Update app.py

app.py

@@ -1,5 +1,5 @@
 # ==============================================================================
-# 🚀 app.py (V38.1 - GEM-Architect: Colored Diagnostics)
+# 🚀 app.py (V61.0 - GEM-Architect: Type Stats UI)
 # ==============================================================================
 
 import os
@@ -45,7 +45,6 @@ try:
     from trade_manager import TradeManager
     from periodic_tuner import AutoTunerScheduler
     
-    # محاولة استيراد محرك الباكتست (اختياري للتشغيل عبر الواجهة)
     try:
         from backtest_engine import run_strategic_optimization_task
         BACKTEST_AVAILABLE = True
@@ -119,14 +118,6 @@ sys_state = SystemState()
 # ------------------------------------------------------------------------------
 # Utilities
 # ------------------------------------------------------------------------------
-def format_crypto_price(price):
-    if price is None: return "0.0"
-    try:
-        p = float(price)
-        if p == 0: return "0.0"
-        return "{:.8f}".format(p).rstrip('0').rstrip('.')
-    except: return str(price)
-
 def calculate_duration_str(timestamp_str):
     if not timestamp_str: return "--:--:--"
     try:
@@ -146,6 +137,10 @@ def calculate_duration_str(timestamp_str):
         return f"{hours:02}:{minutes:02}:{seconds:02}"
     except: return "--:--:--"
 
+def format_pnl_split(profit, loss):
+    """تنسيق الربحية كجزأين (ربح / خسارة) بألوان"""
+    return f"<span style='color:#00ff00'>+${profit:,.0f}</span> / <span style='color:#ff0000'>-${abs(loss):,.0f}</span>"
+
 # ------------------------------------------------------------------------------
 # Auto-Pilot Daemon
 # ------------------------------------------------------------------------------
@@ -156,11 +151,9 @@ async def auto_pilot_loop():
             await asyncio.sleep(5) 
             if not sys_state.ready: continue
             
-            # تحديث حالة الـ Adaptive Hub في الواجهة كل دقيقة
             if adaptive_hub and int(time.time()) % 60 == 0:
                 sys_state.training_status_msg = adaptive_hub.get_status()
 
-            # فحص الحراس (Watchdogs) للصفقات المفتوحة
             if trade_manager and len(trade_manager.open_positions) > 0:
                 wd_status = await trade_manager.ensure_active_guardians()
                 if "No active" not in wd_status:
@@ -168,7 +161,6 @@ async def auto_pilot_loop():
                         sys_state.last_cycle_logs = trade_manager.latest_guardian_log
                 continue 
 
-            # تشغيل دورة المسح (Cycle) إذا كان الطيار الآلي مفعلاً
             if sys_state.auto_pilot and not sys_state.cycle_running and not sys_state.training_running:
                 if sys_state.last_cycle_time:
                     elapsed = (datetime.now() - sys_state.last_cycle_time).total_seconds()
@@ -190,15 +182,13 @@ async def auto_pilot_loop():
 async def lifespan(app: FastAPI):
     global r2, data_manager, ml_processor, adaptive_hub, trade_manager, whale_monitor, news_fetcher, senti_analyzer, sys_state, scheduler
     
-    logger.info("\n🚀 [System] Startup Sequence (Titan V38.1 - Colored Diagnostics)...")
+    logger.info("\n🚀 [System] Startup Sequence (Titan V61.0 - Dual Classification)...")
     try:
-        # 1. الخدمات الأساسية
         r2 = R2Service()
         data_manager = DataManager(contracts_db={}, whale_monitor=None, r2_service=r2)
         await data_manager.initialize() 
         await data_manager.load_contracts_from_r2()
         
-        # 2. المراقبة والتحليل
         whale_monitor = EnhancedWhaleMonitor(contracts_db=data_manager.get_contracts_db(), r2_service=r2)
         rpc_mgr = AdaptiveRpcManager(data_manager.http_client)
         whale_monitor.set_rpc_manager(rpc_mgr)
@@ -207,30 +197,25 @@ async def lifespan(app: FastAPI):
         senti_analyzer = SentimentIntensityAnalyzer()
         data_manager.whale_monitor = whale_monitor
         
-        # 3. العقل الاستراتيجي (Adaptive Hub)
         adaptive_hub = AdaptiveHub(r2_service=r2)
         await adaptive_hub.initialize()
 
-        # 4. المعالج العصبي (Processor)
         ml_processor = MLProcessor(data_manager=data_manager)
         await ml_processor.initialize()
         
-        # 5. مدير التنفيذ (Trade Manager)
         trade_manager = TradeManager(r2_service=r2, data_manager=data_manager, processor=ml_processor)
         trade_manager.learning_hub = adaptive_hub 
         
         await trade_manager.initialize_sentry_exchanges()
         await trade_manager.start_sentry_loops()
         
-        # 6. المجدول التلقائي (Auto-Tuner Scheduler)
         scheduler = AutoTunerScheduler(trade_manager)
         asyncio.create_task(scheduler.start_loop())
         logger.info("🕰️ [Scheduler] Auto-Tuner Background Task Started.")
         
