all changes done over the last 2 weeks

trading_dashboard.py

@@ -38,8 +38,8 @@ conn = get_connection()
 st.title("📊 Trading Bot Dashboard V1.8")
 st.markdown("---")
 
-# Refresh button
-col1, col2, col3 = st.columns([1, 1, 4])
+# Controls
+col1, col2, col3 = st.columns([1, 1, 2])
 with col1:
     if st.button("🔄 Refresh Data"):
         st.cache_data.clear()
@@ -48,6 +48,13 @@ with col1:
 with col2:
     auto_refresh = st.checkbox("Auto-refresh (30s)")
 
+with col3:
+    trade_filter = st.selectbox(
+        "📊 Filter Trades:",
+        ["All Trades", "Live Trades Only", "Historical Only"],
+        index=1  # Default to "Live Trades Only"
+    )
+
 if auto_refresh:
     st.markdown("*Auto-refreshing every 30 seconds...*")
     import time
@@ -96,9 +103,25 @@ def load_bot_status():
     return pd.read_sql_query(query, conn)
 
 # Load data
-df_trades = load_all_trades()
+df_trades_raw = load_all_trades()
 df_status = load_bot_status()
 
+# ==========================================
+# APPLY TRADE FILTER
+# ==========================================
+
+if trade_filter == "Live Trades Only":
+    df_trades = df_trades_raw[df_trades_raw['status'] != 'historical'].copy()
+    st.info(f"📊 Showing **Live Trades Only** (excluding {(df_trades_raw['status'] == 'historical').sum()} historical imports)")
+elif trade_filter == "Historical Only":
+    df_trades = df_trades_raw[df_trades_raw['status'] == 'historical'].copy()
+    st.info(f"📚 Showing **Historical Trades Only** ({len(df_trades)} trades)")
+else:  # All Trades
+    df_trades = df_trades_raw.copy()
+    live_count = (df_trades['status'] != 'historical').sum()
+    hist_count = (df_trades['status'] == 'historical').sum()
+    st.info(f"📊 Showing **All Trades** ({live_count} live + {hist_count} historical)")
+
 # ==========================================
 # TOP METRICS
 # ==========================================
@@ -195,8 +218,8 @@ st.markdown("---")
 st.subheader("⏰ Trades by Hour (UTC)")
 
 if not df_trades.empty:
-    # Extract hour from entry_time
-    df_trades['hour_utc'] = pd.to_datetime(df_trades['entry_time']).dt.hour
+    # Extract hour from entry_time (handle both ISO8601 and standard format)
+    df_trades['hour_utc'] = pd.to_datetime(df_trades['entry_time'], format='mixed').dt.hour
 
     # Count trades by hour
     hourly_dist = df_trades.groupby('hour_utc').size().reset_index(name='count')
@@ -315,7 +338,7 @@ st.subheader("💰 Cumulative Profit Over Time")
 
 if closed_trades > 0:
     profit_timeline = df_trades[df_trades['status'] == 'closed'].copy()
-    profit_timeline['exit_time'] = pd.to_datetime(profit_timeline['exit_time'])
+    profit_timeline['exit_time'] = pd.to_datetime(profit_timeline['exit_time'], format='mixed')
     profit_timeline = profit_timeline.sort_values('exit_time')
     profit_timeline['cumulative_profit'] = profit_timeline['net_profit'].cumsum()