acedanger / Entry and exit signals for stock position

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.

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")