-        # 7. الجاهزية
         sys_state.set_ready()
         asyncio.create_task(auto_pilot_loop())
-        logger.info("✅ [System READY] All modules operational. Cybernetic Link Established.")
+        logger.info("✅ [System READY] All modules operational.")
         yield
         
     except Exception as e:
@@ -244,13 +229,9 @@ async def lifespan(app: FastAPI):
         logger.info("✅ [System] Shutdown Complete.")
 
 # ------------------------------------------------------------------------------
-# Helper Tasks (Modified for Context)
+# Helper Tasks
 # ------------------------------------------------------------------------------
 async def _analyze_symbol_task(candidate_data: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Receives full candidate dict from L1 (Regime, Dynamic Limits),
-    not just the symbol string.
-    """
     try:
         symbol = candidate_data['symbol']
         required_tfs = ["5m", "15m", "1h", "4h"]
@@ -267,22 +248,23 @@ async def _analyze_symbol_task(candidate_data: Dict[str, Any]) -> Dict[str, Any]
         
         current_price = await data_manager.get_latest_price_async(symbol)
         
-        # ✅ FIX: Inject Context (Limits & Regime) into raw_data for L2 Processor
         raw_data = {
             'symbol': symbol, 
             'ohlcv': ohlcv_data, 
             'current_price': current_price, 
             'timestamp': time.time(),
-            # Preserving L1 Context
             'dynamic_limits': candidate_data.get('dynamic_limits', {}),
             'asset_regime': candidate_data.get('asset_regime', 'UNKNOWN'),
             'strategy_tag': candidate_data.get('strategy_tag', 'NONE'),
+            'strategy_type': candidate_data.get('strategy_type', 'NORMAL'), # Passing Type
             'l1_score': candidate_data.get('l1_sort_score', 0)
         }
         
         res = await ml_processor.process_compound_signal(raw_data)
         if not res: return None
-
+        
+        # Pass Type Forward
+        res['strategy_type'] = candidate_data.get('strategy_type', 'NORMAL')
         return res
     except Exception: return None
 
@@ -295,15 +277,12 @@ async def run_unified_cycle():
         logger.info(message)
         log_buffer.write(message + '\n')
 
-    if sys_state.cycle_running or sys_state.training_running:
-        return
-    if not sys_state.ready:
-        return
+    if sys_state.cycle_running or sys_state.training_running: return
+    if not sys_state.ready: return
 
     sys_state.set_cycle_start()
     
     try:
-        # LAYER 0: Guardian & Portfolio Check
         await trade_manager.sync_internal_state_with_r2()
         
         if len(trade_manager.open_positions) > 0:
@@ -314,41 +293,31 @@ async def run_unified_cycle():
                 pnl = ((curr_p - entry_p) / entry_p) * 100 if entry_p > 0 else 0
                 log_and_print(f"   🔒 {sym}: {pnl:+.2f}%")
 
-        # LAYER 1: Adaptive Screening
-        current_regime = getattr(SystemLimits, 'CURRENT_REGIME', 'UNKNOWN')
-        log_and_print(f"   [1/5] 🔍 L1 Screening (Context Aware)...")
-        
-        # Pass adaptive_hub so DataManager can fetch DNA per coin
+        log_and_print(f"   [1/5] 🔍 L1 Screening (Bottom/Momentum)...")
         candidates = await data_manager.layer1_rapid_screening(adaptive_hub_ref=adaptive_hub) 
         if not candidates:
             log_and_print("⚠️ No valid candidates found (Quality Filter).")
             sys_state.set_cycle_end(logs=log_buffer.getvalue())
             return
             
-        # LAYER 2: Deep Analysis
         log_and_print(f"   [2/5] 🧠 L2 Deep Analysis ({len(candidates)} items)...")
-        # ✅ FIX: Pass the WHOLE candidate object, not just symbol string
         tasks = [_analyze_symbol_task(c) for c in candidates]
         results = await asyncio.gather(*tasks)
         valid_l2 = [res for res in results if res is not None]
         
         semi_finalists = sorted(valid_l2, key=lambda x: x.get('enhanced_final_score', 0.0), reverse=True)[:10]
-        
         if not semi_finalists:
             log_and_print("⚠️ No valid L2 candidates.")
             sys_state.set_cycle_end(logs=log_buffer.getvalue())
             return
 
