fixes
This commit is contained in:
Dobromir Popov
@ -170,6 +170,9 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
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self.williams_features = {} # Symbol -> Williams features
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self.symbol_correlation_matrix = {} # Pre-computed correlations
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# Initialize pivot RL trainer (if available)
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self.pivot_rl_trainer = None # Will be initialized if enhanced pivot training is needed
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# Initialize COB Integration for real-time market microstructure
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# PROPERLY INITIALIZED: Create the COB integration instance synchronously
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try:
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@ -2478,7 +2481,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
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threshold_type = "ENTRY"
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# For entries, check if CNN predicts favorable pivot
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if hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model:
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if hasattr(self, 'pivot_rl_trainer') and self.pivot_rl_trainer and hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model:
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try:
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# Get market data for CNN analysis
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current_price = market_state.prices.get(self.timeframes[0], 0)
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@ -2524,7 +2527,7 @@ class EnhancedTradingOrchestrator(TradingOrchestrator):
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'action_type': threshold_type,
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'threshold_used': threshold,
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'pivot_enhanced': True,
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'cnn_integrated': hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model is not None,
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'cnn_integrated': hasattr(self, 'pivot_rl_trainer') and self.pivot_rl_trainer and hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model is not None,
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'timeframe_breakdown': [(tf.timeframe, tf.action, tf.confidence)
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for tf in best_pred.timeframe_predictions],
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'market_regime': market_state.market_regime
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156
web/dashboard.py
156
web/dashboard.py
@ -841,21 +841,35 @@ class TradingDashboard:
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], className="card bg-light", style={"height": "60px"}),
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], style={"display": "grid", "gridTemplateColumns": "repeat(4, 1fr)", "gap": "8px", "width": "60%"}),
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# Right side - Recent Signals & Executions
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# Right side - Merged: Recent Signals & Model Training - 2 columns
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html.Div([
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# Recent Trading Signals Column (50%)
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html.Div([
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html.H6([
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html.I(className="fas fa-robot me-2"),
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"Recent Trading Signals & Executions"
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], className="card-title mb-2"),
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html.Div(id="recent-decisions", style={"height": "160px", "overflowY": "auto"})
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], className="card-body p-2")
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], className="card", style={"width": "48%", "marginLeft": "2%"})
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html.Div([
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html.H6([
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html.I(className="fas fa-robot me-2"),
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"Recent Trading Signals"
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], className="card-title mb-2"),
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html.Div(id="recent-decisions", style={"height": "160px", "overflowY": "auto"})
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], className="card-body p-2")
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], className="card", style={"width": "48%"}),
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# Model Training + COB Buckets Column (50%)
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html.Div([
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html.Div([
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html.H6([
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html.I(className="fas fa-brain me-2"),
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"Training Progress & COB $1 Buckets"
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], className="card-title mb-2"),
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html.Div(id="training-metrics", style={"height": "160px", "overflowY": "auto"})
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], className="card-body p-2")
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], className="card", style={"width": "48%", "marginLeft": "4%"}),
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], style={"width": "48%", "marginLeft": "2%", "display": "flex"})
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], className="d-flex mb-3"),
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# Charts row - More compact
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# Charts row - Now full width since training moved up
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html.Div([
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# Price chart - 70% width
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# Price chart - Full width
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html.Div([
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html.Div([
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html.H6([
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@ -864,18 +878,7 @@ class TradingDashboard:
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], className="card-title mb-2"),
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dcc.Graph(id="price-chart", style={"height": "400px"})
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], className="card-body p-2")
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], className="card", style={"width": "70%"}),
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# Model Training Metrics - 30% width
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html.Div([
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html.Div([
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html.H6([
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html.I(className="fas fa-brain me-2"),
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"Model Training Progress"
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], className="card-title mb-2"),
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html.Div(id="training-metrics", style={"height": "400px", "overflowY": "auto"})
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], className="card-body p-2")
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], className="card", style={"width": "28%", "marginLeft": "2%"}),
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], className="card", style={"width": "100%"}),
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], className="row g-2 mb-3"),
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# CNN Model Monitoring & COB Integration - MERGED into 1 row with 4 columns
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@ -6070,17 +6073,108 @@ class TradingDashboard:
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return self._create_empty_chart("Chart Error", "Chart temporarily unavailable")
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def _create_training_metrics_cached(self):
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"""Cached training metrics with reduced computation"""
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"""Enhanced training metrics with COB $1 buckets"""
