adding model predictions to dash (wip)
This commit is contained in:
Dobromir Popov
@ -1295,12 +1295,19 @@ class DataProvider:
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try:
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cache_file = self.cache_dir / f"{symbol.replace('/', '')}_{timeframe}.parquet"
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if cache_file.exists():
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# Check if cache is recent (less than 1 hour old)
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# Check if cache is recent - stricter rules for startup
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cache_age = time.time() - cache_file.stat().st_mtime
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if cache_age < 3600: # 1 hour
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# For 1m data, use cache only if less than 5 minutes old to avoid gaps
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if timeframe == '1m':
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max_age = 300 # 5 minutes
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else:
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max_age = 3600 # 1 hour for other timeframes
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if cache_age < max_age:
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try:
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df = pd.read_parquet(cache_file)
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logger.debug(f"Loaded {len(df)} rows from cache for {symbol} {timeframe}")
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logger.debug(f"Loaded {len(df)} rows from cache for {symbol} {timeframe} (age: {cache_age/60:.1f}min)")
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return df
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except Exception as parquet_e:
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# Handle corrupted Parquet file
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@ -1314,7 +1321,7 @@ class DataProvider:
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else:
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raise parquet_e
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else:
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logger.debug(f"Cache for {symbol} {timeframe} is too old ({cache_age/3600:.1f}h)")
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logger.debug(f"Cache for {symbol} {timeframe} is too old ({cache_age/60:.1f}min > {max_age/60:.1f}min)")
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return None
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except Exception as e:
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logger.warning(f"Error loading cache for {symbol} {timeframe}: {e}")
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@ -339,9 +339,15 @@ class CleanTradingDashboard:
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def _create_price_chart(self, symbol: str) -> go.Figure:
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"""Create 1-minute main chart with 1-second mini chart - Updated every second"""
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try:
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# FIXED: Merge historical + live data instead of replacing
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# 1. Get historical 1-minute data as base (180 candles = 3 hours)
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df_historical = self.data_provider.get_historical_data(symbol, '1m', limit=180)
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# FIXED: Always get fresh data on startup to avoid gaps
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# 1. Get historical 1-minute data as base (180 candles = 3 hours) - FORCE REFRESH on first load
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is_startup = not hasattr(self, '_chart_initialized') or not self._chart_initialized
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df_historical = self.data_provider.get_historical_data(symbol, '1m', limit=180, refresh=is_startup)
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# Mark chart as initialized to use cache on subsequent loads
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if is_startup:
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self._chart_initialized = True
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logger.info(f"[STARTUP] Fetched fresh {symbol} 1m data to avoid gaps")
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# 2. Get WebSocket 1s data and convert to 1m bars
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ws_data_raw = self._get_websocket_chart_data(symbol, 'raw')
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@ -433,6 +439,12 @@ class CleanTradingDashboard:
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row=1, col=1
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)
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# ADD MODEL PREDICTIONS TO MAIN CHART
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self._add_model_predictions_to_chart(fig, symbol, df_main, row=1)
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# ADD TRADES TO MAIN CHART
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self._add_trades_to_chart(fig, symbol, df_main, row=1)
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# Mini 1-second chart (if available)
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if has_mini_chart:
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fig.add_trace(
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@ -465,7 +477,7 @@ class CleanTradingDashboard:
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fig.update_layout(
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title=f'{symbol} Live Chart - {main_source} (Updated Every Second)',
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template='plotly_dark',
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showlegend=False,
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showlegend=True, # Show legend for model predictions
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height=chart_height,
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margin=dict(l=50, r=50, t=60, b=50),
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xaxis_rangeslider_visible=False
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@ -502,6 +514,246 @@ class CleanTradingDashboard:
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xref="paper", yref="paper",
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x=0.5, y=0.5, showarrow=False)
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def _add_model_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
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"""Add model predictions to the chart"""
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try:
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# Get CNN predictions from orchestrator
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if self.orchestrator and hasattr(self.orchestrator, 'get_recent_predictions'):
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try:
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cnn_predictions = self.orchestrator.get_recent_predictions(symbol)
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if cnn_predictions:
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# Separate by prediction type
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buy_predictions = []
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sell_predictions = []
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for pred in cnn_predictions[-20:]: # Last 20 predictions
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pred_time = pred.get('timestamp')
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pred_price = pred.get('price', 0)
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pred_action = pred.get('action', 'HOLD')
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pred_confidence = pred.get('confidence', 0)