-        # LAYER 3: Deep Dive (Contextual)
         log_and_print(f"   [3/5] 📡 L3 Deep Dive (Whales & News) for TOP {len(semi_finalists)}...")
-        
         final_candidates = []
 
         for sig in semi_finalists:
             symbol = sig['symbol']
             l2_score = sig.get('enhanced_final_score', 0.0)
             
-            # Whale Check
             whale_points = 0.0
             try:
                 if whale_monitor:
@@ -362,7 +331,6 @@ async def run_unified_cycle():
                         elif action == 'SELL': whale_points = -dynamic_impact
             except Exception: pass
 
-            # News Check
             news_points = 0.0
             try:
                 if news_fetcher and senti_analyzer:
@@ -374,7 +342,6 @@ async def run_unified_cycle():
                         news_points = compound_score * SystemLimits.L3_NEWS_IMPACT_MAX
             except Exception: pass
 
-            # MC Advanced
             mc_a_points = 0.0
             try:
                 raw_mc_a = await ml_processor.run_advanced_monte_carlo(symbol, '1h')
@@ -382,30 +349,18 @@ async def run_unified_cycle():
             except Exception: pass
 
             final_score = l2_score + whale_points + news_points + mc_a_points
-            
-            sig['whale_score'] = whale_points
-            sig['news_score'] = news_points
-            sig['mc_advanced_score'] = mc_a_points
             sig['final_total_score'] = final_score 
-            
             final_candidates.append(sig)
 
-        # RE-RANKING
         final_candidates.sort(key=lambda x: x['final_total_score'], reverse=True)
 
         approved_signals = []
-        
-        header = (f"{'SYM':<9} | {'L2(HYB)':<6} | {'TITAN':<5} | {'PATT':<5} | "
-                  f"{'WHALE':<6} | {'MC(A)':<6} | {'FINAL':<6} | {'ORACLE':<6} | {'STATUS'}")
-        log_and_print("-" * 110)
-        log_and_print(header)
         log_and_print("-" * 110)
+        log_and_print(f"{'SYM':<9} | {'TYPE':<12} | {'FINAL':<6} | {'ORACLE':<6} | {'STATUS'}")
         
         for sig in final_candidates:
             symbol = sig['symbol']
-            
             decision = await ml_processor.consult_oracle(sig)
-            
             action = decision.get('action', 'WAIT')
             oracle_conf = decision.get('confidence', 0.0)
             target_class = decision.get('target_class', '')
@@ -416,26 +371,8 @@ async def run_unified_cycle():
                 sig.update(decision)
                 approved_signals.append(sig)
 
-            l2_hybrid = sig.get('enhanced_final_score', 0.0)
-            titan_d = sig.get('titan_score', 0.0)
-            patt_d = sig.get('patterns_score', 0.0)
-            whale_d = sig.get('whale_score', 0.0)
-            mca_d = sig.get('mc_advanced_score', 0.0)
-            final_d = sig.get('final_total_score', 0.0)
-
-            log_and_print(
-                f"{symbol:<9} | "
-                f"{l2_hybrid:.2f}   | "
-                f"{titan_d:.2f}  | "
-                f"{patt_d:.2f}  | "
-                f"{whale_d:+.2f}  | " 
-                f"{mca_d:+.2f}  | " 
-                f"{final_d:.2f}   | " 
-                f"{oracle_conf:.2f}   | "
-                f"{status_str}"
-            )
-
-        # LAYER 4: Sniper & Governance & Execution
+            log_and_print(f"{symbol:<9} | {sig.get('strategy_type','N/A'):<12} | {sig.get('final_total_score',0):.2f}   | {oracle_conf:.2f}   | {status_str}")
+
         if approved_signals:
             log_and_print("-" * 110)
             log_and_print(f"   [4/5] 🎯 L4 Sniper -> 🏛️ Governance -> 💰 Portfolio ({len(approved_signals)} candidates)...")
@@ -462,38 +399,29 @@ async def run_unified_cycle():
 # Handlers
 # ------------------------------------------------------------------------------
 async def trigger_training_cycle():
-    if adaptive_hub:
-        status = adaptive_hub.get_status()
-        return f"🤖 Adaptive System: {status}"
+    if adaptive_hub: return f"🤖 Adaptive System: {adaptive_hub.get_status()}"
     return "⚠️ System not ready."
 
