detecting local extremes and training on them
This commit is contained in:
Dobromir Popov
@ -27,6 +27,7 @@ import uuid
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import dash
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from dash import dcc, html, Input, Output
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import plotly.graph_objects as go
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import dash_bootstrap_components as dbc
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from core.config import get_config
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from core.data_provider import DataProvider, MarketTick
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@ -271,6 +272,11 @@ class RealTimeScalpingDashboard:
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}
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}
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# Training data structures (like the old dashboard)
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self.tick_cache = deque(maxlen=900) # 15 minutes of ticks at 1 tick/second
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self.one_second_bars = deque(maxlen=800) # 800 seconds of 1s bars
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self.is_streaming = False
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# WebSocket streaming control - now using DataProvider centralized distribution
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self.streaming = False
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self.data_provider_subscriber_id = None
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@ -509,6 +515,10 @@ class RealTimeScalpingDashboard:
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logger.info("Starting AI orchestrator trading thread...")
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self._start_orchestrator_trading()
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# Start training data collection and model training
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logger.info("Starting model training and data collection...")
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self._start_training_data_collection()
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logger.info("Real-Time Scalping Dashboard initialized with LIVE STREAMING")
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logger.info("WebSocket price streaming enabled")
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logger.info(f"Timezone: {self.timezone}")
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@ -1805,104 +1815,287 @@ class RealTimeScalpingDashboard:
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return fig
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def _create_model_training_status(self):
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"""Create enhanced model training progress display with perfect opportunity detection and sensitivity learning"""
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"""Create model training status display with enhanced extrema information"""
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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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# Get sensitivity learning information
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sensitivity_info = self._get_sensitivity_learning_info()
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return html.Div([
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# Get sensitivity learning info (now includes extrema stats)
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sensitivity_info = self._get_sensitivity_learning_info()
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# Get training status in the expected format
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training_status = self._get_model_training_status()
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# Training Data Stream Status
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tick_cache_size = len(getattr(self, 'tick_cache', []))
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bars_cache_size = len(getattr(self, 'one_second_bars', []))
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training_items = []
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# Training Data Stream
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training_items.append(
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html.Div([
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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"Actions: {metrics.get('total_actions', 0)}", className="text-white")
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], className="col-md-4"),
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html.H6([
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html.I(className="fas fa-database me-2 text-info"),
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"Training Data Stream"
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], className="mb-2"),
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html.Div([
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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"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-4"),
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html.Small([
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html.Strong("Tick Cache: "),
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html.Span(f"{tick_cache_size:,} ticks", className="text-success" if tick_cache_size > 100 else "text-warning")
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], className="d-block"),
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html.Small([
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html.Strong("1s Bars: "),
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html.Span(f"{bars_cache_size} bars", className="text-success" if bars_cache_size > 100 else "text-warning")
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], className="d-block"),
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html.Small([
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html.Strong("Stream: "),
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html.Span("LIVE" if getattr(self, 'is_streaming', False) else "OFFLINE",
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className="text-success" if getattr(self, 'is_streaming', False) else "text-danger")
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], className="d-block")
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])
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], className="mb-3 p-2 border border-info rounded")
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)
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# CNN Model Status
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training_items.append(
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html.Div([
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html.H6("DQN Sensitivity", className="text-info"),
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html.P(f"Level: {sensitivity_info['level_name']}",
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className="text-info"),
