even better dash
This commit is contained in:
Dobromir Popov
@ -373,14 +373,17 @@ class CleanTradingDashboard:
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if ws_data_1s is not None and len(ws_data_1s) > 5:
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fig = make_subplots(
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rows=3, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.05,
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shared_xaxes=False, # Make 1s chart independent from 1m chart
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vertical_spacing=0.08,
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subplot_titles=(
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f'{symbol} - {main_source} ({len(df_main)} bars)',
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f'1s Mini Chart ({len(ws_data_1s)} bars)',
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f'1s Mini Chart - Independent Axis ({len(ws_data_1s)} bars)',
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'Volume'
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),
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row_heights=[0.5, 0.25, 0.25]
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row_heights=[0.5, 0.25, 0.25],
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specs=[[{"secondary_y": False}],
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[{"secondary_y": False}],
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[{"secondary_y": False}]]
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)
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has_mini_chart = True
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else:
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@ -448,9 +451,23 @@ class CleanTradingDashboard:
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xaxis_rangeslider_visible=False
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)
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# Update axes
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fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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# Update axes with specific configurations for independent charts
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if has_mini_chart:
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# Main 1m chart (row 1)
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fig.update_xaxes(title_text="Time (1m intervals)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=1, col=1)
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fig.update_yaxes(title_text="Price (USD)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=1, col=1)
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# Independent 1s chart (row 2) - can zoom/pan separately
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fig.update_xaxes(title_text="Time (1s ticks)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=2, col=1)
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fig.update_yaxes(title_text="Price (USD)", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=2, col=1)
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# Volume chart (row 3)
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fig.update_xaxes(title_text="Time", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=3, col=1)
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fig.update_yaxes(title_text="Volume", showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)', row=3, col=1)
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else:
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# Main chart only
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fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)')
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chart_info = f"1m bars: {len(df_main)}"
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if has_mini_chart:
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@ -675,15 +692,29 @@ class CleanTradingDashboard:
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"""Handle trading decision from orchestrator"""
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try:
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# Convert orchestrator decision to dashboard format
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dashboard_decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': decision.action if hasattr(decision, 'action') else decision.get('action', 'UNKNOWN'),
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'confidence': decision.confidence if hasattr(decision, 'confidence') else decision.get('confidence', 0),
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'price': decision.price if hasattr(decision, 'price') else decision.get('price', 0),
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'executed': True, # Orchestrator decisions are executed
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'blocked': False,
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'manual': False
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}
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# Handle both TradingDecision objects and dictionary formats
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if hasattr(decision, 'action'):
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# This is a TradingDecision object (dataclass)
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dashboard_decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': decision.action,
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'confidence': decision.confidence,
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'price': decision.price,
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'executed': True, # Orchestrator decisions are executed
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'blocked': False,
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'manual': False
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}
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else:
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# This is a dictionary format
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dashboard_decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': decision.get('action', 'UNKNOWN'),
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'confidence': decision.get('confidence', 0),
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'price': decision.get('price', 0),
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'executed': True, # Orchestrator decisions are executed
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'blocked': False,
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'manual': False
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}
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# Add to recent decisions
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self.recent_decisions.append(dashboard_decision)
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@ -2584,8 +2584,8 @@ class TradingDashboard:
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# If datetime is naive, assume it's UTC
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if dt.tzinfo is None:
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dt = pytz.UTC.localize(dt)
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# Convert to local timezone
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# Convert to local timezone
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return dt.astimezone(self.timezone)
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except Exception as e:
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logger.warning(f"Error converting timezone: {e}")
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@ -2606,7 +2606,7 @@ class TradingDashboard:
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return df
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else:
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# Data has timezone info, convert to local timezone
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df.index = df.index.tz_convert(self.timezone)
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df.index = df.index.tz_convert(self.timezone)
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# Make timezone-naive to prevent browser double-conversion
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df.index = df.index.tz_localize(None)
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@ -3375,7 +3375,7 @@ class TradingDashboard:
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# Minimal cleanup to prevent interference
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if is_cleanup_update:
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try:
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self._cleanup_old_data()
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self._cleanup_old_data()
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except:
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pass # Don't let cleanup interfere with updates
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@ -3503,7 +3503,7 @@ class TradingDashboard:
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# Only recreate chart if data is very old (5 minutes)
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if time.time() - self._cached_chart_data_time > 300:
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needs_new_chart = True
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else:
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else:
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needs_new_chart = True
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if needs_new_chart:
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@ -3514,7 +3514,7 @@ class TradingDashboard:
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if price_chart is not None:
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self._cached_price_chart = price_chart
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self._cached_chart_data_time = time.time()
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else:
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else:
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# If chart creation failed, try cached version or create empty
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if hasattr(self, '_cached_price_chart') and self._cached_price_chart is not None:
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price_chart = self._cached_price_chart
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@ -4508,7 +4508,7 @@ class TradingDashboard:
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except Exception as e:
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logger.debug(f"[COMPREHENSIVE] Error adding trade markers: {e}")
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def _create_price_chart(self, symbol: str) -> go.Figure:
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"""Create price chart with volume and Williams pivot points from cached data"""
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try:
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@ -4523,30 +4523,30 @@ class TradingDashboard:
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logger.debug(f"[CHART] Using WebSocket real-time data: {len(df)} ticks")
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else:
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# Fallback to traditional data provider approach
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# For Williams Market Structure, we need 1s data for proper recursive analysis
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# Get 4 hours (240 minutes) of 1m data for better trade visibility
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df_1s = None
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df_1m = None
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# For Williams Market Structure, we need 1s data for proper recursive analysis
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# Get 4 hours (240 minutes) of 1m data for better trade visibility
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df_1s = None
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df_1m = None
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if ws_df is not None:
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logger.debug(f"[CHART] WebSocket data insufficient ({len(ws_df) if not ws_df.empty else 0} rows), falling back to data provider")
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# Try to get 1s data first for Williams analysis (reduced to 10 minutes for performance)
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try:
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df_1s = self.data_provider.get_historical_data(symbol, '1s', limit=600, refresh=False)
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if df_1s is None or df_1s.empty:
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logger.warning("[CHART] No 1s cached data available, trying fresh 1s data")
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df_1s = self.data_provider.get_historical_data(symbol, '1s', limit=300, refresh=True)
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if df_1s is not None and not df_1s.empty:
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# Aggregate 1s data to 1m for chart display (cleaner visualization)
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df = self._aggregate_1s_to_1m(df_1s)
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actual_timeframe = '1s→1m'
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else:
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df_1s = None
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except Exception as e:
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logger.warning(f"[CHART] Error getting 1s data: {e}")
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# Try to get 1s data first for Williams analysis (reduced to 10 minutes for performance)
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try:
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df_1s = self.data_provider.get_historical_data(symbol, '1s', limit=600, refresh=False)
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if df_1s is None or df_1s.empty:
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logger.warning("[CHART] No 1s cached data available, trying fresh 1s data")
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df_1s = self.data_provider.get_historical_data(symbol, '1s', limit=300, refresh=True)
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if df_1s is not None and not df_1s.empty:
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# Aggregate 1s data to 1m for chart display (cleaner visualization)
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df = self._aggregate_1s_to_1m(df_1s)
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actual_timeframe = '1s→1m'
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else:
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df_1s = None
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except Exception as e:
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logger.warning(f"[CHART] Error getting 1s data: {e}")
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df_1s = None
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# Fallback to 1m data if 1s not available (4 hours for historical trades)
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if df_1s is None:
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@ -4728,7 +4728,7 @@ class TradingDashboard:
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hovertemplate='<b>Volume: %{y:.0f}</b><br>%{x}<extra></extra>'
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),
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row=2, col=1
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)
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)
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# Mark recent trading decisions with proper markers
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if self.recent_decisions and df is not None and not df.empty:
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@ -4769,10 +4769,10 @@ class TradingDashboard:
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decision_time_pd = pd.to_datetime(decision_time_utc)
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if chart_start_utc <= decision_time_pd <= chart_end_utc:
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signal_type = decision.get('signal_type', 'UNKNOWN')
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if decision['action'] == 'BUY':
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buy_decisions.append((decision, signal_type))
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elif decision['action'] == 'SELL':
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sell_decisions.append((decision, signal_type))
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if decision['action'] == 'BUY':
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buy_decisions.append((decision, signal_type))
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elif decision['action'] == 'SELL':
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sell_decisions.append((decision, signal_type))
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@ -4892,30 +4892,30 @@ class TradingDashboard:
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# Convert times to UTC for comparison - FIXED timezone handling
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try:
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if isinstance(entry_time, datetime):
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if isinstance(entry_time, datetime):
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# If naive datetime, assume it's in local timezone
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if entry_time.tzinfo is None:
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entry_time_utc = self.timezone.localize(entry_time).astimezone(timezone.utc).replace(tzinfo=None)
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else:
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entry_time_utc = entry_time.astimezone(timezone.utc).replace(tzinfo=None)
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else:
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continue
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if isinstance(exit_time, datetime):
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else:
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continue
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if isinstance(exit_time, datetime):
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# If naive datetime, assume it's in local timezone
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if exit_time.tzinfo is None:
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exit_time_utc = self.timezone.localize(exit_time).astimezone(timezone.utc).replace(tzinfo=None)
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else:
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exit_time_utc = exit_time.astimezone(timezone.utc).replace(tzinfo=None)
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else:
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continue
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# Check if trade overlaps with chart timeframe
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entry_time_pd = pd.to_datetime(entry_time_utc)
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exit_time_pd = pd.to_datetime(exit_time_utc)
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if (chart_start_utc <= entry_time_pd <= chart_end_utc) or (chart_start_utc <= exit_time_pd <= chart_end_utc):
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chart_trades.append(trade)
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else:
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continue
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# Check if trade overlaps with chart timeframe
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entry_time_pd = pd.to_datetime(entry_time_utc)
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exit_time_pd = pd.to_datetime(exit_time_utc)
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if (chart_start_utc <= entry_time_pd <= chart_end_utc) or (chart_start_utc <= exit_time_pd <= chart_end_utc):
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chart_trades.append(trade)
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except Exception as e:
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logger.debug(f"Error processing trade timestamps: {e}")
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continue
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@ -9094,7 +9094,7 @@ class TradingDashboard:
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except Exception as e:
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logger.debug(f"Error adding Williams pivot points safely: {e}")
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def _add_williams_pivot_points_to_chart(self, fig, pivot_points: Dict, row: int = 1):
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"""Add Williams pivot points as small triangles to the chart with proper timezone conversion"""
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try:
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@ -10176,7 +10176,7 @@ class TradingDashboard:
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except Exception as e:
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logger.error(f"Error creating COB table rows: {e}")
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return [html.Tr([html.Td("Error loading order book", colSpan=4, className="text-danger small")])]
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def _create_cob_status_content(self) -> List:
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"""Create COB status and training pipeline content"""
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try:
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