 async def trigger_strategic_backtest():
-    if not BACKTEST_AVAILABLE:
-        return "⚠️ Backtest Engine not found."
-    
-    if trade_manager and len(trade_manager.open_positions) > 0:
-        return "⛔ Cannot start Backtest: Active trades exist! Close them first."
-    
-    if sys_state.training_running:
-        return "⚠️ Training already in progress."
+    if not BACKTEST_AVAILABLE: return "⚠️ Backtest Engine not found."
+    if trade_manager and len(trade_manager.open_positions) > 0: return "⛔ Active trades exist."
+    if sys_state.training_running: return "⚠️ Running."
         
     async def _run_bg_task():
         sys_state.training_running = True
         sys_state.training_status_msg = "🧪 Strategic Backtest Running..."
         try:
-            logger.info("🧪 [Manual Trigger] Starting Strategic Backtest...")
+            logger.info("🧪 [Manual] Starting Strategic Backtest...")
             await run_strategic_optimization_task()
-            if adaptive_hub: 
-                await adaptive_hub.initialize()
-            logger.info("✅ [Manual Trigger] Backtest Complete. DNA Updated.")
-        except Exception as e:
-            logger.error(f"❌ Backtest Failed: {e}")
+            if adaptive_hub: await adaptive_hub.initialize()
+            logger.info("✅ [Manual] Backtest Complete.")
+        except Exception as e: logger.error(f"❌ Backtest Failed: {e}")
         finally:
             sys_state.training_running = False
             sys_state.training_status_msg = adaptive_hub.get_status() if adaptive_hub else "Ready"
 
     asyncio.create_task(_run_bg_task())
-    return "🧪 Strategic Backtest Started (Safe Mode)."
+    return "🧪 Strategic Backtest Started."
 
 async def manual_close_current_trade():
     if not trade_manager.open_positions: return "⚠️ No trade."
@@ -528,16 +456,13 @@ async def reset_capital_handler():
          await sp._save_state_to_r2()
     return f"✅ Capital Reset to ${INITIAL_CAPITAL} (History Kept)"
 
-# 🔥 زر تصفير مصفوفة التشخيص
 async def reset_diagnostics_handler():
     await r2.reset_diagnostic_stats_async()
     return "✅ Diagnostic Matrix Reset."
 
-# 🔥 زر تصفير إحصائيات الحراس
 async def reset_guardians_handler():
     await r2.reset_guardian_stats_async()
-    if trade_manager:
-        trade_manager.ai_stats = await r2.get_guardian_stats_async()
+    if trade_manager: trade_manager.ai_stats = await r2.get_guardian_stats_async()
     return "✅ Guardian Stats Reset."
 
 async def toggle_auto_pilot(enable):
@@ -550,16 +475,17 @@ async def run_cycle_from_gradio():
     return "🚀 Launched."
 
 # ------------------------------------------------------------------------------
-# UI Updates (Updated for Diagnostics Coloring)
+# UI Updates
 # ------------------------------------------------------------------------------
 async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
     empty_chart = go.Figure()
     empty_chart.update_layout(template="plotly_dark", paper_bgcolor="#0b0f19", plot_bgcolor="#0b0f19", xaxis={'visible':False}, yaxis={'visible':False})
     wl_df_empty = pd.DataFrame(columns=["Coin", "Score"])
     diag_df_empty = pd.DataFrame(columns=["Model", "Wins", "Losses", "PnL (USD)"])
+    type_df_empty = pd.DataFrame(columns=["Coin Type", "Wins", "Losses", "Profitability"])
     
     if not sys_state.ready:
-        return "Initializing...", "...", empty_chart, "0.0", "0.0", "0.0", "0.0", "0.0%", wl_df_empty, diag_df_empty, "Loading...", "Loading...", "Loading..."
+        return "Initializing...", "...", empty_chart, "0.0", "0.0", "0.0", "0.0", "0.0%", wl_df_empty, diag_df_empty, type_df_empty, "Loading...", "Loading...", "Loading..."
 
     try:
         sp = trade_manager.smart_portfolio
@@ -568,8 +494,6 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
         free_cap = max(0.0, equity - allocated)
         daily_pnl = sp.state.get('daily_net_pnl', 0.0)
         is_halted = sp.state.get('is_trading_halted', False)
-        market_mood = sp.market_trend
-        fg_index = sp.fear_greed_index
         
         symbol = None; entry_p = 0.0; tp_p = 0.0; sl_p = 0.0; curr_p = 0.0; pnl_pct = 0.0; pnl_val_unrealized = 0.0
         active_trade_info = ""
@@ -583,19 +507,18 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
             sl_p = float(trade.get('sl_price', 0.0))
             trade_dur_str = calculate_duration_str(trade.get('entry_time'))
             
