3.9 Enhance chart functionality with indicator management and data export features
- Updated `ChartBuilder` to support dynamic indicator integration, allowing users to specify overlay and subplot indicators for enhanced chart analysis. - Implemented a new `get_indicator_data` method in `MarketDataIntegrator` for fetching indicator data based on user configurations. - Added `create_export_controls` in `chart_controls.py` to facilitate data export options (CSV/JSON) for user analysis. - Enhanced error handling and logging throughout the chart and data analysis processes to improve reliability and user feedback. - Updated documentation to reflect new features and usage guidelines for indicator management and data export functionalities.
This commit is contained in:
Vasily.onl
@@ -15,6 +15,12 @@ from decimal import Decimal
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from database.operations import get_database_operations, DatabaseOperationError
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from utils.logger import get_logger
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from .utils import validate_market_data, prepare_chart_data, get_indicator_colors
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from .indicator_manager import get_indicator_manager
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from .layers import (
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LayerManager, CandlestickLayer, VolumeLayer,
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SMALayer, EMALayer, BollingerBandsLayer,
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RSILayer, MACDLayer, IndicatorLayerConfig
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)
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# Initialize logger
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logger = get_logger("default_logger")
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@@ -153,13 +159,16 @@ class ChartBuilder:
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include_volume = kwargs.get('include_volume', has_volume)
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if include_volume and has_volume:
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return self._create_candlestick_with_volume(df, symbol, timeframe, **kwargs)
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fig, df_chart = self._create_candlestick_with_volume(df, symbol, timeframe, **kwargs)
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return fig, df_chart
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else:
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return self._create_basic_candlestick(df, symbol, timeframe, **kwargs)
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fig, df_chart = self._create_basic_candlestick(df, symbol, timeframe, **kwargs)
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return fig, df_chart
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except Exception as e:
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self.logger.error(f"Chart builder: Error creating candlestick chart for {symbol} {timeframe}: {e}")
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return self._create_error_chart(f"Error loading chart: {str(e)}")
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error_fig = self._create_error_chart(f"Error loading chart: {str(e)}")
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return error_fig, pd.DataFrame()
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def _create_basic_candlestick(self, df: pd.DataFrame, symbol: str,
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timeframe: str, **kwargs) -> go.Figure:
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@@ -194,7 +203,7 @@ class ChartBuilder:
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)
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self.logger.debug(f"Chart builder: Created basic candlestick chart for {symbol} {timeframe} with {len(df)} candles")
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return fig
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return fig, df
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def _create_candlestick_with_volume(self, df: pd.DataFrame, symbol: str,
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timeframe: str, **kwargs) -> go.Figure:
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@@ -250,7 +259,8 @@ class ChartBuilder:
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showlegend=False,
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height=height,
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xaxis_rangeslider_visible=False,
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hovermode='x unified'
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hovermode='x unified',
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dragmode='pan'
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)
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# Update axes
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@@ -258,8 +268,8 @@ class ChartBuilder:
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fig.update_yaxes(title_text="Volume", row=2, col=1)
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fig.update_xaxes(title_text="Time", row=2, col=1)
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self.logger.debug(f"Chart builder: Created candlestick chart with volume for {symbol} {timeframe}")
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return fig
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self.logger.debug(f"Chart builder: Created candlestick chart with volume for {symbol} {timeframe} with {len(df)} candles")
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return fig, df
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def _create_empty_chart(self, message: str = "No data available") -> go.Figure:
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"""Create an empty chart with a message."""
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@@ -356,7 +366,7 @@ class ChartBuilder:
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subplot_indicators: List[str] = None,
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days_back: int = 7, **kwargs) -> go.Figure:
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"""
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Create a chart with dynamically selected indicators.
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Create a candlestick chart with specified technical indicators.
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Args:
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symbol: Trading pair
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@@ -367,35 +377,27 @@ class ChartBuilder:
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**kwargs: Additional chart parameters
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Returns:
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Plotly Figure object with selected indicators
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Plotly Figure object and a pandas DataFrame with all chart data.
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"""
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overlay_indicators = overlay_indicators or []
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subplot_indicators = subplot_indicators or []
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try:
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# Fetch market data
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# 1. Fetch and Prepare Base Data
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candles = self.fetch_market_data_enhanced(symbol, timeframe, days_back)
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if not candles:
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self.logger.warning(f"Chart builder: No data available for {symbol} {timeframe}")
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return self._create_empty_chart(f"No data available for {symbol} {timeframe}")
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# Validate and prepare data
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if not validate_market_data(candles):
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self.logger.error(f"Chart builder: Invalid market data for {symbol} {timeframe}")
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return self._create_error_chart("Invalid market data format")
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self.logger.warning(f"No data for {symbol} {timeframe}, creating empty chart.")
