""" Technical Indicators Module for OHLCV Data This module provides technical indicator calculations optimized for sparse OHLCV data as produced by the TCP Trading Platform's aggregation strategy. IMPORTANT: Handles Sparse Data - Missing candles (time gaps) are normal in this system - Indicators properly handle gaps without interpolation - Uses pandas for efficient vectorized calculations - Follows right-aligned timestamp convention Supported Indicators: - Simple Moving Average (SMA) - Exponential Moving Average (EMA) - Relative Strength Index (RSI) - Moving Average Convergence Divergence (MACD) - Bollinger Bands """ from datetime import datetime from typing import Dict, List, Optional, Any, Union import pandas as pd import numpy as np from .result import IndicatorResult from ..data_types import OHLCVCandle from .base import BaseIndicator from .implementations import ( SMAIndicator, EMAIndicator, RSIIndicator, MACDIndicator, BollingerBandsIndicator ) class TechnicalIndicators: """ Technical indicator calculator for OHLCV candle data. This class provides vectorized technical indicator calculations designed to handle sparse data efficiently. All calculations use pandas for performance and handle missing data appropriately. SPARSE DATA HANDLING: - Gaps in timestamps are preserved (no interpolation) - Indicators calculate only on available data points - Periods with insufficient data return NaN - Results maintain original timestamp alignment """ def __init__(self, logger=None): """ Initialize technical indicators calculator. Args: logger: Optional logger instance """ self.logger = logger # Initialize individual indicator calculators self._sma = SMAIndicator(logger) self._ema = EMAIndicator(logger) self._rsi = RSIIndicator(logger) self._macd = MACDIndicator(logger) self._bollinger = BollingerBandsIndicator(logger) if self.logger: self.logger.info("TechnicalIndicators: Initialized indicator calculator") def _prepare_dataframe_from_list(self, candles: List[OHLCVCandle]) -> pd.DataFrame: """ Convert OHLCV candles to pandas DataFrame for efficient calculations. Args: candles: List of OHLCV candles (can be sparse) Returns: DataFrame with OHLCV data, sorted by timestamp """ if not candles: return pd.DataFrame() return self._sma.prepare_dataframe(candles) def sma(self, df: pd.DataFrame, period: int, price_column: str = 'close') -> List[IndicatorResult]: """ Calculate Simple Moving Average (SMA). Args: df: DataFrame with OHLCV data period: Number of periods for moving average price_column: Price column to use ('open', 'high', 'low', 'close') Returns: List of indicator results with SMA values """ return self._sma.calculate(df, period=period, price_column=price_column) def ema(self, df: pd.DataFrame, period: int, price_column: str = 'close') -> List[IndicatorResult]: """ Calculate Exponential Moving Average (EMA). Args: df: DataFrame with OHLCV data period: Number of periods for moving average price_column: Price column to use ('open', 'high', 'low', 'close') Returns: List of indicator results with EMA values """ return self._ema.calculate(df, period=period, price_column=price_column) def rsi(self, df: pd.DataFrame, period: int = 14, price_column: str = 'close') -> List[IndicatorResult]: """ Calculate Relative Strength Index (RSI). Args: df: DataFrame with OHLCV data period: Number of periods for RSI calculation (default 14) price_column: Price column to use ('open', 'high', 'low', 'close') Returns: List of indicator results with RSI values """ return self._rsi.calculate(df, period=period, price_column=price_column) def macd(self, df: pd.DataFrame, fast_period: int = 12, slow_period: int = 26, signal_period: int = 9, price_column: str = 'close') -> List[IndicatorResult]: """ Calculate Moving Average Convergence Divergence (MACD). Args: df: DataFrame with OHLCV data fast_period: Fast EMA period (default 12) slow_period: Slow EMA period (default 26) signal_period: Signal line EMA period (default 9) price_column: Price column to use ('open', 'high', 'low', 