different confidence for open/close position
This commit is contained in:
Dobromir Popov
@ -529,22 +529,22 @@ class RealTimeScalpingDashboard:
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html.Div([
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html.H4(id="current-balance", className="text-success"),
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html.P("Current Balance", className="text-white")
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], className="col-md-2 text-center"),
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], className="col-md-3 text-center"), # Increased from col-md-2
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html.Div([
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html.H4(id="session-duration", className="text-info"),
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html.P("Session Time", className="text-white")
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], className="col-md-2 text-center"),
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], className="col-md-3 text-center"), # Increased from col-md-2
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html.Div([
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html.H4(id="open-positions", className="text-warning"),
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html.Div(id="open-positions", className="text-warning"),
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html.P("Open Positions", className="text-white")
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], className="col-md-2 text-center"),
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], className="col-md-3 text-center"), # Increased from col-md-2 to col-md-3 for more space
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html.Div([
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html.H4("500x", className="text-danger"),
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html.P("Leverage", className="text-white")
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], className="col-md-2 text-center")
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], className="col-md-3 text-center") # Increased from col-md-2
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], className="row mb-3"),
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# Live metrics row
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@ -717,7 +717,34 @@ class RealTimeScalpingDashboard:
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# Update session metrics
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current_balance = f"${dashboard_instance.trading_session.current_balance:.2f}"
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open_positions = str(len(dashboard_instance.trading_session.positions))
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# Create color-coded position display
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positions = dashboard_instance.trading_session.positions
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if positions:
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position_displays = []
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for symbol, pos in positions.items():
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side = pos['side']
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size = pos['size']
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entry_price = pos['entry_price']
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current_price = dashboard_instance.live_prices.get(symbol, entry_price)
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# Calculate unrealized P&L
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if side == 'LONG':
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unrealized_pnl = (current_price - entry_price) * size
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color_class = "text-success" # Green for LONG
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side_display = "[LONG]"
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else: # SHORT
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unrealized_pnl = (entry_price - current_price) * size
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color_class = "text-danger" # Red for SHORT
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side_display = "[SHORT]"
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position_text = f"{side_display} {size:.3f} @ ${entry_price:.2f} | P&L: ${unrealized_pnl:+.2f}"
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position_displays.append(html.P(position_text, className=f"{color_class} mb-1"))
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open_positions = html.Div(position_displays)
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else:
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open_positions = html.P("No open positions", className="text-muted")
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pnl = f"${dashboard_instance.trading_session.total_pnl:+.2f}"
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win_rate = f"{dashboard_instance.trading_session.get_win_rate()*100:.1f}%"
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total_trades = str(dashboard_instance.trading_session.total_trades)
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@ -1767,25 +1794,41 @@ class RealTimeScalpingDashboard:
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return fig
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def _create_model_training_status(self):
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"""Create model training progress display"""
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"""Create enhanced model training progress display with perfect opportunity detection"""
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try:
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# Get model training metrics from orchestrator
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if hasattr(self.orchestrator, 'get_performance_metrics'):
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metrics = self.orchestrator.get_performance_metrics()
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# Get perfect moves for retrospective training
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perfect_moves_count = metrics.get('perfect_moves', 0)
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recent_perfect_moves = []
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if hasattr(self.orchestrator, 'get_recent_perfect_moves'):
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recent_perfect_moves = self.orchestrator.get_recent_perfect_moves(limit=3)
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# Check if models are actively training
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rl_queue_size = metrics.get('rl_queue_size', 0)
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is_rl_training = rl_queue_size > 0
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is_cnn_training = perfect_moves_count > 0
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return html.Div([
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html.Div([
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html.H6("RL Training", className="text-success"),
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html.P(f"Queue Size: {metrics.get('rl_queue_size', 0)}", className="text-white"),
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html.H6("RL Training", className="text-success" if is_rl_training else "text-warning"),
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html.P(f"Status: {'ACTIVE' if is_rl_training else 'IDLE'}",
