rm auto trades from ui

app.py

@@ -482,32 +482,30 @@ def background_auto_trade(app):
     while True:
         start_time = time.time()  # Record the start time of the iteration
         
-        try:
-            sentiment = app.sentiment.get_news_sentiment(app.analyzer.symbols)
-            
-            # Use the shared method to execute trades
-            actions = app._execute_sentiment_trades(sentiment)
-            
-            # Create log entry
-            log_entry = {
-                "timestamp": datetime.now().isoformat(),
-                "actions": actions,
-                "sentiment": sentiment
-            }
-            
-            # Create a temporary file to store the log entry as a workaround for Streamlit session state issues in threads
-            logger.info(f"Auto-trade completed with {len(actions)} actions")
-            
-            # Update the session state directly - this is safer than trying to directly modify st.session_state
-            # from a background thread
-            if "pending_auto_trade" not in st.session_state:
-                st.session_state["pending_auto_trade"] = []
-                
-            st.session_state["pending_auto_trade"].append(log_entry)
-            logger.info(f"Added trade log to pending queue. Queue size: {len(st.session_state.get('pending_auto_trade', []))}")
-                
-        except Exception as e:
-            logger.error(f"Error in background auto-trade: {e}")
+        sentiment = app.sentiment.get_news_sentiment(app.analyzer.symbols)
+        
+        # Use the shared method to execute trades
+        actions = app._execute_sentiment_trades(sentiment)
+        
+        # Create log entry
+        log_entry = {
+            "timestamp": datetime.now().isoformat(),
+            "actions": actions,
+            "sentiment": sentiment
+        }
+        
+        # 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
@@ -520,62 +518,19 @@ def get_auto_trade_log():
     """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] = []
-    
-    # Process any pending trades
-    pending_trades = st.session_state.get("pending_auto_trade", [])
-    if pending_trades:
-        logger.info(f"Processing {len(pending_trades)} pending trades")
-        
-        # Add pending trades to the main log
-        st.session_state[AUTO_TRADE_LOG_KEY].extend(pending_trades)
-        
-        # Clear the pending trades
-        st.session_state["pending_auto_trade"] = []
-        
-        # 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:]
-            
     return st.session_state[AUTO_TRADE_LOG_KEY]
 
-def add_test_trade_entry():
-    """Helper function to add a test trade entry to the log."""
-    # Create a sample log entry
-    test_entry = {
-        "timestamp": datetime.now().isoformat(),
-        "actions": [
-            {
-                "symbol": "AAPL",
-                "company_name": "Apple Inc.",
-                "sentiment": "Positive",
-                "action": "Buy"
-            },
-            {
-                "symbol": "MSFT",
-                "company_name": "Microsoft Corporation",
-                "sentiment": "Neutral",
-                "action": "Hold"
-            },
-            {
-                "symbol": "GOOGL",
-                "company_name": "Alphabet Inc.",
-                "sentiment": "Negative",
-                "action": "Sell"
-            }
-        ],
-        "sentiment": {
-            "AAPL": "Positive",
-            "MSFT": "Neutral",
-            "GOOGL": "Negative"
-        }
-    }
-    
-    # Add to session state
-    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(test_entry)
-    return "Test trade entry added successfully!"
+def get_market_times(alpaca_api):
+    try:
+        clock = alpaca_api.get_clock()
+        is_open = clock.is_open
+        now = pd.Timestamp(clock.timestamp).tz_convert('America/New_York')
+        next_close = pd.Timestamp(clock.next_close).tz_convert('America/New_York')
+        next_open = pd.Timestamp(clock.next_open).tz_convert('America/New_York')
+        return is_open, now, next_open, next_close
+    except Exception as e:
+        logger.error(f"Error fetching market times: {e}")
+        return None, None, None, None
 
 def main():
     st.title("Ben's Stock Trading Application")
@@ -654,72 +609,56 @@ def main():
             st.markdown(f"**Time until open:** {pd.to_timedelta(seconds_left, unit='s')}")
 
 
-    # Initialize auto trade log in session state if needed
-    if AUTO_TRADE_LOG_KEY not in st.session_state:
-        st.session_state[AUTO_TRADE_LOG_KEY] = []
+    # # Initialize auto trade log in session state if needed
+    # if AUTO_TRADE_LOG_KEY not in st.session_state:
+    #     st.session_state[AUTO_TRADE_LOG_KEY] = []
 
