TCPDashboard/data/common/indicators/technical.py

"""
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


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
        
        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()
        
        # Convert to DataFrame
        data = []
        for candle in candles:
            data.append({
                'timestamp': candle.end_time,  # Right-aligned timestamp
                'symbol': candle.symbol,
                'timeframe': candle.timeframe,
                'open': float(candle.open),
                'high': float(candle.high),
                'low': float(candle.low),
                'close': float(candle.close),
                'volume': float(candle.volume),
                'trade_count': candle.trade_count
            })
        
        df = pd.DataFrame(data)
        
        # Sort by timestamp to ensure proper order
        df = df.sort_values('timestamp').reset_index(drop=True)
        
        # Set timestamp as index for time-series operations
        df.set_index('timestamp', inplace=True)
        
        return df
    
    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
        """
        if df.empty or len(df) < period:
            return []
        
        # Calculate SMA using pandas rolling window
        df['sma'] = df[price_column].rolling(window=period, min_periods=period).mean()
        
        # Convert results back to IndicatorResult objects
        results = []
        for timestamp, row in df.iterrows():
            if not pd.isna(row['sma']):
                result = IndicatorResult(
                    timestamp=timestamp,
                    symbol=row['symbol'],
                    timeframe=row['timeframe'],
                    values={'sma': row['sma']},
                    metadata={'period': period, 'price_column': price_column}
                )
                results.append(result)
        
        return results
    
    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
        """
        if df.empty or len(df) < period:
            return []
        
        # Calculate EMA using pandas exponential weighted moving average
        df['ema'] = df[price_column].ewm(span=period, adjust=False).mean()
        
        # Convert results back to IndicatorResult objects
        results = []
        for i, (timestamp, row) in enumerate(df.iterrows()):
            # Only return results after minimum period
            if i >= period - 1 and not pd.isna(row['ema']):
                result = IndicatorResult(
                    timestamp=timestamp,
                    symbol=row['symbol'],
                    timeframe=row['timeframe'],
                    values={'ema': row['ema']},
                    metadata={'period': period, 'price_column': price_column}
                )
                results.append(result)
        
        return results
    
    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
        """
        if df.empty or len(df) < period + 1:
            return []
        
        # Calculate price changes
        df['price_change'] = df[price_column].diff()
        
        # Separate gains and losses
        df['gain'] = df['price_change'].where(df['price_change'] > 0, 0)
        df['loss'] = (-df['price_change']).where(df['price_change'] < 0, 0)
        
        # Calculate average gain and loss using EMA
        df['avg_gain'] = df['gain'].ewm(span=period, adjust=False).mean()
        df['avg_loss'] = df['loss'].ewm(span=period, adjust=False).mean()
        
        # Calculate RS and RSI
        df['rs'] = df['avg_gain'] / df['avg_loss']
        df['rsi'] = 100 - (100 / (1 + df['rs']))
        
        # Handle division by zero
        df['rsi'] = df['rsi'].fillna(50)  # Neutral RSI when no losses
        
        # Convert results back to IndicatorResult objects
        results = []
        for i, (timestamp, row) in enumerate(df.iterrows()):
            # Only return results after minimum period
            if i >= period and not pd.isna(row['rsi']):
                result = IndicatorResult(
                    timestamp=timestamp,
                    symbol=row['symbol'],
                    timeframe=row['timeframe'],
                    values={'rsi': row['rsi']},
                    metadata={'period': period, 'price_column': price_column}
                )
                results.append(result)
        
        return results
    
    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
        """
        if df.empty or len(df) < slow_period:
            return []
        
        # Calculate fast and slow EMAs
        df['ema_fast'] = df[price_column].ewm(span=fast_period, adjust=False).mean()
        df['ema_slow'] = df[price_column].ewm(span=slow_period, adjust=False).mean()
        
        # Calculate MACD line
        df['macd'] = df['ema_fast'] - df['ema_slow']
        
        # Calculate signal line (EMA of MACD)
        df['signal'] = df['macd'].ewm(span=signal_period, adjust=False).mean()
        
        # Calculate histogram
        df['histogram'] = df['macd'] - df['signal']
        
        # Convert results back to IndicatorResult objects
        results = []
        for i, (timestamp, row) in enumerate(df.iterrows()):
            # Only return results after minimum period
            if i >= slow_period - 1:
                if not (pd.isna(row['macd']) or pd.isna(row['signal']) or pd.isna(row['histogram'])):
                    result = IndicatorResult(
                        timestamp=timestamp,
                        symbol=row['symbol'],
                        timeframe=row['timeframe'],
                        values={
                            'macd': row['macd'],
                            'signal': row['signal'],
                            'histogram': row['histogram']
                        },
                        metadata={
                            'fast_period': fast_period,
                            'slow_period': slow_period,
                            'signal_period': signal_period,
                            'price_column': price_column
                        }
                    )
                    results.append(result)
        
        return results
    
    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
        """
        if df.empty or len(df) < period:
            return []
        
        # Calculate middle band (SMA)
        df['middle_band'] = df[price_column].rolling(window=period, min_periods=period).mean()
        
        # Calculate standard deviation
        df['std'] = df[price_column].rolling(window=period, min_periods=period).std()
        
        # Calculate upper and lower bands
        df['upper_band'] = df['middle_band'] + (std_dev * df['std'])
        df['lower_band'] = df['middle_band'] - (std_dev * df['std'])
        
        # Calculate bandwidth and %B
        df['bandwidth'] = (df['upper_band'] - df['lower_band']) / df['middle_band']
        df['percent_b'] = (df[price_column] - df['lower_band']) / (df['upper_band'] - df['lower_band'])
        
        # Convert results back to IndicatorResult objects
        results = []
        for timestamp, row in df.iterrows():
            if not pd.isna(row['middle_band']):
                result = IndicatorResult(
                    timestamp=timestamp,
                    symbol=row['symbol'],
                    timeframe=row['timeframe'],
                    values={
                        'upper_band': row['upper_band'],
                        'middle_band': row['middle_band'],
                        'lower_band': row['lower_band'],
                        'bandwidth': row['bandwidth'],
                        'percent_b': row['percent_b']
                    },
                    metadata={
                        'period': period,
                        'std_dev': std_dev,
                        'price_column': price_column
                    }
                )
                results.append(result)
        
        return results
    
    def calculate_multiple_indicators(self, df: pd.DataFrame,
                                    indicators_config: Dict[str, Dict[str, Any]]) -> Dict[str, List[IndicatorResult]]:
        """
        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"TechnicalIndicators: Unknown indicator type: {indicator_type}")
                    results[indicator_name] = []
            
            except Exception as e:
                if self.logger:
                    self.logger.error(f"TechnicalIndicators: 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"TechnicalIndicators: 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"TechnicalIndicators: Error calculating {indicator_type}: {e}")
            return None