-            # ✅ استخراج بيانات الحوكمة والقرار
             decision_data = trade.get('decision_data', {})
             gov_grade = decision_data.get('governance_grade', 'N/A')
             gov_score = decision_data.get('governance_score', 0.0)
             sys_conf = decision_data.get('system_confidence', 0.0)
+            strat_type = trade.get('strategy_type', 'NORMAL')
             
-            # تلوين الرتبة حسب الجودة
             grade_color = "#ccc"
-            if gov_grade == "ULTRA": grade_color = "#ff00ff" # Magenta
-            elif gov_grade == "STRONG": grade_color = "#00ff00" # Green
-            elif gov_grade == "NORMAL": grade_color = "#00e5ff" # Cyan
-            elif gov_grade == "WEAK": grade_color = "#ffff00" # Yellow
-            elif gov_grade == "REJECT": grade_color = "#ff0000" # Red
+            if gov_grade == "ULTRA": grade_color = "#ff00ff" 
+            elif gov_grade == "STRONG": grade_color = "#00ff00" 
+            elif gov_grade == "NORMAL": grade_color = "#00e5ff" 
+            elif gov_grade == "WEAK": grade_color = "#ffff00" 
+            elif gov_grade == "REJECT": grade_color = "#ff0000" 
 
             curr_p = await data_manager.get_latest_price_async(symbol)
             if curr_p > 0 and entry_p > 0:
@@ -608,16 +531,15 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
                 <span>⏱️ Time:</span> <span style='color: #ffff00;'>{trade_dur_str}</span>
              </div>
              <div style='display: flex; justify-content: space-between; font-size: 12px; color: #ccc; margin-top:5px;'>
-                <span>🏛️ Gov Grade:</span> <span style='color: {grade_color}; font-weight:bold;'>{gov_grade} ({gov_score:.1f})</span>
+                <span>🏛️ Grade:</span> <span style='color: {grade_color}; font-weight:bold;'>{gov_grade} ({gov_score:.1f})</span>
              </div>
              <div style='display: flex; justify-content: space-between; font-size: 12px; color: #ccc; margin-top:5px;'>
-                <span>🔮 System Conf:</span> <span style='color: #00e5ff;'>{sys_conf:.1%}</span>
+                <span>🏷️ Type:</span> <span style='color: #orange;'>{strat_type}</span>
              </div>
             """
 
         virtual_equity = equity + pnl_val_unrealized
-        active_trade_pnl_val = pnl_val_unrealized
-        active_pnl_color = "#00ff00" if active_trade_pnl_val >= 0 else "#ff0000"
+        active_pnl_color = "#00ff00" if pnl_val_unrealized >= 0 else "#ff0000"
         portfolio = await r2.get_portfolio_state_async()
         total_t = portfolio.get('total_trades', 0)
         wins = portfolio.get('winning_trades', 0)
@@ -627,29 +549,24 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
         tot_loss = portfolio.get('total_loss_usd', 0.0)
         net_prof = tot_prof - tot_loss
         win_rate = (wins / total_t * 100) if total_t > 0 else 0.0
-        color = "#00ff00" if daily_pnl >= 0 else "#ff0000"
         halt_status = "<span style='color:red; font-weight:bold;'>HALTED</span>" if is_halted else "<span style='color:#00ff00;'>ACTIVE</span>"
-        current_regime = getattr(SystemLimits, 'CURRENT_REGIME', 'N/A')
 
         wallet_md = f"""
         <div style='background-color: #1a1a1a; padding: 15px; border-radius: 8px; border: 1px solid #333; text-align:center;'>
             <h3 style='margin:0; color:#888; font-size:14px;'>💼 Institutional Portfolio</h3>
             <div style='font-size: 24px; font-weight: bold; color: white; margin: 5px 0 0 0;'>${virtual_equity:,.2f}</div>
-            <div style='font-size: 14px; color: {active_pnl_color}; margin-bottom: 5px;'>({active_trade_pnl_val:+,.2f} USD)</div>
+            <div style='font-size: 14px; color: {active_pnl_color}; margin-bottom: 5px;'>({pnl_val_unrealized:+,.2f} USD)</div>
             