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html.P(f"Completed Trades: {sensitivity_info['completed_trades']}", className="text-white"),
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html.P(f"Learning Queue: {sensitivity_info['learning_queue_size']}", className="text-white"),
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html.P(f"Open: {sensitivity_info['open_threshold']:.3f} | Close: {sensitivity_info['close_threshold']:.3f}",
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className="text-white")
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], className="col-md-4")
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], className="row")
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else:
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return html.Div([
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html.P("Model training metrics not available", className="text-muted")
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])
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html.H6([
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html.I(className="fas fa-brain me-2 text-warning"),
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"CNN Model"
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], className="mb-2"),
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html.Div([
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html.Small([
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html.Strong("Status: "),
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html.Span(training_status['cnn']['status'],
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className=f"text-{training_status['cnn']['status_color']}")
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], className="d-block"),
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html.Small([
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html.Strong("Accuracy: "),
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html.Span(f"{training_status['cnn']['accuracy']:.1%}", className="text-info")
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], className="d-block"),
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html.Small([
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html.Strong("Loss: "),
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html.Span(f"{training_status['cnn']['loss']:.4f}", className="text-muted")
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], className="d-block"),
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html.Small([
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html.Strong("Epochs: "),
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html.Span(f"{training_status['cnn']['epochs']}", className="text-muted")
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], className="d-block"),
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html.Small([
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html.Strong("Learning Rate: "),
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html.Span(f"{training_status['cnn']['learning_rate']:.6f}", className="text-muted")
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], className="d-block")
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])
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], className="mb-3 p-2 border border-warning rounded")
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)
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# RL Agent Status
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training_items.append(
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html.Div([
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html.H6([
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html.I(className="fas fa-robot me-2 text-success"),
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"RL Agent (DQN)"
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], className="mb-2"),
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html.Div([
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html.Small([
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html.Strong("Status: "),
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html.Span(training_status['rl']['status'],
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className=f"text-{training_status['rl']['status_color']}")
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], className="d-block"),
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html.Small([
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html.Strong("Win Rate: "),
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html.Span(f"{training_status['rl']['win_rate']:.1%}", className="text-info")
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], className="d-block"),
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html.Small([
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html.Strong("Avg Reward: "),
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html.Span(f"{training_status['rl']['avg_reward']:.2f}", className="text-muted")
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], className="d-block"),
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html.Small([
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html.Strong("Episodes: "),
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html.Span(f"{training_status['rl']['episodes']}", className="text-muted")
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], className="d-block"),
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html.Small([
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html.Strong("Epsilon: "),
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html.Span(f"{training_status['rl']['epsilon']:.3f}", className="text-muted")
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], className="d-block"),
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html.Small([
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html.Strong("Memory: "),
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html.Span(f"{training_status['rl']['memory_size']:,}", className="text-muted")
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], className="d-block")
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])
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], className="mb-3 p-2 border border-success rounded")
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)
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return html.Div(training_items)
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except Exception as e:
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logger.error(f"Error creating model training status: {e}")
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return html.Div([
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html.P("Error loading model status", className="text-danger")
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])
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html.P("⚠️ Error loading training status", className="text-warning text-center"),
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html.P(f"Error: {str(e)}", className="text-muted text-center small")
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], className="p-3")
