auto trades back in

app.py

@@ -609,55 +609,55 @@ 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):")
-    # 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...")
+    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__":