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if pred_time and pred_price and pred_confidence > 0.5: # Only confident predictions
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if pred_action == 'BUY':
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buy_predictions.append({'x': pred_time, 'y': pred_price, 'confidence': pred_confidence})
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elif pred_action == 'SELL':
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sell_predictions.append({'x': pred_time, 'y': pred_price, 'confidence': pred_confidence})
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# Add BUY predictions (green triangles)
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if buy_predictions:
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in buy_predictions],
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y=[p['y'] for p in buy_predictions],
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mode='markers',
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marker=dict(
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symbol='triangle-up',
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size=12,
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color='rgba(0, 255, 100, 0.8)',
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line=dict(width=2, color='green')
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),
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name='CNN BUY Predictions',
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showlegend=True,
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hovertemplate="<b>CNN BUY Prediction</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"Confidence: %{customdata:.1%}<extra></extra>",
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customdata=[p['confidence'] for p in buy_predictions]
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),
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row=row, col=1
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)
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# Add SELL predictions (red triangles)
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if sell_predictions:
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in sell_predictions],
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y=[p['y'] for p in sell_predictions],
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mode='markers',
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marker=dict(
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symbol='triangle-down',
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size=12,
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color='rgba(255, 100, 100, 0.8)',
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line=dict(width=2, color='red')
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),
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name='CNN SELL Predictions',
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showlegend=True,
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hovertemplate="<b>CNN SELL Prediction</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"Confidence: %{customdata:.1%}<extra></extra>",
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customdata=[p['confidence'] for p in sell_predictions]
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),
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row=row, col=1
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)
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except Exception as e:
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logger.debug(f"Could not get CNN predictions: {e}")
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# Get COB RL predictions
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if hasattr(self, 'cob_predictions') and symbol in self.cob_predictions:
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try:
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cob_preds = self.cob_predictions[symbol][-10:] # Last 10 COB predictions
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up_predictions = []
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down_predictions = []
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for pred in cob_preds:
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pred_time = pred.get('timestamp')
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pred_direction = pred.get('direction', 1) # 0=DOWN, 1=SIDEWAYS, 2=UP
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pred_confidence = pred.get('confidence', 0)
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if pred_time and pred_confidence > 0.7: # Only high confidence COB predictions
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# Get price from main chart at that time
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pred_price = self._get_price_at_time(df_main, pred_time)
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if pred_price:
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if pred_direction == 2: # UP
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up_predictions.append({'x': pred_time, 'y': pred_price, 'confidence': pred_confidence})
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elif pred_direction == 0: # DOWN
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down_predictions.append({'x': pred_time, 'y': pred_price, 'confidence': pred_confidence})
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# Add COB UP predictions (cyan diamonds)
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if up_predictions:
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in up_predictions],
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y=[p['y'] for p in up_predictions],
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mode='markers',
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marker=dict(
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symbol='diamond',
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size=10,
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color='rgba(0, 255, 255, 0.9)',
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line=dict(width=2, color='cyan')
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),
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name='COB RL UP (1B)',
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showlegend=True,
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hovertemplate="<b>COB RL UP Prediction</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"Confidence: %{customdata:.1%}<br>" +
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"Model: 1B Parameters<extra></extra>",
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customdata=[p['confidence'] for p in up_predictions]
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),
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row=row, col=1
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)
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# Add COB DOWN predictions (magenta diamonds)
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if down_predictions:
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in down_predictions],
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y=[p['y'] for p in down_predictions],
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mode='markers',
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marker=dict(
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symbol='diamond',
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size=10,
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color='rgba(255, 0, 255, 0.9)',
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line=dict(width=2, color='magenta')
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),
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name='COB RL DOWN (1B)',
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showlegend=True,
