Implement multi-timeframe support for indicators
- Enhanced the `UserIndicator` class to include an optional `timeframe` attribute for custom indicator timeframes. - Updated the `get_indicator_data` method in `MarketDataIntegrator` to fetch and calculate indicators based on the specified timeframe, ensuring proper data alignment and handling. - Modified the `ChartBuilder` to pass the correct DataFrame for plotting indicators with different timeframes. - Added UI elements in the indicator modal for selecting timeframes, improving user experience. - Updated relevant JSON templates to include the new `timeframe` field for all indicators. - Refactored the `prepare_chart_data` function to ensure it returns a DataFrame with a `DatetimeIndex` for consistent calculations. This commit enhances the flexibility and usability of the indicator system, allowing users to analyze data across various timeframes.
This commit is contained in:
Vasily.onl
@@ -489,7 +489,12 @@ class ChartBuilder:
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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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main_df=df,
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main_timeframe=timeframe,
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indicator_configs=all_indicator_configs,
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indicator_manager=indicator_manager,
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symbol=symbol,
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exchange="okx"
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)
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for indicator_id, indicator_df in indicator_data_map.items():
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@@ -499,7 +504,9 @@ class ChartBuilder:
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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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# Add a suffix to the indicator's columns before joining to prevent overlap
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# when multiple indicators of the same type are added.
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final_df = final_df.join(indicator_df, how='left', rsuffix=f'_{indicator.id}')
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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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@@ -511,7 +518,7 @@ class ChartBuilder:
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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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x_vals = indicator_df.index
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y_upper = indicator_df['upper_band']
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y_lower = indicator_df['lower_band']
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@@ -522,7 +529,7 @@ class ChartBuilder:
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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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x=pd.concat([x_vals.to_series(), x_vals.to_series()[::-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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@@ -540,7 +547,7 @@ class ChartBuilder:
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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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x=indicator_df.index,
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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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@@ -460,8 +460,11 @@ class MarketDataIntegrator:
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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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main_timeframe: str,
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indicator_configs: List['IndicatorLayerConfig'],
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indicator_manager: 'IndicatorManager'
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indicator_manager: 'IndicatorManager',
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symbol: str,
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exchange: str = "okx"
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) -> Dict[str, pd.DataFrame]:
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indicator_data_map = {}
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@@ -477,21 +480,62 @@ class MarketDataIntegrator:
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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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# Determine the timeframe and data to use
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target_timeframe = indicator.timeframe
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if target_timeframe and target_timeframe != main_timeframe:
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# Custom timeframe: fetch new data
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days_back = (main_df.index.max() - main_df.index.min()).days + 2 # Add buffer
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raw_candles, _ = self.get_market_data_for_indicators(
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symbol=symbol,
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timeframe=target_timeframe,
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days_back=days_back,
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exchange=exchange
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)
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if not raw_candles:
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self.logger.warning(f"No data for indicator '{indicator.name}' on timeframe {target_timeframe}")
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continue
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from components.charts.utils import prepare_chart_data
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indicator_df = prepare_chart_data(raw_candles)
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else:
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# Use main chart's dataframe
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indicator_df = main_df
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# Calculate the indicator
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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_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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if indicator_result_pkg and indicator_result_pkg.get('data'):
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indicator_results = indicator_result_pkg['data']
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if not indicator_results:
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self.logger.warning(f"Indicator '{indicator.name}' produced no results.")
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continue
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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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result_df['timestamp'] = pd.to_datetime(result_df['timestamp'])
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result_df.set_index('timestamp', inplace=True)
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# Ensure timezone consistency before reindexing
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if result_df.index.tz is None:
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result_df = result_df.tz_localize('UTC')
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result_df = result_df.tz_convert(main_df.index.tz)
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# Align data to main_df's index to handle different timeframes
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if not result_df.index.equals(main_df.index):
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aligned_df = result_df.reindex(main_df.index, method='ffill')
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indicator_data_map[indicator.id] = aligned_df
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else:
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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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@@ -60,6 +60,7 @@ class UserIndicator:
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display_type: str # DisplayType
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parameters: Dict[str, Any]
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styling: IndicatorStyling
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timeframe: Optional[str] = None
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visible: bool = True
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created_date: str = ""
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modified_date: str = ""
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@@ -82,6 +83,7 @@ class UserIndicator:
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'display_type': self.display_type,
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'parameters': self.parameters,
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'styling': asdict(self.styling),
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'timeframe': self.timeframe,
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'visible': self.visible,
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'created_date': self.created_date,
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'modified_date': self.modified_date
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@@ -101,6 +103,7 @@ class UserIndicator:
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display_type=data['display_type'],
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parameters=data.get('parameters', {}),
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styling=styling,
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timeframe=data.get('timeframe'),
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visible=data.get('visible', True),
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created_date=data.get('created_date', ''),
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modified_date=data.get('modified_date', '')
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@@ -244,7 +247,7 @@ class IndicatorManager:
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def create_indicator(self, name: str, indicator_type: str, parameters: Dict[str, Any],
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description: str = "", color: str = "#007bff",
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display_type: str = None) -> Optional[UserIndicator]:
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display_type: str = None, timeframe: Optional[str] = None) -> Optional[UserIndicator]:
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"""
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Create a new indicator.
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@@ -255,6 +258,7 @@ class IndicatorManager:
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description: Optional description
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color: Color for chart display
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display_type: overlay or subplot (auto-detected if None)
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timeframe: Optional timeframe for the indicator
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Returns:
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Created UserIndicator instance or None if error
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@@ -278,7 +282,8 @@ class IndicatorManager:
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type=indicator_type,
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display_type=display_type,
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parameters=parameters,
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styling=styling
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styling=styling,
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timeframe=timeframe
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)
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# Save to file
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@@ -309,16 +314,19 @@ class IndicatorManager:
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return False
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# Update fields
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for field, value in updates.items():
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if hasattr(indicator, field):
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if field == 'styling' and isinstance(value, dict):
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# Update styling fields
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for style_field, style_value in value.items():
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if hasattr(indicator.styling, style_field):
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setattr(indicator.styling, style_field, style_value)
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for key, value in updates.items():
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if hasattr(indicator, key):
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if key == 'styling' and isinstance(value, dict):
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# Update nested styling fields
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for style_key, style_value in value.items():
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if hasattr(indicator.styling, style_key):
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setattr(indicator.styling, style_key, style_value)
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elif key == 'parameters' and isinstance(value, dict):
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indicator.parameters.update(value)
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else:
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setattr(indicator, field, value)
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setattr(indicator, key, value)
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# Save updated indicator
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return self.save_indicator(indicator)
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except Exception as e:
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@@ -139,9 +139,10 @@ def prepare_chart_data(candles: List[Dict[str, Any]]) -> pd.DataFrame:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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# Sort by timestamp
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df = df.sort_values('timestamp').reset_index(drop=True)
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# Sort by timestamp and set it as the index, keeping the column
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df = df.sort_values('timestamp')
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df.index = pd.to_datetime(df['timestamp'])
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# Handle missing volume data
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if 'volume' not in df.columns:
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df['volume'] = 0
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