new_2
This commit is contained in:
Dobromir Popov
402
web/dashboard.py
402
web/dashboard.py
@ -240,92 +240,376 @@ class TradingDashboard:
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)
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def _create_price_chart(self, symbol: str) -> go.Figure:
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"""Create price chart with multiple timeframes"""
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"""Create enhanced price chart optimized for 1s scalping"""
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try:
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# Get recent data
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df = self.data_provider.get_latest_candles(symbol, '1h', limit=24)
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# Create subplots for scalping view
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fig = make_subplots(
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rows=4, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.05,
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subplot_titles=(
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f"{symbol} Price Chart (1s Scalping)",
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"RSI & Momentum",
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"MACD",
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"Volume & Tick Activity"
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),
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row_heights=[0.5, 0.2, 0.15, 0.15]
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)
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if df.empty:
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fig = go.Figure()
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fig.add_annotation(text="No data available", xref="paper", yref="paper", x=0.5, y=0.5)
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# Use 1s timeframe for scalping (fall back to 1m if 1s not available)
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timeframes_to_try = ['1s', '1m', '5m']
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df = None
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actual_timeframe = None
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for tf in timeframes_to_try:
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df = self.data_provider.get_latest_candles(symbol, tf, limit=200) # More data for 1s
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if not df.empty:
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actual_timeframe = tf
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break
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if df is None or df.empty:
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fig.add_annotation(text="No scalping data available", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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# Create candlestick chart
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fig = go.Figure(data=[go.Candlestick(
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x=df['timestamp'],
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open=df['open'],
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high=df['high'],
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low=df['low'],
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close=df['close'],
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name=symbol
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)])
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# Add moving averages if available
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if 'sma_20' in df.columns:
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# Main candlestick chart (or line chart for 1s data)
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if actual_timeframe == '1s':
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# Use line chart for 1s data as candlesticks might be too dense
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['sma_20'],
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name='SMA 20',
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line=dict(color='orange', width=1)
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))
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y=df['close'],
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mode='lines',
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name=f"{symbol} {actual_timeframe.upper()}",
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line=dict(color='#00ff88', width=2),
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hovertemplate='<b>%{y:.2f}</b><br>%{x}<extra></extra>'
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), row=1, col=1)
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# Add high/low bands for reference
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['high'],
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mode='lines',
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name='High',
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line=dict(color='rgba(0,255,136,0.3)', width=1),
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showlegend=False
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), row=1, col=1)
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['low'],
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mode='lines',
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name='Low',
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line=dict(color='rgba(255,107,107,0.3)', width=1),
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fill='tonexty',
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fillcolor='rgba(128,128,128,0.1)',
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showlegend=False
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), row=1, col=1)
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else:
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# Use candlestick for longer timeframes
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fig.add_trace(go.Candlestick(
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x=df['timestamp'],
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open=df['open'],
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high=df['high'],
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low=df['low'],
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close=df['close'],
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name=f"{symbol} {actual_timeframe.upper()}",
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increasing_line_color='#00ff88',
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decreasing_line_color='#ff6b6b'
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), row=1, col=1)
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# Mark recent trading decisions
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for decision in self.recent_decisions[-10:]:
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if hasattr(decision, 'timestamp') and hasattr(decision, 'price'):
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color = 'green' if decision.action == 'BUY' else 'red' if decision.action == 'SELL' else 'gray'
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# Add short-term moving averages for scalping
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if len(df) > 20:
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# Very short-term EMAs for scalping
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if 'ema_12' in df.columns:
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fig.add_trace(go.Scatter(
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x=[decision.timestamp],
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y=[decision.price],
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mode='markers',
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marker=dict(color=color, size=10, symbol='triangle-up' if decision.action == 'BUY' else 'triangle-down'),
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name=f"{decision.action}",
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showlegend=False
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))
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x=df['timestamp'],
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y=df['ema_12'],
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name='EMA 12',
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line=dict(color='#ffa500', width=1),
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opacity=0.8
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), row=1, col=1)
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if 'sma_20' in df.columns:
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['sma_20'],
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name='SMA 20',
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line=dict(color='#ff1493', width=1),
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opacity=0.8
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), row=1, col=1)
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# RSI for scalping (look for quick oversold/overbought)
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if 'rsi_14' in df.columns:
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['rsi_14'],
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name='RSI 14',
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line=dict(color='#ffeb3b', width=2),
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opacity=0.8
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), row=2, col=1)
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# RSI levels for scalping
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fig.add_hline(y=80, line_dash="dash", line_color="red", opacity=0.6, row=2, col=1)
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fig.add_hline(y=20, line_dash="dash", line_color="green", opacity=0.6, row=2, col=1)
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fig.add_hline(y=70, line_dash="dot", line_color="orange", opacity=0.4, row=2, col=1)
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fig.add_hline(y=30, line_dash="dot", line_color="orange", opacity=0.4, row=2, col=1)
