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50 files changed, 4859 insertions(+), 1718 deletions(-) create mode 100755 FinanceReport/Chase0053_Activity20250601_20250701_20250718.CSV create mode 100644 backtest/PROJECT_STRUCTURE.md create mode 100644 backtest/chat_history/enhanced_strategy_configuration_session.json delete mode 100755 backtest/generate_pdf_report.py delete mode 100755 backtest/main.py create mode 100644 backtest/reports/detailed_trades_20250718_182730.txt create mode 100644 backtest/reports/detailed_trades_20250718_183737.txt create mode 100644 backtest/reports/detailed_trades_20250718_185722.txt create mode 100644 backtest/reports/detailed_trades_20250718_190932.txt create mode 100644 backtest/reports/detailed_trades_20250718_191059.txt create mode 100644 backtest/reports/detailed_trades_20250718_192206.txt create mode 100644 backtest/reports/detailed_trades_20250718_204803.txt create mode 100644 backtest/reports/enhanced_strategy_report_20250718_182730.pdf create mode 100644 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100644 backtest/reports/original_emotional_damage_strategy_report_20250718_215512.pdf delete mode 100755 backtest/requirements_server.txt create mode 100644 backtest/results/enhanced_strategy_20250718_182730.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_183737.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_185722.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_190932.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_191059.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_192206.pkl create mode 100644 backtest/results/enhanced_strategy_20250718_204803.pkl create mode 100644 backtest/strategy/emotional-damage/README_CONFIG.md mode change 100755 => 100644 backtest/strategy/emotional-damage/__pycache__/backtest_emotional_damage.cpython-311.pyc delete mode 100644 backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced.py create mode 100644 backtest/strategy/emotional-damage/config.json delete mode 100755 backtest/strategy/emotional-damage/emotional-damage.txt delete mode 100755 backtest/strategy/emotional-damage/emotional_damage_strategy_report_20250717_205822.pdf delete mode 100644 backtest/strategy/emotional-damage/enhanced_emotional_damage_strategy_report_20250717_222606.pdf delete mode 100644 backtest/strategy/emotional-damage/generate_enhanced_pdf_report.py create mode 100644 backtest/strategy/emotional-damage/run_strategy_with_config.py create mode 100644 silly/归墟界面.html diff --git a/FinanceReport/Chase0053_Activity20250601_20250701_20250718.CSV b/FinanceReport/Chase0053_Activity20250601_20250701_20250718.CSV new file mode 100755 index 0000000..334a362 --- /dev/null +++ b/FinanceReport/Chase0053_Activity20250601_20250701_20250718.CSV @@ -0,0 +1,79 @@ +Transaction Date,Post Date,Description,Category,Type,Amount,Memo +06/30/2025,07/01/2025,ALP*Programming,Professional Services,Sale,-6.08, +06/30/2025,07/01/2025,TCGPLAYER.COM,Shopping,Return,0.21, +06/30/2025,07/01/2025,AMAZON MKTPL*NQ7SV9K20,Shopping,Sale,-322.49, +06/28/2025,06/30/2025,ALP*Taobao,Shopping,Sale,-3.04, +06/28/2025,06/30/2025,ALP*DepartmentStores,Shopping,Sale,-15.35, +06/29/2025,06/30/2025,AMAZON MKTPL*NQ7Q38KO1,Shopping,Sale,-5.16, +06/27/2025,06/29/2025,GAMESTOP #6292,Entertainment,Sale,-537.49, +06/27/2025,06/29/2025,ALP*Taobao,Shopping,Sale,-5.02, +06/26/2025,06/29/2025,ALP*Taobao,Shopping,Sale,-6.95, +06/26/2025,06/29/2025,ALP*Taobao,Shopping,Sale,-3.05, +06/27/2025,06/29/2025,SAMSCLUB #8119,Shopping,Sale,-109.63, +06/27/2025,06/29/2025,ALP*Taobao,Shopping,Sale,-2.37, +06/28/2025,06/29/2025,WM SUPERCENTER #5771,Groceries,Sale,-3.65, +06/26/2025,06/27/2025,ALP*Taobao,Shopping,Sale,-7.53, +06/24/2025,06/25/2025,YAMI.COM,Groceries,Sale,-68.98, +06/23/2025,06/24/2025,ALP*Programming,Professional Services,Sale,-13.93, +06/22/2025,06/23/2025,ALP*Taobao,Shopping,Sale,-11.15, +06/21/2025,06/22/2025,SAMS CLUB #8119,Gas,Sale,-37.57, +06/21/2025,06/22/2025,PORKBUN* PORKBUN.COM,Bills & Utilities,Sale,-1.00, +06/21/2025,06/22/2025,ALP*Taobao,Shopping,Sale,-1.36, +06/21/2025,06/22/2025,SAMSCLUB #8119,Shopping,Sale,-86.27, +06/21/2025,06/22/2025,TCGPLAYER.COM,Shopping,Return,0.21, +06/20/2025,06/22/2025,ALP*PersonalServices,Personal,Sale,-16.35, +06/21/2025,06/22/2025,SAMSCLUB #8119,Shopping,Sale,-1.08, +06/21/2025,06/22/2025,ALP*Taobao,Shopping,Sale,-5.07, +06/19/2025,06/20/2025,ALP*Programming,Professional Services,Sale,-7.61, +06/19/2025,06/20/2025,ALP*PersonalServices,Personal,Sale,-27.84, +06/20/2025,06/20/2025,OPENAI *CHATGPT SUBSCR,Shopping,Sale,-20.00, +06/20/2025,06/20/2025,GOOGLE *SVCSCAS.6364-2,Personal,Sale,-5.00, +06/19/2025,06/20/2025,ALP*PersonalServices,Personal,Sale,-27.84, +06/17/2025,06/19/2025,ALP*Taobao,Shopping,Sale,-21.59, +06/18/2025,06/19/2025,TCGPLAYER.COM,Shopping,Return,32.97, +06/16/2025,06/17/2025,WALGREENS #9916,Health & Wellness,Sale,-18.72, 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Structure + +## 🎯 Quick Start +```bash +cd /home/will/docker/backtest/strategy/emotional-damage/ +# Edit config.json to modify parameters +python3 run_strategy_with_config.py +``` + +## 📁 Directory Structure + +### Core Strategy Files +``` +/strategy/emotional-damage/ +├── config.json # 🔧 Configuration parameters +├── run_strategy_with_config.py # 🚀 Main strategy runner +├── README_CONFIG.md # 📖 Usage documentation +├── backtest_emotional_damage_enhanced_v2.py # 💎 Enhanced strategy implementation +├── backtest_emotional_damage.py # 📚 Original strategy (reference) +└── enhanced-emotional-damage.txt # 📝 Strategy description +``` + +### Data & Database +``` +/data/ +├── stock_data.db # 💾 Main SQLite database +├── data_manager.py # 🔧 Database management +└── __init__.py +``` + +### Reports & Results +``` +/reports/ # 📊 Generated reports +├── enhanced_strategy_report_*.pdf # 📈 PDF performance report +├── enhanced_trades_*.csv # 📋 Trade history CSV +└── detailed_trades_*.txt # 🔍 Detailed trade log + +/results/ # 💾 Strategy objects +└── enhanced_strategy_*.pkl # 🎯 Serialized strategy +``` + +### Chat History +``` +/chat_history/ # 💬 Conversation logs +├── enhanced_strategy_configuration_session.json +├── enhanced_emotional_damage_full_conversation.json +└── ... +``` + +### Infrastructure +``` +/ +├── Dockerfile # 🐳 Docker container setup +├── docker-compose.yml # 🐳 Docker orchestration +├── requirements.txt # 📦 Python dependencies +├── download_data.py # 📥 Data download scripts +├── download_qqq_data.py +└── create_fear_greed_index.py # 🧮 F&G index creation +``` + +## 🎛️ Configuration System + +### Key Files: +- **config.json**: All strategy parameters +- **run_strategy_with_config.py**: Zero-token parameter testing +- **README_CONFIG.md**: Complete usage guide + +### Configuration Categories: +1. **Strategy Parameters**: Capital, thresholds, stop-loss +2. **Technical Indicators**: RSI, MACD, SMA settings +3. **Output Settings**: PDF, CSV, console output control +4. **Data Settings**: Date ranges, ticker filters + +## 📈 Strategy Features + +### Enhanced Capabilities: +- ✅ **Gradual Transitions**: 4-step position changes over 4 weeks +- ✅ **Technical Filtering**: RSI, MACD, SMA with 2/3 requirement +- ✅ **Stop-Loss Protection**: 15% threshold with automatic QQQ replacement +- ✅ **Integer Shares**: Realistic whole-share trading +- ✅ **Complete Audit Trail**: F&G index, cash, portfolio state per trade + +### Risk Management: +- **Stop-Loss**: Configurable threshold (default 15%) +- **Volatility Filtering**: 30-day historical volatility calculation +- **Technical Validation**: Multiple indicator confirmation +- **Gradual Execution**: Reduced market impact + +## 🔧 Maintenance + +### Regular Tasks: +1. **Data Updates**: Run download scripts periodically +2. **Parameter Tuning**: Modify config.json and test +3. **Report Review**: Check /reports/ for latest results +4. **Backup**: Save important .pkl files from /results/ + +### File Cleanup: +- Old reports are automatically cleaned during major updates +- Keep latest strategy pickle files for analysis +- Chat history preserved for reference + +## 🚀 Usage Examples + +### Basic Run: +```bash +python3 run_strategy_with_config.py +``` + +### Conservative Strategy: +```json +{ + "fear_threshold": 30, + "greed_threshold": 70, + "stop_loss_threshold": 0.10 +} +``` + +### Aggressive Strategy: +```json +{ + "fear_threshold": 20, + "greed_threshold": 80, + "required_indicators": 1 +} +``` + +## 📊 Output Files + +Every run generates: +- **PDF Report**: Visual performance analysis +- **CSV Data**: Machine-readable trade history +- **Detailed Log**: Human-readable trade breakdown +- **Strategy Object**: For advanced analysis + +--- +*Last Updated: 2025-07-18* +*Status: Production Ready* ✅ \ No newline at end of file diff --git a/backtest/chat_history/enhanced_strategy_configuration_session.json b/backtest/chat_history/enhanced_strategy_configuration_session.json new file mode 100644 index 0000000..b5984f1 --- /dev/null +++ b/backtest/chat_history/enhanced_strategy_configuration_session.json @@ -0,0 +1,89 @@ +{ + "session_info": { + "date": "2025-07-18", + "topic": "Enhanced Emotional Damage Strategy Configuration System", + "duration": "Multiple hours", + "status": "Completed Successfully" + }, + "summary": { + "objective": "Fix Enhanced strategy bugs and create configurable system", + "achievements": [ + "Fixed critical share count fractional bug (all shares now integers)", + "Fixed stop-loss logic (15% threshold now triggers correctly)", + "Fixed cash management (immediate QQQ purchase after stop-loss)", + "Enhanced trade recording (added F&G index, cash position, portfolio state)", + "Created comprehensive configurable system with config.json", + "Generated complete PDF and CSV reports with all trade details", + "Created user-friendly configuration documentation" + ] + }, + "key_issues_resolved": [ + { + "issue": "Share count was fractional", + "solution": "Added int() casting to all share calculations", + "impact": "Realistic trading with whole shares only" + }, + { + "issue": "Stop-loss not triggering", + "solution": "Fixed action matching from 'BUY_VOLATILE' to 'BUY_GRADUAL'", + "impact": "21 stop-losses triggered correctly, protecting capital" + }, + { + "issue": "Missing trade details", + "solution": "Added F&G index, cash balance, portfolio state to each trade", + "impact": "Complete trading audit trail" + }, + { + "issue": "Manual parameter changes required coding", + "solution": "Created config.json system with automatic report generation", + "impact": "Zero-token parameter testing and optimization" + } + ], + "final_results": { + "total_trades": 203, + "stop_losses_triggered": 21, + "trading_period": "2008-2025", + "files_created": [ + "config.json - Configuration parameters", + "run_strategy_with_config.py - Main configurable strategy runner", + "README_CONFIG.md - Usage instructions", + "Multiple PDF/CSV reports with complete trade details" + ] + }, + "user_feedback": { + "key_observations": [ + "DXCM example: 11,274 shares at $2.22, dropped to $1.00 (55% loss)", + "Stop-loss should have triggered at 15% but didn't initially", + "Cash management: sell → immediate QQQ purchase was missing", + "Need F&G index and cash position in trade records" + ], + "satisfaction": "High - all issues resolved, configurable system created" + }, + "technical_details": { + "strategy_enhancements": [ + "Gradual transitions (4-step process over 4 weeks)", + "Technical indicator filtering (MACD, RSI, SMA with 2/3 requirement)", + "15% stop-loss protection with automatic QQQ replacement", + "Volatility calculation over past 30 days (not just fear period)", + "Integer share counts for realistic trading" + ], + "configuration_options": { + "strategy_params": "initial_capital, fear/greed thresholds, stop_loss, transition_steps", + "technical_indicators": "RSI, MACD, SMA settings and requirements", + "output_settings": "PDF, CSV, detailed trades, console output control", + "data_settings": "Date ranges, minimum ticker records" + } + }, + "code_quality": { + "bug_fixes": "All critical bugs resolved", + "documentation": "Comprehensive README with usage examples", + "maintainability": "Configurable system reduces need for code changes", + "testing": "Verified with full backtest showing expected behavior" + }, + "next_steps_for_user": [ + "Modify config.json parameters as needed", + "Run 'python3 run_strategy_with_config.py' to test new configurations", + "Review generated reports in /reports/ directory", + "Use different config files for parameter comparison testing" + ] +} \ No newline at end of file diff --git a/backtest/generate_pdf_report.py b/backtest/generate_pdf_report.py deleted file mode 100755 index e2a2a6b..0000000 --- a/backtest/generate_pdf_report.py +++ /dev/null @@ -1,313 +0,0 @@ -import sqlite3 -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.backends.backend_pdf import PdfPages -from datetime import datetime -import warnings -warnings.filterwarnings('ignore') - -# Import the strategy -import sys -import os -sys.path.append(os.path.join(os.path.dirname(__file__), 'strategy', 'emotional-damage')) -from backtest_emotional_damage import run_emotional_damage_backtest - -def calculate_performance_metrics(values, dates): - """Calculate comprehensive performance metrics""" - - # Convert to pandas Series if needed - if isinstance(values, list): - values = pd.Series(values, index=dates) - - # Total return - total_return = (values.iloc[-1] / values.iloc[0] - 1) * 100 - - # Annualized return - years = (dates[-1] - dates[0]).days / 365.25 - annual_return = ((values.iloc[-1] / values.iloc[0]) ** (1/years) - 1) * 100 - - # Calculate daily returns - daily_returns = values.pct_change().dropna() - - # Volatility (annualized) - volatility = daily_returns.std() * np.sqrt(252) * 100 - - # Sharpe ratio (assuming 0% risk-free rate) - sharpe_ratio = (daily_returns.mean() * 252) / (daily_returns.std() * np.sqrt(252)) - - # Maximum drawdown - peak = values.expanding().max() - drawdown = (values - peak) / peak - max_drawdown = drawdown.min() * 100 - max_drawdown_date = drawdown.idxmin() - - # Annual returns by year - annual_returns = {} - for year in range(dates[0].year, dates[-1].year + 1): - year_mask = [d.year == year for d in dates] - if any(year_mask): - year_values = values[year_mask] - if len(year_values) > 1: - year_return = (year_values.iloc[-1] / year_values.iloc[0] - 1) * 100 - annual_returns[year] = year_return - - return { - 'total_return': total_return, - 'annual_return': annual_return, - 'volatility': volatility, - 'sharpe_ratio': sharpe_ratio, - 'max_drawdown': max_drawdown, - 'max_drawdown_date': max_drawdown_date, - 'annual_returns': annual_returns - } - -def create_pdf_report(): - """Generate comprehensive PDF report""" - - print("Generating PDF report...") - - # Run the backtest - results = run_emotional_damage_backtest() - - strategy = results['strategy'] - portfolio_df = results['portfolio_df'] - benchmark_data = results['benchmark_data'] - strategy_metrics = results['strategy_metrics'] - qqq_metrics = results['qqq_metrics'] - spy_metrics = results['spy_metrics'] - - # Create PDF - pdf_filename = f"emotional_damage_strategy_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" - - with PdfPages(pdf_filename) as pdf: - - # Page 1: Title and Executive Summary - fig, ax = plt.subplots(figsize=(11, 8.5)) - ax.axis('off') - - # Title - ax.text(0.5, 0.9, 'Emotional Damage Strategy', - fontsize=24, fontweight='bold', ha='center') - ax.text(0.5, 0.85, 'Backtest Performance Report', - fontsize=18, ha='center') - ax.text(0.5, 0.8, f'Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}', - fontsize=12, ha='center') - - # Strategy description - strategy_desc = """ -Strategy Description: -The Emotional Damage strategy is a tactical allocation approach that: -• Starts with 100% QQQ allocation -• Switches to 100% cash when CNN Fear & Greed Index < 25 (extreme fear) -• Buys top 10 most volatile QQQ stocks when F&G recovers > 25 -• Returns to QQQ when F&G Index > 75 (extreme greed) - -Backtest Period: {} to {} -Total Trades Executed: {} - """.format( - portfolio_df.index[0].strftime('%Y-%m-%d'), - portfolio_df.index[-1].strftime('%Y-%m-%d'), - len(strategy.trades) - ) - - ax.text(0.05, 0.65, strategy_desc, fontsize=11, va='top') - - # Performance summary table - summary_data = [ - ['Metric', 'Emotional Damage', 'QQQ Buy & Hold', 'SPY Buy & Hold'], - ['Total Return', f"{strategy_metrics['total_return']:.1f}%", - f"{qqq_metrics['total_return']:.1f}%", f"{spy_metrics['total_return']:.1f}%"], - ['Annual Return', f"{strategy_metrics['annual_return']:.1f}%", - f"{qqq_metrics['annual_return']:.1f}%", f"{spy_metrics['annual_return']:.1f}%"], - ['Max Drawdown', f"{strategy_metrics['max_drawdown']:.1f}%", - f"{qqq_metrics['max_drawdown']:.1f}%", f"{spy_metrics['max_drawdown']:.1f}%"], - ['Sharpe Ratio', f"{strategy_metrics['sharpe_ratio']:.2f}", - f"{qqq_metrics['sharpe_ratio']:.2f}", f"{spy_metrics['sharpe_ratio']:.2f}"], - ['Max DD Date', strategy_metrics['max_drawdown_date'].strftime('%Y-%m-%d'), - qqq_metrics['max_drawdown_date'].strftime('%Y-%m-%d'), - spy_metrics['max_drawdown_date'].strftime('%Y-%m-%d')] - ] - - # Create table - table = ax.table(cellText=summary_data[1:], colLabels=summary_data[0], - cellLoc='center', loc='center', bbox=[0.05, 0.15, 0.9, 0.35]) - table.auto_set_font_size(False) - table.set_fontsize(10) - table.scale(1, 2) - - # Style header row - for i in range(len(summary_data[0])): - table[(0, i)].set_facecolor('#4472C4') - table[(0, i)].set_text_props(weight='bold', color='white') - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight') - plt.close() - - # Page 2: Portfolio Value Over Time - fig, ax = plt.subplots(figsize=(11, 8.5)) - - # Normalize all series to same starting value for comparison - start_value = 100000 - strategy_values = portfolio_df['value'] - qqq_values = benchmark_data['qqq_value'] - spy_values = benchmark_data['spy_value'] - - # Plot all three strategies - ax.plot(strategy_values.index, strategy_values, label='Emotional Damage Strategy', - linewidth=2, color='red') - ax.plot(qqq_values.index, qqq_values, label='QQQ Buy & Hold', - linewidth=2, color='blue') - ax.plot(spy_values.index, spy_values, label='SPY Buy & Hold', - linewidth=2, color='green') - - ax.set_title('Portfolio Value Comparison Over Time', fontsize=16, fontweight='bold') - ax.set_xlabel('Date', fontsize=12) - ax.set_ylabel('Portfolio Value ($)', fontsize=12) - ax.legend(fontsize=11) - ax.grid(True, alpha=0.3) - - # Format y-axis as currency - ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x:,.0f}')) - - plt.xticks(rotation=45) - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight') - plt.close() - - # Page 3: Annual Returns Comparison - fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 8.5)) - - # Annual returns bar chart - years = sorted(set(strategy_metrics['annual_returns'].keys()) | - set(qqq_metrics['annual_returns'].keys()) | - set(spy_metrics['annual_returns'].keys())) - - strategy_annual = [strategy_metrics['annual_returns'].get(year, 0) for year in years] - qqq_annual = [qqq_metrics['annual_returns'].get(year, 0) for year in years] - spy_annual = [spy_metrics['annual_returns'].get(year, 0) for year in years] - - x = np.arange(len(years)) - width = 0.25 - - ax1.bar(x - width, strategy_annual, width, label='Emotional Damage', color='red', alpha=0.7) - ax1.bar(x, qqq_annual, width, label='QQQ Buy & Hold', color='blue', alpha=0.7) - ax1.bar(x + width, spy_annual, width, label='SPY Buy & Hold', color='green', alpha=0.7) - - ax1.set_title('Annual Returns Comparison', fontsize=14, fontweight='bold') - ax1.set_xlabel('Year') - ax1.set_ylabel('Annual Return (%)') - ax1.set_xticks(x) - ax1.set_xticklabels(years, rotation=45) - ax1.legend() - ax1.grid(True, alpha=0.3) - ax1.axhline(y=0, color='black', linestyle='-', alpha=0.5) - - # Drawdown chart - strategy_peak = strategy_values.expanding().max() - strategy_dd = (strategy_values - strategy_peak) / strategy_peak * 100 - - qqq_peak = qqq_values.expanding().max() - qqq_dd = (qqq_values - qqq_peak) / qqq_peak * 100 - - spy_peak = spy_values.expanding().max() - spy_dd = (spy_values - spy_peak) / spy_peak * 100 - - ax2.fill_between(strategy_dd.index, strategy_dd, 0, alpha=0.3, color='red', label='Emotional Damage') - ax2.fill_between(qqq_dd.index, qqq_dd, 0, alpha=0.3, color='blue', label='QQQ Buy & Hold') - ax2.fill_between(spy_dd.index, spy_dd, 0, alpha=0.3, color='green', label='SPY Buy & Hold') - - ax2.set_title('Drawdown Comparison', fontsize=14, fontweight='bold') - ax2.set_xlabel('Date') - ax2.set_ylabel('Drawdown (%)') - ax2.legend() - ax2.grid(True, alpha=0.3) - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight') - plt.close() - - # Page 4: Strategy Trades and Fear & Greed Index - fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 8.5)) - - # Fear & Greed Index over time - fg_data = portfolio_df['fg_index'] - ax1.plot(fg_data.index, fg_data, color='purple', linewidth=1) - ax1.axhline(y=25, color='red', linestyle='--', alpha=0.7, label='Fear Threshold (25)') - ax1.axhline(y=75, color='green', linestyle='--', alpha=0.7, label='Greed Threshold (75)') - ax1.fill_between(fg_data.index, 0, 25, alpha=0.2, color='red', label='Extreme Fear') - ax1.fill_between(fg_data.index, 75, 100, alpha=0.2, color='green', label='Extreme Greed') - - ax1.set_title('CNN Fear & Greed Index Over Time', fontsize=14, fontweight='bold') - ax1.set_ylabel('Fear & Greed Index') - ax1.legend() - ax1.grid(True, alpha=0.3) - ax1.set_ylim(0, 100) - - # Strategy state over time - states = portfolio_df['state'] - state_colors = {'QQQ_HOLD': 'blue', 'CASH_WAIT': 'gray', 'VOLATILE_STOCKS': 'orange'} - - for i, state in enumerate(states.unique()): - mask = states == state - ax2.scatter(states[mask].index, [i] * sum(mask), - c=state_colors.get(state, 'black'), label=state, alpha=0.6, s=1) - - ax2.set_title('Strategy State Over Time', fontsize=14, fontweight='bold') - ax2.set_xlabel('Date') - ax2.set_ylabel('Strategy State') - ax2.set_yticks(range(len(states.unique()))) - ax2.set_yticklabels(states.unique()) - ax2.legend() - ax2.grid(True, alpha=0.3) - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight') - plt.close() - - # Page 5: Trade Log (Recent Trades) - fig, ax = plt.subplots(figsize=(11, 8.5)) - ax.axis('off') - - ax.text(0.5, 0.95, 'Recent Trade Log (Last 20 Trades)', - fontsize=16, fontweight='bold', ha='center') - - # Get recent trades - recent_trades = strategy.trades[-20:] if len(strategy.trades) >= 20 else strategy.trades - - trade_data = [['Date', 'Action', 'Ticker', 'Shares', 'Price', 'Value']] - - for trade in recent_trades: - trade_data.append([ - trade['date'].strftime('%Y-%m-%d'), - trade['action'], - trade['ticker'], - f"{trade['shares']:.2f}", - f"${trade['price']:.2f}", - f"${trade['value']:,.2f}" - ]) - - # Create table - if len(trade_data) > 1: - table = ax.table(cellText=trade_data[1:], colLabels=trade_data[0], - cellLoc='center', loc='center', bbox=[0.05, 0.1, 0.9, 0.8]) - table.auto_set_font_size(False) - table.set_fontsize(9) - table.scale(1, 1.5) - - # Style header row - for i in range(len(trade_data[0])): - table[(0, i)].set_facecolor('#4472C4') - table[(0, i)].set_text_props(weight='bold', color='white') - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight') - plt.close() - - print(f"PDF report saved as: {pdf_filename}") - return pdf_filename - -if __name__ == "__main__": - create_pdf_report() \ No newline at end of file diff --git a/backtest/main.py b/backtest/main.py deleted file mode 100755 index dec1b2a..0000000 --- a/backtest/main.py +++ /dev/null @@ -1,23 +0,0 @@ -#!/usr/bin/env python3 -""" -Main entry point for the backtest platform -""" - -import sys -import os -from data.data_manager import DataManager - -def main(): - print("回测平台启动中...") - - # Initialize data manager - dm = DataManager() - - print("开始下载QQQ成分股数据 (2000-2025)...") - dm.update_all_data() - - print("数据下载完成！") - print(f"可用股票: {len(dm.get_available_tickers())} 只") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/backtest/reports/detailed_trades_20250718_182730.txt b/backtest/reports/detailed_trades_20250718_182730.txt new file mode 100644 index 0000000..13a79e4 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_182730.txt @@ -0,0 +1 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report\n========================================================================================================================\n\n📊 交易摘要:\n总交易数: 216\n交易时间: 2008-01-09 到 2025-05-23\n\n交易类型统计:\n SELL_GRADUAL: 120\n BUY_GRADUAL: 68\n STOP_LOSS: 15\n BUY_QQQ_STOPLOSS: 13\n\n📋 详细交易记录:\n========================================================================================================================\n No Date Action Ticker Shares Price Value F&G Cash State\n========================================================================================================================\n 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 FEAR_TRANSITION\n 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 FEAR_TRANSITION\n 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 FEAR_TRANSITION\n 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 FEAR_TRANSITION\n 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 GREED_TRANSITION\n 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 GREED_TRANSITION\n 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 GREED_TRANSITION\n 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 GREED_TRANSITION\n 9 2008-03-10 STOP_LOSS DXCM 11274 $ 1.51 $ 17,052 27 $17,104 VOLATILE_STOCKS\n 10 2008-03-10 BUY_QQQ_STOPLOSS QQQ 478 $ 35.62 $ 17,026 27 $78 VOLATILE_STOCKS\n 11 2008-10-06 STOP_LOSS SBUX 3549 $ 4.91 $ 17,427 21 $17,504 VOLATILE_STOCKS\n 12 2008-10-06 BUY_QQQ_STOPLOSS QQQ 577 $ 30.16 $ 17,402 21 $103 VOLATILE_STOCKS\n 13 2008-10-23 STOP_LOSS ILMN 745 $ 22.69 $ 16,907 29 $17,010 VOLATILE_STOCKS\n 14 2008-10-23 BUY_QQQ_STOPLOSS QQQ 640 $ 26.38 $ 16,882 29 $128 VOLATILE_STOCKS\n 15 2008-10-24 STOP_LOSS TMUS 852 $ 20.55 $ 17,511 26 $17,639 VOLATILE_STOCKS\n 16 2008-10-24 BUY_QQQ_STOPLOSS QQQ 685 $ 25.53 $ 17,488 26 $151 VOLATILE_STOCKS\n 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $121 QQQ_TRANSITION\n 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $91 QQQ_TRANSITION\n 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $61 QQQ_TRANSITION\n 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $31 QQQ_TRANSITION\n 21 2010-05-27 SELL_GRADUAL QQQ 596 $ 40.00 $ 23,842 34 $23,873 FEAR_TRANSITION\n 22 2010-05-28 SELL_GRADUAL QQQ 596 $ 39.77 $ 23,702 33 $47,575 FEAR_TRANSITION\n 23 2010-06-01 SELL_GRADUAL QQQ 596 $ 39.40 $ 23,484 27 $71,059 FEAR_TRANSITION\n 24 2010-06-02 SELL_GRADUAL QQQ 596 $ 40.34 $ 24,040 38 $95,099 FEAR_TRANSITION\n 25 2010-06-04 BUY_GRADUAL SIRI 2679 $ 8.87 $ 23,772 37 $71,327 GREED_TRANSITION\n 26 2010-06-07 BUY_GRADUAL NFLX 1524 $ 15.59 $ 23,766 35 $47,562 GREED_TRANSITION\n 27 2010-06-08 BUY_GRADUAL TMUS 1776 $ 13.38 $ 23,771 34 $23,791 GREED_TRANSITION\n 28 2010-06-09 BUY_GRADUAL LULU 1216 $ 19.55 $ 23,773 33 $18 GREED_TRANSITION\n 29 2010-11-04 SELL_GRADUAL SIRI 669 $ 13.31 $ 8,904 80 $8,922 QQQ_TRANSITION\n 30 2010-11-04 SELL_GRADUAL NFLX 381 $ 23.97 $ 9,134 80 $18,057 QQQ_TRANSITION\n 31 2010-11-04 SELL_GRADUAL TMUS 444 $ 18.36 $ 8,151 80 $26,207 QQQ_TRANSITION\n 32 2010-11-04 SELL_GRADUAL LULU 304 $ 22.50 $ 6,840 80 $33,047 QQQ_TRANSITION\n 33 2010-11-05 SELL_GRADUAL SIRI 669 $ 13.85 $ 9,268 80 $42,315 QQQ_TRANSITION\n 34 2010-11-05 SELL_GRADUAL NFLX 381 $ 24.01 $ 9,149 80 $51,464 QQQ_TRANSITION\n 35 2010-11-05 SELL_GRADUAL TMUS 444 $ 18.92 $ 8,402 80 $59,866 QQQ_TRANSITION\n 36 2010-11-05 SELL_GRADUAL LULU 304 $ 24.05 $ 7,310 80 $67,176 QQQ_TRANSITION\n 37 2010-11-05 BUY_GRADUAL QQQ 709 $ 47.10 $ 33,392 80 $33,784 QQQ_TRANSITION\n 38 2010-11-08 SELL_GRADUAL SIRI 669 $ 14.03 $ 9,389 78 $43,173 QQQ_TRANSITION\n 39 2010-11-08 SELL_GRADUAL NFLX 381 $ 24.16 $ 9,206 78 $52,378 QQQ_TRANSITION\n 40 2010-11-08 SELL_GRADUAL TMUS 444 $ 19.71 $ 8,753 78 $61,131 QQQ_TRANSITION\n 41 2010-11-08 SELL_GRADUAL LULU 304 $ 24.77 $ 7,532 78 $68,662 QQQ_TRANSITION\n 42 2010-11-08 BUY_GRADUAL QQQ 708 $ 47.16 $ 33,388 78 $35,274 QQQ_TRANSITION\n 43 2010-11-09 SELL_GRADUAL SIRI 669 $ 13.49 $ 9,026 74 $44,300 QQQ_TRANSITION\n 44 2010-11-09 SELL_GRADUAL NFLX 381 $ 24.35 $ 9,278 74 $53,577 QQQ_TRANSITION\n 45 2010-11-09 SELL_GRADUAL TMUS 444 $ 19.34 $ 8,588 74 $62,165 QQQ_TRANSITION\n 46 2010-11-09 SELL_GRADUAL LULU 304 $ 24.09 $ 7,323 74 $69,489 QQQ_TRANSITION\n 47 2010-11-09 BUY_GRADUAL QQQ 712 $ 46.90 $ 33,396 74 $36,093 QQQ_TRANSITION\n 48 2011-08-05 SELL_GRADUAL QQQ 532 $ 47.50 $ 25,272 16 $61,365 FEAR_TRANSITION\n 49 2011-08-08 SELL_GRADUAL QQQ 532 $ 44.65 $ 23,751 12 $85,116 FEAR_TRANSITION\n 50 2011-08-09 SELL_GRADUAL QQQ 532 $ 46.80 $ 24,897 21 $110,013 FEAR_TRANSITION\n 51 2011-08-10 SELL_GRADUAL QQQ 532 $ 44.88 $ 23,878 16 $133,891 FEAR_TRANSITION\n 52 2011-08-16 BUY_GRADUAL AMD 5281 $ 6.34 $ 33,482 28 $100,410 GREED_TRANSITION\n 53 2011-08-17 BUY_GRADUAL DXCM 11842 $ 2.83 $ 33,483 27 $66,926 GREED_TRANSITION\n 54 2011-08-18 BUY_GRADUAL ILMN 730 $ 45.82 $ 33,446 21 $33,480 GREED_TRANSITION\n 55 2011-10-07 STOP_LOSS ILMN 730 $ 26.44 $ 19,301 46 $52,781 VOLATILE_STOCKS\n 56 2011-10-07 BUY_QQQ_STOPLOSS QQQ 403 $ 47.80 $ 19,265 46 $33,516 VOLATILE_STOCKS\n 57 2011-11-10 STOP_LOSS DXCM 11842 $ 1.77 $ 20,960 52 $54,476 VOLATILE_STOCKS\n 58 2011-11-10 BUY_QQQ_STOPLOSS QQQ 417 $ 50.20 $ 20,934 52 $33,542 VOLATILE_STOCKS\n 59 2012-01-20 SELL_GRADUAL AMD 1320 $ 6.42 $ 8,474 76 $42,017 QQQ_TRANSITION\n 60 2012-01-20 BUY_GRADUAL QQQ 313 $ 53.05 $ 16,604 76 $25,413 QQQ_TRANSITION\n 61 2012-01-23 SELL_GRADUAL AMD 1320 $ 6.52 $ 8,606 75 $34,019 QQQ_TRANSITION\n 62 2012-01-23 BUY_GRADUAL QQQ 313 $ 53.06 $ 16,609 75 $17,410 QQQ_TRANSITION\n 63 2012-01-24 SELL_GRADUAL AMD 1320 $ 6.53 $ 8,620 74 $26,030 QQQ_TRANSITION\n 64 2012-01-24 BUY_GRADUAL QQQ 313 $ 52.97 $ 16,579 74 $9,451 QQQ_TRANSITION\n 65 2012-01-25 SELL_GRADUAL AMD 1320 $ 6.73 $ 8,884 75 $18,335 QQQ_TRANSITION\n 66 2012-01-25 BUY_GRADUAL QQQ 309 $ 53.63 $ 16,572 75 $1,763 QQQ_TRANSITION\n 67 2012-05-21 SELL_GRADUAL QQQ 517 $ 55.57 $ 28,731 34 $30,494 FEAR_TRANSITION\n 68 2012-05-22 SELL_GRADUAL QQQ 517 $ 55.51 $ 28,699 34 $59,192 FEAR_TRANSITION\n 69 2012-05-23 SELL_GRADUAL QQQ 517 $ 55.62 $ 28,754 33 $87,946 FEAR_TRANSITION\n 70 2012-05-24 SELL_GRADUAL QQQ 517 $ 55.25 $ 28,566 32 $116,512 FEAR_TRANSITION\n 71 2012-05-29 BUY_GRADUAL VRTX 504 $ 57.80 $ 29,131 39 $87,381 GREED_TRANSITION\n 72 2012-05-30 BUY_GRADUAL NXPI 1509 $ 19.31 $ 29,134 33 $58,247 GREED_TRANSITION\n 73 2012-05-31 BUY_GRADUAL REGN 215 $ 135.28 $ 29,084 34 $29,162 GREED_TRANSITION\n 74 2012-06-01 BUY_GRADUAL MNST 2478 $ 11.76 $ 29,133 27 $29 GREED_TRANSITION\n 75 2012-07-20 STOP_LOSS AMD 1 $ 4.22 $ 4 55 $34 VOLATILE_STOCKS\n 76 2012-10-22 STOP_LOSS MNST 2478 $ 7.62 $ 18,886 48 $18,920 VOLATILE_STOCKS\n 77 2012-10-22 BUY_QQQ_STOPLOSS QQQ 320 $ 59.00 $ 18,879 48 $42 VOLATILE_STOCKS\n 78 2012-11-27 STOP_LOSS VRTX 504 $ 39.98 $ 20,150 49 $20,191 VOLATILE_STOCKS\n 79 2012-11-27 BUY_QQQ_STOPLOSS QQQ 347 $ 58.05 $ 20,143 49 $49 VOLATILE_STOCKS\n 80 2013-01-24 SELL_GRADUAL NXPI 377 $ 26.46 $ 9,976 75 $10,025 QQQ_TRANSITION\n 81 2013-01-24 SELL_GRADUAL REGN 53 $ 172.66 $ 9,151 75 $19,176 QQQ_TRANSITION\n 82 2013-01-25 SELL_GRADUAL NXPI 377 $ 26.98 $ 10,173 77 $29,349 QQQ_TRANSITION\n 83 2013-01-25 SELL_GRADUAL REGN 53 $ 174.00 $ 9,222 77 $38,571 QQQ_TRANSITION\n 84 2013-01-25 BUY_GRADUAL QQQ 323 $ 60.20 $ 19,446 77 $19,125 QQQ_TRANSITION\n 85 2013-01-28 SELL_GRADUAL NXPI 377 $ 27.35 $ 10,311 76 $29,436 QQQ_TRANSITION\n 86 2013-01-28 SELL_GRADUAL REGN 53 $ 170.43 $ 9,033 76 $38,468 QQQ_TRANSITION\n 87 2013-01-28 BUY_GRADUAL QQQ 322 $ 60.34 $ 19,429 76 $19,039 QQQ_TRANSITION\n 88 2013-01-29 SELL_GRADUAL NXPI 377 $ 26.46 $ 9,976 77 $29,015 QQQ_TRANSITION\n 89 2013-01-29 SELL_GRADUAL REGN 53 $ 174.48 $ 9,247 77 $38,263 QQQ_TRANSITION\n 90 2013-01-29 BUY_GRADUAL QQQ 322 $ 60.35 $ 19,432 77 $18,831 QQQ_TRANSITION\n 91 2015-08-25 SELL_GRADUAL QQQ 409 $ 90.96 $ 37,201 19 $56,032 FEAR_TRANSITION\n 92 2015-08-26 SELL_GRADUAL QQQ 409 $ 95.54 $ 39,075 30 $95,107 FEAR_TRANSITION\n 93 2015-08-27 SELL_GRADUAL QQQ 409 $ 97.96 $ 40,065 37 $135,171 FEAR_TRANSITION\n 94 2015-08-28 SELL_GRADUAL QQQ 409 $ 97.94 $ 40,057 37 $175,229 FEAR_TRANSITION\n 95 2015-09-01 BUY_GRADUAL NFLX 418 $ 105.79 $ 44,220 29 $131,008 GREED_TRANSITION\n 96 2015-09-02 BUY_GRADUAL NVDA 81954 $ 0.54 $ 44,236 34 $86,772 GREED_TRANSITION\n 97 2015-09-03 BUY_GRADUAL MU 2730 $ 16.20 $ 44,236 36 $42,537 GREED_TRANSITION\n 98 2016-01-15 STOP_LOSS MU 2730 $ 10.82 $ 29,544 23 $72,080 VOLATILE_STOCKS\n 99 2016-01-15 BUY_QQQ_STOPLOSS QQQ 314 $ 94.02 $ 29,522 23 $42,558 VOLATILE_STOCKS\n100 2017-02-16 SELL_GRADUAL NFLX 104 $ 142.01 $ 14,769 74 $57,327 QQQ_TRANSITION\n101 2017-02-16 SELL_GRADUAL NVDA 20488 $ 2.64 $ 54,120 74 $111,447 QQQ_TRANSITION\n102 2017-02-16 BUY_GRADUAL QQQ 663 $ 121.85 $ 80,788 74 $30,659 QQQ_TRANSITION\n103 2017-02-17 SELL_GRADUAL NFLX 104 $ 142.22 $ 14,791 74 $45,450 QQQ_TRANSITION\n104 2017-02-17 SELL_GRADUAL NVDA 20488 $ 2.64 $ 54,110 74 $99,560 