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try:
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return [
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html.H6("Training Status", className="text-success"),
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html.P(f"Models Active: {len(getattr(self.model_registry, 'models', {})) if self.model_registry else 0}",
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className="text-muted small"),
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html.P(f"Last Update: {datetime.now().strftime('%H:%M:%S')}",
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className="text-muted small")
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]
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except:
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content = []
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# Training Status Section
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content.append(html.H6("Training Status", className="text-success mb-2"))
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content.append(html.P(f"Models Active: {len(getattr(self.model_registry, 'models', {})) if self.model_registry else 0}",
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className="text-muted small"))
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content.append(html.P(f"Last Update: {datetime.now().strftime('%H:%M:%S')}",
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className="text-muted small"))
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# COB $1 Buckets Section
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content.append(html.Hr())
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content.append(html.H6("COB $1 Buckets", className="text-info mb-2"))
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# Get COB bucket data if available
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try:
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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cob_buckets = self._get_cob_dollar_buckets()
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if cob_buckets:
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# Show top 5 buckets by volume
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for i, bucket in enumerate(cob_buckets[:5]):
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price_range = f"${bucket['price']:.0f}-${bucket['price']+1:.0f}"
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volume = bucket['total_volume']
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bid_pct = (bucket['bid_volume'] / volume * 100) if volume > 0 else 0
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ask_pct = (bucket['ask_volume'] / volume * 100) if volume > 0 else 0
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content.append(html.P([
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html.Span(price_range, className="text-warning small fw-bold"),
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html.Br(),
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html.Span(f"Vol: ${volume:,.0f} ", className="text-muted small"),
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html.Span(f"B:{bid_pct:.0f}% ", className="text-success small"),
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html.Span(f"A:{ask_pct:.0f}%", className="text-danger small")
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], className="mb-1"))
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else:
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content.append(html.P("COB buckets loading...", className="text-muted small"))
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else:
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content.append(html.P("COB integration inactive", className="text-warning small"))
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except Exception as e:
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content.append(html.P(f"COB error: {str(e)[:30]}...", className="text-danger small"))
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return content
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except Exception as e:
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return [html.P("Training metrics unavailable", className="text-muted")]
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def _get_cob_dollar_buckets(self) -> List[Dict]:
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"""Get COB data grouped into $1 buckets"""
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try:
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buckets = []
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# Get COB data for primary symbols
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for symbol in ['ETH/USDT', 'BTC/USDT']:
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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try:
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cob_snapshot = self.orchestrator.cob_integration.get_cob_snapshot(symbol)
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if cob_snapshot:
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mid_price = cob_snapshot.volume_weighted_mid
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# Create $1 buckets around mid price (±$50 range)
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price_buckets = {}
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for i in range(-50, 51):
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bucket_price = int(mid_price) + i
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price_buckets[bucket_price] = {
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'price': bucket_price,
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'bid_volume': 0,
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'ask_volume': 0,
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'total_volume': 0
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}
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# Aggregate bid data into buckets
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for level in cob_snapshot.consolidated_bids:
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bucket_price = int(level.price)
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if bucket_price in price_buckets:
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price_buckets[bucket_price]['bid_volume'] += level.total_volume_usd
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price_buckets[bucket_price]['total_volume'] += level.total_volume_usd
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# Aggregate ask data into buckets
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for level in cob_snapshot.consolidated_asks:
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bucket_price = int(level.price)
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if bucket_price in price_buckets:
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price_buckets[bucket_price]['ask_volume'] += level.total_volume_usd
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price_buckets[bucket_price]['total_volume'] += level.total_volume_usd
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# Convert to list and sort by volume
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symbol_buckets = [bucket for bucket in price_buckets.values() if bucket['total_volume'] > 0]
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symbol_buckets.sort(key=lambda x: x['total_volume'], reverse=True)
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# Add symbol info and take top buckets
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for bucket in symbol_buckets[:10]:
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bucket['symbol'] = symbol
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buckets.append(bucket)
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except Exception as e:
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logger.debug(f"Error getting COB buckets for {symbol}: {e}")
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# Sort all buckets by total volume and return top 10
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buckets.sort(key=lambda x: x['total_volume'], reverse=True)
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return buckets[:10]
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except Exception as e:
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logger.error(f"Error creating COB dollar buckets: {e}")
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return []
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def _create_decisions_list_cached(self):
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"""Cached decisions list with limited entries"""
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