For entering positions, I'd focus on a combination of these technical indicators:

**For Entry Signals:**

- **Moving Average Crossovers** - When a shorter-term MA (like 20-day) crosses above a longer-term MA (like 50-day), it can signal upward momentum
- **RSI (Relative Strength Index)** - Looking for oversold conditions (RSI below 30) that might indicate a bounce opportunity
- **MACD** - When the MACD line crosses above the signal line, especially after being in negative territory
- **Volume confirmation** - Ensuring any breakout or signal is accompanied by higher-than-average volume
- **Support and Resistance levels** - Entering near strong support levels or after a clean breakout above resistance

**For Exit Signals:**

- **Trailing stops** based on ATR (Average True Range) to let winners run while protecting against major reversals
- **RSI overbought conditions** (above 70) combined with bearish divergence
- **Moving average violations** - When price closes below a key moving average that previously provided support
- **MACD bearish crossover** - When MACD crosses below the signal line
- **Volume patterns** - Selling into high volume spikes, especially if accompanied by reversal candlestick patterns
- **Predetermined risk/reward ratios** - Taking profits at 2:1 or 3:1 reward-to-risk levels

The key is using multiple indicators together rather than relying on any single one, and always having a clear exit plan before entering any position. Volume confirmation is crucial for validating most technical signals.

```python
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

class TechnicalAnalyzer:
    def __init__(self, data):
        """
        Initialize with price data DataFrame
        Expected columns: ['date', 'open', 'high', 'low', 'close', 'volume']
        """
        self.data = data.copy()
        self.signals = pd.DataFrame()
        
    def calculate_sma(self, period):
        """Simple Moving Average"""
        return self.data['close'].rolling(window=period).mean()
    
    def calculate_ema(self, period):
        """Exponential Moving Average"""
        return self.data['close'].ewm(span=period).mean()
    
    def calculate_rsi(self, period=14):
        """Relative Strength Index"""
        delta = self.data['close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
        rs = gain / loss
        rsi = 100 - (100 / (1 + rs))
        return rsi
    
    def calculate_macd(self, fast=12, slow=26, signal=9):
        """MACD Indicator"""
        ema_fast = self.calculate_ema(fast)
        ema_slow = self.calculate_ema(slow)
        macd_line = ema_fast - ema_slow
        signal_line = macd_line.ewm(span=signal).mean()
        histogram = macd_line - signal_line
        return macd_line, signal_line, histogram
    
    def calculate_bollinger_bands(self, period=20, std_dev=2):
        """Bollinger Bands"""
        sma = self.calculate_sma(period)
        std = self.data['close'].rolling(window=period).std()
        upper_band = sma + (std * std_dev)
        lower_band = sma - (std * std_dev)
        return upper_band, sma, lower_band
    
    def calculate_atr(self, period=14):
        """Average True Range"""
        high_low = self.data['high'] - self.data['low']
        high_close = np.abs(self.data['high'] - self.data['close'].shift())
        low_close = np.abs(self.data['low'] - self.data['close'].shift())
        ranges = pd.concat([high_low, high_close, low_close], axis=1)
        true_range = np.max(ranges, axis=1)
        atr = true_range.rolling(window=period).mean()
        return atr
    
    def calculate_volume_indicators(self):
        """Volume-based indicators"""
        # Volume Moving Average
        vol_sma_20 = self.data['volume'].rolling(window=20).mean()
        vol_ratio = self.data['volume'] / vol_sma_20
        
        # On Balance Volume (OBV)
        obv = (np.sign(self.data['close'].diff()) * self.data['volume']).fillna(0).cumsum()
        
        return vol_ratio, obv
    
    def generate_all_indicators(self):
        """Calculate all technical indicators"""
        # Moving Averages
        self.data['sma_20'] = self.calculate_sma(20)
        self.data['sma_50'] = self.calculate_sma(50)
        self.data['ema_12'] = self.calculate_ema(12)
        self.data['ema_26'] = self.calculate_ema(26)
        
        # RSI
        self.data['rsi'] = self.calculate_rsi()
        
