week4: ai stock trading
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Omar Marie
316
week4/community-contributions/ai_stock_trading/tools/analysis.py
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week4/community-contributions/ai_stock_trading/tools/analysis.py
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"""
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Stock Analysis Module
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This module provides enhanced technical and fundamental analysis capabilities
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for stock data with advanced metrics and indicators.
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Optional, Tuple, Union, Any
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import warnings
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warnings.filterwarnings('ignore')
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class StockAnalyzer:
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"""Enhanced stock analyzer with comprehensive technical indicators"""
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def __init__(self):
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pass
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def analyze_stock(self, data: pd.DataFrame) -> Dict:
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"""
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Comprehensive stock analysis with enhanced metrics
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Args:
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data: DataFrame with OHLCV stock data
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Returns:
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Dictionary with analysis results
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"""
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if data.empty:
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return {'error': 'No data provided for analysis'}
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try:
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analysis = {}
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# Basic price metrics
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analysis.update(self._calculate_price_metrics(data))
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# Technical indicators
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analysis.update(self._calculate_technical_indicators(data))
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# Volatility analysis
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analysis.update(self._calculate_volatility_metrics(data))
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# Volume analysis
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analysis.update(self._calculate_volume_metrics(data))
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# Trend analysis
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analysis.update(self._calculate_trend_metrics(data))
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# Risk metrics
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analysis.update(self._calculate_risk_metrics(data))
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# Performance metrics
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analysis.update(self._calculate_performance_metrics(data))
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return analysis
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except Exception as e:
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return {'error': f'Analysis failed: {str(e)}'}
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def _calculate_price_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate basic price metrics"""
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close_prices = data['Close']
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return {
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'current_price': float(close_prices.iloc[-1]),
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'start_price': float(close_prices.iloc[0]),
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'max_price': float(close_prices.max()),
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'min_price': float(close_prices.min()),
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'price_range_pct': float(((close_prices.max() - close_prices.min()) / close_prices.min()) * 100),
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'total_return_pct': float(((close_prices.iloc[-1] - close_prices.iloc[0]) / close_prices.iloc[0]) * 100)
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}
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def _calculate_technical_indicators(self, data: pd.DataFrame) -> Dict:
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"""Calculate technical indicators"""
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close_prices = data['Close']
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high_prices = data['High']
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low_prices = data['Low']
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indicators = {}
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# Moving averages
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if len(data) >= 20:
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sma_20 = close_prices.rolling(window=20).mean()
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indicators['sma_20'] = float(sma_20.iloc[-1])
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indicators['price_vs_sma_20'] = float(((close_prices.iloc[-1] - sma_20.iloc[-1]) / sma_20.iloc[-1]) * 100)
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if len(data) >= 50:
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sma_50 = close_prices.rolling(window=50).mean()
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indicators['sma_50'] = float(sma_50.iloc[-1])
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indicators['price_vs_sma_50'] = float(((close_prices.iloc[-1] - sma_50.iloc[-1]) / sma_50.iloc[-1]) * 100)
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# Exponential Moving Average
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if len(data) >= 12:
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ema_12 = close_prices.ewm(span=12).mean()
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indicators['ema_12'] = float(ema_12.iloc[-1])
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# RSI (Relative Strength Index)
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if len(data) >= 14:
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rsi = self._calculate_rsi(pd.Series(close_prices), 14)
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indicators['rsi'] = float(rsi.iloc[-1])
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indicators['rsi_signal'] = self._interpret_rsi(float(rsi.iloc[-1]))
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# MACD
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if len(data) >= 26:
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macd_line, signal_line, histogram = self._calculate_macd(pd.Series(close_prices))
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indicators['macd'] = float(macd_line.iloc[-1])
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indicators['macd_signal'] = float(signal_line.iloc[-1])
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indicators['macd_histogram'] = float(histogram.iloc[-1])
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indicators['macd_trend'] = 'bullish' if float(histogram.iloc[-1]) > 0 else 'bearish'
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# Bollinger Bands
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if len(data) >= 20:
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bb_upper, bb_middle, bb_lower = self._calculate_bollinger_bands(pd.Series(close_prices), 20, 2)
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indicators['bb_upper'] = float(bb_upper.iloc[-1])
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indicators['bb_middle'] = float(bb_middle.iloc[-1])
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indicators['bb_lower'] = float(bb_lower.iloc[-1])
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indicators['bb_position'] = self._interpret_bollinger_position(float(close_prices.iloc[-1]), float(bb_upper.iloc[-1]), float(bb_lower.iloc[-1]))
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return indicators
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def _calculate_volatility_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate volatility metrics"""
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close_prices = data['Close']
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daily_returns = close_prices.pct_change().dropna()
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return {
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'volatility_daily': float(daily_returns.std() * 100),
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'volatility_annualized': float(daily_returns.std() * np.sqrt(252) * 100),
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'avg_daily_return': float(daily_returns.mean() * 100),
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'max_daily_gain': float(daily_returns.max() * 100),
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'max_daily_loss': float(daily_returns.min() * 100)
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}
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def _calculate_volume_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate volume metrics"""
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volume = data['Volume']
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metrics: Dict[str, Union[float, str]] = {
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'avg_volume': float(volume.mean()),
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'current_volume': float(volume.iloc[-1]),
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'max_volume': float(volume.max()),
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'min_volume': float(volume.min())
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}
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# Volume trend
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if len(volume) >= 10:
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recent_avg = volume.tail(10).mean()
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overall_avg = volume.mean()
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if recent_avg > overall_avg:
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metrics['volume_trend'] = 'increasing'
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else:
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metrics['volume_trend'] = 'decreasing'
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metrics['volume_vs_avg'] = float(((recent_avg - overall_avg) / overall_avg) * 100)
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return metrics