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className="text-success" if is_rl_training else "text-warning"),
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html.P(f"Queue Size: {rl_queue_size}", className="text-white"),
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html.P(f"Win Rate: {metrics.get('win_rate', 0)*100:.1f}%", className="text-white"),
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html.P(f"Total Actions: {metrics.get('total_actions', 0)}", className="text-white")
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html.P(f"Actions: {metrics.get('total_actions', 0)}", className="text-white")
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], className="col-md-6"),
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html.Div([
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html.H6("CNN Training", className="text-warning"),
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html.P(f"Perfect Moves: {metrics.get('perfect_moves', 0)}", className="text-white"),
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html.H6("CNN Training", className="text-success" if is_cnn_training else "text-warning"),
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html.P(f"Status: {'LEARNING' if is_cnn_training else 'IDLE'}",
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className="text-success" if is_cnn_training else "text-warning"),
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html.P(f"Perfect Moves: {perfect_moves_count}", className="text-white"),
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html.P(f"Confidence: {metrics.get('confidence_threshold', 0.6):.2f}", className="text-white"),
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html.P(f"Frequency: {metrics.get('decision_frequency', 30)}s", className="text-white")
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html.P(f"Retrospective: {'ON' if recent_perfect_moves else 'OFF'}",
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className="text-success" if recent_perfect_moves else "text-muted")
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], className="col-md-6")
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], className="row")
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else:
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@ -1845,54 +1888,87 @@ class RealTimeScalpingDashboard:
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])
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def _create_training_events_log(self):
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"""Create training events log"""
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"""Create enhanced training events log with retrospective learning details"""
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try:
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# Get recent perfect moves and training events
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events = []
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if hasattr(self.orchestrator, 'perfect_moves') and self.orchestrator.perfect_moves:
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perfect_moves = self.orchestrator.perfect_moves[-5:] # Last 5 perfect moves
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perfect_moves = list(self.orchestrator.perfect_moves)[-8:] # Last 8 perfect moves
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for move in perfect_moves:
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timestamp = move.timestamp.strftime('%H:%M:%S')
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outcome_pct = move.actual_outcome * 100
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confidence_gap = move.confidence_should_have_been - 0.6 # vs default threshold
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events.append({
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'time': timestamp,
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'type': 'CNN',
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'event': f"Perfect {move.optimal_action} detected for {move.symbol}",
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'event': f"Perfect {move.optimal_action} {move.symbol} ({outcome_pct:+.2f}%) - Retrospective Learning",
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'confidence': move.confidence_should_have_been,
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'color': 'text-warning'
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'color': 'text-warning',
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'priority': 3 if abs(outcome_pct) > 2 else 2 # High priority for big moves
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})
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# Add confidence adjustment event
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if confidence_gap > 0.1:
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events.append({
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'time': timestamp,
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'type': 'TUNE',
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'event': f"Confidence threshold adjustment needed: +{confidence_gap:.2f}",
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'confidence': confidence_gap,
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'color': 'text-info',
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'priority': 2
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})
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# Add RL training events based on queue activity
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if hasattr(self.orchestrator, 'rl_evaluation_queue') and self.orchestrator.rl_evaluation_queue:
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queue_size = len(self.orchestrator.rl_evaluation_queue)
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current_time = datetime.now()
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if queue_size > 0:
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events.append({
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'time': current_time.strftime('%H:%M:%S'),
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'type': 'RL',
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'event': f'Experience replay active (queue: {queue_size} actions)',
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'confidence': min(1.0, queue_size / 10),
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'color': 'text-success',
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'priority': 3 if queue_size > 5 else 1
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})
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# Add RL training events (mock for now)
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current_time = datetime.now()
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events.extend([
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{
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'time': (current_time - timedelta(minutes=2)).strftime('%H:%M:%S'),
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'type': 'RL',
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'event': 'Experience replay completed (batch_size=128)',
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'confidence': 0.85,
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'color': 'text-success'
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},
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{
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'time': (current_time - timedelta(minutes=5)).strftime('%H:%M:%S'),