        # MACD
        macd, signal, histogram = self.calculate_macd()
        self.data['macd'] = macd
        self.data['macd_signal'] = signal
        self.data['macd_histogram'] = histogram
        
        # Bollinger Bands
        bb_upper, bb_middle, bb_lower = self.calculate_bollinger_bands()
        self.data['bb_upper'] = bb_upper
        self.data['bb_middle'] = bb_middle
        self.data['bb_lower'] = bb_lower
        
        # ATR
        self.data['atr'] = self.calculate_atr()
        
        # Volume indicators
        vol_ratio, obv = self.calculate_volume_indicators()
        self.data['vol_ratio'] = vol_ratio
        self.data['obv'] = obv
        
        return self.data
    
    def identify_entry_signals(self):
        """Identify potential entry points"""
        signals = []
        
        for i in range(1, len(self.data)):
            entry_score = 0
            reasons = []
            
            current = self.data.iloc[i]
            previous = self.data.iloc[i-1]
            
            # Moving Average Crossover (Golden Cross)
            if (current['sma_20'] > current['sma_50'] and 
                previous['sma_20'] <= previous['sma_50']):
                entry_score += 2
                reasons.append("SMA Golden Cross")
            
            # Price above both MAs
            if current['close'] > current['sma_20'] > current['sma_50']:
                entry_score += 1
                reasons.append("Price above MAs")
            
            # RSI oversold recovery
            if previous['rsi'] < 30 and current['rsi'] > 30:
                entry_score += 2
                reasons.append("RSI oversold recovery")
            
            # MACD bullish crossover
            if (current['macd'] > current['macd_signal'] and 
                previous['macd'] <= previous['macd_signal']):
                entry_score += 2
                reasons.append("MACD bullish crossover")
            
            # Bollinger Band bounce
            if previous['close'] <= previous['bb_lower'] and current['close'] > previous['bb_lower']:
                entry_score += 1
                reasons.append("BB lower band bounce")
            
            # Volume confirmation
            if current['vol_ratio'] > 1.5:  # 50% above average
                entry_score += 1
                reasons.append("High volume")
            
            # Strong overall conditions
            if (current['rsi'] > 40 and current['rsi'] < 70 and
                current['macd'] > 0):
                entry_score += 1
                reasons.append("Favorable momentum")
            
            if entry_score >= 3:  # Minimum threshold for entry
                signals.append({
                    'date': current['date'],
                    'type': 'ENTRY',
                    'price': current['close'],
                    'score': entry_score,
                    'reasons': reasons
                })
        
        return signals
    
    def identify_exit_signals(self):
        """Identify potential exit points"""
        signals = []
        
        for i in range(1, len(self.data)):
            exit_score = 0
            reasons = []
            
            current = self.data.iloc[i]
            previous = self.data.iloc[i-1]
            
            # Moving Average bearish cross
            if (current['sma_20'] < current['sma_50'] and 
                previous['sma_20'] >= previous['sma_50']):
                exit_score += 2
                reasons.append("SMA Death Cross")
            
            # Price below key MA
            if current['close'] < current['sma_20']:
                exit_score += 1
                reasons.append("Price below SMA20")
            
            # RSI overbought
            if current['rsi'] > 70:
                exit_score += 1
                reasons.append("RSI overbought")
                
            # RSI bearish divergence (simplified)
            if previous['rsi'] > 70 and current['rsi'] < 70:
                exit_score += 2
                reasons.append("RSI overbought exit")
            
            # MACD bearish crossover
            if (current['macd'] < current['macd_signal'] and 
                previous['macd'] >= previous['macd_signal']):
                exit_score += 2
                reasons.append("MACD bearish crossover")
            