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return self._create_empty_chart(f"No data for {symbol} {timeframe}"), pd.DataFrame()
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df = prepare_chart_data(candles)
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# Import layer classes
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from .layers import (
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LayerManager, CandlestickLayer, VolumeLayer,
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SMALayer, EMALayer, BollingerBandsLayer,
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RSILayer, MACDLayer, IndicatorLayerConfig
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)
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from .indicator_manager import get_indicator_manager
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# Get user indicators instead of default configurations
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indicator_manager = get_indicator_manager()
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# Calculate subplot requirements
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if df.empty:
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self.logger.warning(f"DataFrame empty for {symbol} {timeframe}, creating empty chart.")
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return self._create_empty_chart(f"No data for {symbol} {timeframe}"), pd.DataFrame()
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# Initialize final DataFrame for export
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final_df = df.copy()
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# 2. Setup Subplots
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# Count subplot indicators to configure rows
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subplot_count = 0
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volume_enabled = 'volume' in df.columns and df['volume'].sum() > 0
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if volume_enabled:
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@@ -440,8 +442,8 @@ class ChartBuilder:
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current_row = 1
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# Add candlestick layer (always included)
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candlestick_trace = go.Candlestick(
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# 4. Add Candlestick Trace
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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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@@ -449,72 +451,10 @@ class ChartBuilder:
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close=df['close'],
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name=symbol,
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increasing_line_color=self.default_colors['bullish'],
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decreasing_line_color=self.default_colors['bearish'],
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showlegend=False
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)
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fig.add_trace(candlestick_trace, row=current_row, col=1)
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decreasing_line_color=self.default_colors['bearish']
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), row=current_row, col=1)
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# Add overlay indicators
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if overlay_indicators:
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for indicator_id in overlay_indicators:
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try:
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# Load user indicator
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user_indicator = indicator_manager.load_indicator(indicator_id)
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if user_indicator is None:
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self.logger.warning(f"Overlay indicator {indicator_id} not found")
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continue
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# Create appropriate indicator layer using user configuration
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if user_indicator.type == 'sma':
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period = user_indicator.parameters.get('period', 20)
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layer_config = IndicatorLayerConfig(
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name=user_indicator.name,
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indicator_type='sma',
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color=user_indicator.styling.color,
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parameters={'period': period},
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line_width=user_indicator.styling.line_width
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)
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sma_layer = SMALayer(layer_config)
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traces = sma_layer.create_traces(df.to_dict('records'))
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for trace in traces:
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fig.add_trace(trace, row=current_row, col=1)
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elif user_indicator.type == 'ema':
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period = user_indicator.parameters.get('period', 12)
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layer_config = IndicatorLayerConfig(
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name=user_indicator.name,
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indicator_type='ema',
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color=user_indicator.styling.color,
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parameters={'period': period},
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line_width=user_indicator.styling.line_width
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)
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ema_layer = EMALayer(layer_config)
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traces = ema_layer.create_traces(df.to_dict('records'))
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for trace in traces:
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fig.add_trace(trace, row=current_row, col=1)
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elif user_indicator.type == 'bollinger_bands':
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period = user_indicator.parameters.get('period', 20)
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std_dev = user_indicator.parameters.get('std_dev', 2.0)
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layer_config = IndicatorLayerConfig(
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name=user_indicator.name,
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indicator_type='bollinger_bands',
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color=user_indicator.styling.color,
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parameters={'period': period, 'std_dev': std_dev},
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line_width=user_indicator.styling.line_width,
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show_middle_line=True
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)