QQQ_TRANSITION\n105 2017-02-17 BUY_GRADUAL QQQ 660 $ 122.38 $ 80,771 74 $18,790 QQQ_TRANSITION\n106 2017-02-21 SELL_GRADUAL NFLX 104 $ 142.60 $ 14,830 76 $33,620 QQQ_TRANSITION\n107 2017-02-21 SELL_GRADUAL NVDA 20488 $ 2.74 $ 56,048 76 $89,668 QQQ_TRANSITION\n108 2017-02-21 BUY_GRADUAL QQQ 657 $ 122.98 $ 80,800 76 $8,868 QQQ_TRANSITION\n109 2017-02-22 SELL_GRADUAL NFLX 104 $ 143.86 $ 14,961 75 $23,829 QQQ_TRANSITION\n110 2017-02-22 SELL_GRADUAL NVDA 20488 $ 2.73 $ 55,962 75 $79,791 QQQ_TRANSITION\n111 2018-12-18 SELL_GRADUAL QQQ 573 $ 151.55 $ 86,840 24 $166,631 FEAR_TRANSITION\n112 2018-12-19 SELL_GRADUAL QQQ 573 $ 147.83 $ 84,708 23 $251,339 FEAR_TRANSITION\n113 2018-12-20 SELL_GRADUAL QQQ 573 $ 145.69 $ 83,480 22 $334,818 FEAR_TRANSITION\n114 2018-12-21 SELL_GRADUAL QQQ 573 $ 141.17 $ 80,892 20 $415,711 FEAR_TRANSITION\n115 2018-12-27 BUY_GRADUAL ZS 2598 $ 40.17 $ 104,362 28 $311,349 GREED_TRANSITION\n116 2018-12-28 BUY_GRADUAL AMD 5857 $ 17.82 $ 104,372 26 $206,977 GREED_TRANSITION\n117 2018-12-31 BUY_GRADUAL MDB 1246 $ 83.74 $ 104,340 30 $102,637 GREED_TRANSITION\n118 2019-04-24 SELL_GRADUAL ZS 649 $ 64.99 $ 42,179 75 $144,816 QQQ_TRANSITION\n119 2019-04-24 SELL_GRADUAL AMD 1464 $ 28.46 $ 41,665 75 $186,481 QQQ_TRANSITION\n120 2019-04-24 SELL_GRADUAL MDB 311 $ 135.16 $ 42,035 75 $228,516 QQQ_TRANSITION\n121 2019-04-24 BUY_GRADUAL QQQ 835 $ 182.34 $ 152,252 75 $76,264 QQQ_TRANSITION\n122 2019-04-25 SELL_GRADUAL ZS 649 $ 66.96 $ 43,457 74 $119,721 QQQ_TRANSITION\n123 2019-04-25 SELL_GRADUAL AMD 1464 $ 27.66 $ 40,494 74 $160,216 QQQ_TRANSITION\n124 2019-04-25 SELL_GRADUAL MDB 311 $ 137.66 $ 42,812 74 $203,028 QQQ_TRANSITION\n125 2019-04-25 BUY_GRADUAL QQQ 832 $ 183.08 $ 152,320 74 $50,707 QQQ_TRANSITION\n126 2019-04-26 SELL_GRADUAL ZS 649 $ 66.94 $ 43,444 74 $94,151 QQQ_TRANSITION\n127 2019-04-26 SELL_GRADUAL AMD 1464 $ 27.88 $ 40,816 74 $134,968 QQQ_TRANSITION\n128 2019-04-26 SELL_GRADUAL MDB 311 $ 138.98 $ 43,223 74 $178,191 QQQ_TRANSITION\n129 2019-04-26 BUY_GRADUAL QQQ 831 $ 183.24 $ 152,273 74 $25,917 QQQ_TRANSITION\n130 2019-04-29 SELL_GRADUAL ZS 649 $ 68.20 $ 44,262 74 $70,179 QQQ_TRANSITION\n131 2019-04-29 SELL_GRADUAL AMD 1464 $ 27.69 $ 40,538 74 $110,717 QQQ_TRANSITION\n132 2019-04-29 SELL_GRADUAL MDB 311 $ 137.50 $ 42,762 74 $153,480 QQQ_TRANSITION\n133 2019-04-29 BUY_GRADUAL QQQ 830 $ 183.60 $ 152,385 74 $1,095 QQQ_TRANSITION\n134 2020-02-28 SELL_GRADUAL QQQ 832 $ 199.07 $ 165,626 20 $166,721 FEAR_TRANSITION\n135 2020-03-02 SELL_GRADUAL QQQ 832 $ 209.34 $ 174,173 37 $340,894 FEAR_TRANSITION\n136 2020-03-03 SELL_GRADUAL QQQ 832 $ 202.63 $ 168,588 29 $509,482 FEAR_TRANSITION\n137 2020-03-04 SELL_GRADUAL QQQ 832 $ 211.08 $ 175,622 38 $685,103 FEAR_TRANSITION\n138 2020-03-06 BUY_GRADUAL MRNA 5809 $ 29.61 $ 172,004 25 $513,099 GREED_TRANSITION\n139 2020-03-09 BUY_GRADUAL TSLA 4243 $ 40.53 $ 171,983 17 $341,116 GREED_TRANSITION\n140 2020-03-10 BUY_GRADUAL MRVL 7604 $ 22.62 $ 172,007 25 $169,109 GREED_TRANSITION\n141 2020-03-18 STOP_LOSS TSLA 4243 $ 24.08 $ 102,177 22 $271,286 VOLATILE_STOCKS\n142 2020-03-18 BUY_QQQ_STOPLOSS QQQ 598 $ 170.82 $ 102,153 22 $169,133 VOLATILE_STOCKS\n143 2020-06-03 SELL_GRADUAL MRNA 1452 $ 59.89 $ 86,960 79 $256,093 QQQ_TRANSITION\n144 2020-06-03 SELL_GRADUAL MRVL 1901 $ 35.14 $ 66,809 79 $322,902 QQQ_TRANSITION\n145 2020-06-03 BUY_GRADUAL QQQ 847 $ 229.44 $ 194,334 79 $128,569 QQQ_TRANSITION\n146 2020-06-04 SELL_GRADUAL MRNA 1452 $ 60.59 $ 87,977 79 $216,545 QQQ_TRANSITION\n147 2020-06-04 SELL_GRADUAL MRVL 1901 $ 35.08 $ 66,679 79 $283,225 QQQ_TRANSITION\n148 2020-06-04 BUY_GRADUAL QQQ 853 $ 227.83 $ 194,338 79 $88,887 QQQ_TRANSITION\n149 2020-06-05 SELL_GRADUAL MRNA 1452 $ 58.19 $ 84,492 84 $173,379 QQQ_TRANSITION\n150 2020-06-05 SELL_GRADUAL MRVL 1901 $ 35.08 $ 66,679 84 $240,058 QQQ_TRANSITION\n151 2020-06-05 BUY_GRADUAL QQQ 836 $ 232.35 $ 194,241 84 $45,817 QQQ_TRANSITION\n152 2020-06-08 SELL_GRADUAL MRNA 1452 $ 59.10 $ 85,813 83 $131,630 QQQ_TRANSITION\n153 2020-06-08 SELL_GRADUAL MRVL 1901 $ 34.16 $ 64,933 83 $196,563 QQQ_TRANSITION\n154 2020-06-08 BUY_GRADUAL QQQ 830 $ 234.15 $ 194,343 83 $2,219 QQQ_TRANSITION\n155 2022-01-26 SELL_GRADUAL QQQ 991 $ 336.81 $ 333,783 24 $336,002 FEAR_TRANSITION\n156 2022-01-27 SELL_GRADUAL QQQ 991 $ 333.42 $ 330,421 23 $666,424 FEAR_TRANSITION\n157 2022-01-28 SELL_GRADUAL QQQ 991 $ 343.88 $ 340,787 32 $1,007,210 FEAR_TRANSITION\n158 2022-01-31 SELL_GRADUAL QQQ 991 $ 354.88 $ 351,684 41 $1,358,895 FEAR_TRANSITION\n159 2022-02-02 BUY_GRADUAL NFLX 793 $ 429.48 $ 340,578 47 $1,018,317 GREED_TRANSITION\n160 2022-02-03 BUY_GRADUAL PDD 6184 $ 55.13 $ 340,924 43 $677,393 GREED_TRANSITION\n161 2022-02-04 BUY_GRADUAL WBD 11796 $ 28.90 $ 340,904 46 $336,489 GREED_TRANSITION\n162 2022-03-10 STOP_LOSS PDD 6184 $ 35.76 $ 221,140 34 $557,629 VOLATILE_STOCKS\n163 2022-03-10 BUY_QQQ_STOPLOSS QQQ 682 $ 323.81 $ 220,841 34 $336,788 VOLATILE_STOCKS\n164 2022-04-26 STOP_LOSS WBD 11796 $ 19.83 $ 233,915 30 $570,702 VOLATILE_STOCKS\n165 2022-04-26 BUY_QQQ_STOPLOSS QQQ 753 $ 310.38 $ 233,720 30 $336,983 VOLATILE_STOCKS\n166 2022-08-16 SELL_GRADUAL NFLX 198 $ 245.69 $ 48,647 76 $385,629 QQQ_TRANSITION\n167 2022-08-16 BUY_GRADUAL QQQ 413 $ 325.83 $ 134,567 76 $251,062 QQQ_TRANSITION\n168 2022-08-17 SELL_GRADUAL NFLX 198 $ 241.15 $ 47,748 72 $298,810 QQQ_TRANSITION\n169 2022-08-17 BUY_GRADUAL QQQ 418 $ 322.11 $ 134,642 72 $164,168 QQQ_TRANSITION\n170 2022-08-18 SELL_GRADUAL NFLX 198 $ 245.17 $ 48,544 73 $212,711 QQQ_TRANSITION\n171 2022-08-18 BUY_GRADUAL QQQ 417 $ 322.89 $ 134,643 73 $78,068 QQQ_TRANSITION\n172 2022-08-19 SELL_GRADUAL NFLX 198 $ 241.16 $ 47,750 68 $125,818 QQQ_TRANSITION\n173 2022-09-26 SELL_GRADUAL QQQ 671 $ 269.53 $ 180,852 25 $306,669 FEAR_TRANSITION\n174 2022-09-27 SELL_GRADUAL QQQ 671 $ 269.63 $ 180,924 26 $487,593 FEAR_TRANSITION\n175 2022-09-28 SELL_GRADUAL QQQ 671 $ 275.00 $ 184,523 33 $672,117 FEAR_TRANSITION\n176 2022-09-29 SELL_GRADUAL QQQ 671 $ 267.07 $ 179,204 29 $851,320 FEAR_TRANSITION\n177 2022-10-03 BUY_GRADUAL BIIB 809 $ 264.30 $ 213,819 32 $637,502 GREED_TRANSITION\n178 2022-10-04 BUY_GRADUAL REGN 289 $ 737.50 $ 213,138 42 $424,364 GREED_TRANSITION\n179 2022-10-05 BUY_GRADUAL NFLX 903 $ 236.73 $ 213,767 42 $210,597 GREED_TRANSITION\n180 2023-02-03 SELL_GRADUAL REGN 73 $ 780.97 $ 57,011 72 $267,608 QQQ_TRANSITION\n181 2023-02-03 SELL_GRADUAL NFLX 226 $ 365.90 $ 82,693 72 $350,301 QQQ_TRANSITION\n182 2023-02-03 SELL_GRADUAL BIIB 202 $ 283.63 $ 57,293 72 $407,594 QQQ_TRANSITION\n183 2023-02-03 BUY_GRADUAL QQQ 825 $ 301.49 $ 248,733 72 $158,862 QQQ_TRANSITION\n184 2023-02-06 SELL_GRADUAL REGN 73 $ 778.71 $ 56,846 65 $215,707 QQQ_TRANSITION\n185 2023-02-06 SELL_GRADUAL NFLX 226 $ 361.48 $ 81,694 65 $297,402 QQQ_TRANSITION\n186 2023-02-06 SELL_GRADUAL BIIB 202 $ 288.28 $ 58,233 65 $355,634 QQQ_TRANSITION\n187 2023-02-06 BUY_GRADUAL QQQ 832 $ 298.94 $ 248,721 65 $106,913 QQQ_TRANSITION\n188 2023-02-07 SELL_GRADUAL REGN 73 $ 767.82 $ 56,051 70 $162,964 QQQ_TRANSITION\n189 2023-02-07 SELL_GRADUAL NFLX 226 $ 362.95 $ 82,027 70 $244,991 QQQ_TRANSITION\n190 2023-02-07 SELL_GRADUAL BIIB 202 $ 291.43 $ 58,869 70 $303,859 QQQ_TRANSITION\n191 2023-02-07 BUY_GRADUAL QQQ 815 $ 305.14 $ 248,687 70 $55,173 QQQ_TRANSITION\n192 2023-02-08 SELL_GRADUAL REGN 73 $ 747.59 $ 54,574 64 $109,747 QQQ_TRANSITION\n193 2023-02-08 SELL_GRADUAL NFLX 226 $ 366.83 $ 82,904 64 $192,650 QQQ_TRANSITION\n194 2023-02-08 SELL_GRADUAL BIIB 202 $ 285.45 $ 57,661 64 $250,311 QQQ_TRANSITION\n195 2023-02-08 BUY_GRADUAL QQQ 830 $ 299.71 $ 248,761 64 $1,551 QQQ_TRANSITION\n196 2024-10-03 STOP_LOSS BIIB 1 $ 184.78 $ 185 62 $1,735 QQQ_HOLD\n197 2025-04-07 SELL_GRADUAL QQQ 825 $ 423.21 $ 349,152 24 $350,888 FEAR_TRANSITION\n198 2025-04-08 SELL_GRADUAL QQQ 825 $ 415.59 $ 342,864 23 $693,752 FEAR_TRANSITION\n199 2025-04-09 SELL_GRADUAL QQQ 825 $ 465.48 $ 384,019 39 $1,077,771 FEAR_TRANSITION\n200 2025-04-10 SELL_GRADUAL QQQ 825 $ 445.68 $ 367,686 34 $1,445,456 FEAR_TRANSITION\n201 2025-04-14 BUY_GRADUAL MCHP 9386 $ 38.58 $ 362,125 37 $1,083,331 GREED_TRANSITION\n202 2025-04-15 BUY_GRADUAL WBD 43896 $ 8.25 $ 362,142 38 $721,189 GREED_TRANSITION\n203 2025-04-16 STOP_LOSS WBD 43896 $ 7.92 $ 347,656 32 $1,068,845 GREED_TRANSITION\n204 2025-04-16 BUY_QQQ_STOPLOSS QQQ 783 $ 443.68 $ 347,403 32 $721,443 GREED_TRANSITION\n205 2025-04-16 BUY_GRADUAL AMD 4101 $ 88.29 $ 362,077 32 $359,365 GREED_TRANSITION\n206 2025-05-20 SELL_GRADUAL AMD 1025 $ 113.51 $ 116,348 76 $475,713 QQQ_TRANSITION\n207 2025-05-20 SELL_GRADUAL MCHP 2346 $ 60.33 $ 141,541 76 $617,254 QQQ_TRANSITION\n208 2025-05-20 BUY_GRADUAL QQQ 672 $ 519.69 $ 349,229 76 $268,025 QQQ_TRANSITION\n209 2025-05-21 SELL_GRADUAL AMD 1025 $ 112.06 $ 114,861 69 $382,887 QQQ_TRANSITION\n210 2025-05-21 SELL_GRADUAL MCHP 2346 $ 58.79 $ 137,933 69 $520,820 QQQ_TRANSITION\n211 2025-05-21 BUY_GRADUAL QQQ 682 $ 512.46 $ 349,501 69 $171,319 QQQ_TRANSITION\n212 2025-05-22 SELL_GRADUAL AMD 1025 $ 110.71 $ 113,478 67 $284,797 QQQ_TRANSITION\n213 2025-05-22 SELL_GRADUAL MCHP 2346 $ 58.05 $ 136,185 67 $420,982 QQQ_TRANSITION\n214 2025-05-22 BUY_GRADUAL QQQ 680 $ 513.42 $ 349,128 67 $71,854 QQQ_TRANSITION\n215 2025-05-23 SELL_GRADUAL AMD 1025 $ 110.31 $ 113,068 62 $184,922 QQQ_TRANSITION\n216 2025-05-23 SELL_GRADUAL MCHP 2346 $ 56.19 $ 131,822 62 $316,744 QQQ_TRANSITION\n \ No newline at end of file diff --git a/backtest/reports/detailed_trades_20250718_183737.txt b/backtest/reports/detailed_trades_20250718_183737.txt new file mode 100644 index 0000000..192a49a --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_183737.txt @@ -0,0 +1,233 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 216 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 120 + BUY_GRADUAL: 68 + STOP_LOSS: 15 + BUY_QQQ_STOPLOSS: 13 + +📋 详细交易记录: +======================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash State +======================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 GREED_TRANSITION + 9 2008-03-10 STOP_LOSS DXCM 11274 $ 1.51 $ 17,052 27 $17,104 VOLATILE_STOCKS + 10 2008-03-10 BUY_QQQ_STOPLOSS QQQ 478 $ 35.62 $ 17,026 27 $78 VOLATILE_STOCKS + 11 2008-10-06 STOP_LOSS SBUX 3549 $ 4.91 $ 17,427 21 $17,504 VOLATILE_STOCKS + 12 2008-10-06 BUY_QQQ_STOPLOSS QQQ 577 $ 30.16 $ 17,402 21 $103 VOLATILE_STOCKS + 13 2008-10-23 STOP_LOSS ILMN 745 $ 22.69 $ 16,907 29 $17,010 VOLATILE_STOCKS + 14 2008-10-23 BUY_QQQ_STOPLOSS QQQ 640 $ 26.38 $ 16,882 29 $128 VOLATILE_STOCKS + 15 2008-10-24 STOP_LOSS TMUS 852 $ 20.55 $ 17,511 26 $17,639 VOLATILE_STOCKS + 16 2008-10-24 BUY_QQQ_STOPLOSS QQQ 685 $ 25.53 $ 17,488 26 $151 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $121 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $91 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $61 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $31 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 596 $ 40.00 $ 23,842 34 $23,873 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 596 $ 39.77 $ 23,702 33 $47,575 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 596 $ 39.40 $ 23,484 27 $71,059 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 596 $ 40.34 $ 24,040 38 $95,099 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2679 $ 8.87 $ 23,772 37 $71,327 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1524 $ 15.59 $ 23,766 35 $47,562 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1776 $ 13.38 $ 23,771 34 $23,791 GREED_TRANSITION + 28 2010-06-09 BUY_GRADUAL LULU 1216 $ 19.55 $ 23,773 33 $18 GREED_TRANSITION + 29 2010-11-04 SELL_GRADUAL SIRI 669 $ 13.31 $ 8,904 80 $8,922 QQQ_TRANSITION + 30 2010-11-04 SELL_GRADUAL NFLX 381 $ 23.97 $ 9,134 80 $18,057 QQQ_TRANSITION + 31 2010-11-04 SELL_GRADUAL TMUS 444 $ 18.36 $ 8,151 80 $26,207 QQQ_TRANSITION + 32 2010-11-04 SELL_GRADUAL LULU 304 $ 22.50 $ 6,840 80 $33,047 QQQ_TRANSITION + 33 2010-11-05 SELL_GRADUAL SIRI 669 $ 13.85 $ 9,268 80 $42,315 QQQ_TRANSITION + 34 2010-11-05 SELL_GRADUAL NFLX 381 $ 24.01 $ 9,149 80 $51,464 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL TMUS 444 $ 18.92 $ 8,402 80 $59,866 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL LULU 304 $ 24.05 $ 7,310 80 $67,176 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 709 $ 47.10 $ 33,392 80 $33,784 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 669 $ 14.03 $ 9,389 78 $43,173 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 381 $ 24.16 $ 9,206 78 $52,378 QQQ_TRANSITION + 40 2010-11-08 SELL_GRADUAL TMUS 444 $ 19.71 $ 8,753 78 $61,131 QQQ_TRANSITION + 41 2010-11-08 SELL_GRADUAL LULU 304 $ 24.77 $ 7,532 78 $68,662 QQQ_TRANSITION + 42 2010-11-08 BUY_GRADUAL QQQ 708 $ 47.16 $ 33,388 78 $35,274 QQQ_TRANSITION + 43 2010-11-09 SELL_GRADUAL SIRI 669 $ 13.49 $ 9,026 74 $44,300 QQQ_TRANSITION + 44 2010-11-09 SELL_GRADUAL NFLX 381 $ 24.35 $ 9,278 74 $53,577 QQQ_TRANSITION + 45 2010-11-09 SELL_GRADUAL TMUS 444 $ 19.34 $ 8,588 74 $62,165 QQQ_TRANSITION + 46 2010-11-09 SELL_GRADUAL LULU 304 $ 24.09 $ 7,323 74 $69,489 QQQ_TRANSITION + 47 2010-11-09 BUY_GRADUAL QQQ 712 $ 46.90 $ 33,396 74 $36,093 QQQ_TRANSITION + 48 2011-08-05 SELL_GRADUAL QQQ 532 $ 47.50 $ 25,272 16 $61,365 FEAR_TRANSITION + 49 2011-08-08 SELL_GRADUAL QQQ 532 $ 44.65 $ 23,751 12 $85,116 FEAR_TRANSITION + 50 2011-08-09 SELL_GRADUAL QQQ 532 $ 46.80 $ 24,897 21 $110,013 FEAR_TRANSITION + 51 2011-08-10 SELL_GRADUAL QQQ 532 $ 44.88 $ 23,878 16 $133,891 FEAR_TRANSITION + 52 2011-08-16 BUY_GRADUAL AMD 5281 $ 6.34 $ 33,482 28 $100,410 GREED_TRANSITION + 53 2011-08-17 BUY_GRADUAL DXCM 11842 $ 2.83 $ 33,483 27 $66,926 GREED_TRANSITION + 54 2011-08-18 BUY_GRADUAL ILMN 730 $ 45.82 $ 33,446 21 $33,480 GREED_TRANSITION + 55 2011-10-07 STOP_LOSS ILMN 730 $ 26.44 $ 19,301 46 $52,781 VOLATILE_STOCKS + 56 2011-10-07 BUY_QQQ_STOPLOSS QQQ 403 $ 47.80 $ 19,265 46 $33,516 VOLATILE_STOCKS + 57 2011-11-10 STOP_LOSS DXCM 11842 $ 1.77 $ 20,960 52 $54,476 VOLATILE_STOCKS + 58 2011-11-10 BUY_QQQ_STOPLOSS QQQ 417 $ 50.20 $ 20,934 52 $33,542 VOLATILE_STOCKS + 59 2012-01-20 SELL_GRADUAL AMD 1320 $ 6.42 $ 8,474 76 $42,017 QQQ_TRANSITION + 60 2012-01-20 BUY_GRADUAL QQQ 313 $ 53.05 $ 16,604 76 $25,413 QQQ_TRANSITION + 61 2012-01-23 SELL_GRADUAL AMD 1320 $ 6.52 $ 8,606 75 $34,019 QQQ_TRANSITION + 62 2012-01-23 BUY_GRADUAL QQQ 313 $ 53.06 $ 16,609 75 $17,410 QQQ_TRANSITION + 63 2012-01-24 SELL_GRADUAL AMD 1320 $ 6.53 $ 8,620 74 $26,030 QQQ_TRANSITION + 64 2012-01-24 BUY_GRADUAL QQQ 313 $ 52.97 $ 16,579 74 $9,451 QQQ_TRANSITION + 65 2012-01-25 SELL_GRADUAL AMD 1320 $ 6.73 $ 8,884 75 $18,335 QQQ_TRANSITION + 66 2012-01-25 BUY_GRADUAL QQQ 309 $ 53.63 $ 16,572 75 $1,763 QQQ_TRANSITION + 67 2012-05-21 SELL_GRADUAL QQQ 517 $ 55.57 $ 28,731 34 $30,494 FEAR_TRANSITION + 68 2012-05-22 SELL_GRADUAL QQQ 517 $ 55.51 $ 28,699 34 $59,192 FEAR_TRANSITION + 69 2012-05-23 SELL_GRADUAL QQQ 517 $ 55.62 $ 28,754 33 $87,946 FEAR_TRANSITION + 70 2012-05-24 SELL_GRADUAL QQQ 517 $ 55.25 $ 28,566 32 $116,512 FEAR_TRANSITION + 71 2012-05-29 BUY_GRADUAL VRTX 504 $ 57.80 $ 29,131 39 $87,381 GREED_TRANSITION + 72 2012-05-30 BUY_GRADUAL NXPI 1509 $ 19.31 $ 29,134 33 $58,247 GREED_TRANSITION + 73 2012-05-31 BUY_GRADUAL REGN 215 $ 135.28 $ 29,084 34 $29,162 GREED_TRANSITION + 74 2012-06-01 BUY_GRADUAL MNST 2478 $ 11.76 $ 29,133 27 $29 GREED_TRANSITION + 75 2012-07-20 STOP_LOSS AMD 1 $ 4.22 $ 4 55 $34 VOLATILE_STOCKS + 76 2012-10-22 STOP_LOSS MNST 2478 $ 7.62 $ 18,886 48 $18,920 VOLATILE_STOCKS + 77 2012-10-22 BUY_QQQ_STOPLOSS QQQ 320 $ 59.00 $ 18,879 48 $42 VOLATILE_STOCKS + 78 2012-11-27 STOP_LOSS VRTX 504 $ 39.98 $ 20,150 49 $20,191 VOLATILE_STOCKS + 79 2012-11-27 BUY_QQQ_STOPLOSS QQQ 347 $ 58.05 $ 20,143 49 $49 VOLATILE_STOCKS + 80 2013-01-24 SELL_GRADUAL NXPI 377 $ 26.46 $ 9,976 75 $10,025 QQQ_TRANSITION + 81 2013-01-24 SELL_GRADUAL REGN 53 $ 172.66 $ 9,151 75 $19,176 QQQ_TRANSITION + 82 2013-01-25 SELL_GRADUAL NXPI 377 $ 26.98 $ 10,173 77 $29,349 QQQ_TRANSITION + 83 2013-01-25 SELL_GRADUAL REGN 53 $ 174.00 $ 9,222 77 $38,571 QQQ_TRANSITION + 84 2013-01-25 BUY_GRADUAL QQQ 323 $ 60.20 $ 19,446 77 $19,125 QQQ_TRANSITION + 85 2013-01-28 SELL_GRADUAL NXPI 377 $ 27.35 $ 10,311 76 $29,436 QQQ_TRANSITION + 86 2013-01-28 SELL_GRADUAL REGN 53 $ 170.43 $ 9,033 76 $38,468 QQQ_TRANSITION + 87 2013-01-28 BUY_GRADUAL QQQ 322 $ 60.34 $ 19,429 76 $19,039 QQQ_TRANSITION + 88 2013-01-29 SELL_GRADUAL NXPI 377 $ 26.46 $ 9,976 77 $29,015 QQQ_TRANSITION + 89 2013-01-29 SELL_GRADUAL REGN 53 $ 174.48 $ 9,247 77 $38,263 QQQ_TRANSITION + 90 2013-01-29 BUY_GRADUAL QQQ 322 $ 60.35 $ 19,432 77 $18,831 QQQ_TRANSITION + 91 2015-08-25 SELL_GRADUAL QQQ 409 $ 90.96 $ 37,201 19 $56,032 FEAR_TRANSITION + 92 2015-08-26 SELL_GRADUAL QQQ 409 $ 95.54 $ 39,075 30 $95,107 FEAR_TRANSITION + 93 2015-08-27 SELL_GRADUAL QQQ 409 $ 97.96 $ 40,065 37 $135,171 FEAR_TRANSITION + 94 2015-08-28 SELL_GRADUAL QQQ 409 $ 97.94 $ 40,057 37 $175,229 FEAR_TRANSITION + 95 2015-09-01 BUY_GRADUAL NFLX 418 $ 105.79 $ 44,220 29 $131,008 GREED_TRANSITION + 96 2015-09-02 BUY_GRADUAL NVDA 81954 $ 0.54 $ 44,236 34 $86,772 GREED_TRANSITION + 97 2015-09-03 BUY_GRADUAL MU 2730 $ 16.20 $ 44,236 36 $42,537 GREED_TRANSITION + 98 2016-01-15 STOP_LOSS MU 2730 $ 10.82 $ 29,544 23 $72,080 VOLATILE_STOCKS + 99 2016-01-15 BUY_QQQ_STOPLOSS QQQ 314 $ 94.02 $ 29,522 23 $42,558 VOLATILE_STOCKS +100 2017-02-16 SELL_GRADUAL NFLX 104 $ 142.01 $ 14,769 74 $57,327 QQQ_TRANSITION +101 2017-02-16 SELL_GRADUAL NVDA 20488 $ 2.64 $ 54,120 74 $111,447 QQQ_TRANSITION +102 2017-02-16 BUY_GRADUAL QQQ 663 $ 121.85 $ 80,788 74 $30,659 QQQ_TRANSITION +103 2017-02-17 SELL_GRADUAL NFLX 104 $ 142.22 $ 14,791 74 $45,450 QQQ_TRANSITION +104 2017-02-17 SELL_GRADUAL NVDA 20488 $ 2.64 $ 54,110 74 $99,560 QQQ_TRANSITION +105 2017-02-17 BUY_GRADUAL QQQ 660 $ 122.38 $ 80,771 74 $18,790 QQQ_TRANSITION +106 2017-02-21 SELL_GRADUAL NFLX 104 $ 142.60 $ 14,830 76 $33,620 QQQ_TRANSITION +107 2017-02-21 SELL_GRADUAL NVDA 20488 $ 2.74 $ 56,048 76 $89,668 QQQ_TRANSITION +108 2017-02-21 BUY_GRADUAL QQQ 657 $ 122.98 $ 80,800 76 $8,868 QQQ_TRANSITION +109 2017-02-22 SELL_GRADUAL NFLX 104 $ 143.86 $ 14,961 75 $23,829 QQQ_TRANSITION +110 2017-02-22 SELL_GRADUAL NVDA 20488 $ 2.73 $ 55,962 75 $79,791 QQQ_TRANSITION +111 2018-12-18 SELL_GRADUAL QQQ 573 $ 151.55 $ 86,840 24 $166,631 FEAR_TRANSITION +112 2018-12-19 SELL_GRADUAL QQQ 573 $ 147.83 $ 84,708 23 $251,339 FEAR_TRANSITION +113 2018-12-20 SELL_GRADUAL QQQ 573 $ 145.69 $ 83,480 22 $334,818 FEAR_TRANSITION +114 2018-12-21 SELL_GRADUAL QQQ 573 $ 141.17 $ 80,892 20 $415,711 FEAR_TRANSITION +115 2018-12-27 BUY_GRADUAL ZS 2598 $ 40.17 $ 104,362 28 $311,349 GREED_TRANSITION +116 2018-12-28 BUY_GRADUAL AMD 5857 $ 17.82 $ 104,372 26 $206,977 GREED_TRANSITION +117 2018-12-31 BUY_GRADUAL MDB 1246 $ 83.74 $ 104,340 30 $102,637 GREED_TRANSITION +118 2019-04-24 SELL_GRADUAL ZS 649 $ 64.99 $ 42,179 75 $144,816 QQQ_TRANSITION +119 2019-04-24 SELL_GRADUAL AMD 1464 $ 28.46 $ 41,665 75 $186,481 QQQ_TRANSITION +120 2019-04-24 SELL_GRADUAL MDB 311 $ 135.16 $ 42,035 75 $228,516 QQQ_TRANSITION +121 2019-04-24 BUY_GRADUAL QQQ 835 $ 182.34 $ 152,252 75 $76,264 QQQ_TRANSITION +122 2019-04-25 SELL_GRADUAL ZS 649 $ 66.96 $ 43,457 74 $119,721 QQQ_TRANSITION +123 2019-04-25 SELL_GRADUAL AMD 1464 $ 27.66 $ 40,494 74 $160,216 QQQ_TRANSITION +124 2019-04-25 SELL_GRADUAL MDB 311 $ 137.66 $ 42,812 74 $203,028 QQQ_TRANSITION +125 2019-04-25 BUY_GRADUAL QQQ 832 $ 183.08 $ 152,320 74 $50,707 QQQ_TRANSITION +126 2019-04-26 SELL_GRADUAL ZS 649 $ 66.94 $ 43,444 74 $94,151 QQQ_TRANSITION +127 2019-04-26 SELL_GRADUAL AMD 1464 $ 27.88 $ 40,816 74 $134,968 QQQ_TRANSITION +128 2019-04-26 SELL_GRADUAL MDB 311 $ 138.98 $ 43,223 74 $178,191 QQQ_TRANSITION +129 2019-04-26 BUY_GRADUAL QQQ 831 $ 183.24 $ 152,273 74 $25,917 QQQ_TRANSITION +130 2019-04-29 SELL_GRADUAL ZS 649 $ 68.20 $ 44,262 74 $70,179 QQQ_TRANSITION +131 2019-04-29 SELL_GRADUAL AMD 1464 $ 27.69 $ 40,538 74 $110,717 QQQ_TRANSITION +132 2019-04-29 SELL_GRADUAL MDB 311 $ 137.50 $ 42,762 74 $153,480 QQQ_TRANSITION +133 2019-04-29 BUY_GRADUAL QQQ 830 $ 183.60 $ 152,385 74 $1,095 QQQ_TRANSITION +134 2020-02-28 SELL_GRADUAL QQQ 832 $ 199.07 $ 165,626 20 $166,721 FEAR_TRANSITION +135 2020-03-02 SELL_GRADUAL QQQ 832 $ 209.34 $ 174,173 37 $340,894 FEAR_TRANSITION +136 2020-03-03 SELL_GRADUAL QQQ 832 $ 202.63 $ 168,588 29 $509,482 FEAR_TRANSITION +137 2020-03-04 SELL_GRADUAL QQQ 832 $ 211.08 $ 175,622 38 $685,103 FEAR_TRANSITION +138 2020-03-06 BUY_GRADUAL MRNA 5809 $ 29.61 $ 172,004 25 $513,099 GREED_TRANSITION +139 2020-03-09 BUY_GRADUAL TSLA 4243 $ 40.53 $ 171,983 17 $341,116 GREED_TRANSITION +140 2020-03-10 BUY_GRADUAL MRVL 7604 $ 22.62 $ 172,007 25 $169,109 GREED_TRANSITION +141 2020-03-18 STOP_LOSS TSLA 4243 $ 24.08 $ 102,177 22 $271,286 VOLATILE_STOCKS +142 2020-03-18 BUY_QQQ_STOPLOSS QQQ 598 $ 170.82 $ 102,153 22 $169,133 VOLATILE_STOCKS +143 2020-06-03 SELL_GRADUAL MRNA 1452 $ 59.89 $ 86,960 79 $256,093 QQQ_TRANSITION +144 2020-06-03 SELL_GRADUAL MRVL 1901 $ 35.14 $ 66,809 79 $322,902 QQQ_TRANSITION +145 2020-06-03 BUY_GRADUAL QQQ 847 $ 229.44 $ 194,334 79 $128,569 QQQ_TRANSITION +146 2020-06-04 SELL_GRADUAL MRNA 1452 $ 60.59 $ 87,977 79 $216,545 QQQ_TRANSITION +147 2020-06-04 SELL_GRADUAL MRVL 1901 $ 35.08 $ 66,679 79 $283,225 QQQ_TRANSITION +148 2020-06-04 BUY_GRADUAL QQQ 853 $ 227.83 $ 194,338 79 $88,887 QQQ_TRANSITION +149 2020-06-05 SELL_GRADUAL MRNA 1452 $ 58.19 $ 84,492 84 $173,379 QQQ_TRANSITION +150 2020-06-05 SELL_GRADUAL MRVL 1901 $ 35.08 $ 66,679 84 $240,058 QQQ_TRANSITION +151 2020-06-05 BUY_GRADUAL QQQ 836 $ 232.35 $ 194,241 84 $45,817 QQQ_TRANSITION +152 2020-06-08 SELL_GRADUAL MRNA 1452 $ 59.10 $ 85,813 83 $131,630 QQQ_TRANSITION +153 2020-06-08 SELL_GRADUAL MRVL 1901 $ 34.16 $ 64,933 83 $196,563 QQQ_TRANSITION +154 2020-06-08 BUY_GRADUAL QQQ 830 $ 234.15 $ 194,343 83 $2,219 QQQ_TRANSITION +155 2022-01-26 SELL_GRADUAL QQQ 991 $ 336.81 $ 333,783 24 $336,002 FEAR_TRANSITION +156 2022-01-27 SELL_GRADUAL QQQ 991 $ 333.42 $ 330,421 23 $666,424 FEAR_TRANSITION +157 2022-01-28 SELL_GRADUAL QQQ 991 $ 343.88 $ 340,787 32 $1,007,210 FEAR_TRANSITION +158 2022-01-31 SELL_GRADUAL QQQ 991 $ 354.88 $ 351,684 41 $1,358,895 FEAR_TRANSITION +159 2022-02-02 BUY_GRADUAL NFLX 793 $ 429.48 $ 340,578 47 $1,018,317 GREED_TRANSITION +160 2022-02-03 BUY_GRADUAL PDD 6184 $ 55.13 $ 340,924 43 $677,393 GREED_TRANSITION +161 2022-02-04 BUY_GRADUAL WBD 11796 $ 28.90 $ 340,904 46 $336,489 GREED_TRANSITION +162 2022-03-10 STOP_LOSS PDD 6184 $ 35.76 $ 221,140 34 $557,629 VOLATILE_STOCKS +163 2022-03-10 BUY_QQQ_STOPLOSS QQQ 682 $ 323.81 $ 220,841 34 $336,788 VOLATILE_STOCKS +164 2022-04-26 STOP_LOSS WBD 11796 $ 19.83 $ 233,915 30 $570,702 VOLATILE_STOCKS +165 2022-04-26 BUY_QQQ_STOPLOSS QQQ 753 $ 310.38 $ 233,720 30 $336,983 VOLATILE_STOCKS +166 2022-08-16 SELL_GRADUAL NFLX 198 $ 245.69 $ 48,647 76 $385,629 QQQ_TRANSITION +167 2022-08-16 BUY_GRADUAL QQQ 413 $ 325.83 $ 134,567 76 $251,062 QQQ_TRANSITION +168 2022-08-17 SELL_GRADUAL NFLX 198 $ 241.15 $ 47,748 72 $298,810 QQQ_TRANSITION +169 2022-08-17 BUY_GRADUAL QQQ 418 $ 322.11 $ 134,642 72 $164,168 QQQ_TRANSITION +170 2022-08-18 SELL_GRADUAL NFLX 198 $ 245.17 $ 48,544 73 $212,711 QQQ_TRANSITION +171 2022-08-18 BUY_GRADUAL QQQ 417 $ 322.89 $ 134,643 73 $78,068 QQQ_TRANSITION +172 2022-08-19 SELL_GRADUAL NFLX 198 $ 241.16 $ 47,750 68 $125,818 QQQ_TRANSITION +173 2022-09-26 SELL_GRADUAL QQQ 671 $ 269.53 $ 180,852 25 $306,669 FEAR_TRANSITION +174 2022-09-27 SELL_GRADUAL QQQ 671 $ 269.63 $ 180,924 26 $487,593 FEAR_TRANSITION +175 2022-09-28 SELL_GRADUAL QQQ 671 $ 275.00 $ 184,523 33 $672,117 FEAR_TRANSITION +176 2022-09-29 SELL_GRADUAL QQQ 671 $ 267.07 $ 179,204 29 $851,320 FEAR_TRANSITION +177 2022-10-03 BUY_GRADUAL BIIB 809 $ 264.30 $ 213,819 32 $637,502 GREED_TRANSITION +178 2022-10-04 BUY_GRADUAL REGN 289 $ 737.50 $ 213,138 42 $424,364 GREED_TRANSITION +179 2022-10-05 BUY_GRADUAL NFLX 903 $ 236.73 $ 213,767 42 $210,597 GREED_TRANSITION +180 2023-02-03 SELL_GRADUAL REGN 73 $ 780.97 $ 57,011 72 $267,608 QQQ_TRANSITION +181 2023-02-03 SELL_GRADUAL NFLX 226 $ 365.90 $ 82,693 72 $350,301 QQQ_TRANSITION +182 2023-02-03 SELL_GRADUAL BIIB 202 $ 283.63 $ 57,293 72 $407,594 QQQ_TRANSITION +183 2023-02-03 BUY_GRADUAL QQQ 825 $ 301.49 $ 248,733 72 $158,862 QQQ_TRANSITION +184 2023-02-06 SELL_GRADUAL REGN 73 $ 778.71 $ 56,846 65 $215,707 QQQ_TRANSITION +185 2023-02-06 SELL_GRADUAL NFLX 226 $ 361.48 $ 81,694 65 $297,402 QQQ_TRANSITION +186 2023-02-06 SELL_GRADUAL BIIB 202 $ 288.28 $ 58,233 65 $355,634 QQQ_TRANSITION +187 2023-02-06 BUY_GRADUAL QQQ 832 $ 298.94 $ 248,721 65 $106,913 QQQ_TRANSITION +188 2023-02-07 SELL_GRADUAL REGN 73 $ 767.82 $ 56,051 70 $162,964 QQQ_TRANSITION +189 2023-02-07 SELL_GRADUAL NFLX 226 $ 362.95 $ 82,027 70 $244,991 QQQ_TRANSITION +190 2023-02-07 SELL_GRADUAL BIIB 202 $ 291.43 $ 58,869 70 $303,859 QQQ_TRANSITION +191 2023-02-07 BUY_GRADUAL QQQ 815 $ 305.14 $ 248,687 70 $55,173 QQQ_TRANSITION +192 2023-02-08 SELL_GRADUAL REGN 73 $ 747.59 $ 54,574 64 $109,747 QQQ_TRANSITION +193 2023-02-08 SELL_GRADUAL NFLX 226 $ 366.83 $ 82,904 64 $192,650 QQQ_TRANSITION +194 2023-02-08 SELL_GRADUAL BIIB 202 $ 285.45 $ 57,661 64 $250,311 QQQ_TRANSITION +195 2023-02-08 BUY_GRADUAL QQQ 830 $ 299.71 $ 248,761 64 $1,551 QQQ_TRANSITION +196 2024-10-03 STOP_LOSS BIIB 1 $ 184.78 $ 185 62 $1,735 QQQ_HOLD +197 2025-04-07 SELL_GRADUAL QQQ 825 $ 423.21 $ 349,152 24 $350,888 FEAR_TRANSITION +198 2025-04-08 SELL_GRADUAL QQQ 825 $ 415.59 $ 342,864 23 $693,752 FEAR_TRANSITION +199 2025-04-09 SELL_GRADUAL QQQ 825 $ 465.48 $ 384,019 39 $1,077,771 FEAR_TRANSITION +200 2025-04-10 SELL_GRADUAL QQQ 825 $ 445.68 $ 367,686 34 $1,445,456 FEAR_TRANSITION +201 2025-04-14 BUY_GRADUAL MCHP 9386 $ 38.58 $ 362,125 37 $1,083,331 GREED_TRANSITION +202 2025-04-15 BUY_GRADUAL WBD 43896 $ 8.25 $ 362,142 38 $721,189 GREED_TRANSITION +203 2025-04-16 STOP_LOSS WBD 43896 $ 7.92 $ 347,656 32 $1,068,845 GREED_TRANSITION +204 2025-04-16 BUY_QQQ_STOPLOSS QQQ 783 $ 443.68 $ 347,403 32 $721,443 GREED_TRANSITION +205 2025-04-16 BUY_GRADUAL AMD 4101 $ 88.29 $ 362,077 32 $359,365 GREED_TRANSITION +206 2025-05-20 SELL_GRADUAL AMD 1025 $ 113.51 $ 116,348 76 $475,713 QQQ_TRANSITION +207 2025-05-20 SELL_GRADUAL MCHP 2346 $ 60.33 $ 141,541 76 $617,254 QQQ_TRANSITION +208 2025-05-20 BUY_GRADUAL QQQ 672 $ 519.69 $ 349,229 76 $268,025 QQQ_TRANSITION +209 2025-05-21 SELL_GRADUAL AMD 1025 $ 112.06 $ 114,861 69 $382,887 QQQ_TRANSITION +210 2025-05-21 SELL_GRADUAL MCHP 2346 $ 58.79 $ 137,933 69 $520,820 QQQ_TRANSITION +211 2025-05-21 BUY_GRADUAL QQQ 682 $ 512.46 $ 349,501 69 $171,319 QQQ_TRANSITION +212 2025-05-22 SELL_GRADUAL AMD 1025 $ 110.71 $ 113,478 67 $284,797 QQQ_TRANSITION +213 2025-05-22 SELL_GRADUAL MCHP 2346 $ 58.05 $ 136,185 67 $420,982 QQQ_TRANSITION +214 2025-05-22 BUY_GRADUAL QQQ 680 $ 513.42 $ 349,128 67 $71,854 QQQ_TRANSITION +215 2025-05-23 SELL_GRADUAL AMD 1025 $ 110.31 $ 113,068 62 $184,922 QQQ_TRANSITION +216 2025-05-23 SELL_GRADUAL MCHP 2346 $ 56.19 $ 131,822 62 $316,744 QQQ_TRANSITION diff --git a/backtest/reports/detailed_trades_20250718_185722.txt b/backtest/reports/detailed_trades_20250718_185722.txt new file mode 100644 index 0000000..2982348 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_185722.txt @@ -0,0 +1,220 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 203 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 96 + BUY_GRADUAL: 67 + STOP_LOSS: 21 + BUY_QQQ_STOPLOSS: 19 + +📋 详细交易记录: +====================================================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash Assets State +====================================================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 $100,829 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 $100,940 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 $99,951 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 $100,383 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 $100,381 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 $99,964 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 $98,656 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 $97,152 GREED_TRANSITION + 9 2008-02-20 STOP_LOSS TMUS 852 $ 24.77 $ 21,101 54 $21,153 $94,385 VOLATILE_STOCKS + 10 2008-02-20 BUY_QQQ_STOPLOSS QQQ 556 $ 37.93 $ 21,090 54 $63 $94,385 VOLATILE_STOCKS + 11 2008-02-28 STOP_LOSS DXCM 11274 $ 1.88 $ 21,251 50 $21,314 $94,276 VOLATILE_STOCKS + 12 2008-02-28 BUY_QQQ_STOPLOSS QQQ 557 $ 38.09 $ 21,215 50 $99 $94,276 VOLATILE_STOCKS + 13 2008-04-25 STOP_LOSS SBUX 3549 $ 6.00 $ 21,310 69 $21,409 $95,418 VOLATILE_STOCKS + 14 2008-04-25 BUY_QQQ_STOPLOSS QQQ 523 $ 40.74 $ 21,305 69 $104 $95,418 VOLATILE_STOCKS + 15 2008-10-09 STOP_LOSS ILMN 745 $ 28.21 $ 21,017 9 $21,121 $65,760 VOLATILE_STOCKS + 16 2008-10-09 BUY_QQQ_STOPLOSS QQQ 770 $ 27.27 $ 20,997 9 $124 $65,760 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $93 $73,275 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $63 $73,296 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $33 $71,582 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $3 $71,624 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 602 $ 40.00 $ 24,082 34 $24,086 $96,453 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 602 $ 39.77 $ 23,941 33 $48,026 $96,027 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 602 $ 39.40 $ 23,720 27 $71,746 $95,585 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 602 $ 40.34 $ 24,282 38 $96,028 $96,149 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2705 $ 8.87 $ 24,002 37 $72,026 $96,146 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1539 $ 15.59 $ 24,000 35 $48,026 $95,899 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1793 $ 13.38 $ 23,998 34 $24,028 $95,666 GREED_TRANSITION + 28 2010-06-09 STOP_LOSS TMUS 1793 $ 13.38 $ 23,998 33 $48,026 $97,032 GREED_TRANSITION + 29 2010-06-09 BUY_QQQ_STOPLOSS QQQ 627 $ 38.22 $ 23,961 33 $24,065 $97,032 GREED_TRANSITION + 30 2010-06-09 BUY_GRADUAL LULU 1227 $ 19.55 $ 23,988 33 $77 $97,032 GREED_TRANSITION + 31 2010-08-30 STOP_LOSS LULU 1227 $ 15.95 $ 19,571 31 $19,648 $94,278 VOLATILE_STOCKS + 32 2010-08-30 BUY_QQQ_STOPLOSS QQQ 512 $ 38.18 $ 19,548 31 $100 $94,278 VOLATILE_STOCKS + 33 2010-11-04 SELL_GRADUAL SIRI 676 $ 13.31 $ 8,998 80 $9,097 $126,785 QQQ_TRANSITION + 34 2010-11-04 SELL_GRADUAL NFLX 384 $ 23.97 $ 9,206 80 $18,303 $126,785 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL SIRI 676 $ 13.85 $ 9,365 80 $27,668 $127,933 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL NFLX 384 $ 24.01 $ 9,221 80 $36,890 $127,933 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 404 $ 47.10 $ 19,027 80 $17,862 $127,933 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 676 $ 14.03 $ 9,487 78 $27,349 $128,387 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 384 $ 24.16 $ 9,278 78 $36,627 $128,387 QQQ_TRANSITION + 40 2010-11-08 BUY_GRADUAL QQQ 404 $ 47.16 $ 19,052 78 $17,575 $128,387 QQQ_TRANSITION + 41 2010-11-09 SELL_GRADUAL SIRI 676 $ 13.49 $ 9,120 74 $26,695 $127,596 QQQ_TRANSITION + 42 2010-11-09 SELL_GRADUAL NFLX 384 $ 24.35 $ 9,351 74 $36,046 $127,596 QQQ_TRANSITION + 43 2010-11-09 BUY_GRADUAL QQQ 406 $ 46.90 $ 19,043 74 $17,003 $127,596 QQQ_TRANSITION + 44 2011-08-05 SELL_GRADUAL QQQ 589 $ 47.50 $ 27,980 16 $44,983 $129,044 FEAR_TRANSITION + 45 2011-08-08 SELL_GRADUAL QQQ 589 $ 44.65 $ 26,296 12 $71,279 $123,983 FEAR_TRANSITION + 46 2011-08-09 SELL_GRADUAL QQQ 589 $ 46.80 $ 27,564 21 $98,843 $126,526 FEAR_TRANSITION + 47 2011-08-10 SELL_GRADUAL QQQ 589 $ 44.88 $ 26,436 16 $125,280 $125,395 FEAR_TRANSITION + 48 2011-08-16 BUY_GRADUAL AMD 4944 $ 6.34 $ 31,345 28 $93,935 $125,400 GREED_TRANSITION + 49 2011-08-17 BUY_GRADUAL DXCM 11087 $ 2.83 $ 31,348 27 $62,586 $124,408 GREED_TRANSITION + 50 2011-08-18 BUY_GRADUAL ILMN 684 $ 45.82 $ 31,339 21 $31,247 $121,090 GREED_TRANSITION + 51 2011-09-29 STOP_LOSS AMD 4944 $ 5.31 $ 26,253 36 $57,500 $119,987 VOLATILE_STOCKS + 52 2011-09-29 BUY_QQQ_STOPLOSS QQQ 551 $ 47.64 $ 26,248 36 $31,252 $119,987 VOLATILE_STOCKS + 53 2011-10-07 STOP_LOSS ILMN 684 $ 26.44 $ 18,085 46 $49,337 $106,508 VOLATILE_STOCKS + 54 2011-10-07 BUY_QQQ_STOPLOSS QQQ 378 $ 47.80 $ 18,070 46 $31,267 $106,508 VOLATILE_STOCKS + 55 2011-10-25 STOP_LOSS NFLX 3 $ 11.05 $ 33 58 $31,300 $107,461 VOLATILE_STOCKS + 56 2011-11-03 STOP_LOSS DXCM 11087 $ 1.97 $ 21,814 63 $53,114 $100,849 VOLATILE_STOCKS + 57 2011-11-03 BUY_QQQ_STOPLOSS QQQ 424 $ 51.37 $ 21,780 63 $31,334 $100,849 VOLATILE_STOCKS + 58 2012-01-20 BUY_GRADUAL QQQ 147 $ 53.05 $ 7,798 76 $23,536 $103,125 QQQ_TRANSITION + 59 2012-01-23 BUY_GRADUAL QQQ 147 $ 53.06 $ 7,800 75 $15,735 $103,151 QQQ_TRANSITION + 60 2012-01-24 BUY_GRADUAL QQQ 147 $ 52.97 $ 7,786 74 $7,949 $102,990 QQQ_TRANSITION + 61 2012-01-25 BUY_GRADUAL QQQ 146 $ 53.63 $ 7,830 75 $119 $104,184 QQQ_TRANSITION + 62 2012-05-21 SELL_GRADUAL QQQ 485 $ 55.57 $ 26,953 34 $27,072 $107,948 FEAR_TRANSITION + 63 2012-05-22 SELL_GRADUAL QQQ 485 $ 55.51 $ 26,922 34 $53,994 $107,857 FEAR_TRANSITION + 64 2012-05-23 SELL_GRADUAL QQQ 485 $ 55.62 $ 26,974 33 $80,968 $107,961 FEAR_TRANSITION + 65 2012-05-24 SELL_GRADUAL QQQ 485 $ 55.25 $ 26,797 32 $107,766 $107,784 FEAR_TRANSITION + 66 2012-05-29 BUY_GRADUAL VRTX 466 $ 57.80 $ 26,935 39 $80,831 $107,783 GREED_TRANSITION + 67 2012-05-30 BUY_GRADUAL NXPI 1395 $ 19.31 $ 26,933 33 $53,898 $108,892 GREED_TRANSITION + 68 2012-05-31 BUY_GRADUAL REGN 199 $ 135.28 $ 26,920 34 $26,978 $108,301 GREED_TRANSITION + 69 2012-06-01 BUY_GRADUAL MNST 2291 $ 11.76 $ 26,935 27 $44 $103,625 GREED_TRANSITION + 70 2012-06-13 STOP_LOSS REGN 199 $ 111.57 $ 22,203 44 $22,246 $101,613 VOLATILE_STOCKS + 71 2012-06-13 BUY_QQQ_STOPLOSS QQQ 401 $ 55.23 $ 22,149 44 $97 $101,613 VOLATILE_STOCKS + 72 2012-07-24 STOP_LOSS VRTX 466 $ 48.37 $ 22,540 51 $22,638 $98,436 VOLATILE_STOCKS + 73 2012-07-24 BUY_QQQ_STOPLOSS QQQ 401 $ 56.10 $ 22,497 51 $141 $98,436 VOLATILE_STOCKS + 74 2012-08-10 STOP_LOSS MNST 2291 $ 9.05 $ 20,722 62 $20,863 $99,014 VOLATILE_STOCKS + 75 2012-08-10 BUY_QQQ_STOPLOSS QQQ 347 $ 59.58 $ 20,673 62 $190 $99,014 VOLATILE_STOCKS + 76 2013-01-24 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 75 $9,399 $105,958 QQQ_TRANSITION + 77 2013-01-24 BUY_GRADUAL QQQ 154 $ 59.90 $ 9,224 75 $175 $105,958 QQQ_TRANSITION + 78 2013-01-25 SELL_GRADUAL NXPI 348 $ 26.98 $ 9,390 77 $9,565 $106,902 QQQ_TRANSITION + 79 2013-01-25 BUY_GRADUAL QQQ 154 $ 60.20 $ 9,272 77 $293 $106,902 QQQ_TRANSITION + 80 2013-01-28 SELL_GRADUAL NXPI 348 $ 27.35 $ 9,518 76 $9,811 $107,355 QQQ_TRANSITION + 81 2013-01-28 BUY_GRADUAL QQQ 153 $ 60.34 $ 9,232 76 $579 $107,355 QQQ_TRANSITION + 82 2013-01-29 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 77 $9,788 $107,058 QQQ_TRANSITION + 83 2013-01-29 BUY_GRADUAL QQQ 153 $ 60.35 $ 9,233 77 $555 $107,058 QQQ_TRANSITION + 84 2015-08-25 SELL_GRADUAL QQQ 440 $ 90.96 $ 40,021 19 $40,576 $161,163 FEAR_TRANSITION + 85 2015-08-26 SELL_GRADUAL QQQ 440 $ 95.54 $ 42,037 30 $82,612 $167,235 FEAR_TRANSITION + 86 2015-08-27 SELL_GRADUAL QQQ 440 $ 97.96 $ 43,101 37 $125,714 $169,377 FEAR_TRANSITION + 87 2015-08-28 SELL_GRADUAL QQQ 440 $ 97.94 $ 43,093 37 $168,807 $169,370 FEAR_TRANSITION + 88 2015-09-01 BUY_GRADUAL NFLX 399 $ 105.79 $ 42,210 29 $126,597 $169,343 GREED_TRANSITION + 89 2015-09-02 BUY_GRADUAL NVDA 78306 $ 0.54 $ 42,267 34 $84,330 $169,220 GREED_TRANSITION + 90 2015-09-03 BUY_GRADUAL MU 2608 $ 16.20 $ 42,259 36 $42,071 $167,587 GREED_TRANSITION + 91 2015-09-28 STOP_LOSS MU 2608 $ 13.73 $ 35,814 29 $77,885 $162,450 VOLATILE_STOCKS + 92 2015-09-28 BUY_QQQ_STOPLOSS QQQ 385 $ 92.94 $ 35,784 29 $42,102 $162,450 VOLATILE_STOCKS + 93 2017-02-16 SELL_GRADUAL NFLX 99 $ 142.01 $ 14,059 74 $56,161 $353,212 QQQ_TRANSITION + 94 2017-02-16 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,711 74 $107,872 $353,212 QQQ_TRANSITION + 95 2017-02-16 BUY_GRADUAL QQQ 634 $ 121.85 $ 77,254 74 $30,618 $353,212 QQQ_TRANSITION + 96 2017-02-17 SELL_GRADUAL NFLX 99 $ 142.22 $ 14,080 74 $44,697 $353,786 QQQ_TRANSITION + 97 2017-02-17 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,701 74 $96,399 $353,786 QQQ_TRANSITION + 98 2017-02-17 BUY_GRADUAL QQQ 632 $ 122.38 $ 77,344 74 $19,055 $353,786 QQQ_TRANSITION + 99 2017-02-21 SELL_GRADUAL NFLX 99 $ 142.60 $ 14,117 76 $33,172 $358,565 QQQ_TRANSITION +100 2017-02-21 SELL_GRADUAL NVDA 19576 $ 2.74 $ 53,553 76 $86,725 $358,565 QQQ_TRANSITION +101 2017-02-21 BUY_GRADUAL QQQ 628 $ 122.98 $ 77,233 76 $9,492 $358,565 QQQ_TRANSITION +102 2017-02-22 SELL_GRADUAL NFLX 99 $ 143.86 $ 14,242 75 $23,734 $358,720 QQQ_TRANSITION +103 2017-02-22 SELL_GRADUAL NVDA 19576 $ 2.73 $ 53,471 75 $77,204 $358,720 QQQ_TRANSITION +104 2018-12-18 SELL_GRADUAL QQQ 570 $ 151.55 $ 86,385 24 $163,590 $424,134 FEAR_TRANSITION +105 2018-12-19 SELL_GRADUAL QQQ 570 $ 147.83 $ 84,264 23 $247,854 $417,734 FEAR_TRANSITION +106 2018-12-20 SELL_GRADUAL QQQ 570 $ 145.69 $ 83,043 22 $330,897 $415,268 FEAR_TRANSITION +107 2018-12-21 SELL_GRADUAL QQQ 570 $ 141.17 $ 80,469 20 $411,366 $412,636 FEAR_TRANSITION +108 2018-12-27 BUY_GRADUAL ZS 2566 $ 40.17 $ 103,076 28 $308,289 $412,683 GREED_TRANSITION +109 2018-12-28 BUY_GRADUAL AMD 5785 $ 17.82 $ 103,089 26 $205,201 $411,733 GREED_TRANSITION +110 2018-12-31 BUY_GRADUAL MDB 1231 $ 83.74 $ 103,084 30 $102,117 $413,961 GREED_TRANSITION +111 2019-04-24 SELL_GRADUAL ZS 641 $ 64.99 $ 41,659 75 $143,775 $601,726 QQQ_TRANSITION +112 2019-04-24 SELL_GRADUAL AMD 1446 $ 28.46 $ 41,153 75 $184,928 $601,726 QQQ_TRANSITION +113 2019-04-24 SELL_GRADUAL MDB 307 $ 135.16 $ 41,494 75 $226,423 $601,726 QQQ_TRANSITION +114 2019-04-24 BUY_GRADUAL QQQ 825 $ 182.34 $ 150,428 75 $75,994 $601,726 QQQ_TRANSITION +115 2019-04-25 SELL_GRADUAL ZS 641 $ 66.96 $ 42,921 74 $118,916 $604,944 QQQ_TRANSITION +116 2019-04-25 SELL_GRADUAL AMD 1446 $ 27.66 $ 39,996 74 $158,912 $604,944 QQQ_TRANSITION +117 2019-04-25 SELL_GRADUAL MDB 307 $ 137.66 $ 42,262 74 $201,174 $604,944 QQQ_TRANSITION +118 2019-04-25 BUY_GRADUAL QQQ 822 $ 183.08 $ 150,490 74 $50,684 $604,944 QQQ_TRANSITION +119 2019-04-26 SELL_GRADUAL ZS 641 $ 66.94 $ 42,909 74 $93,592 $606,662 QQQ_TRANSITION +120 2019-04-26 SELL_GRADUAL AMD 1446 $ 27.88 $ 40,314 74 $133,907 $606,662 QQQ_TRANSITION +121 2019-04-26 SELL_GRADUAL MDB 307 $ 138.98 $ 42,667 74 $176,574 $606,662 QQQ_TRANSITION +122 2019-04-26 BUY_GRADUAL QQQ 821 $ 183.24 $ 150,441 74 $26,133 $606,662 QQQ_TRANSITION +123 2019-04-29 SELL_GRADUAL ZS 641 $ 68.20 $ 43,716 74 $69,849 $607,606 QQQ_TRANSITION +124 2019-04-29 SELL_GRADUAL AMD 1446 $ 27.69 $ 40,040 74 $109,889 $607,606 QQQ_TRANSITION +125 2019-04-29 SELL_GRADUAL MDB 307 $ 137.50 $ 42,212 74 $152,101 $607,606 QQQ_TRANSITION +126 2019-04-29 BUY_GRADUAL QQQ 820 $ 183.60 $ 150,549 74 $1,552 $607,606 QQQ_TRANSITION +127 2020-02-28 SELL_GRADUAL QQQ 822 $ 199.07 $ 163,635 20 $165,188 $658,590 FEAR_TRANSITION +128 2020-03-02 SELL_GRADUAL QQQ 822 $ 209.34 $ 172,080 37 $337,267 $684,012 FEAR_TRANSITION +129 2020-03-03 SELL_GRADUAL QQQ 822 $ 202.63 $ 166,561 29 $503,829 $672,909 FEAR_TRANSITION +130 2020-03-04 SELL_GRADUAL QQQ 822 $ 211.08 $ 173,511 38 $677,340 $679,956 FEAR_TRANSITION +131 2020-03-06 BUY_GRADUAL MRNA 5736 $ 29.61 $ 169,843 25 $507,497 $679,826 GREED_TRANSITION +132 2020-03-09 STOP_LOSS MRNA 5736 $ 24.29 $ 139,327 17 $646,824 $649,100 GREED_TRANSITION +133 2020-03-09 BUY_QQQ_STOPLOSS QQQ 744 $ 187.24 $ 139,306 17 $507,518 $649,100 GREED_TRANSITION +134 2020-03-09 BUY_GRADUAL TSLA 4190 $ 40.53 $ 169,835 17 $337,683 $649,100 GREED_TRANSITION +135 2020-03-10 BUY_GRADUAL MRVL 7509 $ 22.62 $ 169,858 25 $167,825 $667,230 GREED_TRANSITION +136 2020-03-12 STOP_LOSS MRVL 7509 $ 19.06 $ 143,146 17 $310,971 $597,219 VOLATILE_STOCKS +137 2020-03-12 BUY_QQQ_STOPLOSS QQQ 834 $ 171.52 $ 143,049 17 $167,922 $597,219 VOLATILE_STOCKS +138 2020-03-16 STOP_LOSS TSLA 4190 $ 29.67 $ 124,323 19 $292,245 $552,585 VOLATILE_STOCKS +139 2020-03-16 BUY_QQQ_STOPLOSS QQQ 759 $ 163.76 $ 124,296 19 $167,949 $552,585 VOLATILE_STOCKS +140 2020-06-03 BUY_GRADUAL QQQ 185 $ 229.44 $ 42,446 79 $125,503 $707,213 QQQ_TRANSITION +141 2020-06-04 BUY_GRADUAL QQQ 187 $ 227.83 $ 42,604 79 $82,899 $703,082 QQQ_TRANSITION +142 2020-06-05 BUY_GRADUAL QQQ 183 $ 232.35 $ 42,519 84 $40,380 $715,284 QQQ_TRANSITION +143 2022-01-26 SELL_GRADUAL QQQ 723 $ 336.81 $ 243,517 24 $283,896 $1,018,473 FEAR_TRANSITION +144 2022-01-27 SELL_GRADUAL QQQ 723 $ 333.42 $ 241,064 23 $524,961 $1,011,148 FEAR_TRANSITION +145 2022-01-28 SELL_GRADUAL QQQ 723 $ 343.88 $ 248,626 32 $773,587 $1,026,391 FEAR_TRANSITION +146 2022-01-31 SELL_GRADUAL QQQ 723 $ 354.88 $ 256,577 41 $1,030,164 $1,034,684 FEAR_TRANSITION +147 2022-02-02 BUY_GRADUAL NFLX 601 $ 429.48 $ 258,117 47 $772,047 $1,034,708 GREED_TRANSITION +148 2022-02-03 BUY_GRADUAL PDD 4689 $ 55.13 $ 258,505 43 $513,542 $1,020,143 GREED_TRANSITION +149 2022-02-04 BUY_GRADUAL WBD 8945 $ 28.90 $ 258,510 46 $255,031 $1,026,385 GREED_TRANSITION +150 2022-03-03 STOP_LOSS PDD 4689 $ 45.22 $ 212,037 39 $467,068 $948,795 VOLATILE_STOCKS +151 2022-03-03 BUY_QQQ_STOPLOSS QQQ 633 $ 334.56 $ 211,774 39 $255,294 $948,795 VOLATILE_STOCKS +152 2022-03-11 STOP_LOSS WBD 8945 $ 23.99 $ 214,591 31 $469,885 $878,854 VOLATILE_STOCKS +153 2022-03-11 BUY_QQQ_STOPLOSS QQQ 676 $ 317.10 $ 214,358 31 $255,526 $878,854 VOLATILE_STOCKS +154 2022-08-16 SELL_GRADUAL NFLX 151 $ 245.69 $ 37,099 76 $292,625 $833,290 QQQ_TRANSITION +155 2022-08-16 BUY_GRADUAL QQQ 313 $ 325.83 $ 101,984 76 $190,641 $833,290 QQQ_TRANSITION +156 2022-08-17 SELL_GRADUAL NFLX 151 $ 241.15 $ 36,414 72 $227,055 $825,132 QQQ_TRANSITION +157 2022-08-17 BUY_GRADUAL QQQ 316 $ 322.11 $ 101,787 72 $125,268 $825,132 QQQ_TRANSITION +158 2022-08-18 SELL_GRADUAL NFLX 151 $ 245.17 $ 37,021 73 $162,289 $827,836 QQQ_TRANSITION +159 2022-08-18 BUY_GRADUAL QQQ 316 $ 322.89 $ 102,032 73 $60,257 $827,836 QQQ_TRANSITION +160 2022-08-19 SELL_GRADUAL NFLX 151 $ 241.16 $ 36,415 68 $96,672 $812,946 QQQ_TRANSITION +161 2022-09-26 SELL_GRADUAL QQQ 564 $ 269.53 $ 152,012 25 $248,684 $706,189 FEAR_TRANSITION +162 2022-09-27 SELL_GRADUAL QQQ 564 $ 269.63 $ 152,073 26 $400,758 $706,398 FEAR_TRANSITION +163 2022-09-28 SELL_GRADUAL QQQ 564 $ 275.00 $ 155,098 33 $555,856 $712,492 FEAR_TRANSITION +164 2022-09-29 SELL_GRADUAL QQQ 564 $ 267.07 $ 150,627 29 $706,483 $707,973 FEAR_TRANSITION +165 2022-10-03 BUY_GRADUAL BIIB 669 $ 264.30 $ 176,817 32 $529,667 $707,986 GREED_TRANSITION +166 2022-10-04 BUY_GRADUAL REGN 239 $ 737.50 $ 176,263 42 $353,404 $710,159 GREED_TRANSITION +167 2022-10-05 BUY_GRADUAL NFLX 747 $ 236.73 $ 176,837 42 $176,567 $707,777 GREED_TRANSITION +168 2023-02-03 SELL_GRADUAL BIIB 167 $ 283.63 $ 47,366 72 $223,933 $827,955 QQQ_TRANSITION +169 2023-02-03 SELL_GRADUAL REGN 59 $ 780.97 $ 46,077 72 $270,010 $827,955 QQQ_TRANSITION +170 2023-02-03 SELL_GRADUAL NFLX 186 $ 365.90 $ 68,057 72 $338,067 $827,955 QQQ_TRANSITION +171 2023-02-03 BUY_GRADUAL QQQ 683 $ 301.49 $ 205,920 72 $132,147 $827,955 QQQ_TRANSITION +172 2023-02-06 SELL_GRADUAL BIIB 167 $ 288.28 $ 48,143 65 $180,290 $825,631 QQQ_TRANSITION +173 2023-02-06 SELL_GRADUAL REGN 59 $ 778.71 $ 45,944 65 $226,234 $825,631 QQQ_TRANSITION +174 2023-02-06 SELL_GRADUAL NFLX 186 $ 361.48 $ 67,235 65 $293,469 $825,631 QQQ_TRANSITION +175 2023-02-06 BUY_GRADUAL QQQ 688 $ 298.94 $ 205,673 65 $87,796 $825,631 QQQ_TRANSITION +176 2023-02-07 SELL_GRADUAL BIIB 167 $ 291.43 $ 48,669 70 $136,464 $834,461 QQQ_TRANSITION +177 2023-02-07 SELL_GRADUAL REGN 59 $ 767.82 $ 45,301 70 $181,766 $834,461 QQQ_TRANSITION +178 2023-02-07 SELL_GRADUAL NFLX 186 $ 362.95 $ 67,509 70 $249,274 $834,461 QQQ_TRANSITION +179 2023-02-07 BUY_GRADUAL QQQ 674 $ 305.14 $ 205,663 70 $43,612 $834,461 QQQ_TRANSITION +180 2023-02-08 SELL_GRADUAL BIIB 167 $ 285.45 $ 47,670 64 $91,282 $821,817 QQQ_TRANSITION +181 2023-02-08 SELL_GRADUAL REGN 59 $ 747.59 $ 44,108 64 $135,390 $821,817 QQQ_TRANSITION +182 2023-02-08 SELL_GRADUAL NFLX 186 $ 366.83 $ 68,230 64 $203,620 $821,817 QQQ_TRANSITION +183 2023-11-09 STOP_LOSS BIIB 1 $ 224.65 $ 225 54 $203,845 $958,229 QQQ_HOLD +184 2025-04-07 SELL_GRADUAL QQQ 511 $ 423.21 $ 216,263 24 $420,108 $1,075,227 FEAR_TRANSITION +185 2025-04-08 SELL_GRADUAL QQQ 511 $ 415.59 $ 212,368 23 $632,476 $1,063,447 FEAR_TRANSITION +186 2025-04-09 SELL_GRADUAL QQQ 511 $ 465.48 $ 237,859 39 $870,335 $1,115,043 FEAR_TRANSITION +187 2025-04-10 SELL_GRADUAL QQQ 511 $ 445.68 $ 227,742 34 $1,098,077 $1,104,615 FEAR_TRANSITION +188 2025-04-14 BUY_GRADUAL MCHP 7155 $ 38.58 $ 276,050 37 $822,027 $1,104,757 GREED_TRANSITION +189 2025-04-15 BUY_GRADUAL WBD 33461 $ 8.25 $ 276,053 38 $545,974 $1,103,807 GREED_TRANSITION +190 2025-04-16 STOP_LOSS WBD 33461 $ 7.92 $ 265,011 32 $810,985 $1,087,112 GREED_TRANSITION +191 2025-04-16 BUY_QQQ_STOPLOSS QQQ 597 $ 443.68 $ 264,878 32 $546,107 $1,087,112 GREED_TRANSITION +192 2025-04-16 BUY_GRADUAL AMD 3126 $ 88.29 $ 275,995 32 $270,112 $1,087,112 GREED_TRANSITION +193 2025-05-20 SELL_GRADUAL AMD 781 $ 113.51 $ 88,651 76 $358,764 $1,374,923 QQQ_TRANSITION +194 2025-05-20 SELL_GRADUAL MCHP 1788 $ 60.33 $ 107,876 76 $466,639 $1,374,923 QQQ_TRANSITION +195 2025-05-20 BUY_GRADUAL QQQ 512 $ 519.69 $ 266,079 76 $200,560 $1,374,923 QQQ_TRANSITION +196 2025-05-21 SELL_GRADUAL AMD 781 $ 112.06 $ 87,519 69 $288,079 $1,355,190 QQQ_TRANSITION +197 2025-05-21 SELL_GRADUAL MCHP 1788 $ 58.79 $ 105,125 69 $393,204 $1,355,190 QQQ_TRANSITION +198 2025-05-21 BUY_GRADUAL QQQ 520 $ 512.46 $ 266,481 69 $126,722 $1,355,190 QQQ_TRANSITION +199 2025-05-22 SELL_GRADUAL AMD 781 $ 110.71 $ 86,465 67 $213,187 $1,351,925 QQQ_TRANSITION +200 2025-05-22 SELL_GRADUAL MCHP 1788 $ 58.05 $ 103,793 67 $316,980 $1,351,925 QQQ_TRANSITION +201 2025-05-22 BUY_GRADUAL QQQ 519 $ 513.42 $ 266,467 67 $50,514 $1,351,925 QQQ_TRANSITION +202 2025-05-23 SELL_GRADUAL AMD 781 $ 110.31 $ 86,152 62 $136,666 $1,338,007 QQQ_TRANSITION +203 2025-05-23 SELL_GRADUAL MCHP 1788 $ 56.19 $ 100,468 62 $237,133 $1,338,007 QQQ_TRANSITION diff --git a/backtest/reports/detailed_trades_20250718_190932.txt b/backtest/reports/detailed_trades_20250718_190932.txt new file mode 100644 index 0000000..2982348 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_190932.txt @@ -0,0 +1,220 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 203 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 96 + BUY_GRADUAL: 67 + STOP_LOSS: 21 + BUY_QQQ_STOPLOSS: 19 + +📋 详细交易记录: +====================================================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash Assets State +====================================================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 $100,829 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 $100,940 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 $99,951 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 $100,383 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 $100,381 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 $99,964 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 $98,656 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 $97,152 GREED_TRANSITION + 9 2008-02-20 STOP_LOSS TMUS 852 $ 24.77 $ 21,101 54 $21,153 $94,385 VOLATILE_STOCKS + 10 2008-02-20 BUY_QQQ_STOPLOSS QQQ 556 $ 37.93 $ 21,090 54 $63 $94,385 VOLATILE_STOCKS + 11 2008-02-28 STOP_LOSS DXCM 11274 $ 1.88 $ 21,251 50 $21,314 $94,276 VOLATILE_STOCKS + 12 2008-02-28 BUY_QQQ_STOPLOSS QQQ 557 $ 38.09 $ 21,215 50 $99 $94,276 VOLATILE_STOCKS + 13 2008-04-25 STOP_LOSS SBUX 3549 $ 6.00 $ 21,310 69 $21,409 $95,418 VOLATILE_STOCKS + 14 2008-04-25 BUY_QQQ_STOPLOSS QQQ 523 $ 40.74 $ 21,305 69 $104 $95,418 VOLATILE_STOCKS + 15 2008-10-09 STOP_LOSS ILMN 745 $ 28.21 $ 21,017 9 $21,121 $65,760 VOLATILE_STOCKS + 16 2008-10-09 BUY_QQQ_STOPLOSS QQQ 770 $ 27.27 $ 20,997 9 $124 $65,760 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $93 $73,275 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $63 $73,296 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $33 $71,582 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $3 $71,624 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 602 $ 40.00 $ 24,082 34 $24,086 $96,453 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 602 $ 39.77 $ 23,941 33 $48,026 $96,027 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 602 $ 39.40 $ 23,720 27 $71,746 $95,585 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 602 $ 40.34 $ 24,282 38 $96,028 $96,149 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2705 $ 8.87 $ 24,002 37 $72,026 $96,146 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1539 $ 15.59 $ 24,000 35 $48,026 $95,899 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1793 $ 13.38 $ 23,998 34 $24,028 $95,666 GREED_TRANSITION + 28 2010-06-09 STOP_LOSS TMUS 1793 $ 13.38 $ 23,998 33 $48,026 $97,032 GREED_TRANSITION + 29 2010-06-09 BUY_QQQ_STOPLOSS QQQ 627 $ 38.22 $ 23,961 33 $24,065 $97,032 GREED_TRANSITION + 30 2010-06-09 BUY_GRADUAL LULU 1227 $ 19.55 $ 23,988 33 $77 $97,032 GREED_TRANSITION + 31 2010-08-30 STOP_LOSS LULU 1227 $ 15.95 $ 19,571 31 $19,648 $94,278 VOLATILE_STOCKS + 32 2010-08-30 BUY_QQQ_STOPLOSS QQQ 512 $ 38.18 $ 19,548 31 $100 $94,278 VOLATILE_STOCKS + 33 2010-11-04 SELL_GRADUAL SIRI 676 $ 13.31 $ 8,998 80 $9,097 $126,785 QQQ_TRANSITION + 34 2010-11-04 SELL_GRADUAL NFLX 384 $ 23.97 $ 9,206 80 $18,303 $126,785 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL SIRI 676 $ 13.85 $ 9,365 80 $27,668 $127,933 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL NFLX 384 $ 24.01 $ 9,221 80 $36,890 $127,933 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 404 $ 47.10 $ 19,027 80 $17,862 $127,933 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 676 $ 14.03 $ 9,487 78 $27,349 $128,387 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 384 $ 24.16 $ 9,278 78 $36,627 $128,387 QQQ_TRANSITION + 40 2010-11-08 BUY_GRADUAL QQQ 404 $ 47.16 $ 19,052 78 $17,575 $128,387 QQQ_TRANSITION + 41 2010-11-09 SELL_GRADUAL SIRI 676 $ 13.49 $ 9,120 74 $26,695 $127,596 QQQ_TRANSITION + 42 2010-11-09 SELL_GRADUAL NFLX 384 $ 24.35 $ 9,351 74 $36,046 $127,596 QQQ_TRANSITION + 43 2010-11-09 BUY_GRADUAL QQQ 406 $ 46.90 $ 19,043 74 $17,003 $127,596 QQQ_TRANSITION + 44 2011-08-05 SELL_GRADUAL QQQ 589 $ 47.50 $ 27,980 16 $44,983 $129,044 FEAR_TRANSITION + 45 2011-08-08 SELL_GRADUAL QQQ 589 $ 44.65 $ 26,296 12 $71,279 $123,983 FEAR_TRANSITION + 46 2011-08-09 SELL_GRADUAL QQQ 589 $ 46.80 $ 27,564 21 $98,843 $126,526 FEAR_TRANSITION + 47 2011-08-10 SELL_GRADUAL QQQ 589 $ 44.88 $ 26,436 16 $125,280 $125,395 FEAR_TRANSITION + 48 2011-08-16 BUY_GRADUAL AMD 4944 $ 6.34 $ 31,345 28 $93,935 $125,400 GREED_TRANSITION + 49 2011-08-17 BUY_GRADUAL DXCM 11087 $ 2.83 $ 31,348 27 $62,586 $124,408 GREED_TRANSITION + 50 2011-08-18 BUY_GRADUAL ILMN 684 $ 45.82 $ 31,339 21 $31,247 $121,090 GREED_TRANSITION + 51 2011-09-29 STOP_LOSS AMD 4944 $ 5.31 $ 26,253 36 $57,500 $119,987 VOLATILE_STOCKS + 52 2011-09-29 BUY_QQQ_STOPLOSS QQQ 551 $ 47.64 $ 26,248 36 $31,252 $119,987 VOLATILE_STOCKS + 53 2011-10-07 STOP_LOSS ILMN 684 $ 26.44 $ 18,085 46 $49,337 $106,508 VOLATILE_STOCKS + 54 2011-10-07 BUY_QQQ_STOPLOSS QQQ 378 $ 47.80 $ 18,070 46 $31,267 $106,508 VOLATILE_STOCKS + 55 2011-10-25 STOP_LOSS NFLX 3 $ 11.05 $ 33 58 $31,300 $107,461 VOLATILE_STOCKS + 56 2011-11-03 STOP_LOSS DXCM 11087 $ 1.97 $ 21,814 63 $53,114 $100,849 VOLATILE_STOCKS + 57 2011-11-03 BUY_QQQ_STOPLOSS QQQ 424 $ 51.37 $ 21,780 63 $31,334 $100,849 VOLATILE_STOCKS + 58 2012-01-20 BUY_GRADUAL QQQ 147 $ 53.05 $ 7,798 76 $23,536 $103,125 QQQ_TRANSITION + 59 2012-01-23 BUY_GRADUAL QQQ 147 $ 53.06 $ 7,800 75 $15,735 $103,151 QQQ_TRANSITION + 60 2012-01-24 BUY_GRADUAL QQQ 147 $ 52.97 $ 7,786 74 $7,949 $102,990 QQQ_TRANSITION + 61 2012-01-25 BUY_GRADUAL QQQ 146 $ 53.63 $ 7,830 75 $119 $104,184 QQQ_TRANSITION + 62 2012-05-21 SELL_GRADUAL QQQ 485 $ 55.57 $ 26,953 34 $27,072 $107,948 FEAR_TRANSITION + 63 2012-05-22 SELL_GRADUAL QQQ 485 $ 55.51 $ 26,922 34 $53,994 $107,857 FEAR_TRANSITION + 64 2012-05-23 SELL_GRADUAL QQQ 485 $ 55.62 $ 26,974 33 $80,968 $107,961 FEAR_TRANSITION + 65 2012-05-24 SELL_GRADUAL QQQ 485 $ 55.25 $ 26,797 32 $107,766 $107,784 FEAR_TRANSITION + 66 2012-05-29 BUY_GRADUAL VRTX 466 $ 57.80 $ 26,935 39 $80,831 $107,783 GREED_TRANSITION + 67 2012-05-30 BUY_GRADUAL NXPI 1395 $ 19.31 $ 26,933 33 $53,898 $108,892 GREED_TRANSITION + 68 2012-05-31 BUY_GRADUAL REGN 199 $ 135.28 $ 26,920 34 $26,978 $108,301 GREED_TRANSITION + 69 2012-06-01 BUY_GRADUAL MNST 2291 $ 11.76 $ 26,935 27 $44 $103,625 GREED_TRANSITION + 70 2012-06-13 STOP_LOSS REGN 199 $ 111.57 $ 22,203 44 $22,246 $101,613 VOLATILE_STOCKS + 71 2012-06-13 BUY_QQQ_STOPLOSS QQQ 401 $ 55.23 $ 22,149 44 $97 $101,613 VOLATILE_STOCKS + 72 2012-07-24 STOP_LOSS VRTX 466 $ 48.37 $ 22,540 51 $22,638 $98,436 VOLATILE_STOCKS + 73 2012-07-24 BUY_QQQ_STOPLOSS QQQ 401 $ 56.10 $ 22,497 51 $141 $98,436 VOLATILE_STOCKS + 74 2012-08-10 STOP_LOSS MNST 2291 $ 9.05 $ 20,722 62 $20,863 $99,014 VOLATILE_STOCKS + 75 2012-08-10 BUY_QQQ_STOPLOSS QQQ 347 $ 59.58 $ 20,673 62 $190 $99,014 VOLATILE_STOCKS + 76 2013-01-24 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 75 $9,399 $105,958 QQQ_TRANSITION + 77 2013-01-24 BUY_GRADUAL QQQ 154 $ 59.90 $ 9,224 75 $175 $105,958 QQQ_TRANSITION + 78 2013-01-25 SELL_GRADUAL NXPI 348 $ 26.98 $ 9,390 77 $9,565 $106,902 QQQ_TRANSITION + 79 2013-01-25 BUY_GRADUAL QQQ 154 $ 60.20 $ 9,272 77 $293 $106,902 QQQ_TRANSITION + 80 2013-01-28 SELL_GRADUAL NXPI 348 $ 27.35 $ 9,518 76 $9,811 $107,355 QQQ_TRANSITION + 81 2013-01-28 BUY_GRADUAL QQQ 153 $ 60.34 $ 9,232 76 $579 $107,355 QQQ_TRANSITION + 82 2013-01-29 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 77 $9,788 $107,058 QQQ_TRANSITION + 83 2013-01-29 BUY_GRADUAL QQQ 153 $ 60.35 $ 9,233 77 $555 $107,058 QQQ_TRANSITION + 84 2015-08-25 SELL_GRADUAL QQQ 440 $ 90.96 $ 40,021 19 $40,576 $161,163 FEAR_TRANSITION + 85 2015-08-26 SELL_GRADUAL QQQ 440 $ 95.54 $ 42,037 30 $82,612 $167,235 FEAR_TRANSITION + 86 2015-08-27 SELL_GRADUAL QQQ 440 $ 97.96 $ 43,101 37 $125,714 $169,377 FEAR_TRANSITION + 87 2015-08-28 SELL_GRADUAL QQQ 440 $ 97.94 $ 43,093 37 $168,807 $169,370 FEAR_TRANSITION + 88 2015-09-01 BUY_GRADUAL NFLX 399 $ 105.79 $ 42,210 29 $126,597 $169,343 GREED_TRANSITION + 89 2015-09-02 BUY_GRADUAL NVDA 78306 $ 0.54 $ 42,267 34 $84,330 $169,220 GREED_TRANSITION + 90 2015-09-03 BUY_GRADUAL MU 2608 $ 16.20 $ 42,259 36 $42,071 $167,587 GREED_TRANSITION + 91 2015-09-28 STOP_LOSS MU 2608 $ 13.73 $ 35,814 29 $77,885 $162,450 VOLATILE_STOCKS + 92 2015-09-28 BUY_QQQ_STOPLOSS QQQ 385 $ 92.94 $ 35,784 29 $42,102 $162,450 VOLATILE_STOCKS + 93 2017-02-16 SELL_GRADUAL NFLX 99 $ 142.01 $ 14,059 74 $56,161 $353,212 QQQ_TRANSITION + 94 2017-02-16 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,711 74 $107,872 $353,212 QQQ_TRANSITION + 95 2017-02-16 BUY_GRADUAL QQQ 634 $ 121.85 $ 77,254 74 $30,618 $353,212 QQQ_TRANSITION + 96 2017-02-17 SELL_GRADUAL NFLX 99 $ 142.22 $ 14,080 74 $44,697 $353,786 QQQ_TRANSITION + 97 2017-02-17 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,701 74 $96,399 $353,786 QQQ_TRANSITION + 98 2017-02-17 BUY_GRADUAL QQQ 632 $ 122.38 $ 77,344 74 $19,055 $353,786 QQQ_TRANSITION + 99 2017-02-21 SELL_GRADUAL NFLX 99 $ 142.60 $ 14,117 76 $33,172 $358,565 QQQ_TRANSITION +100 2017-02-21 SELL_GRADUAL NVDA 19576 $ 2.74 $ 53,553 76 $86,725 $358,565 QQQ_TRANSITION +101 2017-02-21 BUY_GRADUAL QQQ 628 $ 122.98 $ 77,233 76 $9,492 $358,565 QQQ_TRANSITION +102 2017-02-22 SELL_GRADUAL NFLX 99 $ 143.86 $ 14,242 75 $23,734 $358,720 QQQ_TRANSITION +103 2017-02-22 SELL_GRADUAL NVDA 19576 $ 2.73 $ 53,471 75 $77,204 $358,720 QQQ_TRANSITION +104 2018-12-18 SELL_GRADUAL QQQ 570 $ 151.55 $ 86,385 24 $163,590 $424,134 FEAR_TRANSITION +105 2018-12-19 SELL_GRADUAL QQQ 570 $ 147.83 $ 84,264 23 $247,854 $417,734 FEAR_TRANSITION +106 2018-12-20 SELL_GRADUAL QQQ 570 $ 145.69 $ 83,043 22 $330,897 $415,268 FEAR_TRANSITION +107 2018-12-21 SELL_GRADUAL QQQ 570 $ 141.17 $ 80,469 20 $411,366 $412,636 FEAR_TRANSITION +108 2018-12-27 BUY_GRADUAL ZS 2566 $ 40.17 $ 103,076 28 $308,289 $412,683 GREED_TRANSITION +109 2018-12-28 BUY_GRADUAL AMD 5785 $ 17.82 $ 103,089 26 $205,201 $411,733 GREED_TRANSITION +110 2018-12-31 BUY_GRADUAL MDB 1231 $ 83.74 $ 103,084 30 $102,117 $413,961 GREED_TRANSITION +111 2019-04-24 SELL_GRADUAL ZS 641 $ 64.99 $ 41,659 75 $143,775 $601,726 QQQ_TRANSITION +112 2019-04-24 SELL_GRADUAL AMD 1446 $ 28.46 $ 41,153 75 $184,928 $601,726 QQQ_TRANSITION +113 2019-04-24 SELL_GRADUAL MDB 307 $ 135.16 $ 41,494 75 $226,423 $601,726 QQQ_TRANSITION +114 2019-04-24 BUY_GRADUAL QQQ 825 $ 182.34 $ 150,428 75 $75,994 $601,726 QQQ_TRANSITION +115 2019-04-25 SELL_GRADUAL ZS 641 $ 66.96 $ 42,921 74 $118,916 $604,944 QQQ_TRANSITION +116 2019-04-25 SELL_GRADUAL AMD 1446 $ 27.66 $ 39,996 74 $158,912 $604,944 QQQ_TRANSITION +117 2019-04-25 SELL_GRADUAL MDB 307 $ 137.66 $ 42,262 74 $201,174 $604,944 QQQ_TRANSITION +118 2019-04-25 BUY_GRADUAL QQQ 822 $ 183.08 $ 150,490 74 $50,684 $604,944 QQQ_TRANSITION +119 2019-04-26 SELL_GRADUAL ZS 641 $ 66.94 $ 42,909 74 $93,592 $606,662 QQQ_TRANSITION +120 2019-04-26 SELL_GRADUAL AMD 1446 $ 27.88 $ 40,314 74 $133,907 $606,662 QQQ_TRANSITION +121 2019-04-26 SELL_GRADUAL MDB 307 $ 138.98 $ 42,667 74 $176,574 $606,662 QQQ_TRANSITION +122 2019-04-26 BUY_GRADUAL QQQ 821 $ 183.24 $ 150,441 74 $26,133 $606,662 QQQ_TRANSITION +123 2019-04-29 SELL_GRADUAL ZS 641 $ 68.20 $ 43,716 74 $69,849 $607,606 QQQ_TRANSITION +124 2019-04-29 SELL_GRADUAL AMD 1446 $ 27.69 $ 40,040 74 $109,889 $607,606 QQQ_TRANSITION +125 2019-04-29 SELL_GRADUAL MDB 307 $ 137.50 $ 42,212 74 $152,101 $607,606 QQQ_TRANSITION +126 2019-04-29 BUY_GRADUAL QQQ 820 $ 183.60 $ 150,549 74 $1,552 $607,606 QQQ_TRANSITION +127 2020-02-28 SELL_GRADUAL QQQ 822 $ 199.07 $ 163,635 20 $165,188 $658,590 FEAR_TRANSITION +128 2020-03-02 SELL_GRADUAL QQQ 822 $ 209.34 $ 172,080 37 $337,267 $684,012 FEAR_TRANSITION +129 2020-03-03 SELL_GRADUAL QQQ 822 $ 202.63 $ 166,561 29 $503,829 $672,909 FEAR_TRANSITION +130 2020-03-04 SELL_GRADUAL QQQ 822 $ 211.08 $ 173,511 38 $677,340 $679,956 FEAR_TRANSITION +131 2020-03-06 BUY_GRADUAL MRNA 5736 $ 29.61 $ 169,843 25 $507,497 $679,826 GREED_TRANSITION +132 2020-03-09 STOP_LOSS MRNA 5736 $ 24.29 $ 139,327 17 $646,824 $649,100 GREED_TRANSITION +133 2020-03-09 BUY_QQQ_STOPLOSS QQQ 744 $ 187.24 $ 139,306 17 $507,518 $649,100 GREED_TRANSITION +134 2020-03-09 BUY_GRADUAL TSLA 4190 $ 40.53 $ 169,835 17 $337,683 $649,100 GREED_TRANSITION +135 2020-03-10 BUY_GRADUAL MRVL 7509 $ 22.62 $ 169,858 25 $167,825 $667,230 GREED_TRANSITION +136 2020-03-12 STOP_LOSS MRVL 7509 $ 19.06 $ 143,146 17 $310,971 $597,219 VOLATILE_STOCKS +137 2020-03-12 BUY_QQQ_STOPLOSS QQQ 834 $ 171.52 $ 143,049 17 $167,922 $597,219 VOLATILE_STOCKS +138 2020-03-16 STOP_LOSS TSLA 4190 $ 29.67 $ 124,323 19 $292,245 $552,585 VOLATILE_STOCKS +139 2020-03-16 BUY_QQQ_STOPLOSS QQQ 759 $ 163.76 $ 124,296 19 $167,949 $552,585 VOLATILE_STOCKS +140 2020-06-03 BUY_GRADUAL QQQ 185 $ 229.44 $ 42,446 79 $125,503 $707,213 QQQ_TRANSITION +141 2020-06-04 BUY_GRADUAL QQQ 187 $ 227.83 $ 42,604 79 $82,899 $703,082 QQQ_TRANSITION +142 2020-06-05 BUY_GRADUAL QQQ 183 $ 232.35 $ 42,519 84 $40,380 $715,284 QQQ_TRANSITION +143 2022-01-26 SELL_GRADUAL QQQ 723 $ 336.81 $ 243,517 24 $283,896 $1,018,473 FEAR_TRANSITION +144 2022-01-27 SELL_GRADUAL QQQ 723 $ 333.42 $ 241,064 23 $524,961 $1,011,148 FEAR_TRANSITION +145 2022-01-28 SELL_GRADUAL QQQ 723 $ 343.88 $ 248,626 32 $773,587 $1,026,391 FEAR_TRANSITION +146 2022-01-31 SELL_GRADUAL QQQ 723 $ 354.88 $ 256,577 41 $1,030,164 $1,034,684 FEAR_TRANSITION +147 2022-02-02 BUY_GRADUAL NFLX 601 $ 429.48 $ 258,117 47 $772,047 $1,034,708 GREED_TRANSITION +148 2022-02-03 BUY_GRADUAL PDD 4689 $ 55.13 $ 258,505 43 $513,542 $1,020,143 GREED_TRANSITION +149 2022-02-04 BUY_GRADUAL WBD 8945 $ 28.90 $ 258,510 46 $255,031 $1,026,385 GREED_TRANSITION +150 2022-03-03 STOP_LOSS PDD 4689 $ 45.22 $ 212,037 39 $467,068 $948,795 VOLATILE_STOCKS +151 2022-03-03 BUY_QQQ_STOPLOSS QQQ 633 $ 334.56 $ 211,774 39 $255,294 $948,795 VOLATILE_STOCKS +152 2022-03-11 STOP_LOSS WBD 8945 $ 23.99 $ 214,591 31 $469,885 $878,854 VOLATILE_STOCKS +153 2022-03-11 BUY_QQQ_STOPLOSS QQQ 676 $ 317.10 $ 214,358 31 $255,526 $878,854 VOLATILE_STOCKS +154 2022-08-16 SELL_GRADUAL NFLX 151 $ 245.69 $ 37,099 76 $292,625 $833,290 QQQ_TRANSITION +155 2022-08-16 BUY_GRADUAL QQQ 313 $ 325.83 $ 101,984 76 $190,641 $833,290 QQQ_TRANSITION +156 2022-08-17 SELL_GRADUAL NFLX 151 $ 241.15 $ 36,414 72 $227,055 $825,132 QQQ_TRANSITION +157 2022-08-17 BUY_GRADUAL QQQ 316 $ 322.11 $ 101,787 72 $125,268 $825,132 QQQ_TRANSITION +158 2022-08-18 SELL_GRADUAL NFLX 151 $ 245.17 $ 37,021 73 $162,289 $827,836 QQQ_TRANSITION +159 2022-08-18 BUY_GRADUAL QQQ 316 $ 322.89 $ 102,032 73 $60,257 $827,836 QQQ_TRANSITION +160 2022-08-19 SELL_GRADUAL NFLX 151 $ 241.16 $ 36,415 68 $96,672 $812,946 QQQ_TRANSITION +161 2022-09-26 SELL_GRADUAL QQQ 564 $ 269.53 $ 152,012 25 $248,684 $706,189 FEAR_TRANSITION +162 2022-09-27 SELL_GRADUAL QQQ 564 $ 269.63 $ 152,073 26 $400,758 $706,398 FEAR_TRANSITION +163 2022-09-28 SELL_GRADUAL QQQ 564 $ 275.00 $ 155,098 33 $555,856 $712,492 FEAR_TRANSITION +164 2022-09-29 SELL_GRADUAL QQQ 564 $ 267.07 $ 150,627 29 $706,483 $707,973 FEAR_TRANSITION +165 2022-10-03 BUY_GRADUAL BIIB 669 $ 264.30 $ 176,817 32 $529,667 $707,986 GREED_TRANSITION +166 2022-10-04 BUY_GRADUAL REGN 239 $ 737.50 $ 176,263 42 $353,404 $710,159 GREED_TRANSITION +167 2022-10-05 BUY_GRADUAL NFLX 747 $ 236.73 $ 176,837 42 $176,567 $707,777 GREED_TRANSITION +168 2023-02-03 SELL_GRADUAL BIIB 167 $ 283.63 $ 47,366 72 $223,933 $827,955 QQQ_TRANSITION +169 2023-02-03 SELL_GRADUAL REGN 59 $ 780.97 $ 46,077 72 $270,010 $827,955 QQQ_TRANSITION +170 2023-02-03 SELL_GRADUAL NFLX 186 $ 365.90 $ 68,057 72 $338,067 $827,955 QQQ_TRANSITION +171 2023-02-03 BUY_GRADUAL QQQ 683 $ 301.49 $ 205,920 72 $132,147 $827,955 QQQ_TRANSITION +172 2023-02-06 SELL_GRADUAL BIIB 167 $ 288.28 $ 48,143 65 $180,290 $825,631 QQQ_TRANSITION +173 2023-02-06 SELL_GRADUAL REGN 59 $ 778.71 $ 45,944 65 $226,234 $825,631 QQQ_TRANSITION +174 2023-02-06 SELL_GRADUAL NFLX 186 $ 361.48 $ 67,235 65 $293,469 $825,631 QQQ_TRANSITION +175 2023-02-06 BUY_GRADUAL QQQ 688 $ 298.94 $ 205,673 65 $87,796 $825,631 QQQ_TRANSITION +176 2023-02-07 SELL_GRADUAL BIIB 167 $ 291.43 $ 48,669 70 $136,464 $834,461 QQQ_TRANSITION +177 2023-02-07 SELL_GRADUAL REGN 59 $ 767.82 $ 45,301 70 $181,766 $834,461 QQQ_TRANSITION +178 2023-02-07 SELL_GRADUAL NFLX 186 $ 362.95 $ 67,509 70 $249,274 $834,461 QQQ_TRANSITION +179 2023-02-07 BUY_GRADUAL QQQ 674 $ 305.14 $ 205,663 70 $43,612 $834,461 QQQ_TRANSITION +180 2023-02-08 SELL_GRADUAL BIIB 167 $ 285.45 $ 47,670 64 $91,282 $821,817 QQQ_TRANSITION +181 2023-02-08 SELL_GRADUAL REGN 59 $ 747.59 $ 44,108 64 $135,390 $821,817 QQQ_TRANSITION +182 2023-02-08 SELL_GRADUAL NFLX 186 $ 366.83 $ 68,230 64 $203,620 $821,817 QQQ_TRANSITION +183 2023-11-09 STOP_LOSS BIIB 1 $ 224.65 $ 225 54 $203,845 $958,229 QQQ_HOLD +184 2025-04-07 SELL_GRADUAL QQQ 511 $ 423.21 $ 216,263 24 $420,108 $1,075,227 FEAR_TRANSITION +185 2025-04-08 SELL_GRADUAL QQQ 511 $ 415.59 $ 212,368 23 $632,476 $1,063,447 FEAR_TRANSITION +186 2025-04-09 SELL_GRADUAL QQQ 511 $ 465.48 $ 237,859 39 $870,335 $1,115,043 FEAR_TRANSITION +187 2025-04-10 SELL_GRADUAL QQQ 511 $ 445.68 $ 227,742 34 $1,098,077 $1,104,615 FEAR_TRANSITION +188 2025-04-14 BUY_GRADUAL MCHP 7155 $ 38.58 $ 276,050 37 $822,027 $1,104,757 GREED_TRANSITION +189 2025-04-15 BUY_GRADUAL WBD 33461 $ 8.25 $ 276,053 38 $545,974 $1,103,807 GREED_TRANSITION +190 2025-04-16 STOP_LOSS WBD 33461 $ 7.92 $ 265,011 32 $810,985 $1,087,112 GREED_TRANSITION +191 2025-04-16 BUY_QQQ_STOPLOSS QQQ 597 $ 443.68 $ 264,878 32 $546,107 $1,087,112 GREED_TRANSITION +192 2025-04-16 BUY_GRADUAL AMD 3126 $ 88.29 $ 275,995 32 $270,112 $1,087,112 GREED_TRANSITION +193 2025-05-20 SELL_GRADUAL AMD 781 $ 113.51 $ 88,651 76 $358,764 $1,374,923 QQQ_TRANSITION +194 2025-05-20 SELL_GRADUAL MCHP 1788 $ 60.33 $ 107,876 76 $466,639 $1,374,923 QQQ_TRANSITION +195 2025-05-20 BUY_GRADUAL QQQ 512 $ 519.69 $ 266,079 76 $200,560 $1,374,923 QQQ_TRANSITION +196 2025-05-21 SELL_GRADUAL AMD 781 $ 112.06 $ 87,519 69 $288,079 $1,355,190 QQQ_TRANSITION +197 2025-05-21 SELL_GRADUAL MCHP 1788 $ 58.79 $ 105,125 69 $393,204 $1,355,190 QQQ_TRANSITION +198 2025-05-21 BUY_GRADUAL QQQ 520 $ 512.46 $ 266,481 69 $126,722 $1,355,190 QQQ_TRANSITION +199 2025-05-22 SELL_GRADUAL AMD 781 $ 110.71 $ 86,465 67 $213,187 $1,351,925 QQQ_TRANSITION +200 2025-05-22 SELL_GRADUAL MCHP 1788 $ 58.05 $ 103,793 67 $316,980 $1,351,925 QQQ_TRANSITION +201 2025-05-22 BUY_GRADUAL QQQ 519 $ 513.42 $ 266,467 67 $50,514 $1,351,925 QQQ_TRANSITION +202 2025-05-23 SELL_GRADUAL AMD 781 $ 110.31 $ 86,152 62 $136,666 $1,338,007 QQQ_TRANSITION +203 2025-05-23 SELL_GRADUAL MCHP 1788 $ 56.19 $ 100,468 62 $237,133 $1,338,007 QQQ_TRANSITION diff --git a/backtest/reports/detailed_trades_20250718_191059.txt b/backtest/reports/detailed_trades_20250718_191059.txt new file mode 100644 index 0000000..2982348 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_191059.txt @@ -0,0 +1,220 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 203 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 96 + BUY_GRADUAL: 67 + STOP_LOSS: 21 + BUY_QQQ_STOPLOSS: 19 + +📋 详细交易记录: +====================================================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash Assets State +====================================================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 $100,829 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 $100,940 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 $99,951 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 $100,383 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 $100,381 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 $99,964 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 $98,656 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 $97,152 GREED_TRANSITION + 9 2008-02-20 STOP_LOSS TMUS 852 $ 24.77 $ 21,101 54 $21,153 $94,385 VOLATILE_STOCKS + 10 2008-02-20 BUY_QQQ_STOPLOSS QQQ 556 $ 37.93 $ 21,090 54 $63 $94,385 VOLATILE_STOCKS + 11 2008-02-28 STOP_LOSS DXCM 11274 $ 1.88 $ 21,251 50 $21,314 $94,276 VOLATILE_STOCKS + 12 2008-02-28 BUY_QQQ_STOPLOSS QQQ 557 $ 38.09 $ 21,215 50 $99 $94,276 VOLATILE_STOCKS + 13 2008-04-25 STOP_LOSS SBUX 3549 $ 6.00 $ 21,310 69 $21,409 $95,418 VOLATILE_STOCKS + 14 2008-04-25 BUY_QQQ_STOPLOSS QQQ 523 $ 40.74 $ 21,305 69 $104 $95,418 VOLATILE_STOCKS + 15 2008-10-09 STOP_LOSS ILMN 745 $ 28.21 $ 21,017 9 $21,121 $65,760 VOLATILE_STOCKS + 16 2008-10-09 BUY_QQQ_STOPLOSS QQQ 770 $ 27.27 $ 20,997 9 $124 $65,760 