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'type': 'TICK',
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'event': 'High-confidence tick features processed',
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'confidence': 0.92,
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'color': 'text-info'
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}
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])
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# Add tick processing events
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if hasattr(self.orchestrator, 'get_realtime_tick_stats'):
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tick_stats = self.orchestrator.get_realtime_tick_stats()
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patterns_detected = tick_stats.get('patterns_detected', 0)
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if patterns_detected > 0:
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events.append({
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'time': datetime.now().strftime('%H:%M:%S'),
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'type': 'TICK',
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'event': f'Violent move patterns detected: {patterns_detected}',
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'confidence': min(1.0, patterns_detected / 5),
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'color': 'text-info',
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'priority': 2
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})
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# Sort events by priority and time
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events.sort(key=lambda x: (x.get('priority', 1), x['time']), reverse=True)
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if not events:
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return html.Div([
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html.P("No training events yet. Models are initializing...",
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html.P("🤖 Models initializing... Waiting for perfect opportunities to learn from.",
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className="text-muted text-center"),
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html.P("💡 Retrospective learning will activate when significant price moves are detected.",
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className="text-muted text-center")
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])
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log_items = []
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for event in events[-8:]: # Last 8 events
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icon = "🧠" if event['type'] == 'CNN' else "🤖" if event['type'] == 'RL' else "⚡"
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for event in events[:10]: # Show top 10 events
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icon = "🧠" if event['type'] == 'CNN' else "🤖" if event['type'] == 'RL' else "⚙️" if event['type'] == 'TUNE' else "⚡"
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confidence_display = f"{event['confidence']:.2f}" if event['confidence'] <= 1.0 else f"{event['confidence']:.3f}"
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log_items.append(
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html.P(f"{event['time']} {icon} [{event['type']}] {event['event']} (conf: {event['confidence']:.2f})",
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html.P(f"{event['time']} {icon} [{event['type']}] {event['event']} (conf: {confidence_display})",
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className=f"{event['color']} mb-1")
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)
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@ -2121,25 +2197,33 @@ class RealTimeScalpingDashboard:
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logger.error(f"Error logging trade for accounting: {e}")
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def _start_orchestrator_trading(self):
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"""Start orchestrator trading thread"""
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"""Start orchestrator-based trading in background"""
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def orchestrator_loop():
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"""Background thread for orchestrator trading decisions"""
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logger.info("Orchestrator trading thread started")
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"""Background orchestrator trading loop with retrospective learning"""
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logger.info("ORCHESTRATOR: Starting enhanced trading loop with retrospective learning")
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while self.streaming:
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try:
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# Process orchestrator decisions
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self._process_orchestrator_decisions()
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logger.debug("Processing orchestrator decisions...")
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time.sleep(30) # Decision cycle every 30 seconds
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self._process_orchestrator_decisions()
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# Trigger retrospective learning analysis every 5 minutes
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if hasattr(self.orchestrator, 'trigger_retrospective_learning'):
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asyncio.run(self.orchestrator.trigger_retrospective_learning())
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# Sleep for decision frequency
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time.sleep(30) # 30 second intervals for scalping
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except Exception as e:
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logger.error(f"Error in orchestrator trading loop: {e}")
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time.sleep(5)
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logger.error(f"Error in orchestrator loop: {e}")
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time.sleep(5) # Short sleep on error
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logger.info("ORCHESTRATOR: Trading loop stopped")
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# Start the thread
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thread = Thread(target=orchestrator_loop, daemon=True)
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thread.start()
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logger.info("SUCCESS: Orchestrator trading thread running")
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# Start orchestrator in background thread
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orchestrator_thread = Thread(target=orchestrator_loop, daemon=True)
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orchestrator_thread.start()
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logger.info("ORCHESTRATOR: Enhanced trading loop started with retrospective learning")
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def create_scalping_dashboard(data_provider=None, orchestrator=None):
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"""Create real-time dashboard instance"""
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