'close') Returns: List of indicator results with MACD, signal, and histogram values """ return self._macd.calculate( df, fast_period=fast_period, slow_period=slow_period, signal_period=signal_period, price_column=price_column ) def bollinger_bands(self, df: pd.DataFrame, period: int = 20, std_dev: float = 2.0, price_column: str = 'close') -> List[IndicatorResult]: """ Calculate Bollinger Bands. Args: df: DataFrame with OHLCV data period: Number of periods for moving average (default 20) std_dev: Number of standard deviations (default 2.0) price_column: Price column to use ('open', 'high', 'low', 'close') Returns: List of indicator results with upper band, middle band (SMA), and lower band """ return self._bollinger.calculate( df, period=period, std_dev=std_dev, price_column=price_column ) def calculate_multiple_indicators(self, df: pd.DataFrame, indicators_config: Dict[str, Dict[str, Any]]) -> Dict[str, List[IndicatorResult]]: """ TODO: need make more procedural without hardcoding indicators type and so on Calculate multiple indicators at once for efficiency. Args: df: DataFrame with OHLCV data indicators_config: Configuration for indicators to calculate Example: { 'sma_20': {'type': 'sma', 'period': 20}, 'ema_12': {'type': 'ema', 'period': 12}, 'rsi_14': {'type': 'rsi', 'period': 14}, 'macd': {'type': 'macd'}, 'bb_20': {'type': 'bollinger_bands', 'period': 20} } Returns: Dictionary mapping indicator names to their results """ results = {} for indicator_name, config in indicators_config.items(): indicator_type = config.get('type') try: if indicator_type == 'sma': period = config.get('period', 20) price_column = config.get('price_column', 'close') results[indicator_name] = self.sma(df, period, price_column) elif indicator_type == 'ema': period = config.get('period', 20) price_column = config.get('price_column', 'close') results[indicator_name] = self.ema(df, period, price_column) elif indicator_type == 'rsi': period = config.get('period', 14) price_column = config.get('price_column', 'close') results[indicator_name] = self.rsi(df, period, price_column) elif indicator_type == 'macd': fast_period = config.get('fast_period', 12) slow_period = config.get('slow_period', 26) signal_period = config.get('signal_period', 9) price_column = config.get('price_column', 'close') results[indicator_name] = self.macd( df, fast_period, slow_period, signal_period, price_column ) elif indicator_type == 'bollinger_bands': period = config.get('period', 20) std_dev = config.get('std_dev', 2.0) price_column = config.get('price_column', 'close') results[indicator_name] = self.bollinger_bands( df, period, std_dev, price_column ) else: if self.logger: self.logger.warning(f"Unknown indicator type: {indicator_type}") results[indicator_name] = [] except Exception as e: if self.logger: self.logger.error(f"Error calculating {indicator_name}: {e}") results[indicator_name] = [] return results def calculate(self, indicator_type: str, df: pd.DataFrame, **kwargs) -> Optional[Dict[str, Any]]: """ Calculate a single indicator with dynamic dispatch. Args: indicator_type: Name of the indicator (e.g., 'sma', 'ema') df: DataFrame with OHLCV data **kwargs: Indicator-specific parameters (e.g., period=20) Returns: A dictionary containing the indicator results, or None if the type is unknown. """ # Get the indicator calculation method indicator_method = getattr(self, indicator_type, None) if not indicator_method: if self.logger: self.logger.error(f"Unknown indicator type '{indicator_type}'") return None try: if df.empty: return {'data': [], 'metadata': {}} # Call the indicator method raw_result = indicator_method(df, **kwargs) # Extract metadata from the first result if available metadata = raw_result[0].metadata if raw_result else {} # The methods return List[IndicatorResult], let's package that if raw_result: return { "data": raw_result, "metadata": metadata } return None except Exception as e: if self.logger: self.logger.error(f"Error calculating {indicator_type}: {e}") return None