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $93 $73,275 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $63 $73,296 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $33 $71,582 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $3 $71,624 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 602 $ 40.00 $ 24,082 34 $24,086 $96,453 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 602 $ 39.77 $ 23,941 33 $48,026 $96,027 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 602 $ 39.40 $ 23,720 27 $71,746 $95,585 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 602 $ 40.34 $ 24,282 38 $96,028 $96,149 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2705 $ 8.87 $ 24,002 37 $72,026 $96,146 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1539 $ 15.59 $ 24,000 35 $48,026 $95,899 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1793 $ 13.38 $ 23,998 34 $24,028 $95,666 GREED_TRANSITION + 28 2010-06-09 STOP_LOSS TMUS 1793 $ 13.38 $ 23,998 33 $48,026 $97,032 GREED_TRANSITION + 29 2010-06-09 BUY_QQQ_STOPLOSS QQQ 627 $ 38.22 $ 23,961 33 $24,065 $97,032 GREED_TRANSITION + 30 2010-06-09 BUY_GRADUAL LULU 1227 $ 19.55 $ 23,988 33 $77 $97,032 GREED_TRANSITION + 31 2010-08-30 STOP_LOSS LULU 1227 $ 15.95 $ 19,571 31 $19,648 $94,278 VOLATILE_STOCKS + 32 2010-08-30 BUY_QQQ_STOPLOSS QQQ 512 $ 38.18 $ 19,548 31 $100 $94,278 VOLATILE_STOCKS + 33 2010-11-04 SELL_GRADUAL SIRI 676 $ 13.31 $ 8,998 80 $9,097 $126,785 QQQ_TRANSITION + 34 2010-11-04 SELL_GRADUAL NFLX 384 $ 23.97 $ 9,206 80 $18,303 $126,785 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL SIRI 676 $ 13.85 $ 9,365 80 $27,668 $127,933 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL NFLX 384 $ 24.01 $ 9,221 80 $36,890 $127,933 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 404 $ 47.10 $ 19,027 80 $17,862 $127,933 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 676 $ 14.03 $ 9,487 78 $27,349 $128,387 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 384 $ 24.16 $ 9,278 78 $36,627 $128,387 QQQ_TRANSITION + 40 2010-11-08 BUY_GRADUAL QQQ 404 $ 47.16 $ 19,052 78 $17,575 $128,387 QQQ_TRANSITION + 41 2010-11-09 SELL_GRADUAL SIRI 676 $ 13.49 $ 9,120 74 $26,695 $127,596 QQQ_TRANSITION + 42 2010-11-09 SELL_GRADUAL NFLX 384 $ 24.35 $ 9,351 74 $36,046 $127,596 QQQ_TRANSITION + 43 2010-11-09 BUY_GRADUAL QQQ 406 $ 46.90 $ 19,043 74 $17,003 $127,596 QQQ_TRANSITION + 44 2011-08-05 SELL_GRADUAL QQQ 589 $ 47.50 $ 27,980 16 $44,983 $129,044 FEAR_TRANSITION + 45 2011-08-08 SELL_GRADUAL QQQ 589 $ 44.65 $ 26,296 12 $71,279 $123,983 FEAR_TRANSITION + 46 2011-08-09 SELL_GRADUAL QQQ 589 $ 46.80 $ 27,564 21 $98,843 $126,526 FEAR_TRANSITION + 47 2011-08-10 SELL_GRADUAL QQQ 589 $ 44.88 $ 26,436 16 $125,280 $125,395 FEAR_TRANSITION + 48 2011-08-16 BUY_GRADUAL AMD 4944 $ 6.34 $ 31,345 28 $93,935 $125,400 GREED_TRANSITION + 49 2011-08-17 BUY_GRADUAL DXCM 11087 $ 2.83 $ 31,348 27 $62,586 $124,408 GREED_TRANSITION + 50 2011-08-18 BUY_GRADUAL ILMN 684 $ 45.82 $ 31,339 21 $31,247 $121,090 GREED_TRANSITION + 51 2011-09-29 STOP_LOSS AMD 4944 $ 5.31 $ 26,253 36 $57,500 $119,987 VOLATILE_STOCKS + 52 2011-09-29 BUY_QQQ_STOPLOSS QQQ 551 $ 47.64 $ 26,248 36 $31,252 $119,987 VOLATILE_STOCKS + 53 2011-10-07 STOP_LOSS ILMN 684 $ 26.44 $ 18,085 46 $49,337 $106,508 VOLATILE_STOCKS + 54 2011-10-07 BUY_QQQ_STOPLOSS QQQ 378 $ 47.80 $ 18,070 46 $31,267 $106,508 VOLATILE_STOCKS + 55 2011-10-25 STOP_LOSS NFLX 3 $ 11.05 $ 33 58 $31,300 $107,461 VOLATILE_STOCKS + 56 2011-11-03 STOP_LOSS DXCM 11087 $ 1.97 $ 21,814 63 $53,114 $100,849 VOLATILE_STOCKS + 57 2011-11-03 BUY_QQQ_STOPLOSS QQQ 424 $ 51.37 $ 21,780 63 $31,334 $100,849 VOLATILE_STOCKS + 58 2012-01-20 BUY_GRADUAL QQQ 147 $ 53.05 $ 7,798 76 $23,536 $103,125 QQQ_TRANSITION + 59 2012-01-23 BUY_GRADUAL QQQ 147 $ 53.06 $ 7,800 75 $15,735 $103,151 QQQ_TRANSITION + 60 2012-01-24 BUY_GRADUAL QQQ 147 $ 52.97 $ 7,786 74 $7,949 $102,990 QQQ_TRANSITION + 61 2012-01-25 BUY_GRADUAL QQQ 146 $ 53.63 $ 7,830 75 $119 $104,184 QQQ_TRANSITION + 62 2012-05-21 SELL_GRADUAL QQQ 485 $ 55.57 $ 26,953 34 $27,072 $107,948 FEAR_TRANSITION + 63 2012-05-22 SELL_GRADUAL QQQ 485 $ 55.51 $ 26,922 34 $53,994 $107,857 FEAR_TRANSITION + 64 2012-05-23 SELL_GRADUAL QQQ 485 $ 55.62 $ 26,974 33 $80,968 $107,961 FEAR_TRANSITION + 65 2012-05-24 SELL_GRADUAL QQQ 485 $ 55.25 $ 26,797 32 $107,766 $107,784 FEAR_TRANSITION + 66 2012-05-29 BUY_GRADUAL VRTX 466 $ 57.80 $ 26,935 39 $80,831 $107,783 GREED_TRANSITION + 67 2012-05-30 BUY_GRADUAL NXPI 1395 $ 19.31 $ 26,933 33 $53,898 $108,892 GREED_TRANSITION + 68 2012-05-31 BUY_GRADUAL REGN 199 $ 135.28 $ 26,920 34 $26,978 $108,301 GREED_TRANSITION + 69 2012-06-01 BUY_GRADUAL MNST 2291 $ 11.76 $ 26,935 27 $44 $103,625 GREED_TRANSITION + 70 2012-06-13 STOP_LOSS REGN 199 $ 111.57 $ 22,203 44 $22,246 $101,613 VOLATILE_STOCKS + 71 2012-06-13 BUY_QQQ_STOPLOSS QQQ 401 $ 55.23 $ 22,149 44 $97 $101,613 VOLATILE_STOCKS + 72 2012-07-24 STOP_LOSS VRTX 466 $ 48.37 $ 22,540 51 $22,638 $98,436 VOLATILE_STOCKS + 73 2012-07-24 BUY_QQQ_STOPLOSS QQQ 401 $ 56.10 $ 22,497 51 $141 $98,436 VOLATILE_STOCKS + 74 2012-08-10 STOP_LOSS MNST 2291 $ 9.05 $ 20,722 62 $20,863 $99,014 VOLATILE_STOCKS + 75 2012-08-10 BUY_QQQ_STOPLOSS QQQ 347 $ 59.58 $ 20,673 62 $190 $99,014 VOLATILE_STOCKS + 76 2013-01-24 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 75 $9,399 $105,958 QQQ_TRANSITION + 77 2013-01-24 BUY_GRADUAL QQQ 154 $ 59.90 $ 9,224 75 $175 $105,958 QQQ_TRANSITION + 78 2013-01-25 SELL_GRADUAL NXPI 348 $ 26.98 $ 9,390 77 $9,565 $106,902 QQQ_TRANSITION + 79 2013-01-25 BUY_GRADUAL QQQ 154 $ 60.20 $ 9,272 77 $293 $106,902 QQQ_TRANSITION + 80 2013-01-28 SELL_GRADUAL NXPI 348 $ 27.35 $ 9,518 76 $9,811 $107,355 QQQ_TRANSITION + 81 2013-01-28 BUY_GRADUAL QQQ 153 $ 60.34 $ 9,232 76 $579 $107,355 QQQ_TRANSITION + 82 2013-01-29 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 77 $9,788 $107,058 QQQ_TRANSITION + 83 2013-01-29 BUY_GRADUAL QQQ 153 $ 60.35 $ 9,233 77 $555 $107,058 QQQ_TRANSITION + 84 2015-08-25 SELL_GRADUAL QQQ 440 $ 90.96 $ 40,021 19 $40,576 $161,163 FEAR_TRANSITION + 85 2015-08-26 SELL_GRADUAL QQQ 440 $ 95.54 $ 42,037 30 $82,612 $167,235 FEAR_TRANSITION + 86 2015-08-27 SELL_GRADUAL QQQ 440 $ 97.96 $ 43,101 37 $125,714 $169,377 FEAR_TRANSITION + 87 2015-08-28 SELL_GRADUAL QQQ 440 $ 97.94 $ 43,093 37 $168,807 $169,370 FEAR_TRANSITION + 88 2015-09-01 BUY_GRADUAL NFLX 399 $ 105.79 $ 42,210 29 $126,597 $169,343 GREED_TRANSITION + 89 2015-09-02 BUY_GRADUAL NVDA 78306 $ 0.54 $ 42,267 34 $84,330 $169,220 GREED_TRANSITION + 90 2015-09-03 BUY_GRADUAL MU 2608 $ 16.20 $ 42,259 36 $42,071 $167,587 GREED_TRANSITION + 91 2015-09-28 STOP_LOSS MU 2608 $ 13.73 $ 35,814 29 $77,885 $162,450 VOLATILE_STOCKS + 92 2015-09-28 BUY_QQQ_STOPLOSS QQQ 385 $ 92.94 $ 35,784 29 $42,102 $162,450 VOLATILE_STOCKS + 93 2017-02-16 SELL_GRADUAL NFLX 99 $ 142.01 $ 14,059 74 $56,161 $353,212 QQQ_TRANSITION + 94 2017-02-16 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,711 74 $107,872 $353,212 QQQ_TRANSITION + 95 2017-02-16 BUY_GRADUAL QQQ 634 $ 121.85 $ 77,254 74 $30,618 $353,212 QQQ_TRANSITION + 96 2017-02-17 SELL_GRADUAL NFLX 99 $ 142.22 $ 14,080 74 $44,697 $353,786 QQQ_TRANSITION + 97 2017-02-17 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,701 74 $96,399 $353,786 QQQ_TRANSITION + 98 2017-02-17 BUY_GRADUAL QQQ 632 $ 122.38 $ 77,344 74 $19,055 $353,786 QQQ_TRANSITION + 99 2017-02-21 SELL_GRADUAL NFLX 99 $ 142.60 $ 14,117 76 $33,172 $358,565 QQQ_TRANSITION +100 2017-02-21 SELL_GRADUAL NVDA 19576 $ 2.74 $ 53,553 76 $86,725 $358,565 QQQ_TRANSITION +101 2017-02-21 BUY_GRADUAL QQQ 628 $ 122.98 $ 77,233 76 $9,492 $358,565 QQQ_TRANSITION +102 2017-02-22 SELL_GRADUAL NFLX 99 $ 143.86 $ 14,242 75 $23,734 $358,720 QQQ_TRANSITION +103 2017-02-22 SELL_GRADUAL NVDA 19576 $ 2.73 $ 53,471 75 $77,204 $358,720 QQQ_TRANSITION +104 2018-12-18 SELL_GRADUAL QQQ 570 $ 151.55 $ 86,385 24 $163,590 $424,134 FEAR_TRANSITION +105 2018-12-19 SELL_GRADUAL QQQ 570 $ 147.83 $ 84,264 23 $247,854 $417,734 FEAR_TRANSITION +106 2018-12-20 SELL_GRADUAL QQQ 570 $ 145.69 $ 83,043 22 $330,897 $415,268 FEAR_TRANSITION +107 2018-12-21 SELL_GRADUAL QQQ 570 $ 141.17 $ 80,469 20 $411,366 $412,636 FEAR_TRANSITION +108 2018-12-27 BUY_GRADUAL ZS 2566 $ 40.17 $ 103,076 28 $308,289 $412,683 GREED_TRANSITION +109 2018-12-28 BUY_GRADUAL AMD 5785 $ 17.82 $ 103,089 26 $205,201 $411,733 GREED_TRANSITION +110 2018-12-31 BUY_GRADUAL MDB 1231 $ 83.74 $ 103,084 30 $102,117 $413,961 GREED_TRANSITION +111 2019-04-24 SELL_GRADUAL ZS 641 $ 64.99 $ 41,659 75 $143,775 $601,726 QQQ_TRANSITION +112 2019-04-24 SELL_GRADUAL AMD 1446 $ 28.46 $ 41,153 75 $184,928 $601,726 QQQ_TRANSITION +113 2019-04-24 SELL_GRADUAL MDB 307 $ 135.16 $ 41,494 75 $226,423 $601,726 QQQ_TRANSITION +114 2019-04-24 BUY_GRADUAL QQQ 825 $ 182.34 $ 150,428 75 $75,994 $601,726 QQQ_TRANSITION +115 2019-04-25 SELL_GRADUAL ZS 641 $ 66.96 $ 42,921 74 $118,916 $604,944 QQQ_TRANSITION +116 2019-04-25 SELL_GRADUAL AMD 1446 $ 27.66 $ 39,996 74 $158,912 $604,944 QQQ_TRANSITION +117 2019-04-25 SELL_GRADUAL MDB 307 $ 137.66 $ 42,262 74 $201,174 $604,944 QQQ_TRANSITION +118 2019-04-25 BUY_GRADUAL QQQ 822 $ 183.08 $ 150,490 74 $50,684 $604,944 QQQ_TRANSITION +119 2019-04-26 SELL_GRADUAL ZS 641 $ 66.94 $ 42,909 74 $93,592 $606,662 QQQ_TRANSITION +120 2019-04-26 SELL_GRADUAL AMD 1446 $ 27.88 $ 40,314 74 $133,907 $606,662 QQQ_TRANSITION +121 2019-04-26 SELL_GRADUAL MDB 307 $ 138.98 $ 42,667 74 $176,574 $606,662 QQQ_TRANSITION +122 2019-04-26 BUY_GRADUAL QQQ 821 $ 183.24 $ 150,441 74 $26,133 $606,662 QQQ_TRANSITION +123 2019-04-29 SELL_GRADUAL ZS 641 $ 68.20 $ 43,716 74 $69,849 $607,606 QQQ_TRANSITION +124 2019-04-29 SELL_GRADUAL AMD 1446 $ 27.69 $ 40,040 74 $109,889 $607,606 QQQ_TRANSITION +125 2019-04-29 SELL_GRADUAL MDB 307 $ 137.50 $ 42,212 74 $152,101 $607,606 QQQ_TRANSITION +126 2019-04-29 BUY_GRADUAL QQQ 820 $ 183.60 $ 150,549 74 $1,552 $607,606 QQQ_TRANSITION +127 2020-02-28 SELL_GRADUAL QQQ 822 $ 199.07 $ 163,635 20 $165,188 $658,590 FEAR_TRANSITION +128 2020-03-02 SELL_GRADUAL QQQ 822 $ 209.34 $ 172,080 37 $337,267 $684,012 FEAR_TRANSITION +129 2020-03-03 SELL_GRADUAL QQQ 822 $ 202.63 $ 166,561 29 $503,829 $672,909 FEAR_TRANSITION +130 2020-03-04 SELL_GRADUAL QQQ 822 $ 211.08 $ 173,511 38 $677,340 $679,956 FEAR_TRANSITION +131 2020-03-06 BUY_GRADUAL MRNA 5736 $ 29.61 $ 169,843 25 $507,497 $679,826 GREED_TRANSITION +132 2020-03-09 STOP_LOSS MRNA 5736 $ 24.29 $ 139,327 17 $646,824 $649,100 GREED_TRANSITION +133 2020-03-09 BUY_QQQ_STOPLOSS QQQ 744 $ 187.24 $ 139,306 17 $507,518 $649,100 GREED_TRANSITION +134 2020-03-09 BUY_GRADUAL TSLA 4190 $ 40.53 $ 169,835 17 $337,683 $649,100 GREED_TRANSITION +135 2020-03-10 BUY_GRADUAL MRVL 7509 $ 22.62 $ 169,858 25 $167,825 $667,230 GREED_TRANSITION +136 2020-03-12 STOP_LOSS MRVL 7509 $ 19.06 $ 143,146 17 $310,971 $597,219 VOLATILE_STOCKS +137 2020-03-12 BUY_QQQ_STOPLOSS QQQ 834 $ 171.52 $ 143,049 17 $167,922 $597,219 VOLATILE_STOCKS +138 2020-03-16 STOP_LOSS TSLA 4190 $ 29.67 $ 124,323 19 $292,245 $552,585 VOLATILE_STOCKS +139 2020-03-16 BUY_QQQ_STOPLOSS QQQ 759 $ 163.76 $ 124,296 19 $167,949 $552,585 VOLATILE_STOCKS +140 2020-06-03 BUY_GRADUAL QQQ 185 $ 229.44 $ 42,446 79 $125,503 $707,213 QQQ_TRANSITION +141 2020-06-04 BUY_GRADUAL QQQ 187 $ 227.83 $ 42,604 79 $82,899 $703,082 QQQ_TRANSITION +142 2020-06-05 BUY_GRADUAL QQQ 183 $ 232.35 $ 42,519 84 $40,380 $715,284 QQQ_TRANSITION +143 2022-01-26 SELL_GRADUAL QQQ 723 $ 336.81 $ 243,517 24 $283,896 $1,018,473 FEAR_TRANSITION +144 2022-01-27 SELL_GRADUAL QQQ 723 $ 333.42 $ 241,064 23 $524,961 $1,011,148 FEAR_TRANSITION +145 2022-01-28 SELL_GRADUAL QQQ 723 $ 343.88 $ 248,626 32 $773,587 $1,026,391 FEAR_TRANSITION +146 2022-01-31 SELL_GRADUAL QQQ 723 $ 354.88 $ 256,577 41 $1,030,164 $1,034,684 FEAR_TRANSITION +147 2022-02-02 BUY_GRADUAL NFLX 601 $ 429.48 $ 258,117 47 $772,047 $1,034,708 GREED_TRANSITION +148 2022-02-03 BUY_GRADUAL PDD 4689 $ 55.13 $ 258,505 43 $513,542 $1,020,143 GREED_TRANSITION +149 2022-02-04 BUY_GRADUAL WBD 8945 $ 28.90 $ 258,510 46 $255,031 $1,026,385 GREED_TRANSITION +150 2022-03-03 STOP_LOSS PDD 4689 $ 45.22 $ 212,037 39 $467,068 $948,795 VOLATILE_STOCKS +151 2022-03-03 BUY_QQQ_STOPLOSS QQQ 633 $ 334.56 $ 211,774 39 $255,294 $948,795 VOLATILE_STOCKS +152 2022-03-11 STOP_LOSS WBD 8945 $ 23.99 $ 214,591 31 $469,885 $878,854 VOLATILE_STOCKS +153 2022-03-11 BUY_QQQ_STOPLOSS QQQ 676 $ 317.10 $ 214,358 31 $255,526 $878,854 VOLATILE_STOCKS +154 2022-08-16 SELL_GRADUAL NFLX 151 $ 245.69 $ 37,099 76 $292,625 $833,290 QQQ_TRANSITION +155 2022-08-16 BUY_GRADUAL QQQ 313 $ 325.83 $ 101,984 76 $190,641 $833,290 QQQ_TRANSITION +156 2022-08-17 SELL_GRADUAL NFLX 151 $ 241.15 $ 36,414 72 $227,055 $825,132 QQQ_TRANSITION +157 2022-08-17 BUY_GRADUAL QQQ 316 $ 322.11 $ 101,787 72 $125,268 $825,132 QQQ_TRANSITION +158 2022-08-18 SELL_GRADUAL NFLX 151 $ 245.17 $ 37,021 73 $162,289 $827,836 QQQ_TRANSITION +159 2022-08-18 BUY_GRADUAL QQQ 316 $ 322.89 $ 102,032 73 $60,257 $827,836 QQQ_TRANSITION +160 2022-08-19 SELL_GRADUAL NFLX 151 $ 241.16 $ 36,415 68 $96,672 $812,946 QQQ_TRANSITION +161 2022-09-26 SELL_GRADUAL QQQ 564 $ 269.53 $ 152,012 25 $248,684 $706,189 FEAR_TRANSITION +162 2022-09-27 SELL_GRADUAL QQQ 564 $ 269.63 $ 152,073 26 $400,758 $706,398 FEAR_TRANSITION +163 2022-09-28 SELL_GRADUAL QQQ 564 $ 275.00 $ 155,098 33 $555,856 $712,492 FEAR_TRANSITION +164 2022-09-29 SELL_GRADUAL QQQ 564 $ 267.07 $ 150,627 29 $706,483 $707,973 FEAR_TRANSITION +165 2022-10-03 BUY_GRADUAL BIIB 669 $ 264.30 $ 176,817 32 $529,667 $707,986 GREED_TRANSITION +166 2022-10-04 BUY_GRADUAL REGN 239 $ 737.50 $ 176,263 42 $353,404 $710,159 GREED_TRANSITION +167 2022-10-05 BUY_GRADUAL NFLX 747 $ 236.73 $ 176,837 42 $176,567 $707,777 GREED_TRANSITION +168 2023-02-03 SELL_GRADUAL BIIB 167 $ 283.63 $ 47,366 72 $223,933 $827,955 QQQ_TRANSITION +169 2023-02-03 SELL_GRADUAL REGN 59 $ 780.97 $ 46,077 72 $270,010 $827,955 QQQ_TRANSITION +170 2023-02-03 SELL_GRADUAL NFLX 186 $ 365.90 $ 68,057 72 $338,067 $827,955 QQQ_TRANSITION +171 2023-02-03 BUY_GRADUAL QQQ 683 $ 301.49 $ 205,920 72 $132,147 $827,955 QQQ_TRANSITION +172 2023-02-06 SELL_GRADUAL BIIB 167 $ 288.28 $ 48,143 65 $180,290 $825,631 QQQ_TRANSITION +173 2023-02-06 SELL_GRADUAL REGN 59 $ 778.71 $ 45,944 65 $226,234 $825,631 QQQ_TRANSITION +174 2023-02-06 SELL_GRADUAL NFLX 186 $ 361.48 $ 67,235 65 $293,469 $825,631 QQQ_TRANSITION +175 2023-02-06 BUY_GRADUAL QQQ 688 $ 298.94 $ 205,673 65 $87,796 $825,631 QQQ_TRANSITION +176 2023-02-07 SELL_GRADUAL BIIB 167 $ 291.43 $ 48,669 70 $136,464 $834,461 QQQ_TRANSITION +177 2023-02-07 SELL_GRADUAL REGN 59 $ 767.82 $ 45,301 70 $181,766 $834,461 QQQ_TRANSITION +178 2023-02-07 SELL_GRADUAL NFLX 186 $ 362.95 $ 67,509 70 $249,274 $834,461 QQQ_TRANSITION +179 2023-02-07 BUY_GRADUAL QQQ 674 $ 305.14 $ 205,663 70 $43,612 $834,461 QQQ_TRANSITION +180 2023-02-08 SELL_GRADUAL BIIB 167 $ 285.45 $ 47,670 64 $91,282 $821,817 QQQ_TRANSITION +181 2023-02-08 SELL_GRADUAL REGN 59 $ 747.59 $ 44,108 64 $135,390 $821,817 QQQ_TRANSITION +182 2023-02-08 SELL_GRADUAL NFLX 186 $ 366.83 $ 68,230 64 $203,620 $821,817 QQQ_TRANSITION +183 2023-11-09 STOP_LOSS BIIB 1 $ 224.65 $ 225 54 $203,845 $958,229 QQQ_HOLD +184 2025-04-07 SELL_GRADUAL QQQ 511 $ 423.21 $ 216,263 24 $420,108 $1,075,227 FEAR_TRANSITION +185 2025-04-08 SELL_GRADUAL QQQ 511 $ 415.59 $ 212,368 23 $632,476 $1,063,447 FEAR_TRANSITION +186 2025-04-09 SELL_GRADUAL QQQ 511 $ 465.48 $ 237,859 39 $870,335 $1,115,043 FEAR_TRANSITION +187 2025-04-10 SELL_GRADUAL QQQ 511 $ 445.68 $ 227,742 34 $1,098,077 $1,104,615 FEAR_TRANSITION +188 2025-04-14 BUY_GRADUAL MCHP 7155 $ 38.58 $ 276,050 37 $822,027 $1,104,757 GREED_TRANSITION +189 2025-04-15 BUY_GRADUAL WBD 33461 $ 8.25 $ 276,053 38 $545,974 $1,103,807 GREED_TRANSITION +190 2025-04-16 STOP_LOSS WBD 33461 $ 7.92 $ 265,011 32 $810,985 $1,087,112 GREED_TRANSITION +191 2025-04-16 BUY_QQQ_STOPLOSS QQQ 597 $ 443.68 $ 264,878 32 $546,107 $1,087,112 GREED_TRANSITION +192 2025-04-16 BUY_GRADUAL AMD 3126 $ 88.29 $ 275,995 32 $270,112 $1,087,112 GREED_TRANSITION +193 2025-05-20 SELL_GRADUAL AMD 781 $ 113.51 $ 88,651 76 $358,764 $1,374,923 QQQ_TRANSITION +194 2025-05-20 SELL_GRADUAL MCHP 1788 $ 60.33 $ 107,876 76 $466,639 $1,374,923 QQQ_TRANSITION +195 2025-05-20 BUY_GRADUAL QQQ 512 $ 519.69 $ 266,079 76 $200,560 $1,374,923 QQQ_TRANSITION +196 2025-05-21 SELL_GRADUAL AMD 781 $ 112.06 $ 87,519 69 $288,079 $1,355,190 QQQ_TRANSITION +197 2025-05-21 SELL_GRADUAL MCHP 1788 $ 58.79 $ 105,125 69 $393,204 $1,355,190 QQQ_TRANSITION +198 2025-05-21 BUY_GRADUAL QQQ 520 $ 512.46 $ 266,481 69 $126,722 $1,355,190 QQQ_TRANSITION +199 2025-05-22 SELL_GRADUAL AMD 781 $ 110.71 $ 86,465 67 $213,187 $1,351,925 QQQ_TRANSITION +200 2025-05-22 SELL_GRADUAL MCHP 1788 $ 58.05 $ 103,793 67 $316,980 $1,351,925 QQQ_TRANSITION +201 2025-05-22 BUY_GRADUAL QQQ 519 $ 513.42 $ 266,467 67 $50,514 $1,351,925 QQQ_TRANSITION +202 2025-05-23 SELL_GRADUAL AMD 781 $ 110.31 $ 86,152 62 $136,666 $1,338,007 QQQ_TRANSITION +203 2025-05-23 SELL_GRADUAL MCHP 1788 $ 56.19 $ 100,468 62 $237,133 $1,338,007 QQQ_TRANSITION diff --git a/backtest/reports/detailed_trades_20250718_192206.txt b/backtest/reports/detailed_trades_20250718_192206.txt new file mode 100644 index 0000000..2982348 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_192206.txt @@ -0,0 +1,220 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 203 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 96 + BUY_GRADUAL: 67 + STOP_LOSS: 21 + BUY_QQQ_STOPLOSS: 19 + +📋 详细交易记录: +====================================================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash Assets State +====================================================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 $100,829 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 $100,940 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 $99,951 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 $100,383 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 $100,381 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 $99,964 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 $98,656 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 $97,152 GREED_TRANSITION + 9 2008-02-20 STOP_LOSS TMUS 852 $ 24.77 $ 21,101 54 $21,153 $94,385 VOLATILE_STOCKS + 10 2008-02-20 BUY_QQQ_STOPLOSS QQQ 556 $ 37.93 $ 21,090 54 $63 $94,385 VOLATILE_STOCKS + 11 2008-02-28 STOP_LOSS DXCM 11274 $ 1.88 $ 21,251 50 $21,314 $94,276 VOLATILE_STOCKS + 12 2008-02-28 BUY_QQQ_STOPLOSS QQQ 557 $ 38.09 $ 21,215 50 $99 $94,276 VOLATILE_STOCKS + 13 2008-04-25 STOP_LOSS SBUX 3549 $ 6.00 $ 21,310 69 $21,409 $95,418 VOLATILE_STOCKS + 14 2008-04-25 BUY_QQQ_STOPLOSS QQQ 523 $ 40.74 $ 21,305 69 $104 $95,418 VOLATILE_STOCKS + 15 2008-10-09 STOP_LOSS ILMN 745 $ 28.21 $ 21,017 9 $21,121 $65,760 VOLATILE_STOCKS + 16 2008-10-09 BUY_QQQ_STOPLOSS QQQ 770 $ 27.27 $ 20,997 9 $124 $65,760 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $93 $73,275 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $63 $73,296 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $33 $71,582 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $3 $71,624 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 602 $ 40.00 $ 24,082 34 $24,086 $96,453 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 602 $ 39.77 $ 23,941 33 $48,026 $96,027 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 602 $ 39.40 $ 23,720 27 $71,746 $95,585 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 602 $ 40.34 $ 24,282 38 $96,028 $96,149 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2705 $ 8.87 $ 24,002 37 $72,026 $96,146 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1539 $ 15.59 $ 24,000 35 $48,026 $95,899 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1793 $ 13.38 $ 23,998 34 $24,028 $95,666 GREED_TRANSITION + 28 2010-06-09 STOP_LOSS TMUS 1793 $ 13.38 $ 23,998 33 $48,026 $97,032 GREED_TRANSITION + 29 2010-06-09 BUY_QQQ_STOPLOSS QQQ 627 $ 38.22 $ 23,961 33 $24,065 $97,032 GREED_TRANSITION + 30 2010-06-09 BUY_GRADUAL LULU 1227 $ 19.55 $ 23,988 33 $77 $97,032 GREED_TRANSITION + 31 2010-08-30 STOP_LOSS LULU 1227 $ 15.95 $ 19,571 31 $19,648 $94,278 VOLATILE_STOCKS + 32 2010-08-30 BUY_QQQ_STOPLOSS QQQ 512 $ 38.18 $ 19,548 31 $100 $94,278 VOLATILE_STOCKS + 33 2010-11-04 SELL_GRADUAL SIRI 676 $ 13.31 $ 8,998 80 $9,097 $126,785 QQQ_TRANSITION + 34 2010-11-04 SELL_GRADUAL NFLX 384 $ 23.97 $ 9,206 80 $18,303 $126,785 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL SIRI 676 $ 13.85 $ 9,365 80 $27,668 $127,933 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL NFLX 384 $ 24.01 $ 9,221 80 $36,890 $127,933 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 404 $ 47.10 $ 19,027 80 $17,862 $127,933 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 676 $ 14.03 $ 9,487 78 $27,349 $128,387 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 384 $ 24.16 $ 9,278 78 $36,627 $128,387 QQQ_TRANSITION + 40 2010-11-08 BUY_GRADUAL QQQ 404 $ 47.16 $ 19,052 78 $17,575 $128,387 QQQ_TRANSITION + 41 2010-11-09 SELL_GRADUAL SIRI 676 $ 13.49 $ 9,120 74 $26,695 $127,596 QQQ_TRANSITION + 42 2010-11-09 SELL_GRADUAL NFLX 384 $ 24.35 $ 9,351 74 $36,046 $127,596 QQQ_TRANSITION + 43 2010-11-09 BUY_GRADUAL QQQ 406 $ 46.90 $ 19,043 74 $17,003 $127,596 QQQ_TRANSITION + 44 2011-08-05 SELL_GRADUAL QQQ 589 $ 47.50 $ 27,980 16 $44,983 $129,044 FEAR_TRANSITION + 45 2011-08-08 SELL_GRADUAL QQQ 589 $ 44.65 $ 26,296 12 $71,279 $123,983 FEAR_TRANSITION + 46 2011-08-09 SELL_GRADUAL QQQ 589 $ 46.80 $ 27,564 21 $98,843 $126,526 FEAR_TRANSITION + 47 2011-08-10 SELL_GRADUAL QQQ 589 $ 44.88 $ 26,436 16 $125,280 $125,395 FEAR_TRANSITION + 48 2011-08-16 BUY_GRADUAL AMD 4944 $ 6.34 $ 31,345 28 $93,935 $125,400 GREED_TRANSITION + 49 2011-08-17 BUY_GRADUAL DXCM 11087 $ 2.83 $ 31,348 27 $62,586 $124,408 GREED_TRANSITION + 50 2011-08-18 BUY_GRADUAL ILMN 684 $ 45.82 $ 31,339 21 $31,247 $121,090 GREED_TRANSITION + 51 2011-09-29 STOP_LOSS AMD 4944 $ 5.31 $ 26,253 36 $57,500 $119,987 VOLATILE_STOCKS + 52 2011-09-29 BUY_QQQ_STOPLOSS QQQ 551 $ 47.64 $ 26,248 36 $31,252 $119,987 VOLATILE_STOCKS + 53 2011-10-07 STOP_LOSS ILMN 684 $ 26.44 $ 18,085 46 $49,337 $106,508 VOLATILE_STOCKS + 54 2011-10-07 BUY_QQQ_STOPLOSS QQQ 378 $ 47.80 $ 18,070 46 $31,267 $106,508 VOLATILE_STOCKS + 55 2011-10-25 STOP_LOSS NFLX 3 $ 11.05 $ 33 58 $31,300 $107,461 VOLATILE_STOCKS + 56 2011-11-03 STOP_LOSS DXCM 11087 $ 1.97 $ 21,814 63 $53,114 $100,849 VOLATILE_STOCKS + 57 2011-11-03 BUY_QQQ_STOPLOSS QQQ 424 $ 51.37 $ 21,780 63 $31,334 $100,849 VOLATILE_STOCKS + 58 2012-01-20 BUY_GRADUAL QQQ 147 $ 53.05 $ 7,798 76 $23,536 $103,125 QQQ_TRANSITION + 59 2012-01-23 BUY_GRADUAL QQQ 147 $ 53.06 $ 7,800 75 $15,735 $103,151 QQQ_TRANSITION + 60 2012-01-24 BUY_GRADUAL QQQ 147 $ 52.97 $ 7,786 74 $7,949 $102,990 QQQ_TRANSITION + 61 2012-01-25 BUY_GRADUAL QQQ 146 $ 53.63 $ 7,830 75 $119 $104,184 QQQ_TRANSITION + 62 2012-05-21 SELL_GRADUAL QQQ 485 $ 55.57 $ 26,953 34 $27,072 $107,948 FEAR_TRANSITION + 63 2012-05-22 SELL_GRADUAL QQQ 485 $ 55.51 $ 26,922 34 $53,994 $107,857 FEAR_TRANSITION + 64 2012-05-23 SELL_GRADUAL QQQ 485 $ 55.62 $ 26,974 33 $80,968 $107,961 FEAR_TRANSITION + 65 2012-05-24 SELL_GRADUAL QQQ 485 $ 55.25 $ 26,797 32 $107,766 $107,784 FEAR_TRANSITION + 66 2012-05-29 BUY_GRADUAL VRTX 466 $ 57.80 $ 26,935 39 $80,831 $107,783 GREED_TRANSITION + 67 2012-05-30 BUY_GRADUAL NXPI 1395 $ 19.31 $ 26,933 33 $53,898 $108,892 GREED_TRANSITION + 68 2012-05-31 BUY_GRADUAL REGN 199 $ 135.28 $ 26,920 34 $26,978 $108,301 GREED_TRANSITION + 69 2012-06-01 BUY_GRADUAL MNST 2291 $ 11.76 $ 26,935 27 $44 $103,625 GREED_TRANSITION + 70 2012-06-13 STOP_LOSS REGN 199 $ 111.57 $ 22,203 44 $22,246 $101,613 VOLATILE_STOCKS + 71 2012-06-13 BUY_QQQ_STOPLOSS QQQ 401 $ 55.23 $ 22,149 44 $97 $101,613 VOLATILE_STOCKS + 72 2012-07-24 STOP_LOSS VRTX 466 $ 48.37 $ 22,540 51 $22,638 $98,436 VOLATILE_STOCKS + 73 2012-07-24 BUY_QQQ_STOPLOSS QQQ 401 $ 56.10 $ 22,497 51 $141 $98,436 VOLATILE_STOCKS + 74 2012-08-10 STOP_LOSS MNST 2291 $ 9.05 $ 20,722 62 $20,863 $99,014 VOLATILE_STOCKS + 75 2012-08-10 BUY_QQQ_STOPLOSS QQQ 347 $ 59.58 $ 20,673 62 $190 $99,014 VOLATILE_STOCKS + 76 2013-01-24 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 75 $9,399 $105,958 QQQ_TRANSITION + 77 2013-01-24 BUY_GRADUAL QQQ 154 $ 59.90 $ 9,224 75 $175 $105,958 QQQ_TRANSITION + 78 2013-01-25 SELL_GRADUAL NXPI 348 $ 26.98 $ 9,390 77 $9,565 $106,902 QQQ_TRANSITION + 79 2013-01-25 BUY_GRADUAL QQQ 154 $ 60.20 $ 9,272 77 $293 $106,902 QQQ_TRANSITION + 80 2013-01-28 SELL_GRADUAL NXPI 348 $ 27.35 $ 9,518 76 $9,811 $107,355 QQQ_TRANSITION + 81 2013-01-28 BUY_GRADUAL QQQ 153 $ 60.34 $ 9,232 76 $579 $107,355 QQQ_TRANSITION + 82 2013-01-29 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 77 $9,788 $107,058 QQQ_TRANSITION + 83 2013-01-29 BUY_GRADUAL QQQ 153 $ 60.35 $ 9,233 77 $555 $107,058 QQQ_TRANSITION + 84 2015-08-25 SELL_GRADUAL QQQ 440 $ 90.96 $ 40,021 19 $40,576 $161,163 FEAR_TRANSITION + 85 2015-08-26 SELL_GRADUAL QQQ 440 $ 95.54 $ 42,037 30 $82,612 $167,235 FEAR_TRANSITION + 86 2015-08-27 SELL_GRADUAL QQQ 440 $ 97.96 $ 43,101 37 $125,714 $169,377 FEAR_TRANSITION + 87 2015-08-28 SELL_GRADUAL QQQ 440 $ 97.94 $ 43,093 37 $168,807 $169,370 FEAR_TRANSITION + 88 2015-09-01 BUY_GRADUAL NFLX 399 $ 105.79 $ 42,210 29 $126,597 $169,343 GREED_TRANSITION + 89 2015-09-02 BUY_GRADUAL NVDA 78306 $ 0.54 $ 42,267 34 $84,330 $169,220 GREED_TRANSITION + 90 2015-09-03 BUY_GRADUAL MU 2608 $ 16.20 $ 42,259 36 $42,071 $167,587 GREED_TRANSITION + 91 2015-09-28 STOP_LOSS MU 2608 $ 13.73 $ 35,814 29 $77,885 $162,450 VOLATILE_STOCKS + 92 2015-09-28 BUY_QQQ_STOPLOSS QQQ 385 $ 92.94 $ 35,784 29 $42,102 $162,450 VOLATILE_STOCKS + 93 2017-02-16 SELL_GRADUAL NFLX 99 $ 142.01 $ 14,059 74 $56,161 $353,212 QQQ_TRANSITION + 94 2017-02-16 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,711 74 $107,872 $353,212 QQQ_TRANSITION + 95 2017-02-16 BUY_GRADUAL QQQ 634 $ 121.85 $ 77,254 74 $30,618 $353,212 QQQ_TRANSITION + 96 2017-02-17 SELL_GRADUAL NFLX 99 $ 142.22 $ 14,080 74 $44,697 $353,786 QQQ_TRANSITION + 97 2017-02-17 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,701 74 $96,399 $353,786 QQQ_TRANSITION + 98 2017-02-17 BUY_GRADUAL QQQ 632 $ 122.38 $ 77,344 74 $19,055 $353,786 QQQ_TRANSITION + 99 2017-02-21 SELL_GRADUAL NFLX 99 $ 142.60 $ 14,117 76 $33,172 $358,565 QQQ_TRANSITION +100 2017-02-21 SELL_GRADUAL NVDA 19576 $ 2.74 $ 53,553 76 $86,725 $358,565 QQQ_TRANSITION +101 2017-02-21 BUY_GRADUAL QQQ 628 $ 122.98 $ 77,233 76 $9,492 $358,565 QQQ_TRANSITION +102 2017-02-22 SELL_GRADUAL NFLX 99 $ 143.86 $ 14,242 75 $23,734 $358,720 QQQ_TRANSITION +103 2017-02-22 SELL_GRADUAL NVDA 19576 $ 2.73 $ 53,471 75 $77,204 $358,720 QQQ_TRANSITION +104 2018-12-18 SELL_GRADUAL QQQ 570 $ 151.55 $ 86,385 24 $163,590 $424,134 FEAR_TRANSITION +105 2018-12-19 SELL_GRADUAL QQQ 570 $ 147.83 $ 84,264 23 $247,854 $417,734 FEAR_TRANSITION +106 2018-12-20 SELL_GRADUAL QQQ 570 $ 145.69 $ 83,043 22 $330,897 $415,268 FEAR_TRANSITION +107 2018-12-21 SELL_GRADUAL QQQ 570 $ 141.17 $ 80,469 20 $411,366 $412,636 FEAR_TRANSITION +108 2018-12-27 BUY_GRADUAL ZS 2566 $ 40.17 $ 103,076 28 $308,289 $412,683 GREED_TRANSITION +109 2018-12-28 BUY_GRADUAL AMD 5785 $ 17.82 $ 103,089 26 $205,201 $411,733 GREED_TRANSITION +110 2018-12-31 BUY_GRADUAL MDB 1231 $ 83.74 $ 103,084 30 $102,117 $413,961 GREED_TRANSITION +111 2019-04-24 SELL_GRADUAL ZS 641 $ 64.99 $ 41,659 75 $143,775 $601,726 QQQ_TRANSITION +112 2019-04-24 SELL_GRADUAL AMD 1446 $ 28.46 $ 41,153 75 $184,928 $601,726 QQQ_TRANSITION +113 2019-04-24 SELL_GRADUAL MDB 307 $ 135.16 $ 41,494 75 $226,423 $601,726 QQQ_TRANSITION +114 2019-04-24 BUY_GRADUAL QQQ 825 $ 182.34 $ 150,428 75 $75,994 $601,726 QQQ_TRANSITION +115 2019-04-25 SELL_GRADUAL ZS 641 $ 66.96 $ 42,921 74 $118,916 $604,944 QQQ_TRANSITION +116 2019-04-25 SELL_GRADUAL AMD 1446 $ 27.66 $ 39,996 74 $158,912 $604,944 QQQ_TRANSITION +117 2019-04-25 SELL_GRADUAL MDB 307 $ 137.66 $ 42,262 74 $201,174 $604,944 QQQ_TRANSITION +118 2019-04-25 BUY_GRADUAL QQQ 822 $ 183.08 $ 150,490 74 $50,684 $604,944 QQQ_TRANSITION +119 2019-04-26 SELL_GRADUAL ZS 641 $ 66.94 $ 42,909 74 $93,592 $606,662 QQQ_TRANSITION +120 2019-04-26 SELL_GRADUAL AMD 1446 $ 27.88 $ 40,314 74 $133,907 $606,662 QQQ_TRANSITION +121 2019-04-26 SELL_GRADUAL MDB 307 $ 138.98 $ 42,667 74 $176,574 $606,662 QQQ_TRANSITION +122 2019-04-26 BUY_GRADUAL QQQ 821 $ 183.24 $ 150,441 74 $26,133 $606,662 QQQ_TRANSITION +123 2019-04-29 SELL_GRADUAL ZS 641 $ 68.20 $ 43,716 74 $69,849 $607,606 QQQ_TRANSITION +124 2019-04-29 SELL_GRADUAL AMD 1446 $ 27.69 $ 40,040 74 $109,889 $607,606 QQQ_TRANSITION +125 2019-04-29 SELL_GRADUAL MDB 307 $ 137.50 $ 42,212 74 $152,101 $607,606 QQQ_TRANSITION +126 2019-04-29 BUY_GRADUAL QQQ 820 $ 183.60 $ 150,549 74 $1,552 $607,606 QQQ_TRANSITION +127 2020-02-28 SELL_GRADUAL QQQ 822 $ 199.07 $ 163,635 20 $165,188 $658,590 FEAR_TRANSITION +128 2020-03-02 SELL_GRADUAL QQQ 822 $ 209.34 $ 172,080 37 $337,267 $684,012 FEAR_TRANSITION +129 2020-03-03 SELL_GRADUAL QQQ 822 $ 202.63 $ 166,561 29 $503,829 $672,909 FEAR_TRANSITION +130 2020-03-04 SELL_GRADUAL QQQ 822 $ 211.08 $ 173,511 38 $677,340 $679,956 FEAR_TRANSITION +131 2020-03-06 BUY_GRADUAL MRNA 5736 $ 29.61 $ 169,843 25 $507,497 $679,826 GREED_TRANSITION +132 2020-03-09 STOP_LOSS MRNA 5736 $ 24.29 $ 139,327 17 $646,824 $649,100 GREED_TRANSITION +133 2020-03-09 BUY_QQQ_STOPLOSS QQQ 744 $ 187.24 $ 139,306 17 $507,518 $649,100 GREED_TRANSITION +134 2020-03-09 BUY_GRADUAL TSLA 4190 $ 40.53 $ 169,835 17 $337,683 $649,100 GREED_TRANSITION +135 2020-03-10 BUY_GRADUAL MRVL 7509 $ 22.62 $ 169,858 25 $167,825 $667,230 GREED_TRANSITION +136 2020-03-12 STOP_LOSS MRVL 7509 $ 19.06 $ 143,146 17 $310,971 $597,219 VOLATILE_STOCKS +137 2020-03-12 BUY_QQQ_STOPLOSS QQQ 834 $ 171.52 $ 143,049 17 $167,922 $597,219 VOLATILE_STOCKS +138 2020-03-16 STOP_LOSS TSLA 4190 $ 29.67 $ 124,323 19 $292,245 $552,585 VOLATILE_STOCKS +139 2020-03-16 BUY_QQQ_STOPLOSS QQQ 759 $ 163.76 $ 124,296 19 $167,949 $552,585 VOLATILE_STOCKS +140 2020-06-03 BUY_GRADUAL QQQ 185 $ 229.44 $ 42,446 79 $125,503 $707,213 QQQ_TRANSITION +141 2020-06-04 BUY_GRADUAL QQQ 187 $ 227.83 $ 42,604 79 $82,899 $703,082 QQQ_TRANSITION +142 2020-06-05 BUY_GRADUAL QQQ 183 $ 232.35 $ 42,519 84 $40,380 $715,284 QQQ_TRANSITION +143 2022-01-26 SELL_GRADUAL QQQ 723 $ 336.81 $ 243,517 24 $283,896 $1,018,473 FEAR_TRANSITION +144 2022-01-27 SELL_GRADUAL QQQ 723 $ 333.42 $ 241,064 23 $524,961 $1,011,148 FEAR_TRANSITION +145 2022-01-28 SELL_GRADUAL QQQ 723 $ 343.88 $ 248,626 32 $773,587 $1,026,391 FEAR_TRANSITION +146 2022-01-31 SELL_GRADUAL QQQ 723 $ 354.88 $ 256,577 41 $1,030,164 $1,034,684 FEAR_TRANSITION +147 2022-02-02 BUY_GRADUAL NFLX 601 $ 429.48 $ 258,117 47 $772,047 $1,034,708 GREED_TRANSITION +148 2022-02-03 BUY_GRADUAL PDD 4689 $ 55.13 $ 258,505 43 $513,542 $1,020,143 GREED_TRANSITION +149 2022-02-04 BUY_GRADUAL WBD 8945 $ 28.90 $ 258,510 46 $255,031 $1,026,385 GREED_TRANSITION +150 2022-03-03 STOP_LOSS PDD 4689 $ 45.22 $ 212,037 39 $467,068 $948,795 VOLATILE_STOCKS +151 2022-03-03 BUY_QQQ_STOPLOSS QQQ 633 $ 334.56 $ 211,774 39 $255,294 $948,795 VOLATILE_STOCKS +152 2022-03-11 STOP_LOSS WBD 8945 $ 23.99 $ 214,591 31 $469,885 $878,854 VOLATILE_STOCKS +153 2022-03-11 BUY_QQQ_STOPLOSS QQQ 676 $ 317.10 $ 214,358 31 $255,526 $878,854 VOLATILE_STOCKS +154 2022-08-16 SELL_GRADUAL NFLX 151 $ 245.69 $ 37,099 76 $292,625 $833,290 QQQ_TRANSITION +155 2022-08-16 BUY_GRADUAL QQQ 313 $ 325.83 $ 101,984 76 $190,641 $833,290 QQQ_TRANSITION +156 2022-08-17 SELL_GRADUAL NFLX 151 $ 241.15 $ 36,414 72 $227,055 $825,132 QQQ_TRANSITION +157 2022-08-17 BUY_GRADUAL QQQ 316 $ 322.11 $ 101,787 72 $125,268 $825,132 QQQ_TRANSITION +158 2022-08-18 SELL_GRADUAL NFLX 151 $ 245.17 $ 37,021 73 $162,289 $827,836 QQQ_TRANSITION +159 2022-08-18 BUY_GRADUAL QQQ 316 $ 322.89 $ 102,032 73 $60,257 $827,836 QQQ_TRANSITION +160 2022-08-19 SELL_GRADUAL NFLX 151 $ 241.16 $ 36,415 68 $96,672 $812,946 QQQ_TRANSITION +161 2022-09-26 SELL_GRADUAL QQQ 564 $ 269.53 $ 152,012 25 $248,684 $706,189 FEAR_TRANSITION +162 2022-09-27 SELL_GRADUAL QQQ 564 $ 269.63 $ 152,073 26 $400,758 $706,398 FEAR_TRANSITION +163 2022-09-28 SELL_GRADUAL QQQ 564 $ 275.00 $ 155,098 33 $555,856 $712,492 FEAR_TRANSITION +164 2022-09-29 SELL_GRADUAL QQQ 564 $ 267.07 $ 150,627 29 $706,483 $707,973 FEAR_TRANSITION +165 2022-10-03 BUY_GRADUAL BIIB 669 $ 264.30 $ 176,817 32 $529,667 $707,986 GREED_TRANSITION +166 2022-10-04 BUY_GRADUAL REGN 239 $ 737.50 $ 176,263 42 $353,404 $710,159 GREED_TRANSITION +167 2022-10-05 BUY_GRADUAL NFLX 747 $ 236.73 $ 176,837 42 $176,567 $707,777 GREED_TRANSITION +168 2023-02-03 SELL_GRADUAL BIIB 167 $ 283.63 $ 47,366 72 $223,933 $827,955 QQQ_TRANSITION +169 2023-02-03 SELL_GRADUAL REGN 59 $ 780.97 $ 46,077 72 $270,010 $827,955 QQQ_TRANSITION +170 2023-02-03 SELL_GRADUAL NFLX 186 $ 365.90 $ 68,057 72 $338,067 $827,955 QQQ_TRANSITION +171 2023-02-03 BUY_GRADUAL QQQ 683 $ 301.49 $ 205,920 72 $132,147 $827,955 QQQ_TRANSITION +172 2023-02-06 SELL_GRADUAL BIIB 167 $ 288.28 $ 48,143 65 $180,290 $825,631 QQQ_TRANSITION +173 2023-02-06 SELL_GRADUAL REGN 59 $ 778.71 $ 45,944 65 $226,234 $825,631 QQQ_TRANSITION +174 2023-02-06 SELL_GRADUAL NFLX 186 $ 361.48 $ 67,235 65 $293,469 $825,631 