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def _calculate_trend_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate trend analysis metrics"""
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close_prices = data['Close']
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# Linear regression for trend
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x = np.arange(len(close_prices))
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slope, intercept = np.polyfit(x, close_prices, 1)
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# Trend strength
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correlation = np.corrcoef(x, close_prices)[0, 1]
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return {
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'trend_slope': float(slope),
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'trend_direction': 'upward' if slope > 0 else 'downward',
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'trend_strength': float(abs(correlation)),
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'trend_angle': float(np.degrees(np.arctan(slope))),
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'r_squared': float(correlation ** 2)
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}
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def _calculate_risk_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate risk metrics"""
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close_prices = data['Close']
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daily_returns = close_prices.pct_change().dropna()
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# Value at Risk (VaR)
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var_95 = np.percentile(daily_returns, 5)
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var_99 = np.percentile(daily_returns, 1)
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# Maximum Drawdown
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cumulative_returns = (1 + daily_returns).cumprod()
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running_max = cumulative_returns.expanding().max()
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drawdown = (cumulative_returns - running_max) / running_max
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max_drawdown = drawdown.min()
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# Sharpe Ratio (assuming risk-free rate of 2%)
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risk_free_rate = 0.02 / 252 # Daily risk-free rate
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excess_returns = daily_returns - risk_free_rate
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sharpe_ratio = excess_returns.mean() / daily_returns.std() if daily_returns.std() != 0 else 0
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return {
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'var_95': float(var_95 * 100),
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'var_99': float(var_99 * 100),
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'max_drawdown': float(max_drawdown * 100),
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'sharpe_ratio': float(sharpe_ratio * np.sqrt(252)), # Annualized
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'downside_deviation': float(daily_returns[daily_returns < 0].std() * 100)
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}
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def _calculate_performance_metrics(self, data: pd.DataFrame) -> Dict:
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"""Calculate performance metrics"""
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close_prices = data['Close']
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# Different period returns
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periods = {
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'1_week': min(5, len(close_prices) - 1),
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'1_month': min(22, len(close_prices) - 1),
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'3_months': min(66, len(close_prices) - 1),
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'6_months': min(132, len(close_prices) - 1)
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}
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performance = {}
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current_price = close_prices.iloc[-1]
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for period_name, days_back in periods.items():
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if days_back > 0:
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past_price = close_prices.iloc[-(days_back + 1)]
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return_pct = ((current_price - past_price) / past_price) * 100
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performance[f'return_{period_name}'] = float(return_pct)
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return performance
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def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series:
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"""Calculate Relative Strength Index"""
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delta = prices.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def _interpret_rsi(self, rsi_value: float) -> str:
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"""Interpret RSI value"""
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if rsi_value >= 70:
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return 'overbought'
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elif rsi_value <= 30:
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return 'oversold'
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else:
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return 'neutral'
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def _calculate_macd(self, prices: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""Calculate MACD indicator"""
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ema_fast = prices.ewm(span=fast).mean()
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ema_slow = prices.ewm(span=slow).mean()
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macd_line = ema_fast - ema_slow
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signal_line = macd_line.ewm(span=signal).mean()
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histogram = macd_line - signal_line
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return macd_line, signal_line, histogram
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def _calculate_bollinger_bands(self, prices: pd.Series, period: int = 20, std_dev: int = 2) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""Calculate Bollinger Bands"""
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sma = prices.rolling(window=period).mean()
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std = prices.rolling(window=period).std()
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upper_band = sma + (std * std_dev)
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lower_band = sma - (std * std_dev)
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return upper_band, sma, lower_band
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def _interpret_bollinger_position(self, current_price: float, upper_band: float, lower_band: float) -> str:
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"""Interpret position relative to Bollinger Bands"""
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if current_price > upper_band:
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return 'above_upper_band'
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elif current_price < lower_band:
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return 'below_lower_band'
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else:
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return 'within_bands'
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def get_analysis_summary(self, analysis: Dict) -> str:
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"""Generate a human-readable analysis summary"""
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if 'error' in analysis:
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return f"Analysis Error: {analysis['error']}"
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summary = []
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# Price summary
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current_price = analysis.get('current_price', 0)
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total_return = analysis.get('total_return_pct', 0)
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summary.append(f"Current Price: ${current_price:.2f}")
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summary.append(f"Total Return: {total_return:.2f}%")
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# Trend
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trend_direction = analysis.get('trend_direction', 'unknown')
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trend_strength = analysis.get('trend_strength', 0)
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summary.append(f"Trend: {trend_direction.title()} (Strength: {trend_strength:.2f})")
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# Technical indicators
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if 'rsi' in analysis:
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rsi = analysis['rsi']
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rsi_signal = analysis['rsi_signal']
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summary.append(f"RSI: {rsi:.1f} ({rsi_signal})")
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if 'macd_trend' in analysis:
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macd_trend = analysis['macd_trend']
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summary.append(f"MACD: {macd_trend}")
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# Risk
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volatility = analysis.get('volatility_annualized', 0)
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max_drawdown = analysis.get('max_drawdown', 0)
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summary.append(f"Volatility: {volatility:.1f}% (Annual)")
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summary.append(f"Max Drawdown: {max_drawdown:.1f}%")
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return "\n".join(summary)
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# Global instance for easy import
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stock_analyzer = StockAnalyzer()
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# Convenience function
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def analyze_stock(data: pd.DataFrame) -> Dict:
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"""Convenience function to analyze stock data"""
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return stock_analyzer.analyze_stock(data)
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