             <table style='width:100%; font-size:12px; margin-top:5px; color:#ccc;'>
                 <tr><td>Allocated:</td><td style='text-align:right; color:#ffa500;'>${allocated:.2f}</td></tr>
                 <tr><td>Free Cap:</td><td style='text-align:right; color:#00ff00;'>${free_cap:.2f}</td></tr>
-                <tr><td>Daily PnL:</td><td style='text-align:right; color:{color};'>${daily_pnl:+.2f}</td></tr>
+                <tr><td>Daily PnL:</td><td style='text-align:right; color:{"#00ff00" if daily_pnl>=0 else "#ff0000"};'>${daily_pnl:+.2f}</td></tr>
             </table>
             
             <hr style='border-color:#444; margin: 10px 0;'>
             
             <div style='display: flex; justify-content: space-between; font-size: 12px; color: #ccc;'>
-                <span>🦅 Mood:</span> <span style='color: white;'>{market_mood} ({fg_index})</span>
-            </div>
-            <div style='display: flex; justify-content: space-between; font-size: 12px; color: #ccc; margin-top:3px;'>
-                <span>🧬 Regime:</span> <span style='color: #00e5ff; font-weight:bold;'>{current_regime}</span>
+                <span>🦅 Mood:</span> <span style='color: white;'>{sp.market_trend}</span>
             </div>
             <div style='display: flex; justify-content: space-between; font-size: 12px; color: #ccc; margin-top:5px;'>
                 <span>🛡️ Status:</span> {halt_status}
@@ -658,6 +575,7 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
         </div>
         """
 
+        # Guardian Stats
         key_map = {
             "Dual-Core (Hybrid)": "hybrid", 
             "Hydra: Crash (Panic)": "crash", 
@@ -665,7 +583,6 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
             "Hydra: Stagnation (Time)": "stagnation"
         }
         target_key = key_map.get(selected_view, "hybrid")
-        # استخدام النسخة الموجودة في TradeManager مباشرة لضمان الحداثة
         stats_data = trade_manager.ai_stats.get(target_key, {"total":0, "good":0, "saved":0.0, "missed":0.0})
         
         tot_ds = stats_data['total']
@@ -687,7 +604,6 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
                 <tr><td>Interventions:</td><td style='text-align:right;'>{tot_ds}</td></tr>
                 <tr><td>Accuracy:</td><td style='text-align:right; color:#00e5ff;'>{ds_acc:.1f}%</td></tr>
                 <tr><td>Saved:</td><td style='text-align:right; color:#00ff00;'>${stats_data['saved']:.2f}</td></tr>
-                <tr><td>Missed:</td><td style='text-align:right; color:#ff0000;'>${stats_data['missed']:.2f}</td></tr>
             </table>
         </div>
         """
@@ -696,74 +612,57 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
         if adaptive_hub:
             if hasattr(adaptive_hub, 'get_learning_progress'):
                 fast_learn_prog = adaptive_hub.get_learning_progress()
-            else:
-                fast_learn_prog = "N/A"
-        
-        sch_w_time = "Wait"; sch_w_cnt = 0
-        sch_m_time = "Wait"; sch_m_cnt = 0
-        sch_running = False
         
-        if scheduler:
-            metrics = scheduler.get_status_metrics()
-            sch_w_time = metrics["weekly_timer"]
-            sch_w_cnt = metrics["weekly_count"]
-            sch_m_time = metrics["monthly_timer"]
-            sch_m_cnt = metrics["monthly_count"]
-            sch_running = metrics["is_running"]
-
-        running_badge = "<span style='color:#00ff00; float:right; animation: blink 1s infinite;'>RUNNING ⚙️</span>" if sch_running else ""
+        metrics = scheduler.get_status_metrics() if scheduler else {}
+        sch_w_time = metrics.get("weekly_timer", "Wait")
+        sch_w_cnt = metrics.get("weekly_count", 0)
 
         neural_md = f"""
         <div style='background-color: #1a1a1a; padding: 10px; border-radius: 8px; border: 1px solid #333; font-size: 12px; margin-top: 10px;'>
-            <div style='display:flex; justify-content:space-between; align-items:center; margin-bottom:5px;'>
-                <h3 style='margin:0; color:#00e5ff; font-size:14px;'>🧠 Neural Cycles</h3>
-                {running_badge}
-            </div>
+            <h3 style='margin:0; color:#00e5ff; font-size:14px;'>🧠 Neural Cycles</h3>
             <table style='width:100%; color:#ccc;'>
                 <tr style='border-bottom: 1px solid #333;'>
                     <td style='padding:4px 0;'>⚡ Fast Learner:</td>
                     <td style='text-align:right; color:#ffff00; font-weight:bold;'>{fast_learn_prog}</td>
-                    <td style='text-align:right; font-size:10px; color:#666;'>Trades</td>
                 </tr>
                 <tr style='border-bottom: 1px solid #333;'>
                     <td style='padding:4px 0;'>📅 Weekly Tune:</td>
                     <td style='text-align:right; color:#fff;'>{sch_w_time}</td>
-                    <td style='text-align:right; color:#00ff00;'>#{sch_w_cnt}</td>
-                </tr>
-                <tr>
-                    <td style='padding:4px 0;'>🗓️ Monthly Evo:</td>
-                    <td style='text-align:right; color:#fff;'>{sch_m_time}</td>
-                    <td style='text-align:right; color:#00ff00;'>#{sch_m_cnt}</td>
                 </tr>
             </table>
-            <div style='margin-top:5px; font-size:10px; color:#555; text-align:center;'>
-                Adaptive DNA Active: {getattr(SystemLimits, 'CURRENT_REGIME', 'N/A')}
-            </div>
         </div>
         """
         