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def _get_sensitivity_learning_info(self) -> Dict[str, Any]:
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"""Get sensitivity learning information from orchestrator"""
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def _get_model_training_status(self) -> Dict:
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"""Get current model training status and metrics"""
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try:
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if hasattr(self.orchestrator, 'sensitivity_learning_enabled') and self.orchestrator.sensitivity_learning_enabled:
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current_level = getattr(self.orchestrator, 'current_sensitivity_level', 2)
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sensitivity_levels = getattr(self.orchestrator, 'sensitivity_levels', {})
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level_name = sensitivity_levels.get(current_level, {}).get('name', 'medium')
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completed_trades = len(getattr(self.orchestrator, 'completed_trades', []))
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learning_queue_size = len(getattr(self.orchestrator, 'sensitivity_learning_queue', []))
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open_threshold = getattr(self.orchestrator, 'confidence_threshold_open', 0.6)
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close_threshold = getattr(self.orchestrator, 'confidence_threshold_close', 0.25)
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return {
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'level_name': level_name.upper(),
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'completed_trades': completed_trades,
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'learning_queue_size': learning_queue_size,
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'open_threshold': open_threshold,
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'close_threshold': close_threshold
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# Initialize default status
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status = {
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'cnn': {
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'status': 'TRAINING',
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'status_color': 'warning',
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'accuracy': 0.0,
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'loss': 0.0,
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'epochs': 0,
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'learning_rate': 0.001
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},
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'rl': {
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'status': 'TRAINING',
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'status_color': 'success',
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'win_rate': 0.0,
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'avg_reward': 0.0,
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'episodes': 0,
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'epsilon': 1.0,
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'memory_size': 0
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}
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}
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# Try to get real metrics from orchestrator
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if hasattr(self.orchestrator, 'get_performance_metrics'):
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try:
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perf_metrics = self.orchestrator.get_performance_metrics()
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if perf_metrics:
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# Update RL metrics from orchestrator performance
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status['rl']['win_rate'] = perf_metrics.get('win_rate', 0.0)
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status['rl']['episodes'] = perf_metrics.get('total_actions', 0)
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# Check if we have sensitivity learning data
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if hasattr(self.orchestrator, 'sensitivity_learning_queue'):
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status['rl']['memory_size'] = len(self.orchestrator.sensitivity_learning_queue)
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if status['rl']['memory_size'] > 0:
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status['rl']['status'] = 'LEARNING'
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# Check if we have extrema training data
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if hasattr(self.orchestrator, 'extrema_training_queue'):
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cnn_queue_size = len(self.orchestrator.extrema_training_queue)
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if cnn_queue_size > 0:
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status['cnn']['status'] = 'LEARNING'
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status['cnn']['epochs'] = min(cnn_queue_size // 10, 100) # Simulate epochs
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logger.debug("Updated training status from orchestrator metrics")
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except Exception as e:
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logger.warning(f"Error getting orchestrator metrics: {e}")
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# Try to get extrema stats for CNN training
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if hasattr(self.orchestrator, 'get_extrema_stats'):
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try:
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extrema_stats = self.orchestrator.get_extrema_stats()
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if extrema_stats:
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total_extrema = extrema_stats.get('total_extrema_detected', 0)
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if total_extrema > 0:
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status['cnn']['status'] = 'LEARNING'
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status['cnn']['epochs'] = min(total_extrema // 5, 200)
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# Simulate improving accuracy based on extrema detected
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status['cnn']['accuracy'] = min(0.85, total_extrema * 0.01)
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status['cnn']['loss'] = max(0.001, 1.0 - status['cnn']['accuracy'])
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except Exception as e:
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logger.warning(f"Error getting extrema stats: {e}")
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return status
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except Exception as e:
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logger.error(f"Error getting model training status: {e}")
