeframe).df
+            if 'symbol' not in bars.columns:
+                logger.warning("The 'symbol' column is missing in the bars DataFrame.")
+                return {symbol: {'bar_data': None} for symbol in symbols}
+
             for symbol in symbols:
                 symbol_bars = bars[bars['symbol'] == symbol]
                 if not symbol_bars.empty:
@@ -202,7 +206,6 @@ class StockAnalyzer:
                         }
                     }
                 else:
-                    # Only log at debug level to avoid spamming warnings for missing bar data
                     logger.debug(f"No bar data for symbol: {symbol}")
                     bars_data[symbol] = {'bar_data': None}
         except Exception as e:
@@ -434,11 +437,19 @@ class TradingApp:
             st.button("Refresh Portfolio", on_click=refresh_portfolio)
 
     def auto_trade_based_on_sentiment(self, sentiment):
+        """Execute trades based on sentiment analysis and return actions taken."""
+        actions = self._execute_sentiment_trades(sentiment)
+        self.auto_trade_log = actions
+        return actions
+
+    def _execute_sentiment_trades(self, sentiment):
+        """Helper method to execute trades based on sentiment.
+        Used by both auto_trade_based_on_sentiment and background_auto_trade."""
         actions = []
         symbol_to_name = self.analyzer.symbol_to_name
         for symbol, sentiment_value in sentiment.items():
             action = None
-            is_market_open = self.alpaca.get_market_status()  # Use self.alpaca instead of app.alpaca
+            is_market_open = self.alpaca.get_market_status()
             if sentiment_value == 'Positive':
                 order = self.alpaca.buy(symbol, 1, reason="Sentiment: Positive")
                 action = 'Buy'
@@ -464,43 +475,17 @@ class TradingApp:
                 'sentiment': sentiment_value,
                 'action': action
             })
-        self.auto_trade_log = actions
         return actions
 
 def background_auto_trade(app):
-    # This function runs in a background thread and updates session state
+    """This function runs in a background thread and updates session state with automatic trades."""
     while True:
+        start_time = time.time()  # Record the start time of the iteration
+        
         sentiment = app.sentiment.get_news_sentiment(app.analyzer.symbols)
-        symbol_to_name = app.analyzer.symbol_to_name
-        actions = []
-        for symbol, sentiment_value in sentiment.items():
-            action = None
-            is_market_open = app.alpaca.get_market_status()
-            if sentiment_value == 'Positive':
-                order = app.alpaca.buy(symbol, 1, reason="Sentiment: Positive")
-                action = 'Buy'
-            elif sentiment_value == 'Negative':
-                order = app.alpaca.sell(symbol, 1, reason="Sentiment: Negative")
-                action = 'Sell'
-            else:
-                order = None
-                action = 'Hold'
-                logger.info(f"Held {symbol}")
-
-            if order:
-                if not is_market_open:
-                    _, _, next_open, _ = get_market_times(app.alpaca.alpaca)
-                    next_open_time = next_open.strftime('%Y-%m-%d %H:%M:%S') if next_open else "unknown"
-                    logger.warning(f"Market is currently closed. The {action.lower} order for 1 share of {symbol} has been submitted and will execute when the market opens at {next_open_time}.")
-                else:
-                    logger.info(f"Order executed: {action} 1 share of {symbol}")
-
-            actions.append({
-                'symbol': symbol,
-                'company_name': symbol_to_name.get(symbol, ''),
-                'sentiment': sentiment_value,
-                'action': action
-            })
+        
+        # Use the shared method to execute trades
+        actions = app._execute_sentiment_trades(sentiment)
         
         # Create log entry
         log_entry = {
@@ -509,39 +494,30 @@ def background_auto_trade(app):
             "sentiment": sentiment
         }
         
-        # Update session state - need to use a thread lock to safely update
-        if hasattr(threading, "main_thread") and threading.current_thread() is threading.main_thread():
-            # Direct update if in main thread
-            update_auto_trade_log(log_entry)
-        else:
-            # Safer to just store the latest entry, which the main thread will pick up
-            st.session_state["latest_auto_trade_entry"] = log_entry
-            
-        time.sleep(AUTO_TRADE_INTERVAL)
-
-def update_auto_trade_log(log_entry):
-    """Update auto trade log in session state"""
-    if AUTO_TRADE_LOG_KEY not in st.session_state:
-        st.session_state[AUTO_TRADE_LOG_KEY] = []
-    
-    # Append the new entry
-    st.session_state[AUTO_TRADE_LOG_KEY].append(log_entry)
-    
-    # Limit size to avoid memory issues (keep last 50 entries)
-    if len(st.session_state[AUTO_TRADE_LOG_KEY]) > 50:
-        st.session_state[AUTO_TRADE_LOG_KEY] = st.session_state[AUTO_TRADE_LOG_KEY][-50:]
+        # Update session state - ensure the UI reflects the latest data
+        if AUTO_TRADE_LOG_KEY not in st.session_state:
+            st.session_state[AUTO_TRADE_LOG_KEY] = []
+        
+        st.session_state[AUTO_TRADE_LOG_KEY].append(log_entry)
+        
+        # Limit size to avoid memory issues (keep last 50 entries)
+        if len(st.session_state[AUTO_TRADE_LOG_KEY]) > 50:
+            st.session_state[AUTO_TRADE_LOG_KEY] = st.session_state[AUTO_TRADE_LOG_KEY][-50:]
+        
+        # Log the update
+        logger.info(f"Auto-trade completed. Actions: {actions}")
+        
+        # Calculate the time taken for this iteration
+        elapsed_time = time.time() - start_time
+        sleep_time = max(0, AUTO_TRADE_INTERVAL - elapsed_time)  # Ensure non-negative sleep time
+        
+        logger.info(f"Sleeping for {sleep_time:.2f} seconds before the next auto-trade.")
+        time.sleep(sleep_time)
 
 def get_auto_trade_log():
-    """Get the auto trade log from session state"""
+    """Get the auto trade log from session state."""
     if AUTO_TRADE_LOG_KEY not in st.session_state:
         st.session_state[AUTO_TRADE_LOG_KEY] = []
-    
-    # Check if we have a new entry from background thread
-    if "latest_auto_trade_entry" in st.session_state:
-        update_auto_trade_log(st.session_state["latest_auto_trade_entry"])
-        # Clear the latest entry after adding it
-        del st.session_state["latest_auto_trade_entry"]
-        
     return st.session_state[AUTO_TRADE_LOG_KEY]
 
 def get_market_times(alpaca_api):
@@ -561,7 +537,7 @@ def main():
     st.markdown("This is a fun stock trading application that uses Alpaca API for trading and News API for sentiment analysis. Come and trade my money! Well, it's a paper account, so it's not real money. But still, have fun!")
 
     if not st.secrets['ALPACA_API_KEY'] or not st.secrets['NEWS_API_KEY']:
-        st.error("Please configure your API keys in secrets.toml")
+        st.error("Please configure your ALPACA_API_KEY and NEWS_API_KEY")
         return
 
     # Prevent Streamlit from rerunning the script on every widget interaction