            # Bollinger Band upper touch
            if current['close'] >= current['bb_upper']:
                exit_score += 1
                reasons.append("BB upper band resistance")
            
            # Volume spike (could indicate distribution)
            if current['vol_ratio'] > 3.0:
                exit_score += 1
                reasons.append("Extreme volume spike")
            
            if exit_score >= 3:  # Minimum threshold for exit
                signals.append({
                    'date': current['date'],
                    'type': 'EXIT',
                    'price': current['close'],
                    'score': exit_score,
                    'reasons': reasons
                })
        
        return signals
    
    def analyze_stock(self):
        """Complete analysis workflow"""
        # Generate all indicators
        self.generate_all_indicators()
        
        # Get entry and exit signals
        entry_signals = self.identify_entry_signals()
        exit_signals = self.identify_exit_signals()
        
        # Combine all signals
        all_signals = entry_signals + exit_signals
        all_signals = sorted(all_signals, key=lambda x: x['date'])
        
        return all_signals, self.data

# Example usage and demo data generation
def generate_sample_data(days=252):
    """Generate sample stock data for demonstration"""
    np.random.seed(42)  # For reproducible results
    
    start_date = datetime.now() - timedelta(days=days)
    dates = [start_date + timedelta(days=i) for i in range(days)]
    
    # Generate realistic price movement
    returns = np.random.normal(0.001, 0.02, days)  # Daily returns
    price = 100  # Starting price
    prices = [price]
    
    for ret in returns[1:]:
        price *= (1 + ret)
        prices.append(price)
    
    # Generate OHLC data
    data = []
    for i, (date, close) in enumerate(zip(dates, prices)):
        high = close * (1 + abs(np.random.normal(0, 0.015)))
        low = close * (1 - abs(np.random.normal(0, 0.015)))
        open_price = low + (high - low) * np.random.random()
        volume = int(np.random.normal(1000000, 300000))
        
        data.append({
            'date': date,
            'open': open_price,
            'high': high,
            'low': low,
            'close': close,
            'volume': max(volume, 100000)  # Ensure positive volume
        })
    
    return pd.DataFrame(data)

# Demo execution
if __name__ == "__main__":
    # Generate sample data
    print("Generating sample stock data...")
    sample_data = generate_sample_data(180)  # 6 months of data
    
    # Initialize analyzer
    analyzer = TechnicalAnalyzer(sample_data)
    
    # Run complete analysis
    print("Analyzing technical indicators...")
    signals, enhanced_data = analyzer.analyze_stock()
    
    # Display results
    print(f"\n=== TECHNICAL ANALYSIS RESULTS ===")
    print(f"Analysis period: {len(sample_data)} days")
    print(f"Total signals found: {len(signals)}")
    
    # Show recent indicators
    print(f"\n=== LATEST INDICATOR VALUES ===")
    latest = enhanced_data.iloc[-1]
    print(f"Price: ${latest['close']:.2f}")
    print(f"RSI: {latest['rsi']:.2f}")
    print(f"MACD: {latest['macd']:.4f}")
    print(f"Volume Ratio: {latest['vol_ratio']:.2f}x")
    print(f"20-day SMA: ${latest['sma_20']:.2f}")
    print(f"50-day SMA: ${latest['sma_50']:.2f}")
    
    # Show recent signals
    print(f"\n=== RECENT SIGNALS ===")
    recent_signals = [s for s in signals if s['date'] >= (datetime.now() - timedelta(days=30))]
    
    if recent_signals:
        for signal in recent_signals[-5:]:  # Last 5 signals
            print(f"\n{signal['type']} Signal:")
            print(f"  Date: {signal['date'].strftime('%Y-%m-%d')}")
            print(f"  Price: ${signal['price']:.2f}")
            print(f"  Score: {signal['score']}")
            print(f"  Reasons: {', '.join(signal['reasons'])}")
    else:
        print("No recent signals found.")
    
    print(f"\n=== USAGE NOTES ===")
    print("1. Replace sample data with real market data from your preferred source")
    print("2. Adjust indicator parameters based on your trading style")
    print("3. Modify signal thresholds based on backtesting results")
    print("4. Always combine with risk management and position sizing")
    print("5. Consider market conditions and fundamental analysis")
```