-    # Only start the background thread once
-    if "auto_trade_thread_started" not in st.session_state:
-        thread = threading.Thread(target=background_auto_trade, args=(app,), daemon=True)
-        thread.start()
-        st.session_state["auto_trade_thread_started"] = True
+    # # Only start the background thread once
+    # if "auto_trade_thread_started" not in st.session_state:
+    #     thread = threading.Thread(target=background_auto_trade, args=(app,), daemon=True)
+    #     thread.start()
+    #     st.session_state["auto_trade_thread_started"] = True
 
     # Main area: plots and data
     app.manual_trade()
     app.display_charts()
 
     # Read and display latest auto-trade actions
-    st.write("Automatic Trading Actions Based on Sentiment (background):")
-    
-    # Add refresh and test buttons side by side
-    col1, col2 = st.columns(2)
-    with col1:
-        if st.button("Refresh Auto-Trade Log"):
-            st.experimental_rerun()  # Force Streamlit to rerun and update the display
-    with col2:
-        if st.button("Add Test Trade (Debug)"):
-            result = add_test_trade_entry()
-            st.success(result)
-    
-    # Get and display auto trade log
-    auto_trade_log = get_auto_trade_log()
-    st.write(f"Log entries: {len(auto_trade_log)}")
-    
-    # For debugging: Display the size of the log to verify it's being updated
-    st.write(f"Log entries: {len(auto_trade_log)}")
-    
-    if auto_trade_log:
-        # Show the most recent entry
-        last_entry = auto_trade_log[-1]
-        st.write(f"Last checked: {last_entry['timestamp']}")
-        df = pd.DataFrame(last_entry["actions"])
-        if "company_name" in df.columns:
-            df = df[["symbol", "company_name", "sentiment", "action"]]
-        st.dataframe(df)
-        st.write("Sentiment Analysis (latest):")
-        st.write(last_entry["sentiment"])
-
-        # Plot buy/sell actions over time
-        st.write("Auto-Trading History (Buy/Sell Actions Over Time):")
-        history = []
-        for entry in auto_trade_log:
-            ts = entry["timestamp"]
-            for act in entry["actions"]:
-                if act["action"] in ("Buy", "Sell"):
-                    history.append({
-                        "timestamp": ts,
-                        "symbol": act["symbol"],
-                        "action": act["action"]
-                    })
-        if history:
-            hist_df = pd.DataFrame(history)
-            if not hist_df.empty:
-                hist_df["timestamp"] = pd.to_datetime(hist_df["timestamp"])
-                hist_df["action_value"] = hist_df["action"].replace({"Buy": 1, "Sell": -1}).astype(float)
-                pivot = hist_df.pivot_table(index="timestamp", columns="symbol", values="action_value", aggfunc="sum")
-                st.line_chart(pivot.fillna(0))
-    else:
-        st.info("Waiting for first background auto-trade run... Please use the 'Refresh Auto-Trade Log' button if trades have been made.")
+    # st.write("Automatic Trading Actions Based on Sentiment (background):")
+    # auto_trade_log = get_auto_trade_log()
+    # if auto_trade_log:
+    #     # Show the most recent entry
+    #     last_entry = auto_trade_log[-1]
+    #     st.write(f"Last checked: {last_entry['timestamp']}")
+    #     df = pd.DataFrame(last_entry["actions"])
+    #     if "company_name" in df.columns:
+    #         df = df[["symbol", "company_name", "sentiment", "action"]]
+    #     st.dataframe(df)
+    #     st.write("Sentiment Analysis (latest):")
+    #     st.write(last_entry["sentiment"])
+
+    #     # Plot buy/sell actions over time
+    #     st.write("Auto-Trading History (Buy/Sell Actions Over Time):")
+    #     history = []
+    #     for entry in auto_trade_log:
+    #         ts = entry["timestamp"]
+    #         for act in entry["actions"]:
+    #             if act["action"] in ("Buy", "Sell"):
+    #                 history.append({
+    #                     "timestamp": ts,
+    #                     "symbol": act["symbol"],
+    #                     "action": act["action"]
+    #                 })
+    #     if history:
+    #         hist_df = pd.DataFrame(history)
+    #         if not hist_df.empty:
+    #             hist_df["timestamp"] = pd.to_datetime(hist_df["timestamp"])
+    #             hist_df["action_value"] = hist_df["action"].replace({"Buy": 1, "Sell": -1}).astype(float)
+    #             pivot = hist_df.pivot_table(index="timestamp", columns="symbol", values="action_value", aggfunc="sum")
+    #             st.line_chart(pivot.fillna(0))
+    # else:
+    #     st.info("Waiting for first background auto-trade run...")
+
 
 if __name__ == "__main__":
     main()