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bb_layer = BollingerBandsLayer(layer_config)
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traces = bb_layer.create_traces(df.to_dict('records'))
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for trace in traces:
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fig.add_trace(trace, row=current_row, col=1)
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self.logger.debug(f"Added overlay indicator: {user_indicator.name}")
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except Exception as e:
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self.logger.error(f"Chart builder: Error adding overlay indicator {indicator_id}: {e}")
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# Move to next row for volume if enabled
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# 5. Add Volume Trace (if applicable)
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if volume_enabled:
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current_row += 1
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volume_colors = [self.default_colors['bullish'] if close >= open else self.default_colors['bearish']
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@@ -525,56 +465,89 @@ class ChartBuilder:
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y=df['volume'],
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name='Volume',
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marker_color=volume_colors,
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opacity=0.7,
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showlegend=False
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opacity=0.7
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)
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fig.add_trace(volume_trace, row=current_row, col=1)
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fig.update_yaxes(title_text="Volume", row=current_row, col=1)
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# Add subplot indicators
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if subplot_indicators:
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for indicator_id in subplot_indicators:
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current_row += 1
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try:
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# Load user indicator
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user_indicator = indicator_manager.load_indicator(indicator_id)
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if user_indicator is None:
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self.logger.warning(f"Subplot indicator {indicator_id} not found")
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continue
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# Create appropriate subplot indicator layer
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if user_indicator.type == 'rsi':
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period = user_indicator.parameters.get('period', 14)
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rsi_layer = RSILayer(period=period, color=user_indicator.styling.color, name=user_indicator.name)
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# Use the render method
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fig = rsi_layer.render(fig, df, row=current_row, col=1)
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# Add RSI reference lines
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fig.add_hline(y=70, line_dash="dash", line_color="red", opacity=0.5, row=current_row, col=1)
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fig.add_hline(y=30, line_dash="dash", line_color="green", opacity=0.5, row=current_row, col=1)
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fig.update_yaxes(title_text="RSI", range=[0, 100], row=current_row, col=1)
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elif user_indicator.type == 'macd':
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fast_period = user_indicator.parameters.get('fast_period', 12)
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slow_period = user_indicator.parameters.get('slow_period', 26)
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signal_period = user_indicator.parameters.get('signal_period', 9)
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macd_layer = MACDLayer(fast_period=fast_period, slow_period=slow_period,
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signal_period=signal_period, color=user_indicator.styling.color, name=user_indicator.name)
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# Use the render method
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fig = macd_layer.render(fig, df, row=current_row, col=1)
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# Add zero line for MACD
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fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5, row=current_row, col=1)
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fig.update_yaxes(title_text="MACD", row=current_row, col=1)
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self.logger.debug(f"Added subplot indicator: {user_indicator.name}")
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except Exception as e:
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self.logger.error(f"Chart builder: Error adding subplot indicator {indicator_id}: {e}")
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# 6. Add Indicator Traces
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indicator_manager = get_indicator_manager()
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all_indicator_configs = []
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# Create IndicatorLayerConfig objects from indicator IDs
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indicator_ids = (overlay_indicators or []) + (subplot_indicators or [])
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for ind_id in indicator_ids:
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indicator = indicator_manager.load_indicator(ind_id)
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if indicator:
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config = IndicatorLayerConfig(
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id=indicator.id,
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name=indicator.name,
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indicator_type=indicator.type,
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parameters=indicator.parameters
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)
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all_indicator_configs.append(config)
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# Update layout
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if all_indicator_configs:
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indicator_data_map = self.data_integrator.get_indicator_data(
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df, all_indicator_configs, indicator_manager
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)
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for indicator_id, indicator_df in indicator_data_map.items():
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indicator = indicator_manager.load_indicator(indicator_id)
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if not indicator:
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self.logger.warning(f"Could not load indicator '{indicator_id}' for plotting.")
