Trading Signals

MACD Crossover Signal

Moving Average Convergence Divergence crossover detection algorithm

MomentumTechnical AnalysisReal-time

Trend Detection

Identifies market trend changes through MACD and signal line crossovers

Early Signals

Detects momentum shifts before price movements become obvious

Histogram Analysis

Uses histogram divergence to confirm signal strength

Multi-Timeframe

Supports analysis across multiple timeframes for better accuracy

Performance Metrics

68%

Win Rate

+5%

2.3%

Avg Return

+0.8%

1.85

Sharpe Ratio

+0.15

-8.2%

Max Drawdown

-1.2%
Python Implementation
import pandas as pd
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime
import talib

@dataclass
class MACDSignal:
    """MACD Crossover Signal Data Class"""
    timestamp: datetime
    symbol: str
    signal_type: str  # 'golden_cross' or 'death_cross'
    macd_value: float
    signal_value: float
    histogram: float
    price: float
    confidence: float
    strength: str  # 'weak', 'moderate', 'strong'

class MACDCrossoverDetector:
    """MACD Crossover Detection and Analysis Algorithm"""
    
    def __init__(self, fast_period: int = 12, slow_period: int = 26, signal_period: int = 9):
        self.fast_period = fast_period
        self.slow_period = slow_period
        self.signal_period = signal_period
        self.histogram_threshold = 0.001
        
    def calculate_macd(self, prices: pd.Series) -> Tuple[pd.Series, pd.Series, pd.Series]:
        """Calculate MACD indicators"""
        macd, signal, histogram = talib.MACD(
            prices, 
            fastperiod=self.fast_period,
            slowperiod=self.slow_period, 
            signalperiod=self.signal_period
        )
        return macd, signal, histogram
    
    def detect_crossover(self, macd: pd.Series, signal: pd.Series) -> List[MACDSignal]:
        """Detect MACD crossover signals"""
        signals = []
        
        for i in range(1, len(macd)):
            if pd.notna(macd[i]) and pd.notna(signal[i]):
                # Golden Cross (MACD crosses above signal)
                if macd[i-1] <= signal[i-1] and macd[i] > signal[i]:
                    strength = self._calculate_strength(macd[i], signal[i])
                    signals.append(MACDSignal(
                        timestamp=macd.index[i],
                        symbol=self.symbol,
                        signal_type='golden_cross',
                        macd_value=macd[i],
                        signal_value=signal[i],
                        histogram=macd[i] - signal[i],
                        price=self.prices[i],
                        confidence=self._calculate_confidence(i),
                        strength=strength
                    ))
                
                # Death Cross (MACD crosses below signal)
                elif macd[i-1] >= signal[i-1] and macd[i] < signal[i]:
                    strength = self._calculate_strength(macd[i], signal[i])
                    signals.append(MACDSignal(
                        timestamp=macd.index[i],
                        symbol=self.symbol,
                        signal_type='death_cross',
                        macd_value=macd[i],
                        signal_value=signal[i],
                        histogram=macd[i] - signal[i],
                        price=self.prices[i],
                        confidence=self._calculate_confidence(i),
                        strength=strength
                    ))
        
        return signals