-        # 🔥 جلب بيانات مصفوفة التشخيص وتلوينها
+        # Diagnostic Matrix
         diag_data = await r2.get_diagnostic_stats_async()
         diag_list = []
-        ordered_models = ["Titan", "Patterns", "Oracle", "Sniper", "MonteCarlo_L", "MonteCarlo_A", "Governance"]
+        ordered_models = ["Titan", "Patterns", "Oracle", "Sniper", "MonteCarlo_L", "Governance"]
         
         for m in ordered_models:
             stats = diag_data.get(m, {"wins": 0, "losses": 0, "pnl": 0.0})
             pnl_val = stats['pnl']
-            
-            # 🎨 التلوين: أخضر للربح، أحمر للخسارة
             color = "#00ff00" if pnl_val >= 0 else "#ff0000"
             pnl_str = f"<span style='color: {color}; font-weight: bold;'>${pnl_val:+.2f}</span>"
-            
-            diag_list.append([
-                m, 
-                stats['wins'], 
-                stats['losses'], 
-                pnl_str
-            ])
+            diag_list.append([m, stats['wins'], stats['losses'], pnl_str])
         
         diag_df = pd.DataFrame(diag_list, columns=["Model", "Wins", "Losses", "PnL (USD)"])
 
+        # ✅ NEW: Type Stats DataFrame
+        type_stats_list = []
+        if trade_manager and hasattr(trade_manager, 'type_stats'):
+            for t_name, t_data in trade_manager.type_stats.items():
+                name_clean = t_name.replace("_", " ")
+                wins = t_data.get('wins', 0)
+                losses = t_data.get('losses', 0)
+                prof = t_data.get('profit_usd', 0.0)
+                loss_val = t_data.get('loss_usd', 0.0)
+                
+                # Format: +$Profit / -$Loss
+                profitability_html = format_pnl_split(prof, loss_val)
+                type_stats_list.append([name_clean, wins, losses, profitability_html])
+        
+        type_df = pd.DataFrame(type_stats_list, columns=["Coin Type", "Wins", "Losses", "Profitability"])
+
         wl_data = [[k, f"{v.get('final_total_score',0):.2f}"] for k, v in trade_manager.watchlist.items()]
         wl_df = pd.DataFrame(wl_data, columns=["Coin", "Score"])
 
@@ -783,9 +682,9 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
                 if entry_p > 0:
                     fig.add_hline(y=entry_p, line_dash="dash", line_color="white", annotation_text="ENTRY", annotation_position="top left")
                 if tp_p > 0:
-                    fig.add_hline(y=tp_p, line_color="#00ff00", line_width=2, annotation_text="TARGET (TP)", annotation_position="top left")
+                    fig.add_hline(y=tp_p, line_color="#00ff00", line_width=2, annotation_text="TP", annotation_position="top left")
                 if sl_p > 0:
-                    fig.add_hline(y=sl_p, line_color="#ff0000", line_width=2, annotation_text="STOP LOSS", annotation_position="bottom left")
+                    fig.add_hline(y=sl_p, line_color="#ff0000", line_width=2, annotation_text="SL", annotation_position="bottom left")
 
                 fig.update_layout(
                     template="plotly_dark", 
@@ -794,19 +693,18 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
                     margin=dict(l=0, r=40, t=30, b=0), 
                     height=400, 
                     xaxis_rangeslider_visible=False,
-                    title=dict(text=f"{symbol} (Spot Long) | PnL: {pnl_pct:+.2f}%", font=dict(color="white"))
+                    title=dict(text=f"{symbol} (Long) | PnL: {pnl_pct:+.2f}%", font=dict(color="white"))
                 )
 
         train_status = sys_state.training_status_msg
         if sys_state.training_running: train_status = "🧪 Backtest Running..."
 
