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
@@ -74,7 +74,7 @@ class TechnicalIndicators:
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if self.logger:
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self.logger.info("TechnicalIndicators: Initialized indicator calculator")
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def prepare_dataframe(self, candles: List[OHLCVCandle]) -> pd.DataFrame:
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def _prepare_dataframe_from_list(self, candles: List[OHLCVCandle]) -> pd.DataFrame:
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"""
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Convert OHLCV candles to pandas DataFrame for efficient calculations.
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@@ -112,20 +112,19 @@ class TechnicalIndicators:
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return df
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def sma(self, candles: List[OHLCVCandle], period: int,
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def sma(self, df: pd.DataFrame, period: int,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Simple Moving Average (SMA).
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Args:
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candles: List of OHLCV candles
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df: DataFrame with OHLCV data
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period: Number of periods for moving average
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with SMA values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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@@ -147,20 +146,19 @@ class TechnicalIndicators:
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return results
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def ema(self, candles: List[OHLCVCandle], period: int,
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def ema(self, df: pd.DataFrame, period: int,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Exponential Moving Average (EMA).
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Args:
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candles: List of OHLCV candles
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df: DataFrame with OHLCV data
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period: Number of periods for moving average
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with EMA values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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@@ -183,20 +181,19 @@ class TechnicalIndicators:
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return results
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def rsi(self, candles: List[OHLCVCandle], period: int = 14,
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def rsi(self, df: pd.DataFrame, period: int = 14,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Relative Strength Index (RSI).
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Args:
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candles: List of OHLCV candles
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df: DataFrame with OHLCV data
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period: Number of periods for RSI calculation (default 14)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with RSI values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period + 1:
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return []
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@@ -234,14 +231,14 @@ class TechnicalIndicators:
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return results
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def macd(self, candles: List[OHLCVCandle],
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def macd(self, df: pd.DataFrame,
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fast_period: int = 12, slow_period: int = 26, signal_period: int = 9,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Moving Average Convergence Divergence (MACD).
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Args:
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candles: List of OHLCV candles
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df: DataFrame with OHLCV data
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fast_period: Fast EMA period (default 12)
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slow_period: Slow EMA period (default 26)
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signal_period: Signal line EMA period (default 9)
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@@ -250,8 +247,7 @@ class TechnicalIndicators:
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Returns:
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List of indicator results with MACD, signal, and histogram values
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < slow_period + signal_period:
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if df.empty or len(df) < slow_period:
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return []
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# Calculate fast and slow EMAs
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@@ -271,7 +267,7 @@ class TechnicalIndicators:
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results = []
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for i, (timestamp, row) in enumerate(df.iterrows()):
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# Only return results after minimum period
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if i >= slow_period + signal_period - 1:
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if i >= slow_period - 1:
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if not (pd.isna(row['macd']) or pd.isna(row['signal']) or pd.isna(row['histogram'])):
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result = IndicatorResult(
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timestamp=timestamp,
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@@ -293,21 +289,20 @@ class TechnicalIndicators:
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return results
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def bollinger_bands(self, candles: List[OHLCVCandle], period: int = 20,
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def bollinger_bands(self, df: pd.DataFrame, period: int = 20,
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std_dev: float = 2.0, price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Bollinger Bands.
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Args:
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candles: List of OHLCV candles
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df: DataFrame with OHLCV data
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period: Number of periods for moving average (default 20)
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std_dev: Number of standard deviations for bands (default 2.0)
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std_dev: Number of standard deviations (default 2.0)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with upper band, middle band (SMA), and lower band
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"""
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df = self.prepare_dataframe(candles)
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if df.empty or len(df) < period:
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return []
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@@ -417,64 +412,53 @@ class TechnicalIndicators:
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def calculate(self, indicator_type: str, candles: Union[pd.DataFrame, List[OHLCVCandle]], **kwargs) -> Optional[Dict[str, Any]]:
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"""
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Generic method to calculate any supported indicator by type.
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Calculate a single indicator with dynamic dispatch.
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Args:
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indicator_type: The type of indicator to calculate (e.g., 'sma', 'ema').
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candles: The input data, either a DataFrame or a list of OHLCVCandle objects.
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**kwargs: Keyword arguments for the specific indicator function.
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indicator_type: Name of the indicator (e.g., 'sma', 'ema')
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candles: List of OHLCV candles or a pre-prepared DataFrame
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**kwargs: Indicator-specific parameters (e.g., period=20)
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Returns:
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A dictionary containing the indicator results, or None if the type is unknown.
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"""
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# If input is a DataFrame, convert it to list of OHLCVCandle objects.
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# This is a temporary adaptation to the existing methods.
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# Future optimization should standardize on DataFrames.
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if isinstance(candles, pd.DataFrame):
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from .data_types import OHLCVCandle
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# Ensure required columns are present
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required_cols = {'open', 'high', 'low', 'close', 'volume'}
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if not required_cols.issubset(candles.columns):
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if self.logger:
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self.logger.error("Indicators: DataFrame missing required columns for OHLCVCandle conversion.")
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return None
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symbol = kwargs.get('symbol', 'UNKNOWN')
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timeframe = kwargs.get('timeframe', 'UNKNOWN')
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candles_list = [
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OHLCVCandle(
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symbol=symbol,
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timeframe=timeframe,
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start_time=row['timestamp'],
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end_time=row['timestamp'],
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open=Decimal(str(row['open'])),
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high=Decimal(str(row['high'])),
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low=Decimal(str(row['low'])),
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close=Decimal(str(row['close'])),
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volume=Decimal(str(row['volume'])),
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trade_count=int(row.get('trade_count', 0))
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) for _, row in candles.iterrows()
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]
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candles = candles_list
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# Get the indicator calculation method
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indicator_method = getattr(self, indicator_type, None)
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if indicator_method and callable(indicator_method):
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# We need to construct a proper IndicatorResult object here
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# For now, let's adapt to what the methods return
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raw_result = indicator_method(candles, **kwargs)
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if not indicator_method:
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if self.logger:
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self.logger.error(f"TechnicalIndicators: Unknown indicator type '{indicator_type}'")
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return None
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try:
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# Prepare DataFrame if input is a list of candles
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if isinstance(candles, list):
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df = self._prepare_dataframe_from_list(candles)
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elif isinstance(candles, pd.DataFrame):
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df = candles
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else:
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raise TypeError("Input 'candles' must be a list of OHLCVCandle objects or a pandas DataFrame.")
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if df.empty:
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return {'data': [], 'metadata': {}}
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# Call the indicator method
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raw_result = indicator_method(df, **kwargs)
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# Extract metadata from the first result if available
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metadata = raw_result[0].metadata if raw_result else {}
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# The methods return List[IndicatorResult], let's package that
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if raw_result:
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return {
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"data": raw_result
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"data": raw_result,
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"metadata": metadata
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}
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return None
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if self.logger:
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self.logger.warning(f"TechnicalIndicators: Unknown indicator type '{indicator_type}'")
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return None
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except Exception as e:
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if self.logger:
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self.logger.error(f"TechnicalIndicators: Error calculating {indicator_type}: {e}")
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return None
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def create_default_indicators_config() -> Dict[str, Dict[str, Any]]:
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