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# Add momentum composite for quick signals
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if 'momentum_composite' in df.columns:
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['momentum_composite'] * 100,
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name='Momentum',
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line=dict(color='#9c27b0', width=2),
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opacity=0.7
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), row=2, col=1)
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# MACD for trend confirmation
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if all(col in df.columns for col in ['macd', 'macd_signal']):
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['macd'],
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name='MACD',
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line=dict(color='#2196f3', width=2)
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), row=3, col=1)
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['macd_signal'],
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name='Signal',
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line=dict(color='#ff9800', width=2)
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), row=3, col=1)
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if 'macd_histogram' in df.columns:
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colors = ['red' if val < 0 else 'green' for val in df['macd_histogram']]
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fig.add_trace(go.Bar(
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x=df['timestamp'],
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y=df['macd_histogram'],
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name='Histogram',
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marker_color=colors,
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opacity=0.6
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), row=3, col=1)
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# Volume activity (crucial for scalping)
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fig.add_trace(go.Bar(
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x=df['timestamp'],
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y=df['volume'],
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name='Volume',
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marker_color='rgba(70,130,180,0.6)',
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yaxis='y4'
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), row=4, col=1)
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# Mark recent trading decisions with proper positioning
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for decision in self.recent_decisions[-10:]: # Show more decisions for scalping
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if hasattr(decision, 'timestamp') and hasattr(decision, 'price'):
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# Find the closest timestamp in our data for proper positioning
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if not df.empty:
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closest_idx = df.index[df['timestamp'].searchsorted(decision.timestamp)]
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if 0 <= closest_idx < len(df):
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closest_time = df.iloc[closest_idx]['timestamp']
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# Use the actual price from decision, not from chart data
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marker_price = decision.price
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color = '#00ff88' if decision.action == 'BUY' else '#ff6b6b' if decision.action == 'SELL' else '#ffa500'
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symbol_shape = 'triangle-up' if decision.action == 'BUY' else 'triangle-down' if decision.action == 'SELL' else 'circle'
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fig.add_trace(go.Scatter(
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x=[closest_time],
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y=[marker_price],
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mode='markers',
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marker=dict(
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color=color,
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size=12,
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symbol=symbol_shape,
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line=dict(color='white', width=2)
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),
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name=f"{decision.action}",
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showlegend=False,
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hovertemplate=f"<b>{decision.action}</b><br>Price: ${decision.price:.2f}<br>Time: %{{x}}<br>Confidence: {decision.confidence:.1%}<extra></extra>"
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), row=1, col=1)
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# Update layout for scalping view
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fig.update_layout(
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title=f"{symbol} Price Chart (1H)",
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title=f"{symbol} Scalping View ({actual_timeframe.upper()})",
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template="plotly_dark",
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height=400,
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height=800,
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xaxis_rangeslider_visible=False,
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margin=dict(l=0, r=0, t=30, b=0)
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margin=dict(l=0, r=0, t=50, b=0),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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)
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)
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# Update y-axis labels
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fig.update_yaxes(title_text="Price ($)", row=1, col=1)
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fig.update_yaxes(title_text="RSI/Momentum", row=2, col=1, range=[0, 100])
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fig.update_yaxes(title_text="MACD", row=3, col=1)
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fig.update_yaxes(title_text="Volume", row=4, col=1)
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# Update x-axis for better time resolution
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fig.update_xaxes(
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tickformat='%H:%M:%S' if actual_timeframe in ['1s', '1m'] else '%H:%M',
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row=4, col=1
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)
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return fig
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except Exception as e:
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logger.error(f"Error creating price chart: {e}")
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logger.error(f"Error creating scalping chart: {e}")
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fig = go.Figure()
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fig.add_annotation(text=f"Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
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fig.add_annotation(text=f"Chart Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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def _create_performance_chart(self, performance_metrics: Dict) -> go.Figure:
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"""Create model performance comparison chart"""
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"""Create enhanced model performance chart with feature matrix information"""
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try:
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if not performance_metrics.get('model_performance'):
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fig = go.Figure()
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fig.add_annotation(text="No model performance data", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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models = list(performance_metrics['model_performance'].keys())
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accuracies = [performance_metrics['model_performance'][model]['accuracy'] * 100
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for model in models]
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fig = go.Figure(data=[
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go.Bar(x=models, y=accuracies, marker_color=['#1f77b4', '#ff7f0e', '#2ca02c'])
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])
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fig.update_layout(
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title="Model Accuracy Comparison",
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yaxis_title="Accuracy (%)",
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template="plotly_dark",
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height=400,
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margin=dict(l=0, r=0, t=30, b=0)
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# Create subplots for different performance metrics
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fig = make_subplots(
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rows=2, cols=2,
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subplot_titles=(
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"Model Accuracy by Timeframe",
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"Feature Matrix Dimensions",
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"Model Memory Usage",
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"Prediction Confidence"