-        # إضافة diag_df إلى مخرجات الدالة
         return (status_txt, status_line, fig, f"{curr_p:.6f}", f"{entry_p:.6f}", f"{tp_p:.6f}", f"{sl_p:.6f}",
-                f"{pnl_pct:+.2f}%", wl_df, diag_df, wallet_md, history_md, neural_md)
+                f"{pnl_pct:+.2f}%", wl_df, diag_df, type_df, wallet_md, history_md, neural_md)
 
     except Exception:
         traceback.print_exc()
-        return "Error", "Error", empty_chart, "0", "0", "0", "0", "0%", wl_df_empty, diag_df_empty, "Err", "Err", "Err"
+        return "Error", "Error", empty_chart, "0", "0", "0", "0", "0%", wl_df_empty, diag_df_empty, type_df_empty, "Err", "Err", "Err"
 
 # ------------------------------------------------------------------------------
 # Gradio UI Construction
@@ -814,10 +712,10 @@ async def check_live_pnl_and_status(selected_view="Dual-Core (Hybrid)"):
 def create_gradio_ui():
     custom_css = ".gradio-container {background:#0b0f19} .dataframe {background:#1a1a1a!important} .html-box {min-height:180px}"
     
-    with gr.Blocks(title="Titan V38.1 (Neural Dashboard)") as demo:
+    with gr.Blocks(title="Titan V61.0 (Dual-Type Dashboard)") as demo:
         gr.HTML(f"<style>{custom_css}</style>")
         
-        gr.Markdown("# 🚀 Titan V38.1 (Cybernetic: Neural Dashboard)")
+        gr.Markdown("# 🚀 Titan V61.0 (Cybernetic: Dual-Type Engine)")
         
         with gr.Row():
             with gr.Column(scale=3):
@@ -834,11 +732,19 @@ def create_gradio_ui():
                 wallet_out = gr.HTML(label="Smart Wallet", elem_classes="html-box")
                 neural_out = gr.HTML(label="Neural Cycles", elem_classes="html-box")
                 
-                # 🔥 إضافة مصفوفة التشخيص الجديدة مع دعم HTML للألوان
+                # ✅ NEW: Type Stats Table
+                gr.Markdown("### 💎 Opportunity Types")
+                type_stats_out = gr.Dataframe(
+                    headers=["Coin Type", "Wins", "Losses", "Profitability"],
+                    datatype=["str", "number", "number", "html"],
+                    interactive=False,
+                    label="Type Performance"
+                )
+
                 gr.Markdown("### 🕵️ Diagnostic Matrix")
                 diagnostic_out = gr.Dataframe(
                     headers=["Model", "Wins", "Losses", "PnL (USD)"],
-                    datatype=["str", "number", "number", "html"], # ✅ html لتفعيل الألوان
+                    datatype=["str", "number", "number", "html"], 
                     interactive=False,
                     label="Model Performance"
                 )
@@ -880,7 +786,6 @@ def create_gradio_ui():
                 logs = gr.Textbox(label="Logs", lines=14, autoscroll=True, elem_classes="log-box", type="text")
                 gr.HTML("<style>.log-box textarea { font-family: 'Consolas', 'Monaco', monospace !important; font-size: 12px !important; white-space: pre !important; }</style>")
 
-        # Event Handlers
         btn_run.click(fn=run_cycle_from_gradio, outputs=alert)
         btn_close.click(fn=manual_close_current_trade, outputs=alert)
         btn_history_reset.click(fn=reset_history_handler, outputs=alert)
@@ -889,12 +794,12 @@ def create_gradio_ui():
         btn_backtest.click(fn=trigger_strategic_backtest, outputs=alert)
         auto_pilot.change(fn=toggle_auto_pilot, inputs=auto_pilot, outputs=alert)
         
-        # ربط أزرار التصفير الجديدة
         btn_reset_diag.click(fn=reset_diagnostics_handler, outputs=alert)
         btn_reset_guard.click(fn=reset_guardians_handler, outputs=alert)
         
+        # Added type_stats_out to outputs
         gr.Timer(3).tick(fn=check_live_pnl_and_status, inputs=stats_dd, 
-                         outputs=[logs, status, live_chart, t_price, t_entry, t_tp, t_sl, t_pnl, watchlist_out, diagnostic_out, wallet_out, history_out, neural_out])
+                         outputs=[logs, status, live_chart, t_price, t_entry, t_tp, t_sl, t_pnl, watchlist_out, diagnostic_out, type_stats_out, wallet_out, history_out, neural_out])
     return demo
 
 fast_api_server = FastAPI(lifespan=lifespan)