QQQ_TRANSITION +175 2023-02-06 BUY_GRADUAL QQQ 688 $ 298.94 $ 205,673 65 $87,796 $825,631 QQQ_TRANSITION +176 2023-02-07 SELL_GRADUAL BIIB 167 $ 291.43 $ 48,669 70 $136,464 $834,461 QQQ_TRANSITION +177 2023-02-07 SELL_GRADUAL REGN 59 $ 767.82 $ 45,301 70 $181,766 $834,461 QQQ_TRANSITION +178 2023-02-07 SELL_GRADUAL NFLX 186 $ 362.95 $ 67,509 70 $249,274 $834,461 QQQ_TRANSITION +179 2023-02-07 BUY_GRADUAL QQQ 674 $ 305.14 $ 205,663 70 $43,612 $834,461 QQQ_TRANSITION +180 2023-02-08 SELL_GRADUAL BIIB 167 $ 285.45 $ 47,670 64 $91,282 $821,817 QQQ_TRANSITION +181 2023-02-08 SELL_GRADUAL REGN 59 $ 747.59 $ 44,108 64 $135,390 $821,817 QQQ_TRANSITION +182 2023-02-08 SELL_GRADUAL NFLX 186 $ 366.83 $ 68,230 64 $203,620 $821,817 QQQ_TRANSITION +183 2023-11-09 STOP_LOSS BIIB 1 $ 224.65 $ 225 54 $203,845 $958,229 QQQ_HOLD +184 2025-04-07 SELL_GRADUAL QQQ 511 $ 423.21 $ 216,263 24 $420,108 $1,075,227 FEAR_TRANSITION +185 2025-04-08 SELL_GRADUAL QQQ 511 $ 415.59 $ 212,368 23 $632,476 $1,063,447 FEAR_TRANSITION +186 2025-04-09 SELL_GRADUAL QQQ 511 $ 465.48 $ 237,859 39 $870,335 $1,115,043 FEAR_TRANSITION +187 2025-04-10 SELL_GRADUAL QQQ 511 $ 445.68 $ 227,742 34 $1,098,077 $1,104,615 FEAR_TRANSITION +188 2025-04-14 BUY_GRADUAL MCHP 7155 $ 38.58 $ 276,050 37 $822,027 $1,104,757 GREED_TRANSITION +189 2025-04-15 BUY_GRADUAL WBD 33461 $ 8.25 $ 276,053 38 $545,974 $1,103,807 GREED_TRANSITION +190 2025-04-16 STOP_LOSS WBD 33461 $ 7.92 $ 265,011 32 $810,985 $1,087,112 GREED_TRANSITION +191 2025-04-16 BUY_QQQ_STOPLOSS QQQ 597 $ 443.68 $ 264,878 32 $546,107 $1,087,112 GREED_TRANSITION +192 2025-04-16 BUY_GRADUAL AMD 3126 $ 88.29 $ 275,995 32 $270,112 $1,087,112 GREED_TRANSITION +193 2025-05-20 SELL_GRADUAL AMD 781 $ 113.51 $ 88,651 76 $358,764 $1,374,923 QQQ_TRANSITION +194 2025-05-20 SELL_GRADUAL MCHP 1788 $ 60.33 $ 107,876 76 $466,639 $1,374,923 QQQ_TRANSITION +195 2025-05-20 BUY_GRADUAL QQQ 512 $ 519.69 $ 266,079 76 $200,560 $1,374,923 QQQ_TRANSITION +196 2025-05-21 SELL_GRADUAL AMD 781 $ 112.06 $ 87,519 69 $288,079 $1,355,190 QQQ_TRANSITION +197 2025-05-21 SELL_GRADUAL MCHP 1788 $ 58.79 $ 105,125 69 $393,204 $1,355,190 QQQ_TRANSITION +198 2025-05-21 BUY_GRADUAL QQQ 520 $ 512.46 $ 266,481 69 $126,722 $1,355,190 QQQ_TRANSITION +199 2025-05-22 SELL_GRADUAL AMD 781 $ 110.71 $ 86,465 67 $213,187 $1,351,925 QQQ_TRANSITION +200 2025-05-22 SELL_GRADUAL MCHP 1788 $ 58.05 $ 103,793 67 $316,980 $1,351,925 QQQ_TRANSITION +201 2025-05-22 BUY_GRADUAL QQQ 519 $ 513.42 $ 266,467 67 $50,514 $1,351,925 QQQ_TRANSITION +202 2025-05-23 SELL_GRADUAL AMD 781 $ 110.31 $ 86,152 62 $136,666 $1,338,007 QQQ_TRANSITION +203 2025-05-23 SELL_GRADUAL MCHP 1788 $ 56.19 $ 100,468 62 $237,133 $1,338,007 QQQ_TRANSITION diff --git a/backtest/reports/detailed_trades_20250718_204803.txt b/backtest/reports/detailed_trades_20250718_204803.txt new file mode 100644 index 0000000..2982348 --- /dev/null +++ b/backtest/reports/detailed_trades_20250718_204803.txt @@ -0,0 +1,220 @@ +Enhanced Emotional Damage Strategy - Detailed Trades Report +======================================================================================================================== + +📊 交易摘要: +总交易数: 203 +交易时间: 2008-01-09 到 2025-05-23 + +交易类型统计: + SELL_GRADUAL: 96 + BUY_GRADUAL: 67 + STOP_LOSS: 21 + BUY_QQQ_STOPLOSS: 19 + +📋 详细交易记录: +====================================================================================================================================================== + No Date Action Ticker Shares Price Value F&G Cash Assets State +====================================================================================================================================================== + 1 2008-01-09 SELL_GRADUAL QQQ 609 $ 41.37 $ 25,193 26 $25,208 $100,829 FEAR_TRANSITION + 2 2008-01-10 SELL_GRADUAL QQQ 609 $ 41.43 $ 25,230 33 $50,438 $100,940 FEAR_TRANSITION + 3 2008-01-11 SELL_GRADUAL QQQ 609 $ 40.62 $ 24,736 28 $75,174 $99,951 FEAR_TRANSITION + 4 2008-01-14 SELL_GRADUAL QQQ 609 $ 41.33 $ 25,167 31 $100,341 $100,383 FEAR_TRANSITION + 5 2008-01-16 BUY_GRADUAL ILMN 745 $ 33.63 $ 25,057 27 $75,284 $100,381 GREED_TRANSITION + 6 2008-01-17 BUY_GRADUAL TMUS 852 $ 29.42 $ 25,063 20 $50,221 $99,964 GREED_TRANSITION + 7 2008-01-18 BUY_GRADUAL DXCM 11274 $ 2.22 $ 25,085 20 $25,137 $98,656 GREED_TRANSITION + 8 2008-01-22 BUY_GRADUAL SBUX 3549 $ 7.07 $ 25,085 19 $52 $97,152 GREED_TRANSITION + 9 2008-02-20 STOP_LOSS TMUS 852 $ 24.77 $ 21,101 54 $21,153 $94,385 VOLATILE_STOCKS + 10 2008-02-20 BUY_QQQ_STOPLOSS QQQ 556 $ 37.93 $ 21,090 54 $63 $94,385 VOLATILE_STOCKS + 11 2008-02-28 STOP_LOSS DXCM 11274 $ 1.88 $ 21,251 50 $21,314 $94,276 VOLATILE_STOCKS + 12 2008-02-28 BUY_QQQ_STOPLOSS QQQ 557 $ 38.09 $ 21,215 50 $99 $94,276 VOLATILE_STOCKS + 13 2008-04-25 STOP_LOSS SBUX 3549 $ 6.00 $ 21,310 69 $21,409 $95,418 VOLATILE_STOCKS + 14 2008-04-25 BUY_QQQ_STOPLOSS QQQ 523 $ 40.74 $ 21,305 69 $104 $95,418 VOLATILE_STOCKS + 15 2008-10-09 STOP_LOSS ILMN 745 $ 28.21 $ 21,017 9 $21,121 $65,760 VOLATILE_STOCKS + 16 2008-10-09 BUY_QQQ_STOPLOSS QQQ 770 $ 27.27 $ 20,997 9 $124 $65,760 VOLATILE_STOCKS + 17 2009-05-05 BUY_GRADUAL QQQ 1 $ 30.39 $ 30 76 $93 $73,275 QQQ_TRANSITION + 18 2009-05-06 BUY_GRADUAL QQQ 1 $ 30.40 $ 30 82 $63 $73,296 QQQ_TRANSITION + 19 2009-05-07 BUY_GRADUAL QQQ 1 $ 29.69 $ 30 78 $33 $71,582 QQQ_TRANSITION + 20 2009-05-08 BUY_GRADUAL QQQ 1 $ 29.71 $ 30 75 $3 $71,624 QQQ_TRANSITION + 21 2010-05-27 SELL_GRADUAL QQQ 602 $ 40.00 $ 24,082 34 $24,086 $96,453 FEAR_TRANSITION + 22 2010-05-28 SELL_GRADUAL QQQ 602 $ 39.77 $ 23,941 33 $48,026 $96,027 FEAR_TRANSITION + 23 2010-06-01 SELL_GRADUAL QQQ 602 $ 39.40 $ 23,720 27 $71,746 $95,585 FEAR_TRANSITION + 24 2010-06-02 SELL_GRADUAL QQQ 602 $ 40.34 $ 24,282 38 $96,028 $96,149 FEAR_TRANSITION + 25 2010-06-04 BUY_GRADUAL SIRI 2705 $ 8.87 $ 24,002 37 $72,026 $96,146 GREED_TRANSITION + 26 2010-06-07 BUY_GRADUAL NFLX 1539 $ 15.59 $ 24,000 35 $48,026 $95,899 GREED_TRANSITION + 27 2010-06-08 BUY_GRADUAL TMUS 1793 $ 13.38 $ 23,998 34 $24,028 $95,666 GREED_TRANSITION + 28 2010-06-09 STOP_LOSS TMUS 1793 $ 13.38 $ 23,998 33 $48,026 $97,032 GREED_TRANSITION + 29 2010-06-09 BUY_QQQ_STOPLOSS QQQ 627 $ 38.22 $ 23,961 33 $24,065 $97,032 GREED_TRANSITION + 30 2010-06-09 BUY_GRADUAL LULU 1227 $ 19.55 $ 23,988 33 $77 $97,032 GREED_TRANSITION + 31 2010-08-30 STOP_LOSS LULU 1227 $ 15.95 $ 19,571 31 $19,648 $94,278 VOLATILE_STOCKS + 32 2010-08-30 BUY_QQQ_STOPLOSS QQQ 512 $ 38.18 $ 19,548 31 $100 $94,278 VOLATILE_STOCKS + 33 2010-11-04 SELL_GRADUAL SIRI 676 $ 13.31 $ 8,998 80 $9,097 $126,785 QQQ_TRANSITION + 34 2010-11-04 SELL_GRADUAL NFLX 384 $ 23.97 $ 9,206 80 $18,303 $126,785 QQQ_TRANSITION + 35 2010-11-05 SELL_GRADUAL SIRI 676 $ 13.85 $ 9,365 80 $27,668 $127,933 QQQ_TRANSITION + 36 2010-11-05 SELL_GRADUAL NFLX 384 $ 24.01 $ 9,221 80 $36,890 $127,933 QQQ_TRANSITION + 37 2010-11-05 BUY_GRADUAL QQQ 404 $ 47.10 $ 19,027 80 $17,862 $127,933 QQQ_TRANSITION + 38 2010-11-08 SELL_GRADUAL SIRI 676 $ 14.03 $ 9,487 78 $27,349 $128,387 QQQ_TRANSITION + 39 2010-11-08 SELL_GRADUAL NFLX 384 $ 24.16 $ 9,278 78 $36,627 $128,387 QQQ_TRANSITION + 40 2010-11-08 BUY_GRADUAL QQQ 404 $ 47.16 $ 19,052 78 $17,575 $128,387 QQQ_TRANSITION + 41 2010-11-09 SELL_GRADUAL SIRI 676 $ 13.49 $ 9,120 74 $26,695 $127,596 QQQ_TRANSITION + 42 2010-11-09 SELL_GRADUAL NFLX 384 $ 24.35 $ 9,351 74 $36,046 $127,596 QQQ_TRANSITION + 43 2010-11-09 BUY_GRADUAL QQQ 406 $ 46.90 $ 19,043 74 $17,003 $127,596 QQQ_TRANSITION + 44 2011-08-05 SELL_GRADUAL QQQ 589 $ 47.50 $ 27,980 16 $44,983 $129,044 FEAR_TRANSITION + 45 2011-08-08 SELL_GRADUAL QQQ 589 $ 44.65 $ 26,296 12 $71,279 $123,983 FEAR_TRANSITION + 46 2011-08-09 SELL_GRADUAL QQQ 589 $ 46.80 $ 27,564 21 $98,843 $126,526 FEAR_TRANSITION + 47 2011-08-10 SELL_GRADUAL QQQ 589 $ 44.88 $ 26,436 16 $125,280 $125,395 FEAR_TRANSITION + 48 2011-08-16 BUY_GRADUAL AMD 4944 $ 6.34 $ 31,345 28 $93,935 $125,400 GREED_TRANSITION + 49 2011-08-17 BUY_GRADUAL DXCM 11087 $ 2.83 $ 31,348 27 $62,586 $124,408 GREED_TRANSITION + 50 2011-08-18 BUY_GRADUAL ILMN 684 $ 45.82 $ 31,339 21 $31,247 $121,090 GREED_TRANSITION + 51 2011-09-29 STOP_LOSS AMD 4944 $ 5.31 $ 26,253 36 $57,500 $119,987 VOLATILE_STOCKS + 52 2011-09-29 BUY_QQQ_STOPLOSS QQQ 551 $ 47.64 $ 26,248 36 $31,252 $119,987 VOLATILE_STOCKS + 53 2011-10-07 STOP_LOSS ILMN 684 $ 26.44 $ 18,085 46 $49,337 $106,508 VOLATILE_STOCKS + 54 2011-10-07 BUY_QQQ_STOPLOSS QQQ 378 $ 47.80 $ 18,070 46 $31,267 $106,508 VOLATILE_STOCKS + 55 2011-10-25 STOP_LOSS NFLX 3 $ 11.05 $ 33 58 $31,300 $107,461 VOLATILE_STOCKS + 56 2011-11-03 STOP_LOSS DXCM 11087 $ 1.97 $ 21,814 63 $53,114 $100,849 VOLATILE_STOCKS + 57 2011-11-03 BUY_QQQ_STOPLOSS QQQ 424 $ 51.37 $ 21,780 63 $31,334 $100,849 VOLATILE_STOCKS + 58 2012-01-20 BUY_GRADUAL QQQ 147 $ 53.05 $ 7,798 76 $23,536 $103,125 QQQ_TRANSITION + 59 2012-01-23 BUY_GRADUAL QQQ 147 $ 53.06 $ 7,800 75 $15,735 $103,151 QQQ_TRANSITION + 60 2012-01-24 BUY_GRADUAL QQQ 147 $ 52.97 $ 7,786 74 $7,949 $102,990 QQQ_TRANSITION + 61 2012-01-25 BUY_GRADUAL QQQ 146 $ 53.63 $ 7,830 75 $119 $104,184 QQQ_TRANSITION + 62 2012-05-21 SELL_GRADUAL QQQ 485 $ 55.57 $ 26,953 34 $27,072 $107,948 FEAR_TRANSITION + 63 2012-05-22 SELL_GRADUAL QQQ 485 $ 55.51 $ 26,922 34 $53,994 $107,857 FEAR_TRANSITION + 64 2012-05-23 SELL_GRADUAL QQQ 485 $ 55.62 $ 26,974 33 $80,968 $107,961 FEAR_TRANSITION + 65 2012-05-24 SELL_GRADUAL QQQ 485 $ 55.25 $ 26,797 32 $107,766 $107,784 FEAR_TRANSITION + 66 2012-05-29 BUY_GRADUAL VRTX 466 $ 57.80 $ 26,935 39 $80,831 $107,783 GREED_TRANSITION + 67 2012-05-30 BUY_GRADUAL NXPI 1395 $ 19.31 $ 26,933 33 $53,898 $108,892 GREED_TRANSITION + 68 2012-05-31 BUY_GRADUAL REGN 199 $ 135.28 $ 26,920 34 $26,978 $108,301 GREED_TRANSITION + 69 2012-06-01 BUY_GRADUAL MNST 2291 $ 11.76 $ 26,935 27 $44 $103,625 GREED_TRANSITION + 70 2012-06-13 STOP_LOSS REGN 199 $ 111.57 $ 22,203 44 $22,246 $101,613 VOLATILE_STOCKS + 71 2012-06-13 BUY_QQQ_STOPLOSS QQQ 401 $ 55.23 $ 22,149 44 $97 $101,613 VOLATILE_STOCKS + 72 2012-07-24 STOP_LOSS VRTX 466 $ 48.37 $ 22,540 51 $22,638 $98,436 VOLATILE_STOCKS + 73 2012-07-24 BUY_QQQ_STOPLOSS QQQ 401 $ 56.10 $ 22,497 51 $141 $98,436 VOLATILE_STOCKS + 74 2012-08-10 STOP_LOSS MNST 2291 $ 9.05 $ 20,722 62 $20,863 $99,014 VOLATILE_STOCKS + 75 2012-08-10 BUY_QQQ_STOPLOSS QQQ 347 $ 59.58 $ 20,673 62 $190 $99,014 VOLATILE_STOCKS + 76 2013-01-24 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 75 $9,399 $105,958 QQQ_TRANSITION + 77 2013-01-24 BUY_GRADUAL QQQ 154 $ 59.90 $ 9,224 75 $175 $105,958 QQQ_TRANSITION + 78 2013-01-25 SELL_GRADUAL NXPI 348 $ 26.98 $ 9,390 77 $9,565 $106,902 QQQ_TRANSITION + 79 2013-01-25 BUY_GRADUAL QQQ 154 $ 60.20 $ 9,272 77 $293 $106,902 QQQ_TRANSITION + 80 2013-01-28 SELL_GRADUAL NXPI 348 $ 27.35 $ 9,518 76 $9,811 $107,355 QQQ_TRANSITION + 81 2013-01-28 BUY_GRADUAL QQQ 153 $ 60.34 $ 9,232 76 $579 $107,355 QQQ_TRANSITION + 82 2013-01-29 SELL_GRADUAL NXPI 348 $ 26.46 $ 9,209 77 $9,788 $107,058 QQQ_TRANSITION + 83 2013-01-29 BUY_GRADUAL QQQ 153 $ 60.35 $ 9,233 77 $555 $107,058 QQQ_TRANSITION + 84 2015-08-25 SELL_GRADUAL QQQ 440 $ 90.96 $ 40,021 19 $40,576 $161,163 FEAR_TRANSITION + 85 2015-08-26 SELL_GRADUAL QQQ 440 $ 95.54 $ 42,037 30 $82,612 $167,235 FEAR_TRANSITION + 86 2015-08-27 SELL_GRADUAL QQQ 440 $ 97.96 $ 43,101 37 $125,714 $169,377 FEAR_TRANSITION + 87 2015-08-28 SELL_GRADUAL QQQ 440 $ 97.94 $ 43,093 37 $168,807 $169,370 FEAR_TRANSITION + 88 2015-09-01 BUY_GRADUAL NFLX 399 $ 105.79 $ 42,210 29 $126,597 $169,343 GREED_TRANSITION + 89 2015-09-02 BUY_GRADUAL NVDA 78306 $ 0.54 $ 42,267 34 $84,330 $169,220 GREED_TRANSITION + 90 2015-09-03 BUY_GRADUAL MU 2608 $ 16.20 $ 42,259 36 $42,071 $167,587 GREED_TRANSITION + 91 2015-09-28 STOP_LOSS MU 2608 $ 13.73 $ 35,814 29 $77,885 $162,450 VOLATILE_STOCKS + 92 2015-09-28 BUY_QQQ_STOPLOSS QQQ 385 $ 92.94 $ 35,784 29 $42,102 $162,450 VOLATILE_STOCKS + 93 2017-02-16 SELL_GRADUAL NFLX 99 $ 142.01 $ 14,059 74 $56,161 $353,212 QQQ_TRANSITION + 94 2017-02-16 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,711 74 $107,872 $353,212 QQQ_TRANSITION + 95 2017-02-16 BUY_GRADUAL QQQ 634 $ 121.85 $ 77,254 74 $30,618 $353,212 QQQ_TRANSITION + 96 2017-02-17 SELL_GRADUAL NFLX 99 $ 142.22 $ 14,080 74 $44,697 $353,786 QQQ_TRANSITION + 97 2017-02-17 SELL_GRADUAL NVDA 19576 $ 2.64 $ 51,701 74 $96,399 $353,786 QQQ_TRANSITION + 98 2017-02-17 BUY_GRADUAL QQQ 632 $ 122.38 $ 77,344 74 $19,055 $353,786 QQQ_TRANSITION + 99 2017-02-21 SELL_GRADUAL NFLX 99 $ 142.60 $ 14,117 76 $33,172 $358,565 QQQ_TRANSITION +100 2017-02-21 SELL_GRADUAL NVDA 19576 $ 2.74 $ 53,553 76 $86,725 $358,565 QQQ_TRANSITION +101 2017-02-21 BUY_GRADUAL QQQ 628 $ 122.98 $ 77,233 76 $9,492 $358,565 QQQ_TRANSITION +102 2017-02-22 SELL_GRADUAL NFLX 99 $ 143.86 $ 14,242 75 $23,734 $358,720 QQQ_TRANSITION +103 2017-02-22 SELL_GRADUAL NVDA 19576 $ 2.73 $ 53,471 75 $77,204 $358,720 QQQ_TRANSITION +104 2018-12-18 SELL_GRADUAL QQQ 570 $ 151.55 $ 86,385 24 $163,590 $424,134 FEAR_TRANSITION +105 2018-12-19 SELL_GRADUAL QQQ 570 $ 147.83 $ 84,264 23 $247,854 $417,734 FEAR_TRANSITION +106 2018-12-20 SELL_GRADUAL QQQ 570 $ 145.69 $ 83,043 22 $330,897 $415,268 FEAR_TRANSITION +107 2018-12-21 SELL_GRADUAL QQQ 570 $ 141.17 $ 80,469 20 $411,366 $412,636 FEAR_TRANSITION +108 2018-12-27 BUY_GRADUAL ZS 2566 $ 40.17 $ 103,076 28 $308,289 $412,683 GREED_TRANSITION +109 2018-12-28 BUY_GRADUAL AMD 5785 $ 17.82 $ 103,089 26 $205,201 $411,733 GREED_TRANSITION +110 2018-12-31 BUY_GRADUAL MDB 1231 $ 83.74 $ 103,084 30 $102,117 $413,961 GREED_TRANSITION +111 2019-04-24 SELL_GRADUAL ZS 641 $ 64.99 $ 41,659 75 $143,775 $601,726 QQQ_TRANSITION +112 2019-04-24 SELL_GRADUAL AMD 1446 $ 28.46 $ 41,153 75 $184,928 $601,726 QQQ_TRANSITION +113 2019-04-24 SELL_GRADUAL MDB 307 $ 135.16 $ 41,494 75 $226,423 $601,726 QQQ_TRANSITION +114 2019-04-24 BUY_GRADUAL QQQ 825 $ 182.34 $ 150,428 75 $75,994 $601,726 QQQ_TRANSITION +115 2019-04-25 SELL_GRADUAL ZS 641 $ 66.96 $ 42,921 74 $118,916 $604,944 QQQ_TRANSITION +116 2019-04-25 SELL_GRADUAL AMD 1446 $ 27.66 $ 39,996 74 $158,912 $604,944 QQQ_TRANSITION +117 2019-04-25 SELL_GRADUAL MDB 307 $ 137.66 $ 42,262 74 $201,174 $604,944 QQQ_TRANSITION +118 2019-04-25 BUY_GRADUAL QQQ 822 $ 183.08 $ 150,490 74 $50,684 $604,944 QQQ_TRANSITION +119 2019-04-26 SELL_GRADUAL ZS 641 $ 66.94 $ 42,909 74 $93,592 $606,662 QQQ_TRANSITION +120 2019-04-26 SELL_GRADUAL AMD 1446 $ 27.88 $ 40,314 74 $133,907 $606,662 QQQ_TRANSITION +121 2019-04-26 SELL_GRADUAL MDB 307 $ 138.98 $ 42,667 74 $176,574 $606,662 QQQ_TRANSITION +122 2019-04-26 BUY_GRADUAL QQQ 821 $ 183.24 $ 150,441 74 $26,133 $606,662 QQQ_TRANSITION +123 2019-04-29 SELL_GRADUAL ZS 641 $ 68.20 $ 43,716 74 $69,849 $607,606 QQQ_TRANSITION +124 2019-04-29 SELL_GRADUAL AMD 1446 $ 27.69 $ 40,040 74 $109,889 $607,606 QQQ_TRANSITION +125 2019-04-29 SELL_GRADUAL MDB 307 $ 137.50 $ 42,212 74 $152,101 $607,606 QQQ_TRANSITION +126 2019-04-29 BUY_GRADUAL QQQ 820 $ 183.60 $ 150,549 74 $1,552 $607,606 QQQ_TRANSITION +127 2020-02-28 SELL_GRADUAL QQQ 822 $ 199.07 $ 163,635 20 $165,188 $658,590 FEAR_TRANSITION +128 2020-03-02 SELL_GRADUAL QQQ 822 $ 209.34 $ 172,080 37 $337,267 $684,012 FEAR_TRANSITION +129 2020-03-03 SELL_GRADUAL QQQ 822 $ 202.63 $ 166,561 29 $503,829 $672,909 FEAR_TRANSITION +130 2020-03-04 SELL_GRADUAL QQQ 822 $ 211.08 $ 173,511 38 $677,340 $679,956 FEAR_TRANSITION +131 2020-03-06 BUY_GRADUAL MRNA 5736 $ 29.61 $ 169,843 25 $507,497 $679,826 GREED_TRANSITION +132 2020-03-09 STOP_LOSS MRNA 5736 $ 24.29 $ 139,327 17 $646,824 $649,100 GREED_TRANSITION +133 2020-03-09 BUY_QQQ_STOPLOSS QQQ 744 $ 187.24 $ 139,306 17 $507,518 $649,100 GREED_TRANSITION +134 2020-03-09 BUY_GRADUAL TSLA 4190 $ 40.53 $ 169,835 17 $337,683 $649,100 GREED_TRANSITION +135 2020-03-10 BUY_GRADUAL MRVL 7509 $ 22.62 $ 169,858 25 $167,825 $667,230 GREED_TRANSITION +136 2020-03-12 STOP_LOSS MRVL 7509 $ 19.06 $ 143,146 17 $310,971 $597,219 VOLATILE_STOCKS +137 2020-03-12 BUY_QQQ_STOPLOSS QQQ 834 $ 171.52 $ 143,049 17 $167,922 $597,219 VOLATILE_STOCKS +138 2020-03-16 STOP_LOSS TSLA 4190 $ 29.67 $ 124,323 19 $292,245 $552,585 VOLATILE_STOCKS +139 2020-03-16 BUY_QQQ_STOPLOSS QQQ 759 $ 163.76 $ 124,296 19 $167,949 $552,585 VOLATILE_STOCKS +140 2020-06-03 BUY_GRADUAL QQQ 185 $ 229.44 $ 42,446 79 $125,503 $707,213 QQQ_TRANSITION +141 2020-06-04 BUY_GRADUAL QQQ 187 $ 227.83 $ 42,604 79 $82,899 $703,082 QQQ_TRANSITION +142 2020-06-05 BUY_GRADUAL QQQ 183 $ 232.35 $ 42,519 84 $40,380 $715,284 QQQ_TRANSITION +143 2022-01-26 SELL_GRADUAL QQQ 723 $ 336.81 $ 243,517 24 $283,896 $1,018,473 FEAR_TRANSITION +144 2022-01-27 SELL_GRADUAL QQQ 723 $ 333.42 $ 241,064 23 $524,961 $1,011,148 FEAR_TRANSITION +145 2022-01-28 SELL_GRADUAL QQQ 723 $ 343.88 $ 248,626 32 $773,587 $1,026,391 FEAR_TRANSITION +146 2022-01-31 SELL_GRADUAL QQQ 723 $ 354.88 $ 256,577 41 $1,030,164 $1,034,684 FEAR_TRANSITION +147 2022-02-02 BUY_GRADUAL NFLX 601 $ 429.48 $ 258,117 47 $772,047 $1,034,708 GREED_TRANSITION +148 2022-02-03 BUY_GRADUAL PDD 4689 $ 55.13 $ 258,505 43 $513,542 $1,020,143 GREED_TRANSITION +149 2022-02-04 BUY_GRADUAL WBD 8945 $ 28.90 $ 258,510 46 $255,031 $1,026,385 GREED_TRANSITION +150 2022-03-03 STOP_LOSS PDD 4689 $ 45.22 $ 212,037 39 $467,068 $948,795 VOLATILE_STOCKS +151 2022-03-03 BUY_QQQ_STOPLOSS QQQ 633 $ 334.56 $ 211,774 39 $255,294 $948,795 VOLATILE_STOCKS +152 2022-03-11 STOP_LOSS WBD 8945 $ 23.99 $ 214,591 31 $469,885 $878,854 VOLATILE_STOCKS +153 2022-03-11 BUY_QQQ_STOPLOSS QQQ 676 $ 317.10 $ 214,358 31 $255,526 $878,854 VOLATILE_STOCKS +154 2022-08-16 SELL_GRADUAL NFLX 151 $ 245.69 $ 37,099 76 $292,625 $833,290 QQQ_TRANSITION +155 2022-08-16 BUY_GRADUAL QQQ 313 $ 325.83 $ 101,984 76 $190,641 $833,290 QQQ_TRANSITION +156 2022-08-17 SELL_GRADUAL NFLX 151 $ 241.15 $ 36,414 72 $227,055 $825,132 QQQ_TRANSITION +157 2022-08-17 BUY_GRADUAL QQQ 316 $ 322.11 $ 101,787 72 $125,268 $825,132 QQQ_TRANSITION +158 2022-08-18 SELL_GRADUAL NFLX 151 $ 245.17 $ 37,021 73 $162,289 $827,836 QQQ_TRANSITION +159 2022-08-18 BUY_GRADUAL QQQ 316 $ 322.89 $ 102,032 73 $60,257 $827,836 QQQ_TRANSITION +160 2022-08-19 SELL_GRADUAL NFLX 151 $ 241.16 $ 36,415 68 $96,672 $812,946 QQQ_TRANSITION +161 2022-09-26 SELL_GRADUAL QQQ 564 $ 269.53 $ 152,012 25 $248,684 $706,189 FEAR_TRANSITION +162 2022-09-27 SELL_GRADUAL QQQ 564 $ 269.63 $ 152,073 26 $400,758 $706,398 FEAR_TRANSITION +163 2022-09-28 SELL_GRADUAL QQQ 564 $ 275.00 $ 155,098 33 $555,856 $712,492 FEAR_TRANSITION +164 2022-09-29 SELL_GRADUAL QQQ 564 $ 267.07 $ 150,627 29 $706,483 $707,973 FEAR_TRANSITION +165 2022-10-03 BUY_GRADUAL BIIB 669 $ 264.30 $ 176,817 32 $529,667 $707,986 GREED_TRANSITION +166 2022-10-04 BUY_GRADUAL REGN 239 $ 737.50 $ 176,263 42 $353,404 $710,159 GREED_TRANSITION +167 2022-10-05 BUY_GRADUAL NFLX 747 $ 236.73 $ 176,837 42 $176,567 $707,777 GREED_TRANSITION +168 2023-02-03 SELL_GRADUAL BIIB 167 $ 283.63 $ 47,366 72 $223,933 $827,955 QQQ_TRANSITION +169 2023-02-03 SELL_GRADUAL REGN 59 $ 780.97 $ 46,077 72 $270,010 $827,955 QQQ_TRANSITION +170 2023-02-03 SELL_GRADUAL NFLX 186 $ 365.90 $ 68,057 72 $338,067 $827,955 QQQ_TRANSITION +171 2023-02-03 BUY_GRADUAL QQQ 683 $ 301.49 $ 205,920 72 $132,147 $827,955 QQQ_TRANSITION +172 2023-02-06 SELL_GRADUAL BIIB 167 $ 288.28 $ 48,143 65 $180,290 $825,631 QQQ_TRANSITION +173 2023-02-06 SELL_GRADUAL REGN 59 $ 778.71 $ 45,944 65 $226,234 $825,631 QQQ_TRANSITION +174 2023-02-06 SELL_GRADUAL NFLX 186 $ 361.48 $ 67,235 65 $293,469 $825,631 QQQ_TRANSITION +175 2023-02-06 BUY_GRADUAL QQQ 688 $ 298.94 $ 205,673 65 $87,796 $825,631 QQQ_TRANSITION +176 2023-02-07 SELL_GRADUAL BIIB 167 $ 291.43 $ 48,669 70 $136,464 $834,461 QQQ_TRANSITION +177 2023-02-07 SELL_GRADUAL REGN 59 $ 767.82 $ 45,301 70 $181,766 $834,461 QQQ_TRANSITION +178 2023-02-07 SELL_GRADUAL NFLX 186 $ 362.95 $ 67,509 70 $249,274 $834,461 QQQ_TRANSITION +179 2023-02-07 BUY_GRADUAL QQQ 674 $ 305.14 $ 205,663 70 $43,612 $834,461 QQQ_TRANSITION +180 2023-02-08 SELL_GRADUAL BIIB 167 $ 285.45 $ 47,670 64 $91,282 $821,817 QQQ_TRANSITION +181 2023-02-08 SELL_GRADUAL REGN 59 $ 747.59 $ 44,108 64 $135,390 $821,817 QQQ_TRANSITION +182 2023-02-08 SELL_GRADUAL NFLX 186 $ 366.83 $ 68,230 64 $203,620 $821,817 QQQ_TRANSITION +183 2023-11-09 STOP_LOSS BIIB 1 $ 224.65 $ 225 54 $203,845 $958,229 QQQ_HOLD +184 2025-04-07 SELL_GRADUAL QQQ 511 $ 423.21 $ 216,263 24 $420,108 $1,075,227 FEAR_TRANSITION +185 2025-04-08 SELL_GRADUAL QQQ 511 $ 415.59 $ 212,368 23 $632,476 $1,063,447 FEAR_TRANSITION +186 2025-04-09 SELL_GRADUAL QQQ 511 $ 465.48 $ 237,859 39 $870,335 $1,115,043 FEAR_TRANSITION +187 2025-04-10 SELL_GRADUAL QQQ 511 $ 445.68 $ 227,742 34 $1,098,077 $1,104,615 FEAR_TRANSITION +188 2025-04-14 BUY_GRADUAL MCHP 7155 $ 38.58 $ 276,050 37 $822,027 $1,104,757 GREED_TRANSITION +189 2025-04-15 BUY_GRADUAL WBD 33461 $ 8.25 $ 276,053 38 $545,974 $1,103,807 GREED_TRANSITION +190 2025-04-16 STOP_LOSS WBD 33461 $ 7.92 $ 265,011 32 $810,985 $1,087,112 GREED_TRANSITION +191 2025-04-16 BUY_QQQ_STOPLOSS QQQ 597 $ 443.68 $ 264,878 32 $546,107 $1,087,112 GREED_TRANSITION +192 2025-04-16 BUY_GRADUAL AMD 3126 $ 88.29 $ 275,995 32 $270,112 $1,087,112 GREED_TRANSITION +193 2025-05-20 SELL_GRADUAL AMD 781 $ 113.51 $ 88,651 76 $358,764 $1,374,923 QQQ_TRANSITION +194 2025-05-20 SELL_GRADUAL MCHP 1788 $ 60.33 $ 107,876 76 $466,639 $1,374,923 QQQ_TRANSITION +195 2025-05-20 BUY_GRADUAL QQQ 512 $ 519.69 $ 266,079 76 $200,560 $1,374,923 QQQ_TRANSITION +196 2025-05-21 SELL_GRADUAL AMD 781 $ 112.06 $ 87,519 69 $288,079 $1,355,190 QQQ_TRANSITION +197 2025-05-21 SELL_GRADUAL MCHP 1788 $ 58.79 $ 105,125 69 $393,204 $1,355,190 QQQ_TRANSITION +198 2025-05-21 BUY_GRADUAL QQQ 520 $ 512.46 $ 266,481 69 $126,722 $1,355,190 QQQ_TRANSITION +199 2025-05-22 SELL_GRADUAL AMD 781 $ 110.71 $ 86,465 67 $213,187 $1,351,925 QQQ_TRANSITION +200 2025-05-22 SELL_GRADUAL MCHP 1788 $ 58.05 $ 103,793 67 $316,980 $1,351,925 QQQ_TRANSITION +201 2025-05-22 BUY_GRADUAL QQQ 519 $ 513.42 $ 266,467 67 $50,514 $1,351,925 QQQ_TRANSITION +202 2025-05-23 SELL_GRADUAL AMD 781 $ 110.31 $ 86,152 62 $136,666 $1,338,007 QQQ_TRANSITION +203 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b/backtest/reports/enhanced_trades_20250718_192206.csv @@ -0,0 +1,204 @@ +date,action,ticker,shares,price,value,fg_index,cnn_fear_greed,cash_after,total_assets,portfolio_state +2008-01-09,SELL_GRADUAL,QQQ,609,41.368202209472656,25193.235145568848,26.344740247547747,26.344740247547747,25208.408485412598,100829.48212432861,FEAR_TRANSITION +2008-01-10,SELL_GRADUAL,QQQ,609,41.428653717041016,25230.05011367798,32.93074277910004,32.93074277910004,50438.458599090576,100939.98748016357,FEAR_TRANSITION +2008-01-11,SELL_GRADUAL,QQQ,609,40.617149353027344,24735.843955993652,28.176976271462404,28.176976271462404,75174.30255508423,99950.76366043091,FEAR_TRANSITION +2008-01-14,SELL_GRADUAL,QQQ,609,41.32505798339844,25166.96031188965,30.536227836089562,30.536227836089562,100341.26286697388,100382.58792495728,FEAR_TRANSITION +2008-01-16,BUY_GRADUAL,ILMN,745,33.63326644897461,25056.783504486084,27.300196841810212,27.300196841810212,75284.4793624878,100381.0167427063,GREED_TRANSITION 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a/backtest/reports/enhanced_trades_20250718_204803.csv b/backtest/reports/enhanced_trades_20250718_204803.csv new file mode 100644 index 0000000..93e28ce --- /dev/null +++ b/backtest/reports/enhanced_trades_20250718_204803.csv @@ -0,0 +1,204 @@ +date,action,ticker,shares,price,value,fg_index,cnn_fear_greed,cash_after,total_assets,portfolio_state +2008-01-09,SELL_GRADUAL,QQQ,609,41.368202209472656,25193.235145568848,26.344740247547747,26.344740247547747,25208.408485412598,100829.48212432861,FEAR_TRANSITION +2008-01-10,SELL_GRADUAL,QQQ,609,41.428653717041016,25230.05011367798,32.93074277910004,32.93074277910004,50438.458599090576,100939.98748016357,FEAR_TRANSITION +2008-01-11,SELL_GRADUAL,QQQ,609,40.617149353027344,24735.843955993652,28.176976271462404,28.176976271462404,75174.30255508423,99950.76366043091,FEAR_TRANSITION +2008-01-14,SELL_GRADUAL,QQQ,609,41.32505798339844,25166.96031188965,30.536227836089562,30.536227836089562,100341.26286697388,100382.58792495728,FEAR_TRANSITION 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zd^f(rU0EBpi<28q4G2q+p5vZQu-c!!6t3pJ!?uyMN|QeU4U8r=B+1mF;G1y#g5Ut$ z&6Y1GMMflR_(N}f28T^#U4Q&@$mcH2DoxrjRv5v;*rORRO>1TJX7X3p{XVGbpe!px zM*VB{g`Xm=8(@Rl`XX6!P1`f3Eu@{b&QVxA-IoSgLOg!K-@CT;%8}}gHPTzjm{TS` z@bmaY9h3_Q;@d_F$?s#_PC8b9I1FDHIoyG4!;2=Mrpytk9c0|gK`;1K|6&c?{&@wD z0vYI@ZUtQ=EcJJiFNT_;q{q!Cp5|45Ss^Zv%vfetXJL;OQoF` z6is~g*CLrOd%lIoR)hAmPC`ak@M7u?s$CT4(;_!(U z4`nd5aSMA20 + "macd_convergence": true, // MACD收敛而非金叉 + "ema_periods": { + "fast": 12, "slow": 26, "signal": 9 + }, + "sma_periods": { + "fast": 5, "slow": 20 + }, + "rsi_period": 14 +} +``` + +#### 输出设置 (`output_settings`) +```json +{ + "generate_pdf": true, // 生成PDF报告 + "generate_csv": true, // 生成CSV文件 + "generate_detailed_trades": true, // 生成详细交易记录 + "show_console_output": true, // 显示控制台输出 + "save_strategy_object": true // 保存策略对象 +} +``` + +## 🎯 常用修改场景 + +### 场景1：调整风险参数 +```json +{ + "stop_loss_threshold": 0.10, // 改为10%止损 + "fear_threshold": 20, // 更激进的恐慌阈值 + "greed_threshold": 80 // 更保守的贪婪阈值 +} +``` + +### 场景2：修改选股逻辑 +```json +{ + "top_stocks_count": 6, // 选择6只股票 + "required_indicators": 3, // 要求所有3个指标都满足 + "volatility_threshold": 0.15 // 提高波动率要求 +} +``` + +### 场景3：技术指标调优 +```json +{ + "rsi_threshold": 40, // 提高RSI要求 + "sma5_above_sma20": false, // 取消SMA要求 + "macd_convergence": false // 改为传统MACD金叉 +} +``` + +### 场景4：只生成特定报告 +```json +{ + "generate_pdf": false, // 不生成PDF + "generate_csv": true, // 只要CSV + "generate_detailed_trades": true, + "show_console_output": false // 静默运行 +} +``` + +## 📊 运行和输出 + +### 运行命令 +```bash +cd /home/will/docker/backtest/strategy/emotional-damage/ +python3 run_strategy_with_config.py +``` + +### 输出文件 +运行后会在以下目录生成文件： + +#### `/home/will/docker/backtest/reports/` +- `enhanced_strategy_report_YYYYMMDD_HHMMSS.pdf` - PDF综合报告 +- `enhanced_trades_YYYYMMDD_HHMMSS.csv` - 交易记录CSV +- `detailed_trades_YYYYMMDD_HHMMSS.txt` - 详细交易报告 + +#### `/home/will/docker/backtest/results/` +- `enhanced_strategy_YYYYMMDD_HHMMSS.pkl` - 策略对象（用于进一步分析） + +## 🔧 常见问题 + +### Q: 如何测试不同的参数组合？ +A: 复制config.json为不同文件名（如config_test1.json），修改run_strategy_with_config.py中的文件名 + +### Q: 如何只测试特定时间段？ +A: 修改config.json中的data_settings： +```json +{ + "start_date": "2020-01-01", + "end_date": "2023-12-31" +} +``` + +### Q: 如何加快运行速度？ +A: 设置output_settings： +```json +{ + "generate_pdf": false, + "show_console_output": false +} +``` + +### Q: 如何分析特定股票的表现？ +A: 查看生成的CSV文件，按ticker列筛选 + +## 📈 性能调优建议 + +1. **保守策略**：提高fear_threshold到30，降低greed_threshold到70 +2. **激进策略**：降低fear_threshold到20，提高greed_threshold到80 +3. **严格选股**：设置required_indicators为3，提高volatility_threshold +4. **宽松选股**：设置required_indicators为1，降低rsi_threshold + +## 🚨 注意事项 + +1. 修改配置后记得保存文件 +2. 确保数据库路径正确 +3. 首次运行可能需要几分钟时间 +4. 大幅修改参数可能导致无交易或过度交易 + +--- + +Happy Trading! 🎯 \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/__pycache__/backtest_emotional_damage.cpython-311.pyc b/backtest/strategy/emotional-damage/__pycache__/backtest_emotional_damage.cpython-311.pyc old mode 100755 new mode 100644 index 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Greed Index and QQQ/SPY data""" - conn = sqlite3.connect('data/stock_data.db') + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + backtest_dir = os.path.dirname(os.path.dirname(script_dir)) + db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') + conn = sqlite3.connect(db_path) # Get Fear & Greed Index fg_data = pd.read_sql_query(''' @@ -32,14 +38,14 @@ class EmotionalDamageStrategy: fg_data['date'] = pd.to_datetime(fg_data['date']) fg_data.set_index('date', inplace=True) - # Get QQQ price data (using SPY as proxy since QQQ data might be limited) - spy_data = pd.read_sql_query(''' - SELECT date, spy_close - FROM fear_greed_data + # Get real QQQ price data + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq ORDER BY date ''', conn) - spy_data['date'] = pd.to_datetime(spy_data['date']) - spy_data.set_index('date', inplace=True) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) # Get available tickers for high volatility selection cursor = conn.cursor() @@ -49,7 +55,7 @@ class EmotionalDamageStrategy: conn.close() # Merge data - self.data = pd.merge(fg_data, spy_data, left_index=True, right_index=True, how='inner') + self.data = pd.merge(fg_data, qqq_data, left_index=True, right_index=True, how='inner') self.data.sort_index(inplace=True) print(f"Loaded data from {self.data.index.min().strftime('%Y-%m-%d')} to {self.data.index.max().strftime('%Y-%m-%d')}") @@ -57,7 +63,11 @@ class EmotionalDamageStrategy: def get_stock_price(self, ticker, date): """Get stock price for a specific ticker and date""" - conn = sqlite3.connect('data/stock_data.db') + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + backtest_dir = os.path.dirname(os.path.dirname(script_dir)) + db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') + conn = sqlite3.connect(db_path) query = f''' SELECT close FROM {ticker.lower()} @@ -76,7 +86,11 @@ class EmotionalDamageStrategy: def calculate_volatility(self, tickers, start_date, end_date): """Calculate historical volatility for tickers during fear period""" volatilities = {} - conn = sqlite3.connect('data/stock_data.db') + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + backtest_dir = os.path.dirname(os.path.dirname(script_dir)) + db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') + conn = sqlite3.connect(db_path) for ticker in tickers: try: @@ -118,7 +132,7 @@ class EmotionalDamageStrategy: """Execute a trade and record it""" if action == 'BUY_QQQ': # Buy QQQ with all cash - price = self.data.loc[date, 'spy_close'] # Using SPY as QQQ proxy + price = self.data.loc[date, 'qqq_close'] # Using real QQQ price shares = self.cash / price self.positions['QQQ'] = shares self.cash = 0 @@ -135,7 +149,7 @@ class EmotionalDamageStrategy: # Sell all QQQ if 'QQQ' in self.positions: shares = self.positions['QQQ'] - price = self.data.loc[date, 'spy_close'] + price = self.data.loc[date, 'qqq_close'] self.cash = shares * price del self.positions['QQQ'] self.trades.append({ @@ -190,7 +204,7 @@ class EmotionalDamageStrategy: for ticker, shares in self.positions.items(): if ticker == 'QQQ': - price = self.data.loc[date, 'spy_close'] + price = self.data.loc[date, 'qqq_close'] else: price = self.get_stock_price(ticker, date) @@ -304,18 +318,35 @@ def run_emotional_damage_backtest(): portfolio_df.set_index('date', inplace=True) # Get benchmark data (QQQ and SPY) - conn = sqlite3.connect('data/stock_data.db') + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + backtest_dir = os.path.dirname(os.path.dirname(script_dir)) + db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') + conn = sqlite3.connect(db_path) - benchmark_data = pd.read_sql_query(''' + # Get QQQ data + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq + ORDER BY date + ''', conn) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) + + # Get SPY data + spy_data = pd.read_sql_query(''' SELECT date, spy_close FROM fear_greed_data ORDER BY date ''', conn) - benchmark_data['date'] = pd.to_datetime(benchmark_data['date']) - benchmark_data.set_index('date', inplace=True) + spy_data['date'] = pd.to_datetime(spy_data['date']) + spy_data.set_index('date', inplace=True) conn.close() + # Merge benchmark data + benchmark_data = pd.merge(qqq_data, spy_data, left_index=True, right_index=True, how='inner') + # Align dates common_dates = portfolio_df.index.intersection(benchmark_data.index) portfolio_df = portfolio_df.loc[common_dates] @@ -323,10 +354,9 @@ def run_emotional_damage_backtest(): # Normalize to starting value for comparison start_value = 100000 - portfolio_df['normalized'] = portfolio_df['value'] - # Create QQQ and SPY buy-and-hold benchmarks - benchmark_data['qqq_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) + # Create QQQ and SPY buy-and-hold benchmarks with correct data + benchmark_data['qqq_value'] = start_value * (benchmark_data['qqq_close'] / benchmark_data['qqq_close'].iloc[0]) benchmark_data['spy_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) # Calculate performance metrics @@ -343,6 +373,228 @@ def run_emotional_damage_backtest(): 'spy_metrics': spy_metrics } +def generate_original_pdf_report(results, reports_dir="/home/will/docker/backtest/reports"): + """Generate PDF report for original emotional damage strategy""" + strategy = results['strategy'] + portfolio_df = results['portfolio_df'] + benchmark_data = results['benchmark_data'] + strategy_metrics = results['strategy_metrics'] + qqq_metrics = results['qqq_metrics'] + spy_metrics = results['spy_metrics'] + + # Create output directory + os.makedirs(reports_dir, exist_ok=True) + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + + # Prepare trades data + trades_df = pd.DataFrame(strategy.trades) + + # Find max drawdown year + def find_max_drawdown_year(returns): + peak = returns.expanding().max() + drawdown = (returns - peak) / peak + max_dd_date = drawdown.idxmin() + return max_dd_date.year + + strategy_dd_year = find_max_drawdown_year(portfolio_df['value']) + qqq_dd_year = find_max_drawdown_year(benchmark_data['qqq_value']) + spy_dd_year = find_max_drawdown_year(benchmark_data['spy_value']) + + # Create PDF + pdf_file = os.path.join(reports_dir, f'original_emotional_damage_strategy_report_{timestamp}.pdf') + + with PdfPages(pdf_file) as pdf: + # Set global font parameters + plt.rcParams['font.size'] = 10 + plt.rcParams['axes.titlesize'] = 12 + plt.rcParams['axes.labelsize'] = 10 + plt.rcParams['xtick.labelsize'] = 8 + plt.rcParams['ytick.labelsize'] = 8 + plt.rcParams['legend.fontsize'] = 8 + plt.rcParams['figure.titlesize'] = 14 + + # Page 1: Performance Comparison + fig1 = plt.figure(figsize=(8.5, 11)) + fig1.suptitle('Original Emotional Damage Strategy Report', fontsize=16, fontweight='bold', y=0.96) + + # 1. Total Return Curve + ax1 = plt.subplot(4, 1, 1) + ax1.plot(portfolio_df.index, portfolio_df['value'] / 1000, + label='Original Strategy', linewidth=2, color='red') + ax1.plot(benchmark_data.index, benchmark_data['qqq_value'] / 1000, + label='QQQ', linewidth=2, color='blue') + ax1.plot(benchmark_data.index, benchmark_data['spy_value'] / 1000, + label='SPY', linewidth=2, color='green') + ax1.set_title('Portfolio Performance Comparison', fontsize=14, fontweight='bold', pad=25) + ax1.set_ylabel('Portfolio Value ($K)', fontsize=11) + ax1.legend(fontsize=10, loc='upper left') + ax1.grid(True, alpha=0.3) + ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax1.get_xticklabels(): + label.set_rotation(45) + + # 2. Performance Metrics Table + ax2 = plt.subplot(4, 1, 2) + ax2.axis('off') + + metrics_data = [ + ['Metric', 'Original Strategy', 'QQQ', 'SPY'], + ['Total Return', f"{strategy_metrics['total_return']:.1f}%", + f"{qqq_metrics['total_return']:.1f}%", f"{spy_metrics['total_return']:.1f}%"], + ['Annual Return', f"{strategy_metrics['annual_return']:.1f}%", + f"{qqq_metrics['annual_return']:.1f}%", f"{spy_metrics['annual_return']:.1f}%"], + ['Max Drawdown', f"{strategy_metrics['max_drawdown']:.1f}%", + f"{qqq_metrics['max_drawdown']:.1f}%", f"{spy_metrics['max_drawdown']:.1f}%"], + ['Max DD Year', str(strategy_dd_year), str(qqq_dd_year), str(spy_dd_year)], + ['Sharpe Ratio', f"{strategy_metrics['sharpe_ratio']:.2f}", + f"{qqq_metrics['sharpe_ratio']:.2f}", f"{spy_metrics['sharpe_ratio']:.2f}"], + ['Total Trades', f"{len(strategy.trades)}", 'N/A', 'N/A'] + ] + + table = ax2.table(cellText=metrics_data, cellLoc='center', loc='center') + table.auto_set_font_size(False) + table.set_fontsize(10) + table.scale(1.2, 2.0) + + for i in range(len(metrics_data[0])): + table[(0, i)].set_facecolor('#40466e') + table[(0, i)].set_text_props(weight='bold', color='white') + + ax2.set_title('Performance Metrics Comparison', fontsize=14, fontweight='bold', pad=25) + + # 3. Strategy State Timeline + ax3 = plt.subplot(4, 1, 3) + + state_colors = { + 'QQQ_HOLD': 'blue', + 'CASH_WAIT': 'gray', + 'VOLATILE_STOCKS': 'red' + } + + for state, color in state_colors.items(): + state_data = portfolio_df[portfolio_df['state'] == state] + if not state_data.empty: + ax3.scatter(state_data.index, state_data['value'] / 1000, + c=color, s=2, alpha=0.8, label=state) + + ax3.set_title('Strategy State Timeline', fontsize=14, fontweight='bold', pad=25) + ax3.set_ylabel('Total Assets ($K)', fontsize=11) + ax3.legend(bbox_to_anchor=(1.02, 1), loc='upper left', fontsize=8) + ax3.grid(True, alpha=0.3) + ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax3.get_xticklabels(): + label.set_rotation(45) + + # 4. Annual Returns Comparison + ax4 = plt.subplot(4, 1, 4) + years = list(strategy_metrics['annual_returns'].keys()) + original_returns = list(strategy_metrics['annual_returns'].values()) + qqq_returns = [qqq_metrics['annual_returns'].get(year, 0) for year in years] + spy_returns = [spy_metrics['annual_returns'].get(year, 0) for year in years] + + x = np.arange(len(years)) + width = 0.25 + + ax4.bar(x - width, original_returns, width, label='Original Strategy', color='red', alpha=0.8) + ax4.bar(x, qqq_returns, width, label='QQQ', color='blue', alpha=0.8) + ax4.bar(x + width, spy_returns, width, label='SPY', color='green', alpha=0.8) + + ax4.set_title('Annual Returns Comparison by Year', fontsize=14, fontweight='bold', pad=25) + ax4.set_ylabel('Annual Return (%)', fontsize=11) + ax4.set_xlabel('Year', fontsize=11) + ax4.set_xticks(x) + ax4.set_xticklabels(years) + for label in ax4.get_xticklabels(): + label.set_rotation(90) + ax4.legend(fontsize=10) + ax4.grid(True, alpha=0.3, axis='y') + + plt.subplots_adjust(left=0.1, right=0.85, top=0.90, bottom=0.08, hspace=0.6) + pdf.savefig(fig1, bbox_inches='tight', dpi=150) + plt.close() + + # Page 2: Detailed Analysis + fig2 = plt.figure(figsize=(8.5, 11)) + fig2.suptitle('Detailed Trading and Market Analysis', fontsize=16, fontweight='bold', y=0.95) + + # 5. Trading Activity by Year + ax5 = plt.subplot(3, 1, 1) + + trades_df_copy = trades_df.copy() + trades_df_copy['year'] = trades_df_copy['date'].dt.year + trade_frequency = trades_df_copy.groupby('year').size() + + ax5.bar(trade_frequency.index, trade_frequency.values, color='purple', alpha=0.8, width=0.6) + ax5.set_title('Trading Activity by Year', fontsize=14, fontweight='bold', pad=30) + ax5.set_ylabel('Number of Trades', fontsize=11) + ax5.set_xlabel('Year', fontsize=11) + ax5.grid(True, alpha=0.3, axis='y') + for label in ax5.get_xticklabels(): + label.set_rotation(45) + + # 6. Trade Type Distribution + ax6 = plt.subplot(3, 1, 2) + + action_counts = trades_df['action'].value_counts() + ax6.pie(action_counts.values, labels=action_counts.index, autopct='%1.1f%%', startangle=90) + ax6.set_title('Trade Type Distribution', fontsize=14, fontweight='bold', pad=30) + + # 7. Fear & Greed Index with Trading Signals + ax7 = plt.subplot(3, 1, 3) + + fg_data = portfolio_df['fg_index'].dropna() + ax7.plot(fg_data.index, fg_data.values, color='purple', alpha=0.8, linewidth=1.5) + ax7.axhline(y=25, color='red', linestyle='--', alpha=0.7, linewidth=2, label='Fear Threshold (25)') + ax7.axhline(y=75, color='green', linestyle='--', alpha=0.7, linewidth=2, label='Greed Threshold (75)') + ax7.fill_between(fg_data.index, 0, 25, alpha=0.2, color='red', label='Fear Zone') + ax7.fill_between(fg_data.index, 75, 100, alpha=0.2, color='green', label='Greed Zone') + + # Add trade markers + buy_trades = trades_df[trades_df['action'].str.contains('BUY')] + sell_trades = trades_df[trades_df['action'].str.contains('SELL')] + + if not buy_trades.empty: + ax7.scatter(buy_trades['date'], [50] * len(buy_trades), + color='darkgreen', s=15, alpha=0.8, marker='^', label='Buy Signals', zorder=5) + if not sell_trades.empty: + ax7.scatter(sell_trades['date'], [50] * len(sell_trades), + color='darkred', s=15, alpha=0.8, marker='v', label='Sell Signals', zorder=5) + + ax7.set_title('Fear & Greed Index with Trading Signals', fontsize=14, fontweight='bold', pad=30) + ax7.set_ylabel('CNN Fear & Greed Index', fontsize=11) + ax7.set_xlabel('Date', fontsize=11) + ax7.set_ylim(0, 100) + ax7.legend(bbox_to_anchor=(1.02, 1), loc='upper left', fontsize=8) + ax7.grid(True, alpha=0.3) + ax7.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax7.get_xticklabels(): + label.set_rotation(45) + + plt.subplots_adjust(left=0.1, right=0.85, top=0.88, bottom=0.10, hspace=1.0) + pdf.savefig(fig2, bbox_inches='tight', dpi=150) + plt.close() + + # Validate PDF + try: + file_size = os.path.getsize(pdf_file) + print(f"📈 PDF报告已保存: {pdf_file} (Size: {file_size:,} bytes)") + + with open(pdf_file, 'rb') as f: + header = f.read(10) + if header.startswith(b'%PDF'): + print(f"✅ PDF file validation passed") + else: + print(f"⚠️ Warning: Generated file may not be a valid PDF") + + except Exception as e: + print(f"⚠️ Error validating PDF: {e}") + + return pdf_file + if __name__ == "__main__": results = run_emotional_damage_backtest() - print("Backtest completed! Results ready for PDF generation.") \ No newline at end of file + print("Backtest completed! Generating PDF report...") + + # Generate PDF report + pdf_file = generate_original_pdf_report(results) + print(f"🎉 Original Emotional Damage Strategy report generated: {pdf_file}") \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced.py b/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced.py deleted file mode 100644 index cb2dc8d..0000000 --- a/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced.py +++ /dev/null @@ -1,600 +0,0 @@ -import sqlite3 -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.backends.backend_pdf import PdfPages -import warnings -warnings.filterwarnings('ignore') - -class EnhancedEmotionalDamageStrategy: - def __init__(self, initial_capital=100000): - self.initial_capital = initial_capital - self.cash = initial_capital - self.positions = {} # ticker: shares - self.portfolio_value = [] - self.trades = [] - self.fear_threshold = 25 - self.greed_threshold = 75 - self.top_stocks_count = 10 - self.stop_loss_threshold = 0.15 # 15% stop loss - - # New state management for gradual transitions - self.state = 'QQQ_HOLD' # QQQ_HOLD, FEAR_TRANSITION, GREED_TRANSITION, VOLATILE_STOCKS - self.transition_steps = 4 - self.current_transition_step = 0 - self.transition_target = None - self.transition_stocks = [] - self.last_fear_date = None - self.last_greed_date = None - - def get_data(self): - """Load Fear & Greed Index and stock data""" - conn = sqlite3.connect('data/stock_data.db') - - # Get Fear & Greed Index - fg_data = pd.read_sql_query(''' - SELECT date, fear_greed_index - FROM fear_greed_index - ORDER BY date - ''', conn) - fg_data['date'] = pd.to_datetime(fg_data['date']) - fg_data.set_index('date', inplace=True) - - # Get QQQ price data - spy_data = pd.read_sql_query(''' - SELECT date, spy_close - FROM fear_greed_data - ORDER BY date - ''', conn) - spy_data['date'] = pd.to_datetime(spy_data['date']) - spy_data.set_index('date', inplace=True) - - # Get available tickers - cursor = conn.cursor() - cursor.execute('SELECT ticker FROM ticker_list WHERE records > 1000') - self.available_tickers = [row[0] for row in cursor.fetchall()] - - conn.close() - - # Merge data - self.data = pd.merge(fg_data, spy_data, left_index=True, right_index=True, how='inner') - self.data.sort_index(inplace=True) - - print(f"Loaded data from {self.data.index.min().strftime('%Y-%m-%d')} to {self.data.index.max().strftime('%Y-%m-%d')}") - print(f"Available tickers for selection: {len(self.available_tickers)}") - - def get_stock_price(self, ticker, date): - """Get stock price for a specific ticker and date""" - conn = sqlite3.connect('data/stock_data.db') - - query = f''' - SELECT close FROM {ticker.lower()} - WHERE date <= ? - ORDER BY date DESC - LIMIT 1 - ''' - - cursor = conn.cursor() - cursor.execute(query, (date.strftime('%Y-%m-%d'),)) - result = cursor.fetchone() - conn.close() - - return result[0] if result else None - - def get_stock_data(self, ticker, start_date, end_date): - """Get historical stock data for technical analysis""" - conn = sqlite3.connect('data/stock_data.db') - - query = f''' - SELECT date, open, high, low, close, volume - FROM {ticker.lower()} - WHERE date >= ? AND date <= ? - ORDER BY date - ''' - - df = pd.read_sql_query(query, conn, params=( - start_date.strftime('%Y-%m-%d'), - end_date.strftime('%Y-%m-%d') - )) - conn.close() - - if not df.empty: - df['date'] = pd.to_datetime(df['date']) - df.set_index('date', inplace=True) - return df - return None - - def calculate_technical_indicators(self, df): - """Calculate MACD, RSI, and EMA indicators""" - if len(df) < 50: # Need sufficient data - return None - - # RSI - delta = df['close'].diff() - gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() - loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() - rs = gain / loss - rsi = 100 - (100 / (1 + rs)) - - # MACD - exp1 = df['close'].ewm(span=12).mean() - exp2 = df['close'].ewm(span=26).mean() - macd = exp1 - exp2 - signal = macd.ewm(span=9).mean() - - # EMA - ema5 = df['close'].ewm(span=5).mean() - ema20 = df['close'].ewm(span=20).mean() - - return { - 'rsi': rsi, - 'macd': macd, - 'signal': signal, - 'ema5': ema5, - 'ema20': ema20 - } - - def check_signal_direction(self, indicators, df): - """Check if indicators are turning upward""" - if indicators is None: - return False - - signals = [] - - # RSI upward turn (first derivative positive) - if len(indicators['rsi']) >= 3: - rsi_current = indicators['rsi'].iloc[-1] - rsi_prev = indicators['rsi'].iloc[-2] - rsi_slope = rsi_current - rsi_prev - signals.append(rsi_slope > 0) - - # MACD golden cross (MACD crosses above signal) - if len(indicators['macd']) >= 3: - macd_current = indicators['macd'].iloc[-1] - signal_current = indicators['signal'].iloc[-1] - macd_prev = indicators['macd'].iloc[-2] - signal_prev = indicators['signal'].iloc[-2] - - # Golden cross: macd crosses above signal - golden_cross = (macd_prev <= signal_prev) and (macd_current > signal_current) - signals.append(golden_cross) - - # EMA crossover (EMA5 crosses above EMA20) - if len(indicators['ema5']) >= 3: - ema5_current = indicators['ema5'].iloc[-1] - ema20_current = indicators['ema20'].iloc[-1] - ema5_prev = indicators['ema5'].iloc[-2] - ema20_prev = indicators['ema20'].iloc[-2] - - ema_crossover = (ema5_prev <= ema20_prev) and (ema5_current > ema20_current) - signals.append(ema_crossover) - - # Need at least 2 out of 3 signals positive - return sum(signals) >= 2 - - def calculate_volatility(self, ticker, start_date, end_date): - """Calculate historical volatility for a single ticker""" - conn = sqlite3.connect('data/stock_data.db') - - try: - query = f''' - SELECT date, close FROM {ticker.lower()} - WHERE date >= ? AND date <= ? - ORDER BY date - ''' - - df = pd.read_sql_query(query, conn, params=( - start_date.strftime('%Y-%m-%d'), - end_date.strftime('%Y-%m-%d') - )) - - if len(df) > 10: - df['returns'] = df['close'].pct_change() - volatility = df['returns'].std() * np.sqrt(252) - conn.close() - return volatility - - except Exception as e: - pass - - conn.close() - return 0 - - def select_stocks_with_technical_filter(self, fear_start_date, fear_end_date): - """Select stocks using technical indicators + volatility ranking""" - candidates = [] - - # Extend the period for more data - extended_start = fear_start_date - timedelta(days=30) - extended_end = fear_end_date + timedelta(days=5) - - for ticker in self.available_tickers: - stock_data = self.get_stock_data(ticker, extended_start, extended_end) - if stock_data is not None and len(stock_data) >= 30: - volatility = self.calculate_volatility(ticker, fear_start_date, fear_end_date) - if volatility > 0.1: # Minimum volatility threshold - - # Check technical indicators on recent data - recent_data = stock_data.tail(30) - indicators = self.calculate_technical_indicators(recent_data) - - # Be more lenient - accept if at least some indicators are positive - technical_score = 0 - if indicators is not None: - # Simplified scoring - just check if recent trend is up - recent_trend = recent_data['close'].pct_change().tail(5).sum() - if recent_trend > -0.02: # Not strongly declining - technical_score += 1 - - # Check if RSI is not oversold - if len(indicators['rsi']) > 0 and indicators['rsi'].iloc[-1] > 30: - technical_score += 1 - - # Accept if basic criteria met or if volatility is high - if technical_score >= 1 or volatility > 0.5: - candidates.append((ticker, volatility)) - - # Sort by volatility and select top stocks - candidates.sort(key=lambda x: x[1], reverse=True) - selected = [ticker for ticker, vol in candidates[:self.top_stocks_count]] - - print(f"Selected {len(selected)} stocks from {len(candidates)} candidates") - if selected: - print(f"Top stocks: {selected}") - return selected - - def execute_gradual_transition(self, date, target_state): - """Execute gradual 4-step position transitions""" - if target_state == 'CASH': - # Gradually sell to cash - if self.current_transition_step < self.transition_steps: - step_size = 1.0 / self.transition_steps - step_pct = step_size * (self.current_transition_step + 1) - - # Sell portion of holdings - for ticker in list(self.positions.keys()): - if ticker != 'QQQ': - shares_to_sell = int(self.positions[ticker] * step_pct) - if shares_to_sell > 0: - price = self.get_stock_price(ticker, date) - if price: - value = shares_to_sell * price - self.cash += value - self.positions[ticker] -= shares_to_sell - if self.positions[ticker] <= 0: - del self.positions[ticker] - - self.trades.append({ - 'date': date, - 'action': 'SELL_GRADUAL', - 'ticker': ticker, - 'shares': shares_to_sell, - 'price': price, - 'value': value - }) - - self.current_transition_step += 1 - if self.current_transition_step >= self.transition_steps: - return True # Transition complete - - elif target_state == 'VOLATILE': - # Gradually buy volatile stocks - if self.current_transition_step < self.transition_steps: - step_size = 1.0 / self.transition_steps - step_pct = step_size * (self.current_transition_step + 1) - - if self.transition_stocks: - amount_per_stock = (self.cash * step_pct) / len(self.transition_stocks) - - for ticker in self.transition_stocks: - price = self.get_stock_price(ticker, date) - if price: - shares = amount_per_stock / price - if ticker in self.positions: - self.positions[ticker] += shares - else: - self.positions[ticker] = shares - - self.cash -= amount_per_stock - - self.trades.append({ - 'date': date, - 'action': 'BUY_GRADUAL', - 'ticker': ticker, - 'shares': shares, - 'price': price, - 'value': amount_per_stock - }) - - self.current_transition_step += 1 - if self.current_transition_step >= self.transition_steps: - return True # Transition complete - - elif target_state == 'QQQ': - # Gradually buy QQQ - if self.current_transition_step < self.transition_steps: - step_size = 1.0 / self.transition_steps - step_pct = step_size * (self.current_transition_step + 1) - - qqq_price = self.data.loc[date, 'spy_close'] - total_value = self.calculate_portfolio_value(date) - target_qqq_value = total_value * step_pct - - if 'QQQ' not in self.positions: - self.positions['QQQ'] = 0 - - shares_to_buy = (target_qqq_value - (self.positions.get('QQQ', 0) * qqq_price)) / qqq_price - if shares_to_buy > 0: - self.positions['QQQ'] += shares_to_buy - - # Sell other positions proportionally - other_positions = {k: v for k, v in self.positions.items() if k != 'QQQ'} - for ticker, shares in other_positions.items(): - shares_to_sell = shares * (step_size / (1 - (self.current_transition_step * step_size))) - price = self.get_stock_price(ticker, date) - if price: - value = shares_to_sell * price - self.cash += value - self.positions[ticker] -= shares_to_sell - if self.positions[ticker] <= 0: - del self.positions[ticker] - - self.current_transition_step += 1 - if self.current_transition_step >= self.transition_steps: - return True # Transition complete - - return False # Transition ongoing - - def check_stop_loss(self, date): - """Check for 15% stop loss and replace with QQQ""" - stop_loss_trades = [] - - for ticker, shares in list(self.positions.items()): - if ticker == 'QQQ': - continue - - current_price = self.get_stock_price(ticker, date) - if current_price: - # Find buy price from recent trades - buy_trades = [t for t in self.trades if t['ticker'] == ticker and t['action'] in ['BUY_VOLATILE', 'BUY_GRADUAL']] - if buy_trades: - avg_buy_price = sum(t['price'] * t['shares'] for t in buy_trades) / sum(t['shares'] for t in buy_trades) - loss_pct = (current_price - avg_buy_price) / avg_buy_price - - if loss_pct <= -self.stop_loss_threshold: - # Sell the losing position - value = shares * current_price - self.cash += value - del self.positions[ticker] - - stop_loss_trades.append({ - 'date': date, - 'action': 'STOP_LOSS', - 'ticker': ticker, - 'shares': shares, - 'price': current_price, - 'value': value, - 'loss_pct': loss_pct * 100 - }) - - # Immediately buy QQQ with the proceeds - qqq_price = self.data.loc[date, 'spy_close'] - qqq_shares = value / qqq_price - self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares - - stop_loss_trades.append({ - 'date': date, - 'action': 'BUY_QQQ_STOPLOSS', - 'ticker': 'QQQ', - 'shares': qqq_shares, - 'price': qqq_price, - 'value': value - }) - - self.trades.extend(stop_loss_trades) - return len(stop_loss_trades) > 0 - - def calculate_portfolio_value(self, date): - """Calculate total portfolio value at given date""" - total_value = self.cash - - for ticker, shares in self.positions.items(): - if ticker == 'QQQ': - price = self.data.loc[date, 'spy_close'] - else: - price = self.get_stock_price(ticker, date) - - if price: - total_value += shares * price - - return total_value - - def run_backtest(self): - """Run the enhanced emotional damage strategy backtest""" - print("Running Enhanced Emotional Damage Strategy Backtest...") - - self.get_data() - - # Start with QQQ - first_date = self.data.index[0] - qqq_price = self.data.loc[first_date, 'spy_close'] - qqq_shares = self.cash / qqq_price - self.positions['QQQ'] = qqq_shares - self.cash = 0 - - fear_start_date = None - greed_start_date = None - - for i, (date, row) in enumerate(self.data.iterrows()): - fg_index = row['fear_greed_index'] - - # Check stop loss first - self.check_stop_loss(date) - - if self.state == 'QQQ_HOLD': - # Check if Fear & Greed drops below 25 - if fg_index < self.fear_threshold: - self.state = 'FEAR_TRANSITION' - self.transition_target = 'CASH' - self.current_transition_step = 0 - self.last_fear_date = date - print(f"{date.strftime('%Y-%m-%d')}: Fear & Greed {fg_index:.1f} < 25, starting gradual transition to cash") - - elif self.state == 'FEAR_TRANSITION': - # Continue gradual transition to cash - completed = self.execute_gradual_transition(date, 'CASH') - if completed: - # Check if we should transition to volatile stocks - if fg_index >= self.fear_threshold and self.last_fear_date: - # Select stocks using technical filters - top_stocks = self.select_stocks_with_technical_filter( - self.last_fear_date, date - ) - - if top_stocks: - self.transition_stocks = top_stocks - self.state = 'GREED_TRANSITION' # Transition to volatile stocks - self.transition_target = 'VOLATILE' - self.current_transition_step = 0 - print(f"{date.strftime('%Y-%m-%d')}: Fear & Greed recovered, starting transition to volatile stocks: {top_stocks}") - - # If fear continues, stay in cash - elif fg_index < self.fear_threshold: - self.state = 'CASH_WAIT' - print(f"{date.strftime('%Y-%m-%d')}: Transition to cash complete, holding cash") - - elif self.state == 'CASH_WAIT': - # Waiting in cash, check for recovery - if fg_index >= self.fear_threshold and self.last_fear_date: - # Select stocks using technical filters - top_stocks = self.select_stocks_with_technical_filter( - self.last_fear_date, date - ) - - if top_stocks: - self.transition_stocks = top_stocks - self.state = 'GREED_TRANSITION' # Transition to volatile stocks - self.transition_target = 'VOLATILE' - self.current_transition_step = 0 - print(f"{date.strftime('%Y-%m-%d')}: Fear & Greed recovered, starting transition to volatile stocks: {top_stocks}") - - elif self.state == 'GREED_TRANSITION': - # Continue gradual transition to volatile stocks - completed = self.execute_gradual_transition(date, 'VOLATILE') - if completed: - self.state = 'VOLATILE_STOCKS' - self.last_greed_date = date - print(f"{date.strftime('%Y-%m-%d')}: Transition to volatile stocks complete") - - elif self.state == 'VOLATILE_STOCKS': - # Check if Fear & Greed exceeds 75 (extreme greed) - if fg_index > self.greed_threshold: - self.state = 'FEAR_TRANSITION' # Transition to QQQ - self.transition_target = 'QQQ' - self.current_transition_step = 0 - self.last_greed_date = date - print(f"{date.strftime('%Y-%m-%d')}: Fear & Greed {fg_index:.1f} > 75, starting transition to QQQ") - - # Record portfolio value - portfolio_value = self.calculate_portfolio_value(date) - self.portfolio_value.append({ - 'date': date, - 'value': portfolio_value, - 'state': self.state, - 'fg_index': fg_index, - 'cash': self.cash - }) - - print(f"Backtest completed! Total trades: {len(self.trades)}") - - def calculate_performance_metrics(self, returns): - """Calculate performance metrics""" - total_return = (returns.iloc[-1] / returns.iloc[0] - 1) * 100 - annual_return = ((returns.iloc[-1] / returns.iloc[0]) ** (252 / len(returns)) - 1) * 100 - - # Calculate max drawdown - peak = returns.expanding().max() - drawdown = (returns - peak) / peak - max_drawdown = drawdown.min() * 100 - - # Find max drawdown period - max_dd_date = drawdown.idxmin() - max_dd_year = max_dd_date.year - - # Calculate Sharpe ratio - daily_returns = returns.pct_change().dropna() - sharpe_ratio = np.sqrt(252) * daily_returns.mean() / daily_returns.std() - - # Annual returns by year - annual_rets = {} - for year in returns.index.year.unique(): - year_data = returns[returns.index.year == year] - if len(year_data) > 1: - year_return = (year_data.iloc[-1] / year_data.iloc[0] - 1) * 100 - annual_rets[year] = year_return - - return { - 'total_return': total_return, - 'annual_return': annual_return, - 'max_drawdown': max_drawdown, - 'max_drawdown_date': max_dd_date, - 'max_drawdown_year': max_dd_year, - 'sharpe_ratio': sharpe_ratio, - 'annual_returns': annual_rets - } - -def run_enhanced_backtest(): - """Run the enhanced emotional damage strategy""" - - # Run strategy - strategy = EnhancedEmotionalDamageStrategy(initial_capital=100000) - strategy.run_backtest() - - # Convert results to DataFrame - portfolio_df = pd.DataFrame(strategy.portfolio_value) - portfolio_df.set_index('date', inplace=True) - - # Get benchmark data - conn = sqlite3.connect('data/stock_data.db') - - benchmark_data = pd.read_sql_query(''' - SELECT date, spy_close - FROM fear_greed_data - ORDER BY date - ''', conn) - benchmark_data['date'] = pd.to_datetime(benchmark_data['date']) - benchmark_data.set_index('date', inplace=True) - - conn.close() - - # Align dates - common_dates = portfolio_df.index.intersection(benchmark_data.index) - portfolio_df = portfolio_df.loc[common_dates] - benchmark_data = benchmark_data.loc[common_dates] - - # Normalize to starting value for comparison - start_value = 100000 - - # Create QQQ and SPY buy-and-hold benchmarks - benchmark_data['qqq_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) - benchmark_data['spy_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) - - # Calculate performance metrics - strategy_metrics = strategy.calculate_performance_metrics(portfolio_df['value']) - qqq_metrics = strategy.calculate_performance_metrics(benchmark_data['qqq_value']) - spy_metrics = strategy.calculate_performance_metrics(benchmark_data['spy_value']) - - return { - 'strategy': strategy, - 'portfolio_df': portfolio_df, - 'benchmark_data': benchmark_data, - 'strategy_metrics': strategy_metrics, - 'qqq_metrics': qqq_metrics, - 'spy_metrics': spy_metrics - } - -if __name__ == "__main__": - results = run_enhanced_backtest() - print("Enhanced backtest completed! Results ready for PDF generation.") \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced_v2.py b/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced_v2.py index a7facb7..38d2ed3 100644 --- a/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced_v2.py +++ b/backtest/strategy/emotional-damage/backtest_emotional_damage_enhanced_v2.py @@ -46,14 +46,14 @@ class EnhancedEmotionalDamageStrategy: fg_data['date'] = pd.to_datetime(fg_data['date']) fg_data.set_index('date', inplace=True) - # Get SPY price data as QQQ proxy - spy_data = pd.read_sql_query(''' - SELECT date, spy_close - FROM fear_greed_data + # Get real QQQ price data + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq ORDER BY date ''', conn) - spy_data['date'] = pd.to_datetime(spy_data['date']) - spy_data.set_index('date', inplace=True) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) # Get available tickers cursor = conn.cursor() @@ -63,7 +63,7 @@ class EnhancedEmotionalDamageStrategy: conn.close() # Merge data - self.data = pd.merge(fg_data, spy_data, left_index=True, right_index=True, how='inner') + self.data = pd.merge(fg_data, qqq_data, left_index=True, right_index=True, how='inner') self.data.sort_index(inplace=True) print(f"Loaded data from {self.data.index.min().strftime('%Y-%m-%d')} to {self.data.index.max().strftime('%Y-%m-%d')}") @@ -91,15 +91,19 @@ class EnhancedEmotionalDamageStrategy: return result[0] if result else None - def calculate_volatility(self, ticker, start_date, end_date): - """Calculate historical volatility""" + def calculate_volatility(self, ticker, current_date): + """Calculate historical volatility over the past month""" import os + from datetime import timedelta script_dir = os.path.dirname(os.path.abspath(__file__)) backtest_dir = os.path.dirname(os.path.dirname(script_dir)) db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') conn = sqlite3.connect(db_path) try: + # Calculate volatility for the past 30 days + start_date = current_date - timedelta(days=30) + query = f''' SELECT date, close FROM {ticker.lower()} WHERE date >= ? AND date <= ? @@ -108,7 +112,7 @@ class EnhancedEmotionalDamageStrategy: df = pd.read_sql_query(query, conn, params=( start_date.strftime('%Y-%m-%d'), - end_date.strftime('%Y-%m-%d') + current_date.strftime('%Y-%m-%d') )) if len(df) > 10: @@ -123,30 +127,119 @@ class EnhancedEmotionalDamageStrategy: conn.close() return 0 + def check_technical_indicators(self, ticker, date): + """Check RSI, MACD, and SMA technical indicators""" + import os + script_dir = os.path.dirname(os.path.abspath(__file__)) + backtest_dir = os.path.dirname(os.path.dirname(script_dir)) + db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') + conn = sqlite3.connect(db_path) + + try: + # Get 50 days of data for technical analysis + query = f''' + SELECT date, close FROM {ticker.lower()} + WHERE date <= ? + ORDER BY date DESC + LIMIT 50 + ''' + + df = pd.read_sql_query(query, conn, params=(date.strftime('%Y-%m-%d'),)) + + if len(df) < 20: + conn.close() + return False + + df = df.sort_values('date') + df.reset_index(drop=True, inplace=True) + + # Calculate RSI + delta = df['close'].diff() + gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() + rs = gain / loss + rsi = 100 - (100 / (1 + rs)) + + # Calculate MACD + ema12 = df['close'].ewm(span=12).mean() + ema26 = df['close'].ewm(span=26).mean() + macd = ema12 - ema26 + signal = macd.ewm(span=9).mean() + + # Calculate SMA + sma5 = df['close'].rolling(window=5).mean() + sma20 = df['close'].rolling(window=20).mean() + + # Check conditions (use latest values) + latest_rsi = rsi.iloc[-1] + latest_macd = macd.iloc[-1] + latest_signal = signal.iloc[-1] + latest_sma5 = sma5.iloc[-1] + latest_sma20 = sma20.iloc[-1] + + # RSI should be above 30 (not oversold) + rsi_ok = latest_rsi > 30 + + # MACD convergence: MACD and Signal lines are converging + # Check if MACD is getting closer to signal line (momentum improving) + if len(macd) >= 2 and len(signal) >= 2: + prev_macd = macd.iloc[-2] + prev_signal = signal.iloc[-2] + prev_diff = abs(prev_macd - prev_signal) + current_diff = abs(latest_macd - latest_signal) + macd_ok = current_diff < prev_diff # Lines are converging + else: + macd_ok = latest_macd > latest_signal # Fallback to original condition + + # SMA5 should be above SMA20 (uptrend) + sma_ok = latest_sma5 > latest_sma20 + + # Need at least 2 out of 3 indicators to be positive + score = sum([rsi_ok, macd_ok, sma_ok]) + + conn.close() + return score >= 2 + + except Exception as e: + conn.close() + return False + def select_volatile_stocks(self, fear_start_date, fear_end_date): - """Select top volatile stocks""" - volatilities = {} + """Select stocks using technical indicators, then sort by volatility""" + qualified_stocks = [] + # Filter stocks using technical indicators for ticker in self.available_tickers: - vol = self.calculate_volatility(ticker, fear_start_date, fear_end_date) - if vol > 0.2: # Minimum volatility threshold - volatilities[ticker] = vol + if self.check_technical_indicators(ticker, fear_end_date): + vol = self.calculate_volatility(ticker, fear_end_date) # Calculate past month volatility + + if vol > 0.1: # Normal volatility threshold + qualified_stocks.append((ticker, vol)) - # Sort by volatility and select top N - sorted_vol = sorted(volatilities.items(), key=lambda x: x[1], reverse=True) - top_stocks = [ticker for ticker, vol in sorted_vol[:self.top_stocks_count]] + # Sort by volatility and select top 4 + qualified_stocks.sort(key=lambda x: x[1], reverse=True) + top_stocks = [ticker for ticker, vol in qualified_stocks[:4]] return top_stocks def execute_trade(self, date, action, ticker=None, shares=None, price=None, value=None): - """Record a trade""" + """Execute and record a trade""" + # Note: In enhanced strategy, actual execution is handled by gradual_transition + # This method is only for recording trades + + # Get F&G index for this date + fg_index = self.data.loc[date, 'fear_greed_index'] if date in self.data.index else None + self.trades.append({ 'date': date, 'action': action, 'ticker': ticker, 'shares': shares, 'price': price, - 'value': value + 'value': value, + 'fg_index': fg_index, + 'cash_after': self.cash, + 'portfolio_state': self.state }) def calculate_portfolio_value(self, date): @@ -155,7 +248,7 @@ class EnhancedEmotionalDamageStrategy: for ticker, shares in self.positions.items(): if ticker == 'QQQ': - price = self.data.loc[date, 'spy_close'] + price = self.data.loc[date, 'qqq_close'] else: price = self.get_stock_price(ticker, date) @@ -176,7 +269,7 @@ class EnhancedEmotionalDamageStrategy: # Find average buy price buy_trades = [t for t in self.trades - if t['ticker'] == ticker and t['action'] in ['BUY_VOLATILE']] + if t['ticker'] == ticker and t['action'] in ['BUY_GRADUAL']] if buy_trades: total_cost = sum(t['price'] * t['shares'] for t in buy_trades) total_shares = sum(t['shares'] for t in buy_trades) @@ -192,12 +285,15 @@ class EnhancedEmotionalDamageStrategy: self.execute_trade(date, 'STOP_LOSS', ticker, shares, current_price, value) - # Buy QQQ - qqq_price = self.data.loc[date, 'spy_close'] - qqq_shares = value / qqq_price - self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares + # Buy QQQ with integer shares + qqq_price = self.data.loc[date, 'qqq_close'] + qqq_shares = int(value / qqq_price) - self.execute_trade(date, 'BUY_QQQ_STOPLOSS', 'QQQ', qqq_shares, qqq_price, value) + if qqq_shares > 0: + actual_qqq_value = qqq_shares * qqq_price + self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares + self.cash -= actual_qqq_value # Subtract the actual QQQ purchase value + self.execute_trade(date, 'BUY_QQQ_STOPLOSS', 'QQQ', qqq_shares, qqq_price, actual_qqq_value) print(f"{date.strftime('%Y-%m-%d')}: Stop loss triggered for {ticker}, loss: {loss_pct*100:.1f}%") @@ -206,11 +302,10 @@ class EnhancedEmotionalDamageStrategy: self.transition_plan = {'type': target_type, 'stocks': stocks} if target_type == 'CASH': - # Plan to sell all non-QQQ positions over 4 steps + # Plan to sell ALL positions (including QQQ) over 4 steps self.transition_plan['positions_to_sell'] = {} for ticker in self.positions: - if ticker != 'QQQ': - self.transition_plan['positions_to_sell'][ticker] = self.positions[ticker] + self.transition_plan['positions_to_sell'][ticker] = self.positions[ticker] elif target_type == 'QQQ': # Plan to sell all non-QQQ positions and convert to cash pool @@ -232,7 +327,16 @@ class EnhancedEmotionalDamageStrategy: elif target_type == 'VOLATILE' and stocks: # Plan to invest available cash in volatile stocks - self.transition_plan['total_cash_to_invest'] = self.cash + # Include cash from any remaining positions (should be mostly cash by now) + cash_from_positions = 0 + for ticker in self.positions: + if ticker != 'QQQ': + price = self.get_stock_price(ticker, date) + if price: + cash_from_positions += self.positions[ticker] * price + + total_available_cash = self.cash + cash_from_positions + self.transition_plan['total_cash_to_invest'] = total_available_cash def gradual_transition(self, date, target_type, stocks=None): """Handle 4-step gradual transitions with fixed allocation""" @@ -256,19 +360,23 @@ class EnhancedEmotionalDamageStrategy: self.execute_trade(date, 'SELL_GRADUAL', ticker, shares_to_sell, price, value) elif target_type == 'VOLATILE' and stocks: - # Buy volatile stocks gradually using fixed cash allocation + # Buy only the most volatile stock each step (up to 4 different stocks) total_cash = self.transition_plan.get('total_cash_to_invest', 0) cash_this_step = total_cash * step_size if cash_this_step > 0 and self.cash >= cash_this_step: - amount_per_stock = cash_this_step / len(stocks) - for ticker in stocks: - price = self.get_stock_price(ticker, date) - if price and amount_per_stock > 0: - shares = amount_per_stock / price + # Buy only the most volatile stock this step + current_step_index = min(self.current_step, len(stocks) - 1) + ticker = stocks[current_step_index] # stocks are already sorted by volatility + + price = self.get_stock_price(ticker, date) + if price and cash_this_step > 0: + shares = int(cash_this_step / price) # Integer shares only + if shares > 0: + actual_value = shares * price self.positions[ticker] = self.positions.get(ticker, 0) + shares - self.cash -= amount_per_stock - self.execute_trade(date, 'BUY_GRADUAL', ticker, shares, price, amount_per_stock) + self.cash -= actual_value + self.execute_trade(date, 'BUY_GRADUAL', ticker, shares, price, actual_value) elif target_type == 'QQQ': # Sell positions gradually and buy QQQ with fixed allocation @@ -293,12 +401,14 @@ class EnhancedEmotionalDamageStrategy: cash_this_step = total_cash * step_size if cash_this_step > 0 and self.cash >= cash_this_step: - qqq_price = self.data.loc[date, 'spy_close'] - qqq_shares = cash_this_step / qqq_price + qqq_price = self.data.loc[date, 'qqq_close'] + qqq_shares = int(cash_this_step / qqq_price) # Integer shares only - self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares - self.cash -= cash_this_step - self.execute_trade(date, 'BUY_GRADUAL', 'QQQ', qqq_shares, qqq_price, cash_this_step) + if qqq_shares > 0: + actual_value = qqq_shares * qqq_price + self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares + self.cash -= actual_value + self.execute_trade(date, 'BUY_GRADUAL', 'QQQ', qqq_shares, qqq_price, actual_value) def run_backtest(self): """Run the enhanced strategy backtest""" @@ -308,9 +418,10 @@ class EnhancedEmotionalDamageStrategy: # Start with 100% QQQ first_date = self.data.index[0] - qqq_price = self.data.loc[first_date, 'spy_close'] - qqq_shares = self.initial_capital / qqq_price + qqq_price = self.data.loc[first_date, 'qqq_close'] + qqq_shares = int(self.initial_capital / qqq_price) # Integer shares only self.positions['QQQ'] = qqq_shares + self.cash = self.initial_capital - (qqq_shares * qqq_price) # Remaining cash after buying integer shares fear_start_date = None @@ -438,21 +549,36 @@ def run_enhanced_backtest(): portfolio_df = pd.DataFrame(strategy.portfolio_value) portfolio_df.set_index('date', inplace=True) - # Get benchmark data + # Get benchmark data (both QQQ and SPY) import os script_dir = os.path.dirname(os.path.abspath(__file__)) backtest_dir = os.path.dirname(os.path.dirname(script_dir)) db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') conn = sqlite3.connect(db_path) - benchmark_data = pd.read_sql_query(''' + + # Get QQQ data + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq + ORDER BY date + ''', conn) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) + + # Get SPY data + spy_data = pd.read_sql_query(''' SELECT date, spy_close FROM fear_greed_data ORDER BY date ''', conn) - benchmark_data['date'] = pd.to_datetime(benchmark_data['date']) - benchmark_data.set_index('date', inplace=True) + spy_data['date'] = pd.to_datetime(spy_data['date']) + spy_data.set_index('date', inplace=True) + conn.close() + # Merge benchmark data + benchmark_data = pd.merge(qqq_data, spy_data, left_index=True, right_index=True, how='inner') + # Align dates common_dates = portfolio_df.index.intersection(benchmark_data.index) portfolio_df = portfolio_df.loc[common_dates] @@ -460,7 +586,7 @@ def run_enhanced_backtest(): # Normalize benchmarks start_value = 100000 - benchmark_data['qqq_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) + benchmark_data['qqq_value'] = start_value * (benchmark_data['qqq_close'] / benchmark_data['qqq_close'].iloc[0]) benchmark_data['spy_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) # Calculate metrics diff --git a/backtest/strategy/emotional-damage/config.json b/backtest/strategy/emotional-damage/config.json new file mode 100644 index 0000000..42c6915 --- /dev/null +++ b/backtest/strategy/emotional-damage/config.json @@ -0,0 +1,45 @@ +{ + "strategy_params": { + "initial_capital": 100000, + "fear_threshold": 25, + "greed_threshold": 75, + "stop_loss_threshold": 0.15, + "transition_steps": 4, + "top_stocks_count": 4, + "volatility_threshold": 0.1, + "volatility_calculation_days": 30 + }, + "technical_indicators": { + "rsi_threshold": 30, + "required_indicators": 2, + "sma5_above_sma20": true, + "macd_convergence": true, + "ema_periods": { + "fast": 12, + "slow": 26, + "signal": 9 + }, + "sma_periods": { + "fast": 5, + "slow": 20 + }, + "rsi_period": 14 + }, + "output_settings": { + "generate_pdf": true, + "generate_csv": true, + "generate_detailed_trades": true, + "show_console_output": true, + "save_strategy_object": true + }, + "paths": { + "database_path": "/home/will/docker/backtest/data/stock_data.db", + "reports_dir": "/home/will/docker/backtest/reports", + "results_dir": "/home/will/docker/backtest/results" + }, + "data_settings": { + "min_ticker_records": 1000, + "start_date": null, + "end_date": null + } +} \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/emotional-damage.txt b/backtest/strategy/emotional-damage/emotional-damage.txt deleted file mode 100755 index c8cbf30..0000000 --- a/backtest/strategy/emotional-damage/emotional-damage.txt +++ /dev/null @@ -1,15 +0,0 @@ -默认状态下，100%满仓QQQ。 - -当CNN Fear & Greed 指数跌破25（极端恐惧）： - -清仓QQQ，100%现金状态等待。 - -当指数从25以下反弹回25以上时： - -计算在指数<25期间，QQQ成分股的历史波动率。 - -选出波动性最大的前10支股票，满仓持有这10只股票。 - -一直持有，直到CNN Fear & Greed 指数突破75（极端贪婪）： - -清仓10支股票，重新100%满仓QQQ，回到初始状态。 \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/emotional_damage_strategy_report_20250717_205822.pdf b/backtest/strategy/emotional-damage/emotional_damage_strategy_report_20250717_205822.pdf deleted file mode 100755 index 934d68d90e1863436972176976778306f1ced147..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 597784 zcmbT6bx<5l_wN%T1PBn^7Wd%677y-AaM#7%b%Edx!3i3II|K`E3yTxnb#Y(Zxp{u? 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z*8#STy?slUC2qXZWtHofI=;T)wP$?nf%Y@qbyH-{7u6M&vq@diIlID<0N2lTeBFa< zOI79NY69o0;2SHnN#-seIvm&L8hz)(%V%&lPA(zWmb%K@B>DoU7enCFf{iN++-FXP zK_>#9@)`7D7B>yvuCu 25: Select volatile stocks (recovery play) - - When F&G > 75: Move to QQQ (safe haven) +### 1. 基于恐惧与贪婪指数的市场择时 +- **数据源**: CNN恐惧与贪婪指数（0-100刻度） +- **恐惧阈值**: 25（市场恐慌触发点） +- **贪婪阈值**: 75（获利了结触发点） +- **逻辑**: + - 当F&G < 25时：转为现金（避免进一步损失） + - 当F&G恢复 > 25时：选择高波动股票（恢复阶段投资） + - 当F&G > 75时：转为QQQ（安全港） -### 2. Gradual Transition System (4-Step Process) -**Critical Fix Applied**: Original algorithm had compounding error creating impossible returns. +### 2. 渐进式调仓系统（4步调仓） +**关键修复**: 原算法存在复利错误，产生了不可能的收益率。 -#### Fixed Implementation: +#### 修复后的实现: ```python def start_transition(self, date, target_type, stocks=None): - # Calculate TOTAL funds available for transition at START - # Store fixed allocation plan to prevent compounding + # 在开始时计算总资金，存储固定分配计划防止复利错误 def gradual_transition(self, date, target_type, stocks=None): - # Execute 1/4 of predetermined allocation each step - # NO recalculation of total value per step + # 每步执行预定分配的1/4，不重新计算总价值 ``` -**Transition Types**: -- **TO_CASH**: Sell all non-QQQ positions over 4 trading days -- **TO_VOLATILE**: Buy selected volatile stocks with available cash -- **TO_QQQ**: Sell all positions and buy QQQ over 4 days +**调仓类型**: +- **转现金**: 在4个交易日内卖出所有非QQQ持仓 +- **转波动股**: 用可用现金买入选定的高波动股票 +- **转QQQ**: 在4天内卖出所有持仓并买入QQQ -**Why 4 Steps**: Reduces market impact and slippage from large position changes +**为什么4步**: 减少大额仓位变化的市场冲击和滑点 -### 3. Technical Indicator Filtering -For volatile stock selection, requires 2 out of 3 indicators showing upward trend: +### 3. 技术指标过滤系统 +高波动股票选择需要满足3个指标中至少2个显示上涨趋势： -#### MACD Golden Cross +#### MACD黄金交叉 ```python -macd > macd_signal # Current MACD above signal line +macd > macd_signal # 当前MACD高于信号线 ``` -#### RSI First Derivative Positive +#### RSI向上趋势 ```python -current_rsi > previous_rsi # RSI trending upward +current_rsi > 30 # RSI大于30（避免超卖） ``` -#### EMA Crossover (5/20 period) +#### 移动平均线交叉（5/20周期） ```python -ema_5 > ema_20 # Short-term momentum over long-term +sma_5 > sma_20 # 短期动量超过长期 ``` -### 4. Stop-Loss Protection (15% Rule) -- **Trigger**: Any volatile stock position drops 15% below average purchase price -- **Action**: Immediately sell position and buy equivalent QQQ shares -- **Purpose**: Risk management to prevent large losses +### 4. 止损保护（15%规则） +- **触发条件**: 任何波动股持仓跌破平均买入价15% +- **执行动作**: 立即卖出该持仓并买入等值QQQ +- **目的**: 风险管理，防止大额损失 -## State Machine Implementation +## 状态机实现 ``` QQQ_HOLD → [F&G < 25] → FEAR_TRANSITION → CASH_WAIT - ↓ (4 steps) + ↓ (4步) CASH_WAIT → [F&G ≥ 25] → GREED_TRANSITION → VOLATILE_STOCKS - ↓ (4 steps) ↓ [F&G > 75] + ↓ (4步) ↓ [F&G > 75] QQQ_TRANSITION - ↓ (4 steps) + ↓ (4步) QQQ_HOLD ``` -## Key Algorithm Fixes Applied +## 关键算法修复 -### Original Bug: Compounding Error +### 原始BUG: 初始化错误 ```python -# WRONG (caused 129M% impossible returns): -total_value = self.calculate_portfolio_value(date) -target_qqq_value = total_value * step_size # Created money each step! +# 错误（导致资金翻倍）: +self.cash = initial_capital # 设置现金 +self.positions['QQQ'] = qqq_shares # 买入QQQ但没有清零现金 ``` -### Fixed Algorithm: +### 修复后的算法: ```python -# CORRECT (realistic 516% over 17 years): -def start_transition(self, date, target_type): - self.transition_cash_pool = calculate_total_available_cash() - -def gradual_transition(self, date, target_type): - cash_this_step = self.transition_cash_pool * step_size # Fixed amount +# 正确（资金正常）: +self.positions['QQQ'] = qqq_shares +self.cash = 0 # 修复: 买入QQQ后清零现金 ``` -## Performance Characteristics +### 股票选择逻辑修复 +```python +# 原来: 只使用波动率过滤（过于简单） +if vol > 0.2: # 20%波动率阈值过高 + volatilities[ticker] = vol -### Risk Management Features: -- **Maximum Drawdown Control**: Move to cash during market panic -- **Position Sizing**: Equal-weight allocation across selected stocks -- **Stop-Loss**: 15% maximum loss per position -- **Gradual Execution**: 4-step transitions reduce market impact +# 修复后: 技术指标过滤 + 波动率排序 +if self.check_technical_indicators(ticker, date): # RSI+MACD+SMA过滤 + if vol > 0.1: # 降低波动率阈值 + qualified_stocks.append((ticker, vol)) +``` -### Expected Behavior: -- **Bull Markets**: Moderate participation via QQQ -- **Bear Markets**: Cash preservation during panic phases -- **Recovery Phases**: Aggressive positioning in volatile stocks -- **Overheated Markets**: Defensive shift back to QQQ +### 交易逻辑修复 +```python +# 原来: 等权重买入所有股票 +amount_per_stock = cash / len(stocks) # 分散投资 +for ticker in stocks: + buy(ticker, amount_per_stock) -## Technical Implementation Details +# 修复后: 每次买入波动性最强的1支股票 +current_step_index = min(self.current_step, len(stocks) - 1) +ticker = stocks[current_step_index] # 按波动性排序，每次买最强的 +buy(ticker, cash_this_step) # 最多同时持有4支股票 +``` -### Database Dependencies: -- `fear_greed_index`: CNN F&G historical data -- `{ticker}`: Individual stock OHLCV + technical indicators -- `qqq`: QQQ ETF historical prices (to be added) -- `fear_greed_data.spy_close`: SPY benchmark data +## 性能特征 -### Performance Metrics Calculated: -- Total Return, Annual Return, Sharpe Ratio -- Maximum Drawdown and recovery periods -- Win/Loss ratio and trade frequency -- Correlation with market benchmarks +### 风险管理功能: +- **最大回撤控制**: 市场恐慌时转为现金 +- **仓位管理**: 最多同时持有4支高波动股票 +- **止损保护**: 每个持仓最大损失15% +- **渐进执行**: 4步调仓减少市场冲击 -## Known Limitations & Future Improvements +### 预期行为: +- **牛市**: 通过QQQ适度参与 +- **熊市**: 恐慌阶段现金保值 +- **恢复阶段**: 高波动股票攻击性持仓 +- **过热市场**: 防御性回到QQQ -### Current Issues: -1. **QQQ Data**: Currently using SPY prices for QQQ trades (unrealistic) -2. **Network Dependency**: Requires external data feeds for F&G index -3. **Market Regime Changes**: May not adapt to structural market shifts +## 技术实现细节 -### Planned Enhancements: -1. **Real QQQ Data**: Download actual QQQ historical prices -2. **Dynamic Thresholds**: Adjust F&G thresholds based on market volatility -3. **Sector Rotation**: Add sector-specific volatile stock selection -4. **Risk Scaling**: Position size based on volatility estimates +### 数据库依赖: +- `fear_greed_index`: CNN恐惧与贪婪历史数据 +- `{ticker}`: 个股OHLCV + 技术指标数据 +- `qqq`: QQQ ETF历史价格（真实数据） +- `fear_greed_data.spy_close`: SPY基准数据 -## Backtest Results (After Bug Fix) -- **Period**: 2007-2025 (17+ years) -- **Total Return**: 516.8% (vs impossible 129M% before fix) -- **Annual Return**: ~11% (realistic performance) -- **Max Drawdown**: TBD (pending QQQ data fix) -- **Trade Count**: 4 major transitions (simple strategy behavior) +### 计算的性能指标: +- 总回报、年化回报、夏普比率 +- 最大回撤和恢复期 +- 胜率和交易频率 +- 与市场基准的相关性 -## Files Structure: +## 已知限制与未来改进 + +### 当前问题: +1. **技术指标过滤过严**: 导致大部分恐慌期找不到合适股票 +2. **交易记录不完整**: gradual_transition中的交易未被记录 +3. **市场制度变化**: 可能无法适应结构性市场变化 + +### 计划增强: +1. **放宽技术指标**: 降低过滤标准，提高股票选择成功率 +2. **完善交易记录**: 确保所有交易都被正确记录 +3. **动态阈值**: 根据市场波动调整F&G阈值 +4. **风险缩放**: 基于波动率估算调整仓位大小 + +## 回测结果（修复后） +- **期间**: 2007-2025（18年） +- **总回报**: 1258.3%（修复初始化BUG后） +- **年化回报**: 15.7%（略低于QQQ的16.3%） +- **最大回撤**: 待优化 +- **交易次数**: 0（技术指标过滤太严格） + +## 文件结构: ``` strategy/emotional-damage/ -├── backtest_emotional_damage_enhanced_v2.py # Main strategy (fixed) -├── generate_enhanced_pdf_report.py # Report generator -├── enhanced_emotional_damage_strategy_report_*.pdf # Results -└── enhanced-emotional-damage.txt # This documentation +├── backtest_emotional_damage_enhanced_v2.py # 主策略（已修复） +├── generate_enhanced_pdf_report.py # 报告生成器 +├── enhanced_emotional_damage_strategy_report_*.pdf # 结果报告 +└── enhanced-emotional-damage.txt # 本文档 ``` -**Status**: Strategy logic fixed, awaiting real QQQ data for complete validation. \ No newline at end of file +**状态**: 策略逻辑已修复，但技术指标过滤需要优化以提高选股成功率。目前表现接近QQQ买入持有策略，但缺少波动股票投资机会。 \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/enhanced_emotional_damage_strategy_report_20250717_222606.pdf b/backtest/strategy/emotional-damage/enhanced_emotional_damage_strategy_report_20250717_222606.pdf deleted file mode 100644 index 0f3bf710d0d42698241c806dda7b657feb675cd6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 396351 zcmZU(Wl&sQ&^4M6+%-UOcXv;K;I6^lZScW^LvTrO2p)8T4-SFg?lQo@;6Au>^L+1n z>wZ 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a/backtest/strategy/emotional-damage/generate_enhanced_pdf_report.py +++ /dev/null @@ -1,440 +0,0 @@ -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.backends.backend_pdf import PdfPages -import pandas as pd -import numpy as np -from datetime import datetime -import sqlite3 -import sys -import os - -# Add the strategy path -sys.path.insert(0, os.path.join(os.path.dirname(__file__))) -from backtest_emotional_damage_enhanced_v2 import EnhancedEmotionalDamageStrategy - -def run_enhanced_backtest_local(): - """Run enhanced strategy backtest locally""" - - strategy = EnhancedEmotionalDamageStrategy(initial_capital=100000) - strategy.run_backtest() - - # Convert results to DataFrame - portfolio_df = pd.DataFrame(strategy.portfolio_value) - portfolio_df.set_index('date', inplace=True) - - # Get benchmark data (QQQ and SPY) - import os - script_dir = os.path.dirname(os.path.abspath(__file__)) - backtest_dir = os.path.dirname(os.path.dirname(script_dir)) - db_path = os.path.join(backtest_dir, 'data', 'stock_data.db') - print(f"Connecting to database at: {db_path}") - conn = sqlite3.connect(db_path) - - benchmark_data = pd.read_sql_query(''' - SELECT date, spy_close - FROM fear_greed_data - ORDER BY date - ''', conn) - benchmark_data['date'] = pd.to_datetime(benchmark_data['date']) - benchmark_data.set_index('date', inplace=True) - - conn.close() - - # Align dates - common_dates = portfolio_df.index.intersection(benchmark_data.index) - portfolio_df = portfolio_df.loc[common_dates] - benchmark_data = benchmark_data.loc[common_dates] - - # Normalize to starting value for comparison - start_value = 100000 - portfolio_df['normalized'] = portfolio_df['value'] - - # Create QQQ and SPY buy-and-hold benchmarks - benchmark_data['qqq_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) - benchmark_data['spy_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) - - # Calculate performance metrics - def calculate_performance_metrics(returns): - """Calculate performance metrics""" - total_return = (returns.iloc[-1] / returns.iloc[0] - 1) * 100 - annual_return = ((returns.iloc[-1] / returns.iloc[0]) ** (252 / len(returns)) - 1) * 100 - - # Calculate max drawdown - peak = returns.expanding().max() - drawdown = (returns - peak) / peak - max_drawdown = drawdown.min() * 100 - - # Find max drawdown period - max_dd_date = drawdown.idxmin() - max_dd_year = max_dd_date.year - - # Calculate Sharpe ratio - daily_returns = returns.pct_change().dropna() - sharpe_ratio = np.sqrt(252) * daily_returns.mean() / daily_returns.std() - - # Annual returns by year - annual_rets = {} - for year in returns.index.year.unique(): - year_data = returns[returns.index.year == year] - if len(year_data) > 1: - year_return = (year_data.iloc[-1] / year_data.iloc[0] - 1) * 100 - annual_rets[year] = year_return - - return { - 'total_return': total_return, - 'annual_return': annual_return, - 'max_drawdown': max_drawdown, - 'max_drawdown_date': max_dd_date, - 'max_drawdown_year': max_dd_year, - 'sharpe_ratio': sharpe_ratio, - 'annual_returns': annual_rets - } - - strategy_metrics = calculate_performance_metrics(portfolio_df['value']) - qqq_metrics = calculate_performance_metrics(benchmark_data['qqq_value']) - spy_metrics = calculate_performance_metrics(benchmark_data['spy_value']) - - return { - 'strategy': strategy, - 'portfolio_df': portfolio_df, - 'benchmark_data': benchmark_data, - 'strategy_metrics': strategy_metrics, - 'qqq_metrics': qqq_metrics, - 'spy_metrics': spy_metrics - } - -def generate_enhanced_pdf_report(): - """Generate comprehensive PDF report for enhanced strategy""" - - print("Running enhanced strategy backtest...") - results = run_enhanced_backtest_local() - - strategy = results['strategy'] - portfolio_df = results['portfolio_df'] - benchmark_data = results['benchmark_data'] - strategy_metrics = results['strategy_metrics'] - qqq_metrics = results['qqq_metrics'] - spy_metrics = results['spy_metrics'] - - # Create PDF report in the strategy directory - report_filename = f"enhanced_emotional_damage_strategy_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" - report_path = os.path.join(os.path.dirname(__file__), report_filename) - - with PdfPages(report_path) as pdf: - - # Page 1: Executive Summary - fig = plt.figure(figsize=(16, 12)) - fig.suptitle('Enhanced Emotional Damage Strategy - Comprehensive Analysis', fontsize=20, fontweight='bold') - - # Create grid layout - gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3) - - # Performance comparison table - ax1 = fig.add_subplot(gs[0, :]) - ax1.axis('tight') - ax1.axis('off') - - table_data = [ - ['Metric', 'Enhanced Strategy', 'QQQ Buy & Hold', 'SPY Buy & Hold'], - [f'Total Return', f'{strategy_metrics["total_return"]:.1f}%', f'{qqq_metrics["total_return"]:.1f}%', f'{spy_metrics["total_return"]:.1f}%'], - [f'Annual Return', f'{strategy_metrics["annual_return"]:.1f}%', f'{qqq_metrics["annual_return"]:.1f}%', f'{spy_metrics["annual_return"]:.1f}%'], - [f'Max Drawdown', f'{strategy_metrics["max_drawdown"]:.1f}%', f'{qqq_metrics["max_drawdown"]:.1f}%', f'{spy_metrics["max_drawdown"]:.1f}%'], - [f'Sharpe Ratio', f'{strategy_metrics["sharpe_ratio"]:.2f}', f'{qqq_metrics["sharpe_ratio"]:.2f}', f'{spy_metrics["sharpe_ratio"]:.2f}'], - [f'Max DD Year', f'{strategy_metrics["max_drawdown_year"]}', f'{qqq_metrics.get("max_drawdown_year", "N/A")}', f'{spy_metrics.get("max_drawdown_year", "N/A")}'] - ] - - table = ax1.table(cellText=table_data[1:], - colLabels=table_data[0], - cellLoc='center', - loc='upper center', - colWidths=[0.25, 0.25, 0.25, 0.25]) - table.auto_set_font_size(False) - table.set_fontsize(11) - table.scale(1.2, 2.5) - table.auto_set_column_width(col=list(range(len(table_data[0])))) - - # Color the header row - for i in range(len(table_data[0])): - table[(0, i)].set_facecolor('#4CAF50') - table[(0, i)].set_text_props(weight='bold', color='white') - - ax1.set_title('Performance Summary (18+ Years Backtest)', fontsize=16, fontweight='bold', pad=30) - - # Portfolio value over time - ax2 = fig.add_subplot(gs[1, :2]) - ax2.plot(portfolio_df.index, portfolio_df['value'], label='Enhanced Strategy', linewidth=2.5, color='#2E86AB') - ax2.plot(benchmark_data.index, benchmark_data['qqq_value'], label='QQQ', alpha=0.7, color='#A23B72') - ax2.plot(benchmark_data.index, benchmark_data['spy_value'], label='SPY', alpha=0.7, color='#F18F01') - ax2.set_title('Portfolio Value Over Time (Starting from $100,000)', fontsize=14, fontweight='bold') - ax2.set_ylabel('Portfolio Value ($)', fontsize=12) - ax2.legend(fontsize=11) - ax2.grid(True, alpha=0.3) - ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x/1000:.0f}K')) - - # Fear & Greed Index with strategy states - ax3 = fig.add_subplot(gs[1, 2]) - ax3_twin = ax3.twinx() - - # Fear & Greed Index - ax3.plot(portfolio_df.index, portfolio_df['fg_index'], color='red', alpha=0.7, linewidth=1) - ax3.axhline(y=25, color='red', linestyle='--', alpha=0.5, label='Fear (25)') - ax3.axhline(y=75, color='green', linestyle='--', alpha=0.5, label='Greed (75)') - ax3.set_ylabel('Fear & Greed Index', color='red', fontsize=10) - ax3.tick_params(axis='y', labelcolor='red') - ax3.set_ylim(0, 100) - ax3.set_title('Fear & Greed Index', fontsize=12) - - # Strategy states as background - state_colors = {'QQQ_HOLD': '#E8F5E8', 'FEAR_TRANSITION': '#FFE6E6', - 'CASH_WAIT': '#FFF2E6', 'GREED_TRANSITION': '#E6F3FF', - 'VOLATILE_STOCKS': '#FFE6CC', 'QQQ_TRANSITION': '#FFE6F0'} - - current_state = None - start_idx = 0 - for i, (idx, row) in enumerate(portfolio_df.iterrows()): - if row['state'] != current_state: - if current_state is not None: - ax3.axvspan(portfolio_df.index[start_idx], idx, - alpha=0.2, color=state_colors.get(current_state, 'gray')) - current_state = row['state'] - start_idx = i - - if current_state is not None: - ax3.axvspan(portfolio_df.index[start_idx], portfolio_df.index[-1], - alpha=0.2, color=state_colors.get(current_state, 'gray')) - - # Annual returns comparison - ax4 = fig.add_subplot(gs[2, :]) - years = sorted(strategy_metrics['annual_returns'].keys()) - strategy_rets = [strategy_metrics['annual_returns'][y] for y in years] - qqq_rets = [qqq_metrics['annual_returns'][y] for y in years] - spy_rets = [spy_metrics['annual_returns'][y] for y in years] - - x = np.arange(len(years)) - width = 0.25 - - bars1 = ax4.bar(x - width, strategy_rets, width, label='Enhanced Strategy', - color='#2E86AB', alpha=0.8) - bars2 = ax4.bar(x, qqq_rets, width, label='QQQ', color='#A23B72', alpha=0.8) - bars3 = ax4.bar(x + width, spy_rets, width, label='SPY', color='#F18F01', alpha=0.8) - - ax4.set_xlabel('Year', fontsize=12) - ax4.set_ylabel('Annual Return (%)', fontsize=12) - ax4.set_title('Annual Returns Comparison', fontsize=14, fontweight='bold') - ax4.set_xticks(x) - ax4.set_xticklabels(years, rotation=45) - ax4.legend(fontsize=11) - ax4.grid(True, alpha=0.3) - ax4.axhline(y=0, color='black', linestyle='-', alpha=0.3) - - # Add value labels on bars - for bars in [bars1, bars2, bars3]: - for bar in bars: - height = bar.get_height() - if abs(height) > 5: # Only label significant returns - ax4.annotate(f'{height:.0f}%', - xy=(bar.get_x() + bar.get_width() / 2, height), - xytext=(0, 3 if height > 0 else -15), - textcoords="offset points", - ha='center', va='bottom' if height > 0 else 'top', - fontsize=9) - - plt.suptitle('Enhanced Emotional Damage Strategy - Comprehensive Analysis', fontsize=20, fontweight='bold') - pdf.savefig(fig, bbox_inches='tight', dpi=300) - plt.close() - - # Page 2: Drawdown Analysis - fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12)) - fig.suptitle('Risk Analysis', fontsize=16, fontweight='bold') - - # Calculate drawdowns - def calculate_drawdown(returns): - peak = returns.expanding().max() - drawdown = (returns - peak) / peak - return drawdown * 100 - - strategy_dd = calculate_drawdown(portfolio_df['value']) - qqq_dd = calculate_drawdown(benchmark_data['qqq_value']) - spy_dd = calculate_drawdown(benchmark_data['spy_value']) - - # Drawdown comparison - ax1.plot(portfolio_df.index, strategy_dd, label='Enhanced Strategy', - linewidth=2, color='#2E86AB') - ax1.plot(benchmark_data.index, qqq_dd, label='QQQ', alpha=0.7, color='#A23B72') - ax1.plot(benchmark_data.index, spy_dd, label='SPY', alpha=0.7, color='#F18F01') - ax1.axhline(y=0, color='black', linestyle='-', alpha=0.3) - ax1.fill_between(portfolio_df.index, strategy_dd, 0, alpha=0.2, color='#2E86AB') - ax1.set_title('Drawdown Comparison Over Time') - ax1.set_ylabel('Drawdown (%)') - ax1.legend() - ax1.grid(True, alpha=0.3) - - # Drawdown histogram - significant_dd = strategy_dd[strategy_dd < -10] # > 10% drawdowns - if len(significant_dd) > 0: - ax2.hist(significant_dd, bins=20, alpha=0.7, color='#A23B72', edgecolor='black') - ax2.axvline(x=significant_dd.min(), color='red', linestyle='--', - label=f'Max DD: {significant_dd.min():.1f}%') - ax2.set_title('Distribution of Significant Drawdowns') - ax2.set_xlabel('Drawdown (%)') - ax2.set_ylabel('Frequency') - ax2.legend() - ax2.grid(True, alpha=0.3) - else: - ax2.text(0.5, 0.5, 'No significant drawdowns > 10%', - ha='center', va='center', transform=ax2.transAxes, - fontsize=12) - - # Rolling 252-day returns - window = 252 - strategy_rolling = portfolio_df['value'].pct_change(window).rolling(window).mean() * 100 - qqq_rolling = benchmark_data['qqq_value'].pct_change(window).rolling(window).mean() * 100 - - ax3.plot(portfolio_df.index, strategy_rolling, label='Enhanced Strategy', - linewidth=2, color='#2E86AB') - ax3.plot(benchmark_data.index, qqq_rolling, label='QQQ', alpha=0.7, color='#A23B72') - ax3.set_title(f'Rolling {window}-Day Annualized Returns') - ax3.set_ylabel('Return (%)') - ax3.legend() - ax3.grid(True, alpha=0.3) - - # Risk-adjusted returns scatter - strategies = ['Enhanced Strategy', 'QQQ', 'SPY'] - returns = [strategy_metrics['annual_return'], qqq_metrics['annual_return'], spy_metrics['annual_return']] - risks = [abs(strategy_metrics['max_drawdown']), abs(qqq_metrics['max_drawdown']), abs(spy_metrics['max_drawdown'])] - sharpes = [strategy_metrics['sharpe_ratio'], qqq_metrics['sharpe_ratio'], spy_metrics['sharpe_ratio']] - - colors = ['#2E86AB', '#A23B72', '#F18F01'] - - for i, (strat, ret, risk, sharpe, color) in enumerate(zip(strategies, returns, risks, sharpes, colors)): - ax4.scatter(risk, ret, s=sharpe*100, alpha=0.7, color=color, label=f'{strat} (Sharpe: {sharpe:.2f})') - ax4.annotate(strat, (risk, ret), xytext=(5, 5), textcoords='offset points', - fontsize=10, fontweight='bold') - - ax4.set_xlabel('Maximum Drawdown (%)') - ax4.set_ylabel('Annual Return (%)') - ax4.set_title('Risk vs Return (bubble size = Sharpe Ratio)') - ax4.grid(True, alpha=0.3) - ax4.legend() - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight', dpi=300) - plt.close() - - # Page 3: Trading Activity and Strategy Features - fig = plt.figure(figsize=(16, 12)) - gs = fig.add_gridspec(3, 2, hspace=0.3, wspace=0.3) - fig.suptitle('Trading Activity and Enhanced Features', fontsize=16, fontweight='bold') - - # Trading frequency - trades_df = pd.DataFrame(strategy.trades) - if len(trades_df) > 0: - trades_df['date'] = pd.to_datetime(trades_df['date']) - trades_df['year'] = trades_df['date'].dt.year - - ax1 = fig.add_subplot(gs[0, 0]) - trades_by_year = trades_df.groupby('year').size() - ax1.bar(trades_by_year.index, trades_by_year.values, - color='#2E86AB', alpha=0.7, edgecolor='black') - ax1.set_title('Trading Frequency by Year') - ax1.set_xlabel('Year') - ax1.set_ylabel('Number of Trades') - ax1.grid(True, alpha=0.3) - - # Trade types - ax2 = fig.add_subplot(gs[0, 1]) - trade_types = trades_df['action'].value_counts() - colors = plt.cm.Set3(np.linspace(0, 1, len(trade_types))) - wedges, texts, autotexts = ax2.pie(trade_types.values, - labels=trade_types.index, - autopct='%1.1f%%', - colors=colors) - ax2.set_title('Trade Types Distribution') - - # Recent trades table - ax3 = fig.add_subplot(gs[1, :]) - ax3.axis('tight') - ax3.axis('off') - - recent_trades = trades_df.tail(15) if len(trades_df) > 15 else trades_df - if len(recent_trades) > 0: - trade_table_data = [] - for _, trade in recent_trades.iterrows(): - trade_table_data.append([ - trade['date'].strftime('%Y-%m-%d'), - trade['action'][:15], - trade['ticker'], - f"{trade['shares']:.0f}", - f"${trade['price']:.2f}", - f"${trade['value']:,.0f}" - ]) - - trade_table = ax3.table(cellText=trade_table_data, - colLabels=['Date', 'Action', 'Ticker', 'Shares', 'Price', 'Value'], - cellLoc='center', - loc='center') - trade_table.auto_set_font_size(False) - trade_table.set_fontsize(9) - trade_table.scale(1.2, 1.5) - ax3.set_title('Recent 15 Trades', fontsize=14, fontweight='bold', pad=20) - - # Strategy features - ax4 = fig.add_subplot(gs[2, :]) - features_text = """ -ENHANCED STRATEGY FEATURES: - -1. 4-Step Gradual Position Transitions - • Reduces market impact and slippage - • Provides better entry/exit timing - • Smooth transitions between QQQ, cash, and volatile stocks - -2. 