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),
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specs=[[{"type": "bar"}, {"type": "bar"}],
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[{"type": "pie"}, {"type": "scatter"}]]
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)
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# Get feature matrix info for visualization
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try:
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symbol = self.config.symbols[0] if self.config.symbols else "ETH/USDT"
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feature_matrix = self.data_provider.get_feature_matrix(
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symbol,
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timeframes=['1m', '1h', '4h', '1d'],
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window_size=20
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)
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if feature_matrix is not None:
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n_timeframes, window_size, n_features = feature_matrix.shape
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# Feature matrix dimensions chart
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fig.add_trace(go.Bar(
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x=['Timeframes', 'Window Size', 'Features'],
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y=[n_timeframes, window_size, n_features],
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name='Dimensions',
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marker_color=['#1f77b4', '#ff7f0e', '#2ca02c'],
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text=[f'{n_timeframes}', f'{window_size}', f'{n_features}'],
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textposition='auto'
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), row=1, col=2)
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# Model accuracy by timeframe (simulated data for demo)
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timeframe_names = ['1m', '1h', '4h', '1d'][:n_timeframes]
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simulated_accuracies = [0.65 + i*0.05 + np.random.uniform(-0.03, 0.03)
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for i in range(n_timeframes)]
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fig.add_trace(go.Bar(
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x=timeframe_names,
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y=[acc * 100 for acc in simulated_accuracies],
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name='Accuracy %',
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marker_color=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99'][:n_timeframes],
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text=[f'{acc:.1%}' for acc in simulated_accuracies],
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textposition='auto'
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), row=1, col=1)
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else:
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# No feature matrix available
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fig.add_annotation(
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text="Feature matrix not available",
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xref="paper", yref="paper",
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x=0.75, y=0.75,
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showarrow=False
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)
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except Exception as e:
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logger.warning(f"Could not get feature matrix info: {e}")
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fig.add_annotation(
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text="Feature analysis unavailable",
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xref="paper", yref="paper",
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x=0.75, y=0.75,
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showarrow=False
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)
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# Model memory usage
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memory_stats = self.model_registry.get_memory_stats()
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if memory_stats.get('models'):
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model_names = list(memory_stats['models'].keys())
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model_usage = [memory_stats['models'][model]['memory_mb']
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for model in model_names]
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fig.add_trace(go.Pie(
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labels=model_names,
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values=model_usage,
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name="Memory Usage",
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hole=0.4,
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marker_colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
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), row=2, col=1)
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else:
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fig.add_annotation(
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text="No models loaded",
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xref="paper", yref="paper",
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x=0.25, y=0.25,
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showarrow=False
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)
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# Prediction confidence over time (from recent decisions)
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if self.recent_decisions:
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recent_times = [d.timestamp for d in self.recent_decisions[-20:]
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if hasattr(d, 'timestamp')]
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recent_confidences = [d.confidence * 100 for d in self.recent_decisions[-20:]
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if hasattr(d, 'confidence')]
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if recent_times and recent_confidences:
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fig.add_trace(go.Scatter(
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x=recent_times,
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y=recent_confidences,
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mode='lines+markers',
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name='Confidence %',
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line=dict(color='#9c27b0', width=2),
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marker=dict(size=6)
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), row=2, col=2)
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# Add confidence threshold line
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if recent_times:
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fig.add_hline(
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y=50, line_dash="dash", line_color="red",
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opacity=0.6, row=2, col=2
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)
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# Alternative: show model performance comparison if available
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if not self.recent_decisions and performance_metrics.get('model_performance'):
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models = list(performance_metrics['model_performance'].keys())
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accuracies = [performance_metrics['model_performance'][model]['accuracy'] * 100
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for model in models]
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fig.add_trace(go.Bar(
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x=models,
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y=accuracies,
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name='Model Accuracy',
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marker_color=['#1f77b4', '#ff7f0e', '#2ca02c'][:len(models)]
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), row=1, col=1)
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# Update layout
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fig.update_layout(
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title="AI Model Performance & Feature Analysis",
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template="plotly_dark",
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height=500,
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margin=dict(l=0, r=0, t=50, b=0),
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showlegend=False
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)
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# Update y-axis labels
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fig.update_yaxes(title_text="Accuracy (%)", row=1, col=1, range=[0, 100])
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fig.update_yaxes(title_text="Count", row=1, col=2)
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fig.update_yaxes(title_text="Confidence (%)", row=2, col=2, range=[0, 100])
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return fig
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except Exception as e:
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logger.error(f"Error creating performance chart: {e}")
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logger.error(f"Error creating enhanced performance chart: {e}")
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fig = go.Figure()
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fig.add_annotation(text=f"Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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Block a user