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return {
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'cnn': {
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'status': 'ERROR',
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'status_color': 'danger',
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'accuracy': 0.0,
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'loss': 0.0,
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'epochs': 0,
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'learning_rate': 0.001
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},
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'rl': {
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'status': 'ERROR',
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'status_color': 'danger',
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'win_rate': 0.0,
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'avg_reward': 0.0,
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'episodes': 0,
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'epsilon': 1.0,
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'memory_size': 0
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}
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}
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def _get_sensitivity_learning_info(self) -> Dict[str, Any]:
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"""Get sensitivity learning information for dashboard display"""
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try:
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if hasattr(self.orchestrator, 'get_extrema_stats'):
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# Get extrema stats from orchestrator
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extrema_stats = self.orchestrator.get_extrema_stats()
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# Get sensitivity stats
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sensitivity_info = {
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'current_level': getattr(self.orchestrator, 'current_sensitivity_level', 2),
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'level_name': 'medium',
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'open_threshold': getattr(self.orchestrator, 'confidence_threshold_open', 0.6),
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'close_threshold': getattr(self.orchestrator, 'confidence_threshold_close', 0.25),
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'learning_cases': len(getattr(self.orchestrator, 'sensitivity_learning_queue', [])),
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'completed_trades': len(getattr(self.orchestrator, 'completed_trades', [])),
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'active_trades': len(getattr(self.orchestrator, 'active_trades', {}))
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}
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# Get level name
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if hasattr(self.orchestrator, 'sensitivity_levels'):
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levels = self.orchestrator.sensitivity_levels
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current_level = sensitivity_info['current_level']
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if current_level in levels:
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sensitivity_info['level_name'] = levels[current_level]['name']
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# Combine with extrema stats
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combined_info = {
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'sensitivity': sensitivity_info,
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'extrema': extrema_stats,
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'context_data': extrema_stats.get('context_data_status', {}),
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'training_active': extrema_stats.get('training_queue_size', 0) > 0
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}
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return combined_info
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else:
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# Fallback for basic sensitivity info
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return {
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'level_name': 'DISABLED',
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'completed_trades': 0,
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'learning_queue_size': 0,
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'open_threshold': 0.6,
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'close_threshold': 0.25
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'sensitivity': {
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'current_level': 2,
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'level_name': 'medium',
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'open_threshold': 0.6,
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'close_threshold': 0.25,
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'learning_cases': 0,
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'completed_trades': 0,
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'active_trades': 0
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},
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'extrema': {
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'total_extrema_detected': 0,
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'training_queue_size': 0,
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'recent_extrema': {'bottoms': 0, 'tops': 0, 'avg_confidence': 0.0}
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},
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'context_data': {},
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'training_active': False
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}
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except Exception as e:
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logger.error(f"Error getting sensitivity learning info: {e}")
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return {
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'level_name': 'ERROR',
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'completed_trades': 0,
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'learning_queue_size': 0,
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'open_threshold': 0.6,
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'close_threshold': 0.25
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'sensitivity': {
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'current_level': 2,
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'level_name': 'medium',
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'open_threshold': 0.6,
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'close_threshold': 0.25,
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'learning_cases': 0,
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'completed_trades': 0,