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hovertemplate="<b>COB RL DOWN Prediction</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"Confidence: %{customdata:.1%}<br>" +
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"Model: 1B Parameters<extra></extra>",
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customdata=[p['confidence'] for p in down_predictions]
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),
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row=row, col=1
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)
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except Exception as e:
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logger.debug(f"Could not get COB predictions: {e}")
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except Exception as e:
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logger.warning(f"Error adding model predictions to chart: {e}")
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def _add_trades_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
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"""Add executed trades to the chart"""
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try:
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if not self.closed_trades:
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return
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buy_trades = []
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sell_trades = []
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for trade in self.closed_trades[-20:]: # Last 20 trades
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entry_time = trade.get('entry_time')
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side = trade.get('side', 'UNKNOWN')
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entry_price = trade.get('entry_price', 0)
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pnl = trade.get('pnl', 0)
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if entry_time and entry_price:
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trade_data = {'x': entry_time, 'y': entry_price, 'pnl': pnl}
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if side == 'BUY':
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buy_trades.append(trade_data)
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elif side == 'SELL':
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sell_trades.append(trade_data)
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# Add BUY trades (green circles)
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if buy_trades:
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fig.add_trace(
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go.Scatter(
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x=[t['x'] for t in buy_trades],
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y=[t['y'] for t in buy_trades],
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mode='markers',
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marker=dict(
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symbol='circle',
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size=8,
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color='rgba(0, 255, 0, 0.7)',
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line=dict(width=2, color='green')
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),
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name='BUY Trades',
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showlegend=True,
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hovertemplate="<b>BUY Trade Executed</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"P&L: $%{customdata:.2f}<extra></extra>",
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customdata=[t['pnl'] for t in buy_trades]
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),
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row=row, col=1
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)
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# Add SELL trades (red circles)
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if sell_trades:
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fig.add_trace(
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go.Scatter(
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x=[t['x'] for t in sell_trades],
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y=[t['y'] for t in sell_trades],
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mode='markers',
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marker=dict(
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symbol='circle',
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size=8,
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color='rgba(255, 0, 0, 0.7)',
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line=dict(width=2, color='red')
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),
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name='SELL Trades',
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showlegend=True,
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hovertemplate="<b>SELL Trade Executed</b><br>" +
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"Price: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"P&L: $%{customdata:.2f}<extra></extra>",
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customdata=[t['pnl'] for t in sell_trades]
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),
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row=row, col=1
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)
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except Exception as e:
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logger.warning(f"Error adding trades to chart: {e}")
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def _get_price_at_time(self, df: pd.DataFrame, timestamp) -> Optional[float]:
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"""Get price from dataframe at specific timestamp"""
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try:
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if isinstance(timestamp, str):
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timestamp = pd.to_datetime(timestamp)
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# Find closest timestamp in dataframe
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closest_idx = df.index.get_indexer([timestamp], method='nearest')[0]
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if closest_idx >= 0 and closest_idx < len(df):
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return float(df.iloc[closest_idx]['close'])
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return None
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except Exception:
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return None
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def _get_websocket_chart_data(self, symbol: str, timeframe: str = '1m') -> Optional[pd.DataFrame]:
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"""Get WebSocket chart data - supports both 1m and 1s timeframes"""
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try:
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@ -22,7 +22,7 @@ class DashboardComponentManager:
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return [html.P("No recent signals", className="text-muted small")]
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signals = []
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for decision in recent_decisions[-10:]: # Last 10 signals
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for decision in reversed(recent_decisions[-10:]): # Last 10 signals, reversed
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# Handle both TradingDecision objects and dictionary formats
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if hasattr(decision, 'timestamp'):
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# This is a TradingDecision object (dataclass)
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