TCPDashboard/strategies/implementations/macd.py

"""
MACD Strategy Implementation

This module implements a Moving Average Convergence Divergence (MACD) trading strategy.
It extends the BaseStrategy and generates buy/sell signals based on the crossover
of the MACD line and its signal line.
"""

import pandas as pd
from typing import List, Dict, Any

from ..base import BaseStrategy
from ..data_types import StrategyResult, StrategySignal, SignalType
from ..utils import create_indicator_key, detect_crossover_signals_vectorized


class MACDStrategy(BaseStrategy):
    """
    MACD Strategy.

    Generates buy/sell signals when the MACD line crosses above or below its signal line.
    """

    def __init__(self, strategy_name: str, logger=None):
        super().__init__(strategy_name, logger)

    def get_required_indicators(self) -> List[Dict[str, Any]]:
        """
        Defines the indicators required by the MACD strategy.
        It needs one MACD indicator.
        """
        # Default periods for MACD, can be overridden by strategy config
        return [
            {'type': 'macd', 'fast_period': 12, 'slow_period': 26, 'signal_period': 9, 'price_column': 'close'}
        ]

    def calculate(self, df: pd.DataFrame, indicators_data: Dict[str, pd.DataFrame], **kwargs) -> List[StrategyResult]:
        """
        Calculate MACD strategy signals.

        Args:
            df: DataFrame with OHLCV data.
            indicators_data: Dictionary of pre-calculated indicator DataFrames.
                             Expected key: 'macd_fast_period_12_slow_period_26_signal_period_9'.
            **kwargs: Additional strategy parameters (e.g., fast_period, slow_period, signal_period, price_column).

        Returns:
            List of StrategyResult objects, each containing generated signals.
        """
        # Extract parameters from kwargs or use defaults
        fast_period = kwargs.get('fast_period', 12)
        slow_period = kwargs.get('slow_period', 26)
        signal_period = kwargs.get('signal_period', 9)
        price_column = kwargs.get('price_column', 'close')

        # Generate indicator key using shared utility function
        macd_key = create_indicator_key({
            'type': 'macd', 
            'fast_period': fast_period, 
            'slow_period': slow_period, 
            'signal_period': signal_period
        })

        # Validate that the main DataFrame has enough data for strategy calculation
        min_periods = max(slow_period, signal_period)
        if not self.validate_dataframe(df, min_periods):
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Insufficient main DataFrame for calculation.")
            return []

        # Validate that the required MACD indicator data is present and sufficient
        required_indicators = [
            {'type': 'macd', 'fast_period': fast_period, 'slow_period': slow_period, 'signal_period': signal_period}
        ]
        if not self.validate_indicators_data(indicators_data, required_indicators):
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Missing or insufficient MACD indicator data.")
            return []

        macd_df = indicators_data.get(macd_key)

        if macd_df is None or macd_df.empty:
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: MACD indicator DataFrame is not found or empty.")
            return []

        # Merge all necessary data into a single DataFrame for easier processing
        merged_df = pd.merge(df[[price_column, 'symbol', 'timeframe']], 
                             macd_df[['macd', 'signal', 'histogram']], 
                             left_index=True, right_index=True, how='inner')

        if merged_df.empty:
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Merged DataFrame is empty after indicator alignment. Check data ranges.")
            return []

        # Use vectorized signal detection for better performance
        bullish_crossover, bearish_crossover = detect_crossover_signals_vectorized(
            merged_df, 'macd', 'signal'
        )
        
        results: List[StrategyResult] = []
        strategy_metadata = {
            'fast_period': fast_period,
            'slow_period': slow_period,
            'signal_period': signal_period
        }

        # Process bullish crossover signals (MACD crosses above Signal)
        bullish_indices = merged_df[bullish_crossover].index
        for timestamp in bullish_indices:
            row = merged_df.loc[timestamp]
            
            # Skip if any MACD values are NaN
            if pd.isna(row['macd']) or pd.isna(row['signal']):
                continue
                
            signal = StrategySignal(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                signal_type=SignalType.BUY,
                price=float(row[price_column]),
                confidence=0.9,
                metadata={'macd_cross': 'bullish', **strategy_metadata}
            )
            
            results.append(StrategyResult(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                strategy_name=self.strategy_name,
                signals=[signal],
                indicators_used={
                    'macd': float(row['macd']),
                    'signal': float(row['signal'])
                },
                metadata=strategy_metadata
            ))
            
            if self.logger:
                self.logger.info(f"{self.strategy_name}: BUY signal at {timestamp} for {row['symbol']} (MACD: {row['macd']:.2f}, Signal: {row['signal']:.2f})")

        # Process bearish crossover signals (MACD crosses below Signal)
        bearish_indices = merged_df[bearish_crossover].index
        for timestamp in bearish_indices:
            row = merged_df.loc[timestamp]
            
            # Skip if any MACD values are NaN
            if pd.isna(row['macd']) or pd.isna(row['signal']):
                continue
                
            signal = StrategySignal(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                signal_type=SignalType.SELL,
                price=float(row[price_column]),
                confidence=0.9,
                metadata={'macd_cross': 'bearish', **strategy_metadata}
            )
            
            results.append(StrategyResult(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                strategy_name=self.strategy_name,
                signals=[signal],
                indicators_used={
                    'macd': float(row['macd']),
                    'signal': float(row['signal'])
                },
                metadata=strategy_metadata
            ))
            
            if self.logger:
                self.logger.info(f"{self.strategy_name}: SELL signal at {timestamp} for {row['symbol']} (MACD: {row['macd']:.2f}, Signal: {row['signal']:.2f})")

        return results