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continue
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if indicator_df is not None and not indicator_df.empty:
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final_df = pd.merge(final_df, indicator_df, on='timestamp', how='left')
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# Determine target row for plotting
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target_row = 1 # Default to overlay on the main chart
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if indicator.id in subplot_indicators:
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current_row += 1
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target_row = current_row
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fig.update_yaxes(title_text=indicator.name, row=target_row, col=1)
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if indicator.type == 'bollinger_bands':
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if all(c in indicator_df.columns for c in ['upper_band', 'lower_band', 'middle_band']):
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# Prepare data for the filled area
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x_vals = indicator_df['timestamp']
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y_upper = indicator_df['upper_band']
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y_lower = indicator_df['lower_band']
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# Convert hex color to rgba for the fill
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hex_color = indicator.styling.color.lstrip('#')
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rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
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fill_color = f'rgba({rgb[0]}, {rgb[1]}, {rgb[2]}, 0.1)'
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# Add the transparent fill trace
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fig.add_trace(go.Scatter(
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x=pd.concat([x_vals, x_vals[::-1]]),
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y=pd.concat([y_upper, y_lower[::-1]]),
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fill='toself',
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fillcolor=fill_color,
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line={'color': 'rgba(255,255,255,0)'},
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hoverinfo='none',
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showlegend=False
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), row=target_row, col=1)
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# Add the visible line traces for the bands
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fig.add_trace(go.Scatter(x=x_vals, y=y_upper, name=f'{indicator.name} Upper', mode='lines', line=dict(color=indicator.styling.color, width=1.5)), row=target_row, col=1)
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fig.add_trace(go.Scatter(x=x_vals, y=y_lower, name=f'{indicator.name} Lower', mode='lines', line=dict(color=indicator.styling.color, width=1.5)), row=target_row, col=1)
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fig.add_trace(go.Scatter(x=x_vals, y=indicator_df['middle_band'], name=f'{indicator.name} Middle', mode='lines', line=dict(color=indicator.styling.color, width=1.5, dash='dash')), row=target_row, col=1)
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else:
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# Generic plotting for other indicators
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for col in indicator_df.columns:
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if col != 'timestamp':
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fig.add_trace(go.Scatter(
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x=indicator_df['timestamp'],
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y=indicator_df[col],
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mode='lines',
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name=f"{indicator.name} ({col})",
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line=dict(color=indicator.styling.color)
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), row=target_row, col=1)
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# 7. Final Layout Updates
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height = kwargs.get('height', self.default_height)
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template = kwargs.get('template', self.default_template)
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@@ -594,8 +567,9 @@ class ChartBuilder:
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indicator_count = len(overlay_indicators or []) + len(subplot_indicators or [])
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self.logger.debug(f"Created chart for {symbol} {timeframe} with {indicator_count} indicators")
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return fig
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self.logger.info(f"Successfully created chart for {symbol} with {len(overlay_indicators + subplot_indicators)} indicators.")
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return fig, final_df
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except Exception as e:
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self.logger.error(f"Chart builder: Error creating chart with indicators: {e}")
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return self._create_error_chart(f"Chart creation failed: {str(e)}")
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self.logger.error(f"Error in create_chart_with_indicators for {symbol}: {e}", exc_info=True)
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return self._create_error_chart(f"Error generating indicator chart: {e}"), pd.DataFrame()
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@@ -457,6 +457,49 @@ class MarketDataIntegrator:
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self._cache.clear()
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self.logger.debug("Data Integration: Data cache cleared")
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def get_indicator_data(
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self,
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main_df: pd.DataFrame,
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indicator_configs: List['IndicatorLayerConfig'],
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indicator_manager: 'IndicatorManager'
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) -> Dict[str, pd.DataFrame]:
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indicator_data_map = {}
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if main_df.empty:
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return indicator_data_map
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for config in indicator_configs:
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indicator_id = config.id
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indicator = indicator_manager.load_indicator(indicator_id)
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if not indicator:
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logger.warning(f"Data Integrator: Could not load indicator with ID: {indicator_id}")
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continue
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try:
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# The new `calculate` method in TechnicalIndicators handles DataFrame input
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indicator_result_pkg = self.indicators.calculate(
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indicator.type,
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main_df,
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**indicator.parameters
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)
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if indicator_result_pkg and 'data' in indicator_result_pkg and indicator_result_pkg['data']:
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# The result is a list of IndicatorResult objects. Convert to DataFrame.
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indicator_results = indicator_result_pkg['data']
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result_df = pd.DataFrame([
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{'timestamp': r.timestamp, **r.values}
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for r in indicator_results
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])
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indicator_data_map[indicator.id] = result_df
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else:
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self.logger.warning(f"No data returned for indicator '{indicator.name}'")
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except Exception as e:
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self.logger.error(f"Error calculating indicator '{indicator.name}': {e}", exc_info=True)
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return indicator_data_map
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# Convenience functions for common operations
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def get_market_data_integrator(config: DataIntegrationConfig = None) -> MarketDataIntegrator:
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@@ -28,6 +28,7 @@ logger = get_logger("default_logger")
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||||
@dataclass
|
||||
class IndicatorLayerConfig(LayerConfig):
|
||||
"""Extended configuration for indicator layers"""
|
||||
id: str = ""
|
||||
indicator_type: str = "" # e.g., 'sma', 'ema', 'rsi'
|
||||
parameters: Dict[str, Any] = None # Indicator-specific parameters
|
||||
line_width: int = 2
|
||||
|
||||
Reference in New Issue
Block a user