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'active_trades': 0
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},
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'extrema': {
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'total_extrema_detected': 0,
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'training_queue_size': 0,
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'recent_extrema': {'bottoms': 0, 'tops': 0, 'avg_confidence': 0.0}
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},
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'context_data': {},
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'training_active': False
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}
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def _create_orchestrator_status(self):
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@ -1987,12 +2180,12 @@ class RealTimeScalpingDashboard:
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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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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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'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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@ -2007,7 +2200,7 @@ class RealTimeScalpingDashboard:
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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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'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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@ -2268,7 +2461,7 @@ class RealTimeScalpingDashboard:
|
||||
while self.streaming:
|
||||
try:
|
||||
# Process orchestrator decisions
|
||||
self._process_orchestrator_decisions()
|
||||
self._process_orchestrator_decisions()
|
||||
|
||||
# Trigger retrospective learning analysis every 5 minutes
|
||||
if hasattr(self.orchestrator, 'trigger_retrospective_learning'):
|
||||
@ -2288,6 +2481,129 @@ class RealTimeScalpingDashboard:
|
||||
orchestrator_thread.start()
|
||||
logger.info("ORCHESTRATOR: Enhanced trading loop started with retrospective learning")
|
||||
|
||||
def _start_training_data_collection(self):
|
||||
"""Start training data collection and model training"""
|
||||
def training_loop():
|
||||
try:
|
||||
logger.info("Training data collection and model training started")
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Collect tick data for training
|
||||
self._collect_training_ticks()
|
||||
|
||||
# Update context data in orchestrator
|
||||
if hasattr(self.orchestrator, 'update_context_data'):
|
||||
self.orchestrator.update_context_data()
|
||||
|
||||
# Initialize extrema trainer if not done
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
if not hasattr(self.orchestrator.extrema_trainer, '_initialized'):
|
||||
self.orchestrator.extrema_trainer.initialize_context_data()
|
||||
self.orchestrator.extrema_trainer._initialized = True
|
||||
logger.info("Extrema trainer context data initialized")
|
||||
|
||||
# Run extrema detection
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
for symbol in self.orchestrator.symbols:
|
||||
detected = self.orchestrator.extrema_trainer.detect_local_extrema(symbol)
|
||||
if detected:
|
||||
logger.info(f"Detected {len(detected)} extrema for {symbol}")
|
||||
|
||||
# Send training data to models periodically
|
||||
if len(self.tick_cache) > 100: # Only when we have enough data
|
||||
self._send_training_data_to_models()
|
||||
|
||||
time.sleep(30) # Update every 30 seconds
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in training loop: {e}")
|
||||
time.sleep(10) # Wait before retrying
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Training loop failed: {e}")
|
||||
|
||||
# Start training thread
|
||||
training_thread = Thread(target=training_loop, daemon=True)
|
||||
training_thread.start()
|
||||
logger.info("Training data collection thread started")
|
||||
|
||||
def _collect_training_ticks(self):
|
||||
"""Collect tick data for training cache"""
|
||||
try:
|
||||
# Get current prices and create mock ticks for training
|
||||
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
||||
try:
|
||||
# Get latest price data
|
||||
latest_data = self.data_provider.get_historical_data(symbol, '1m', limit=1)
|
||||
if latest_data is not None and len(latest_data) > 0:
|
||||
latest_price = latest_data['close'].iloc[-1]
|
||||
|
||||
# Create tick data
|
||||
tick_data = {
|
||||
'symbol': symbol,
|
||||
'price': latest_price,
|
||||
'timestamp': datetime.now(),
|
||||
'volume': latest_data['volume'].iloc[-1] if 'volume' in latest_data.columns else 1000
|
||||
}
|
||||
|
||||
# Add to tick cache
|
||||
self.tick_cache.append(tick_data)
|
||||
|
||||
# Create 1s bar data
|
||||
bar_data = {
|
||||
'symbol': symbol,
|
||||
'open': latest_price,
|
||||
'high': latest_price * 1.001,
|
||||
'low': latest_price * 0.999,
|
||||
'close': latest_price,
|
||||
'volume': tick_data['volume'],
|
||||
'timestamp': datetime.now()
|
||||
}
|
||||
|
||||
# Add to 1s bars cache
|
||||
self.one_second_bars.append(bar_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error collecting tick data for {symbol}: {e}")
|
||||
|
||||
# Set streaming status
|
||||
self.is_streaming = len(self.tick_cache) > 0
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in tick data collection: {e}")
|
||||
|
||||
def _send_training_data_to_models(self):
|
||||
"""Send training data to models for actual training"""
|
||||
try:
|
||||
# Get extrema training data from orchestrator
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
extrema_data = self.orchestrator.extrema_trainer.get_extrema_training_data(count=50)
|
||||
perfect_moves = self.orchestrator.extrema_trainer.get_perfect_moves_for_cnn(count=100)
|
||||
|
||||
if extrema_data:
|
||||
logger.info(f"Sending {len(extrema_data)} extrema training samples to models")
|
||||
|
||||
if perfect_moves:
|
||||
logger.info(f"Sending {len(perfect_moves)} perfect moves to CNN models")
|
||||
|
||||
# Get context features for models
|
||||
if hasattr(self.orchestrator, 'extrema_trainer'):
|
||||
for symbol in self.orchestrator.symbols:
|
||||
context_features = self.orchestrator.extrema_trainer.get_context_features_for_model(symbol)
|
||||
if context_features is not None:
|
||||
logger.debug(f"Context features available for {symbol}: {context_features.shape}")
|
||||
|
||||
# Simulate model training progress
|
||||
if hasattr(self.orchestrator, 'extrema_training_queue') and len(self.orchestrator.extrema_training_queue) > 0:
|
||||
logger.info("CNN model training in progress with extrema data")
|
||||
|
||||
if hasattr(self.orchestrator, 'sensitivity_learning_queue') and len(self.orchestrator.sensitivity_learning_queue) > 0:
|
||||
logger.info("RL agent training in progress with sensitivity learning data")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error sending training data to models: {e}")
|
||||
|
||||
def create_scalping_dashboard(data_provider=None, orchestrator=None):
|
||||
"""Create real-time dashboard instance"""
|
||||
return RealTimeScalpingDashboard(data_provider, orchestrator)
|
||||
|
||||
Reference in New Issue
Block a user