15% Stop-Loss Protection - • Individual stock risk management - • Automatic QQQ replacement on stop-loss triggers - • Protects against significant losses - -3. Technical Indicator Filtering - • MACD: Identifies trend reversals and momentum shifts - • RSI: Avoids oversold conditions (RSI > 30 filter) - • EMA: Uses EMA5/EMA20 crossover for trend confirmation - -4. Enhanced Volatility Selection - • Combines technical signals with historical volatility - • More selective stock picking process - • Dynamic selection based on recent market conditions - -5. Fear & Greed Based Market Timing - • Systematic entry/exit based on CNN Fear & Greed Index - • Counter-emotional trading biases - • Proven market sentiment indicator - -PERFORMANCE SUMMARY: -""" - - # Add performance summary to features - perf_summary = f""" -Backtest Period: {portfolio_df.index.min().strftime('%Y-%m-%d')} to {portfolio_df.index.max().strftime('%Y-%m-%d')} -Total Trades: {len(strategy.trades)} -Total Return: {strategy_metrics['total_return']:.1f}% -Annual Return: {strategy_metrics['annual_return']:.1f}% -Max Drawdown: {strategy_metrics['max_drawdown']:.1f}% -Sharpe Ratio: {strategy_metrics['sharpe_ratio']:.2f} -""" - - full_text = features_text + perf_summary - - ax4.text(0.05, 0.95, full_text, transform=ax4.transAxes, - fontsize=10, verticalalignment='top', fontfamily='monospace', - bbox=dict(boxstyle="round,pad=0.3", facecolor='lightgray', alpha=0.3)) - ax4.axis('off') - - plt.tight_layout() - pdf.savefig(fig, bbox_inches='tight', dpi=300) - plt.close() - - print(f"Enhanced PDF report generated: {report_filename}") - return report_path - -if __name__ == "__main__": - filename = generate_enhanced_pdf_report() - print(f"Report saved as: {filename}") \ No newline at end of file diff --git a/backtest/strategy/emotional-damage/run_strategy_with_config.py b/backtest/strategy/emotional-damage/run_strategy_with_config.py new file mode 100644 index 0000000..4ece1af --- /dev/null +++ b/backtest/strategy/emotional-damage/run_strategy_with_config.py @@ -0,0 +1,960 @@ +#!/usr/bin/env python3 + +import json +import sqlite3 +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +import warnings +import os +import pickle +import matplotlib.pyplot as plt +import seaborn as sns +warnings.filterwarnings('ignore') + +class ConfigurableEmotionalDamageStrategy: + def __init__(self, config_path='config.json'): + """Initialize strategy with config file""" + self.load_config(config_path) + self.cash = self.config['strategy_params']['initial_capital'] + self.positions = {} + self.portfolio_value = [] + self.trades = [] + + # State management + self.state = 'QQQ_HOLD' + self.current_step = 0 + self.target_allocation = {} + self.last_fear_date = None + + # For gradual transitions + self.transition_plan = {} + self.transition_cash_pool = 0 + + def load_config(self, config_path): + """Load configuration from JSON file""" + with open(config_path, 'r') as f: + self.config = json.load(f) + + # Set strategy parameters as attributes for easy access + params = self.config['strategy_params'] + self.initial_capital = params['initial_capital'] + self.fear_threshold = params['fear_threshold'] + self.greed_threshold = params['greed_threshold'] + self.stop_loss_threshold = params['stop_loss_threshold'] + self.top_stocks_count = params['top_stocks_count'] + self.volatility_threshold = params['volatility_threshold'] + self.volatility_calculation_days = params['volatility_calculation_days'] + self.transition_steps = params['transition_steps'] + + # Technical indicators + tech = self.config['technical_indicators'] + self.rsi_threshold = tech['rsi_threshold'] + self.required_indicators = tech['required_indicators'] + self.sma5_above_sma20 = tech['sma5_above_sma20'] + self.macd_convergence = tech['macd_convergence'] + + if self.config['output_settings']['show_console_output']: + print(f"✅ 配置已加载:") + print(f" 初始资金: ${self.initial_capital:,}") + print(f" 恐慌阈值: {self.fear_threshold}") + print(f" 贪婪阈值: {self.greed_threshold}") + print(f" 止损阈值: {self.stop_loss_threshold*100}%") + print(f" 转换步数: {self.transition_steps}") + print(f" 选股数量: {self.top_stocks_count}") + print("") + + def get_data(self): + """Load Fear & Greed Index and stock data""" + db_path = self.config['paths']['database_path'] + if self.config['output_settings']['show_console_output']: + print(f"连接数据库: {db_path}") + + conn = sqlite3.connect(db_path) + + # Get Fear & Greed Index + fg_data = pd.read_sql_query(''' + SELECT date, fear_greed_index + FROM fear_greed_index + ORDER BY date + ''', conn) + fg_data['date'] = pd.to_datetime(fg_data['date']) + fg_data.set_index('date', inplace=True) + + # Get real QQQ price data + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq + ORDER BY date + ''', conn) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) + + # Get available tickers + min_records = self.config['data_settings']['min_ticker_records'] + cursor = conn.cursor() + cursor.execute(f'SELECT ticker FROM ticker_list WHERE records > {min_records}') + self.available_tickers = [row[0] for row in cursor.fetchall()] + + conn.close() + + # Merge data + self.data = pd.merge(fg_data, qqq_data, left_index=True, right_index=True, how='inner') + + # Apply date filters if specified + if self.config['data_settings']['start_date']: + start_date = pd.to_datetime(self.config['data_settings']['start_date']) + self.data = self.data[self.data.index >= start_date] + + if self.config['data_settings']['end_date']: + end_date = pd.to_datetime(self.config['data_settings']['end_date']) + self.data = self.data[self.data.index <= end_date] + + self.data.sort_index(inplace=True) + + if self.config['output_settings']['show_console_output']: + print(f"数据加载完成: {self.data.index.min().strftime('%Y-%m-%d')} 到 {self.data.index.max().strftime('%Y-%m-%d')}") + print(f"可用股票数量: {len(self.available_tickers)}") + print("") + + def get_stock_price(self, ticker, date): + """Get stock price for a specific ticker and date""" + db_path = self.config['paths']['database_path'] + conn = sqlite3.connect(db_path) + + query = f''' + SELECT close FROM {ticker.lower()} + WHERE date <= ? + ORDER BY date DESC + LIMIT 1 + ''' + + cursor = conn.cursor() + cursor.execute(query, (date.strftime('%Y-%m-%d'),)) + result = cursor.fetchone() + conn.close() + + return result[0] if result else None + + def calculate_volatility(self, ticker, current_date): + """Calculate historical volatility over configured period""" + db_path = self.config['paths']['database_path'] + conn = sqlite3.connect(db_path) + + try: + start_date = current_date - timedelta(days=self.volatility_calculation_days) + + query = f''' + SELECT date, close FROM {ticker.lower()} + WHERE date >= ? AND date <= ? + ORDER BY date + ''' + + df = pd.read_sql_query(query, conn, params=( + start_date.strftime('%Y-%m-%d'), + current_date.strftime('%Y-%m-%d') + )) + + if len(df) > 10: + df['returns'] = df['close'].pct_change() + volatility = df['returns'].std() * np.sqrt(252) + conn.close() + return volatility + + except Exception as e: + pass + + conn.close() + return 0 + + def check_technical_indicators(self, ticker, date): + """Check RSI, MACD, and SMA technical indicators""" + db_path = self.config['paths']['database_path'] + conn = sqlite3.connect(db_path) + + try: + query = f''' + SELECT date, close FROM {ticker.lower()} + WHERE date <= ? + ORDER BY date DESC + LIMIT 50 + ''' + + df = pd.read_sql_query(query, conn, params=(date.strftime('%Y-%m-%d'),)) + + if len(df) < 20: + conn.close() + return False + + df = df.sort_values('date') + df.reset_index(drop=True, inplace=True) + + # Calculate RSI + rsi_period = self.config['technical_indicators']['rsi_period'] + delta = df['close'].diff() + gain = (delta.where(delta > 0, 0)).rolling(window=rsi_period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=rsi_period).mean() + rs = gain / loss + rsi = 100 - (100 / (1 + rs)) + + # Calculate MACD + ema_fast = self.config['technical_indicators']['ema_periods']['fast'] + ema_slow = self.config['technical_indicators']['ema_periods']['slow'] + ema_signal = self.config['technical_indicators']['ema_periods']['signal'] + + ema12 = df['close'].ewm(span=ema_fast).mean() + ema26 = df['close'].ewm(span=ema_slow).mean() + macd = ema12 - ema26 + signal = macd.ewm(span=ema_signal).mean() + + # Calculate SMA + sma_fast = self.config['technical_indicators']['sma_periods']['fast'] + sma_slow = self.config['technical_indicators']['sma_periods']['slow'] + sma5 = df['close'].rolling(window=sma_fast).mean() + sma20 = df['close'].rolling(window=sma_slow).mean() + + # Check conditions + latest_rsi = rsi.iloc[-1] + latest_macd = macd.iloc[-1] + latest_signal = signal.iloc[-1] + latest_sma5 = sma5.iloc[-1] + latest_sma20 = sma20.iloc[-1] + + # RSI condition + rsi_ok = latest_rsi > self.rsi_threshold + + # MACD condition + if self.macd_convergence and len(macd) >= 2 and len(signal) >= 2: + prev_macd = macd.iloc[-2] + prev_signal = signal.iloc[-2] + prev_diff = abs(prev_macd - prev_signal) + current_diff = abs(latest_macd - latest_signal) + macd_ok = current_diff < prev_diff # Lines are converging + else: + macd_ok = latest_macd > latest_signal # Traditional golden cross + + # SMA condition + if self.sma5_above_sma20: + sma_ok = latest_sma5 > latest_sma20 + else: + sma_ok = True # Skip SMA check if disabled + + # Check if enough indicators are positive + conditions = [rsi_ok, macd_ok, sma_ok] + score = sum(conditions) + + conn.close() + return score >= self.required_indicators + + except Exception as e: + conn.close() + return False + + def select_volatile_stocks(self, fear_start_date, fear_end_date): + """Select stocks using technical indicators, then sort by volatility""" + qualified_stocks = [] + + for ticker in self.available_tickers: + if self.check_technical_indicators(ticker, fear_end_date): + vol = self.calculate_volatility(ticker, fear_end_date) + + if vol > self.volatility_threshold: + qualified_stocks.append((ticker, vol)) + + # Sort by volatility and select top stocks + qualified_stocks.sort(key=lambda x: x[1], reverse=True) + top_stocks = [ticker for ticker, vol in qualified_stocks[:self.top_stocks_count]] + + return top_stocks + + def execute_trade(self, date, action, ticker=None, shares=None, price=None, value=None): + """Execute and record a trade""" + fg_index = self.data.loc[date, 'fear_greed_index'] if date in self.data.index else None + total_assets = self.calculate_portfolio_value(date) + + self.trades.append({ + 'date': date, + 'action': action, + 'ticker': ticker, + 'shares': shares, + 'price': price, + 'value': value, + 'fg_index': fg_index, + 'cnn_fear_greed': fg_index, # Same as fg_index but with clearer name + 'cash_after': self.cash, + 'total_assets': total_assets, + 'portfolio_state': self.state + }) + + def calculate_portfolio_value(self, date): + """Calculate total portfolio value""" + total_value = self.cash + + for ticker, shares in self.positions.items(): + if ticker == 'QQQ': + price = self.data.loc[date, 'qqq_close'] + else: + price = self.get_stock_price(ticker, date) + + if price: + total_value += shares * price + + return total_value + + def check_stop_loss(self, date): + """Check stop loss threshold""" + for ticker in list(self.positions.keys()): + if ticker == 'QQQ': + continue + + current_price = self.get_stock_price(ticker, date) + if not current_price: + continue + + # Find average buy price + buy_trades = [t for t in self.trades + if t['ticker'] == ticker and t['action'] in ['BUY_GRADUAL']] + if buy_trades: + total_cost = sum(t['price'] * t['shares'] for t in buy_trades) + total_shares = sum(t['shares'] for t in buy_trades) + avg_price = total_cost / total_shares + + loss_pct = (current_price - avg_price) / avg_price + if loss_pct <= -self.stop_loss_threshold: + # Sell and buy QQQ + shares = self.positions[ticker] + value = shares * current_price + self.cash += value + del self.positions[ticker] + + self.execute_trade(date, 'STOP_LOSS', ticker, shares, current_price, value) + + # Buy QQQ with integer shares + qqq_price = self.data.loc[date, 'qqq_close'] + qqq_shares = int(value / qqq_price) + + if qqq_shares > 0: + actual_qqq_value = qqq_shares * qqq_price + self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares + self.cash -= actual_qqq_value + self.execute_trade(date, 'BUY_QQQ_STOPLOSS', 'QQQ', qqq_shares, qqq_price, actual_qqq_value) + + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Stop loss triggered for {ticker}, loss: {loss_pct*100:.1f}%") + + def start_transition(self, date, target_type, stocks=None): + """Initialize transition plan""" + self.transition_plan = {'type': target_type, 'stocks': stocks} + + if target_type == 'CASH': + self.transition_plan['positions_to_sell'] = {} + for ticker in self.positions: + self.transition_plan['positions_to_sell'][ticker] = self.positions[ticker] + + elif target_type == 'QQQ': + cash_from_positions = 0 + for ticker in self.positions: + if ticker != 'QQQ': + price = self.get_stock_price(ticker, date) + if price: + cash_from_positions += self.positions[ticker] * price + + self.transition_cash_pool = self.cash + cash_from_positions + self.transition_plan['total_cash_to_invest'] = self.transition_cash_pool + self.transition_plan['positions_to_sell'] = {} + for ticker in self.positions: + if ticker != 'QQQ': + self.transition_plan['positions_to_sell'][ticker] = self.positions[ticker] + + elif target_type == 'VOLATILE' and stocks: + cash_from_positions = 0 + for ticker in self.positions: + if ticker != 'QQQ': + price = self.get_stock_price(ticker, date) + if price: + cash_from_positions += self.positions[ticker] * price + + total_available_cash = self.cash + cash_from_positions + self.transition_plan['total_cash_to_invest'] = total_available_cash + + def gradual_transition(self, date, target_type, stocks=None): + """Handle gradual transitions with integer shares""" + step_size = 1.0 / self.transition_steps + + if target_type == 'CASH': + for ticker in list(self.transition_plan.get('positions_to_sell', {})): + if ticker in self.positions: + total_shares_to_sell = self.transition_plan['positions_to_sell'][ticker] + shares_to_sell = int(total_shares_to_sell * step_size) + + if shares_to_sell > 0 and shares_to_sell <= self.positions[ticker]: + price = self.get_stock_price(ticker, date) + if price: + value = shares_to_sell * price + self.cash += value + self.positions[ticker] -= shares_to_sell + if self.positions[ticker] <= 0: + del self.positions[ticker] + self.execute_trade(date, 'SELL_GRADUAL', ticker, shares_to_sell, price, value) + + elif target_type == 'VOLATILE' and stocks: + total_cash = self.transition_plan.get('total_cash_to_invest', 0) + cash_this_step = total_cash * step_size + + if cash_this_step > 0 and self.cash >= cash_this_step: + current_step_index = min(self.current_step, len(stocks) - 1) + ticker = stocks[current_step_index] + + price = self.get_stock_price(ticker, date) + if price and cash_this_step > 0: + shares = int(cash_this_step / price) + if shares > 0: + actual_value = shares * price + self.positions[ticker] = self.positions.get(ticker, 0) + shares + self.cash -= actual_value + self.execute_trade(date, 'BUY_GRADUAL', ticker, shares, price, actual_value) + + elif target_type == 'QQQ': + # Sell positions gradually + for ticker in list(self.transition_plan.get('positions_to_sell', {})): + if ticker in self.positions: + total_shares_to_sell = self.transition_plan['positions_to_sell'][ticker] + shares_to_sell = int(total_shares_to_sell * step_size) + + if shares_to_sell > 0 and shares_to_sell <= self.positions[ticker]: + price = self.get_stock_price(ticker, date) + if price: + value = shares_to_sell * price + self.cash += value + self.positions[ticker] -= shares_to_sell + if self.positions[ticker] <= 0: + del self.positions[ticker] + self.execute_trade(date, 'SELL_GRADUAL', ticker, shares_to_sell, price, value) + + # Buy QQQ + total_cash = self.transition_plan.get('total_cash_to_invest', 0) + cash_this_step = total_cash * step_size + + if cash_this_step > 0 and self.cash >= cash_this_step: + qqq_price = self.data.loc[date, 'qqq_close'] + qqq_shares = int(cash_this_step / qqq_price) + + if qqq_shares > 0: + actual_value = qqq_shares * qqq_price + self.positions['QQQ'] = self.positions.get('QQQ', 0) + qqq_shares + self.cash -= actual_value + self.execute_trade(date, 'BUY_GRADUAL', 'QQQ', qqq_shares, qqq_price, actual_value) + + def run_backtest(self): + """Run the strategy backtest""" + if self.config['output_settings']['show_console_output']: + print("🚀 开始运行Enhanced Emotional Damage Strategy...") + print("") + + self.get_data() + + # Start with 100% QQQ + first_date = self.data.index[0] + qqq_price = self.data.loc[first_date, 'qqq_close'] + qqq_shares = int(self.initial_capital / qqq_price) + self.positions['QQQ'] = qqq_shares + self.cash = self.initial_capital - (qqq_shares * qqq_price) + + fear_start_date = None + + for date, row in self.data.iterrows(): + fg_index = row['fear_greed_index'] + + # Check stop loss + self.check_stop_loss(date) + + if self.state == 'QQQ_HOLD': + if fg_index < self.fear_threshold: + fear_start_date = date + self.state = 'FEAR_TRANSITION' + self.current_step = 0 + self.start_transition(date, 'CASH') + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Fear threshold hit ({fg_index:.1f}), starting transition to cash") + + elif self.state == 'FEAR_TRANSITION': + self.gradual_transition(date, 'CASH') + self.current_step += 1 + + if self.current_step >= self.transition_steps: + self.state = 'CASH_WAIT' + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Transition to cash complete") + + elif self.state == 'CASH_WAIT': + if fg_index >= self.fear_threshold and fear_start_date: + top_stocks = self.select_volatile_stocks(fear_start_date, date) + + if top_stocks: + self.state = 'GREED_TRANSITION' + self.current_step = 0 + self.transition_stocks = top_stocks + self.start_transition(date, 'VOLATILE', top_stocks) + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Fear recovered, starting transition to volatile stocks: {top_stocks}") + else: + self.state = 'QQQ_TRANSITION' + self.current_step = 0 + self.start_transition(date, 'QQQ') + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Fear recovered, no suitable stocks, returning to QQQ") + + elif self.state == 'GREED_TRANSITION': + self.gradual_transition(date, 'VOLATILE', self.transition_stocks) + self.current_step += 1 + + if self.current_step >= self.transition_steps: + self.state = 'VOLATILE_STOCKS' + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Transition to volatile stocks complete") + + elif self.state == 'VOLATILE_STOCKS': + if fg_index > self.greed_threshold: + self.state = 'QQQ_TRANSITION' + self.current_step = 0 + self.start_transition(date, 'QQQ') + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Greed threshold hit ({fg_index:.1f}), starting transition to QQQ") + + elif self.state == 'QQQ_TRANSITION': + self.gradual_transition(date, 'QQQ') + self.current_step += 1 + + if self.current_step >= self.transition_steps: + self.state = 'QQQ_HOLD' + if self.config['output_settings']['show_console_output']: + print(f"{date.strftime('%Y-%m-%d')}: Transition to QQQ complete") + + # Record portfolio value + portfolio_value = self.calculate_portfolio_value(date) + self.portfolio_value.append({ + 'date': date, + 'value': portfolio_value, + 'state': self.state, + 'fg_index': fg_index + }) + + if self.config['output_settings']['show_console_output']: + print("") + print(f"✅ 回测完成! 总交易数: {len(self.trades)}") + print("") + + def calculate_performance_metrics(self, returns): + """Calculate performance metrics""" + total_return = (returns.iloc[-1] / returns.iloc[0] - 1) * 100 + annual_return = ((returns.iloc[-1] / returns.iloc[0]) ** (252 / len(returns)) - 1) * 100 + + # Calculate max drawdown + peak = returns.expanding().max() + drawdown = (returns - peak) / peak + max_drawdown = drawdown.min() * 100 + + # Calculate Sharpe ratio + daily_returns = returns.pct_change().dropna() + sharpe_ratio = np.sqrt(252) * daily_returns.mean() / daily_returns.std() + + # Annual returns by year + annual_rets = {} + for year in returns.index.year.unique(): + year_data = returns[returns.index.year == year] + if len(year_data) > 1: + year_return = (year_data.iloc[-1] / year_data.iloc[0] - 1) * 100 + annual_rets[year] = year_return + + return { + 'total_return': total_return, + 'annual_return': annual_return, + 'max_drawdown': max_drawdown, + 'sharpe_ratio': sharpe_ratio, + 'annual_returns': annual_rets + } + + +def generate_reports(strategy): + """Generate all reports based on config settings""" + config = strategy.config + + # Create output directories + reports_dir = config['paths']['reports_dir'] + results_dir = config['paths']['results_dir'] + os.makedirs(reports_dir, exist_ok=True) + os.makedirs(results_dir, exist_ok=True) + + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + + # Save strategy object if requested + if config['output_settings']['save_strategy_object']: + strategy_file = os.path.join(results_dir, f'enhanced_strategy_{timestamp}.pkl') + with open(strategy_file, 'wb') as f: + pickle.dump(strategy, f) + print(f"📦 策略对象已保存: {strategy_file}") + + # Generate CSV reports + if config['output_settings']['generate_csv'] or config['output_settings']['generate_detailed_trades']: + trades_df = pd.DataFrame(strategy.trades) + + if config['output_settings']['generate_csv']: + csv_file = os.path.join(reports_dir, f'enhanced_trades_{timestamp}.csv') + trades_df.to_csv(csv_file, index=False) + print(f"📊 交易CSV已保存: {csv_file}") + + if config['output_settings']['generate_detailed_trades']: + detailed_file = os.path.join(reports_dir, f'detailed_trades_{timestamp}.txt') + with open(detailed_file, 'w', encoding='utf-8') as f: + f.write("Enhanced Emotional Damage Strategy - Detailed Trades Report\n") + f.write("=" * 120 + "\n\n") + + # Summary + f.write(f"📊 交易摘要:\n") + f.write(f"总交易数: {len(strategy.trades)}\n") + f.write(f"交易时间: {trades_df['date'].min().strftime('%Y-%m-%d')} 到 {trades_df['date'].max().strftime('%Y-%m-%d')}\n") + + # Trade types + action_counts = trades_df['action'].value_counts() + f.write(f"\n交易类型统计:\n") + for action, count in action_counts.items(): + f.write(f" {action}: {count}\n") + + # Detailed trades + f.write(f"\n📋 详细交易记录:\n") + f.write("=" * 150 + "\n") + f.write(f"{'No':>3s} {'Date':>10s} {'Action':>15s} {'Ticker':>5s} {'Shares':>8s} {'Price':>7s} {'Value':>12s} {'F&G':>4s} {'Cash':>12s} {'Assets':>12s} {'State':>15s}\n") + f.write("=" * 150 + "\n") + + for i, trade in enumerate(strategy.trades, 1): + fg_str = f"{trade.get('fg_index', 0):.0f}" if trade.get('fg_index') else "N/A" + cash_str = f"${trade.get('cash_after', 0):,.0f}" if trade.get('cash_after') else "N/A" + assets_str = f"${trade.get('total_assets', 0):,.0f}" if trade.get('total_assets') else "N/A" + state_str = trade.get('portfolio_state', 'N/A') + f.write(f"{i:3d} {trade['date'].strftime('%Y-%m-%d'):>10s} {trade['action']:>15s} {trade['ticker']:>5s} " + f"{trade['shares']:>8.0f} ${trade['price']:>7.2f} ${trade['value']:>12,.0f} {fg_str:>4s} {cash_str:>12s} {assets_str:>12s} {state_str:>15s}\n") + + print(f"📝 详细交易报告已保存: {detailed_file}") + + # Generate PDF report + if config['output_settings']['generate_pdf']: + try: + generate_enhanced_pdf_report(strategy, reports_dir, timestamp) + + except Exception as e: + print(f"⚠️ PDF生成失败: {e}") + import traceback + traceback.print_exc() + + print("\n🎉 所有报告生成完成!") + + +def generate_enhanced_pdf_report(strategy, reports_dir, timestamp): + """Generate comprehensive PDF report with enhanced layout and proper spacing""" + import matplotlib.pyplot as plt + import matplotlib.dates as mdates + from matplotlib.backends.backend_pdf import PdfPages + import seaborn as sns + import sqlite3 + import os + + # Prepare data + portfolio_df = pd.DataFrame(strategy.portfolio_value) + portfolio_df.set_index('date', inplace=True) + trades_df = pd.DataFrame(strategy.trades) + + # Get benchmark data + db_path = strategy.config['paths']['database_path'] + conn = sqlite3.connect(db_path) + + qqq_data = pd.read_sql_query(''' + SELECT date, close as qqq_close + FROM qqq + ORDER BY date + ''', conn) + qqq_data['date'] = pd.to_datetime(qqq_data['date']) + qqq_data.set_index('date', inplace=True) + + spy_data = pd.read_sql_query(''' + SELECT date, spy_close + FROM fear_greed_data + ORDER BY date + ''', conn) + spy_data['date'] = pd.to_datetime(spy_data['date']) + spy_data.set_index('date', inplace=True) + + conn.close() + + # Merge and align data + benchmark_data = pd.merge(qqq_data, spy_data, left_index=True, right_index=True, how='inner') + common_dates = portfolio_df.index.intersection(benchmark_data.index) + portfolio_df = portfolio_df.loc[common_dates] + benchmark_data = benchmark_data.loc[common_dates] + + # Normalize benchmarks + start_value = strategy.initial_capital + benchmark_data['qqq_value'] = start_value * (benchmark_data['qqq_close'] / benchmark_data['qqq_close'].iloc[0]) + benchmark_data['spy_value'] = start_value * (benchmark_data['spy_close'] / benchmark_data['spy_close'].iloc[0]) + + # Calculate metrics + strategy_metrics = strategy.calculate_performance_metrics(portfolio_df['value']) + qqq_metrics = strategy.calculate_performance_metrics(benchmark_data['qqq_value']) + spy_metrics = strategy.calculate_performance_metrics(benchmark_data['spy_value']) + + # Find max drawdown year + def find_max_drawdown_year(returns): + peak = returns.expanding().max() + drawdown = (returns - peak) / peak + max_dd_date = drawdown.idxmin() + return max_dd_date.year + + strategy_dd_year = find_max_drawdown_year(portfolio_df['value']) + qqq_dd_year = find_max_drawdown_year(benchmark_data['qqq_value']) + spy_dd_year = find_max_drawdown_year(benchmark_data['spy_value']) + + # Create PDF with multiple pages + pdf_file = os.path.join(reports_dir, f'enhanced_strategy_report_{timestamp}.pdf') + + with PdfPages(pdf_file) as pdf: + # Set global font to support text and better spacing + plt.rcParams['font.size'] = 10 + plt.rcParams['axes.titlesize'] = 12 + plt.rcParams['axes.labelsize'] = 10 + plt.rcParams['xtick.labelsize'] = 8 + plt.rcParams['ytick.labelsize'] = 8 + plt.rcParams['legend.fontsize'] = 8 + plt.rcParams['figure.titlesize'] = 14 + + # Page 1: Performance Comparison (Full Width) + fig1 = plt.figure(figsize=(8.5, 11)) + fig1.suptitle('Enhanced Emotional Damage Strategy Report', fontsize=16, fontweight='bold', y=0.96) + + # 1. Total Return Curve (Full width) + ax1 = plt.subplot(4, 1, 1) + ax1.plot(portfolio_df.index, portfolio_df['value'] / 1000, + label='Enhanced Strategy', linewidth=2, color='red') + ax1.plot(benchmark_data.index, benchmark_data['qqq_value'] / 1000, + label='QQQ', linewidth=2, color='blue') + ax1.plot(benchmark_data.index, benchmark_data['spy_value'] / 1000, + label='SPY', linewidth=2, color='green') + ax1.set_title('Portfolio Performance Comparison', fontsize=14, fontweight='bold', pad=25) + ax1.set_ylabel('Portfolio Value ($K)', fontsize=11) + ax1.legend(fontsize=10, loc='upper left') + ax1.grid(True, alpha=0.3) + ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax1.get_xticklabels(): + label.set_rotation(45) + + # 2. Performance Metrics Table (Full width) + ax2 = plt.subplot(4, 1, 2) + ax2.axis('off') + + metrics_data = [ + ['Metric', 'Enhanced Strategy', 'QQQ', 'SPY'], + ['Total Return', f"{strategy_metrics['total_return']:.1f}%", + f"{qqq_metrics['total_return']:.1f}%", f"{spy_metrics['total_return']:.1f}%"], + ['Annual Return', f"{strategy_metrics['annual_return']:.1f}%", + f"{qqq_metrics['annual_return']:.1f}%", f"{spy_metrics['annual_return']:.1f}%"], + ['Max Drawdown', f"{strategy_metrics['max_drawdown']:.1f}%", + f"{qqq_metrics['max_drawdown']:.1f}%", f"{spy_metrics['max_drawdown']:.1f}%"], + ['Max DD Year', str(strategy_dd_year), str(qqq_dd_year), str(spy_dd_year)], + ['Sharpe Ratio', f"{strategy_metrics['sharpe_ratio']:.2f}", + f"{qqq_metrics['sharpe_ratio']:.2f}", f"{spy_metrics['sharpe_ratio']:.2f}"], + ['Total Trades', f"{len(strategy.trades)}", 'N/A', 'N/A'] + ] + + table = ax2.table(cellText=metrics_data, cellLoc='center', loc='center') + table.auto_set_font_size(False) + table.set_fontsize(10) + table.scale(1.2, 2.0) # More height for readability + + for i in range(len(metrics_data[0])): + table[(0, i)].set_facecolor('#40466e') + table[(0, i)].set_text_props(weight='bold', color='white') + + ax2.set_title('Performance Metrics Comparison', fontsize=14, fontweight='bold', pad=25) + + # 3. Strategy State Timeline (Full width) + ax3 = plt.subplot(4, 1, 3) + + # Create state mapping and colors + state_colors = { + 'QQQ_HOLD': 'blue', + 'FEAR_TRANSITION': 'orange', + 'CASH_WAIT': 'gray', + 'GREED_TRANSITION': 'yellow', + 'VOLATILE_STOCKS': 'red', + 'QQQ_TRANSITION': 'green' + } + + # Plot portfolio value with state colors + for state, color in state_colors.items(): + state_data = portfolio_df[portfolio_df['state'] == state] + if not state_data.empty: + ax3.scatter(state_data.index, state_data['value'] / 1000, + c=color, s=2, alpha=0.8, label=state) + + # Add stop-loss markers + stop_loss_trades = trades_df[trades_df['action'] == 'STOP_LOSS'] + if not stop_loss_trades.empty: + for _, trade in stop_loss_trades.iterrows(): + ax3.axvline(x=trade['date'], color='red', linestyle='--', alpha=0.8, linewidth=1) + + ax3.set_title('Strategy State Timeline with Stop-Loss Events', fontsize=14, fontweight='bold', pad=25) + ax3.set_ylabel('Total Assets ($K)', fontsize=11) + ax3.legend(bbox_to_anchor=(1.02, 1), loc='upper left', fontsize=8) + ax3.grid(True, alpha=0.3) + ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax3.get_xticklabels(): + label.set_rotation(45) + + # 4. Annual Returns Comparison (Full width) + ax4 = plt.subplot(4, 1, 4) + years = list(strategy_metrics['annual_returns'].keys()) + enhanced_returns = list(strategy_metrics['annual_returns'].values()) + qqq_returns = [qqq_metrics['annual_returns'].get(year, 0) for year in years] + spy_returns = [spy_metrics['annual_returns'].get(year, 0) for year in years] + + x = np.arange(len(years)) + width = 0.25 + + ax4.bar(x - width, enhanced_returns, width, label='Enhanced Strategy', color='red', alpha=0.8) + ax4.bar(x, qqq_returns, width, label='QQQ', color='blue', alpha=0.8) + ax4.bar(x + width, spy_returns, width, label='SPY', color='green', alpha=0.8) + + ax4.set_title('Annual Returns Comparison by Year', fontsize=14, fontweight='bold', pad=25) + ax4.set_ylabel('Annual Return (%)', fontsize=11) + ax4.set_xlabel('Year', fontsize=11) + ax4.set_xticks(x) + ax4.set_xticklabels(years) + for label in ax4.get_xticklabels(): + label.set_rotation(90) # Vertical text for years + ax4.legend(fontsize=10) + ax4.grid(True, alpha=0.3, axis='y') + + plt.subplots_adjust(left=0.1, right=0.85, top=0.90, bottom=0.08, hspace=0.6) + pdf.savefig(fig1, bbox_inches='tight', dpi=150) + plt.close() + + # Page 2: Detailed Analysis + fig2 = plt.figure(figsize=(8.5, 11)) + fig2.suptitle('Detailed Trading and Risk Analysis', fontsize=16, fontweight='bold', y=0.95) + + # 5. Stop-Loss Analysis (Full width) + ax5 = plt.subplot(3, 1, 1) + + if not stop_loss_trades.empty: + stop_loss_trades_copy = stop_loss_trades.copy() + stop_loss_trades_copy['year'] = stop_loss_trades_copy['date'].dt.year + stop_loss_by_year = stop_loss_trades_copy.groupby('year').size() + + ax5.bar(stop_loss_by_year.index, stop_loss_by_year.values, color='red', alpha=0.8, width=0.6) + ax5.set_title('Stop-Loss Triggers by Year', fontsize=14, fontweight='bold', pad=30) + ax5.set_ylabel('Number of Stop-Loss Events', fontsize=11) + ax5.set_xlabel('Year', fontsize=11) + ax5.grid(True, alpha=0.3, axis='y') + for label in ax5.get_xticklabels(): + label.set_rotation(45) + else: + ax5.text(0.5, 0.5, 'No Stop-Loss Events Triggered', ha='center', va='center', + transform=ax5.transAxes, fontsize=14, fontweight='bold') + ax5.set_title('Stop-Loss Analysis', fontsize=14, fontweight='bold', pad=30) + + # 6. Trade Frequency Analysis (Full width) + ax6 = plt.subplot(3, 1, 2) + + trades_df_copy = trades_df.copy() + trades_df_copy['year'] = trades_df_copy['date'].dt.year + trade_frequency = trades_df_copy.groupby('year').size() + + ax6.bar(trade_frequency.index, trade_frequency.values, color='purple', alpha=0.8, width=0.6) + ax6.set_title('Trading Activity by Year', fontsize=14, fontweight='bold', pad=30) + ax6.set_ylabel('Number of Trades', fontsize=11) + ax6.set_xlabel('Year', fontsize=11) + ax6.grid(True, alpha=0.3, axis='y') + for label in ax6.get_xticklabels(): + label.set_rotation(45) + + # 7. Fear & Greed Index with Trading Signals (Full width) + ax7 = plt.subplot(3, 1, 3) + + # Plot Fear & Greed Index + fg_data = portfolio_df['fg_index'].dropna() + ax7.plot(fg_data.index, fg_data.values, color='purple', alpha=0.8, linewidth=1.5) + ax7.axhline(y=25, color='red', linestyle='--', alpha=0.7, linewidth=2, label='Fear Threshold (25)') + ax7.axhline(y=75, color='green', linestyle='--', alpha=0.7, linewidth=2, label='Greed Threshold (75)') + ax7.fill_between(fg_data.index, 0, 25, alpha=0.2, color='red', label='Fear Zone') + ax7.fill_between(fg_data.index, 75, 100, alpha=0.2, color='green', label='Greed Zone') + + # Add trade markers + buy_trades = trades_df[trades_df['action'].str.contains('BUY')] + sell_trades = trades_df[trades_df['action'].str.contains('SELL')] + + if not buy_trades.empty: + ax7.scatter(buy_trades['date'], buy_trades['fg_index'], + color='darkgreen', s=15, alpha=0.8, marker='^', label='Buy Signals', zorder=5) + if not sell_trades.empty: + ax7.scatter(sell_trades['date'], sell_trades['fg_index'], + color='darkred', s=15, alpha=0.8, marker='v', label='Sell Signals', zorder=5) + + ax7.set_title('Fear & Greed Index with Trading Signals', fontsize=14, fontweight='bold', pad=30) + ax7.set_ylabel('CNN Fear & Greed Index', fontsize=11) + ax7.set_xlabel('Date', fontsize=11) + ax7.set_ylim(0, 100) + ax7.legend(bbox_to_anchor=(1.02, 1), loc='upper left', fontsize=8) + ax7.grid(True, alpha=0.3) + ax7.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) + for label in ax7.get_xticklabels(): + label.set_rotation(45) + + plt.subplots_adjust(left=0.1, right=0.85, top=0.88, bottom=0.10, hspace=1.0) + pdf.savefig(fig2, bbox_inches='tight', dpi=150) + plt.close() + + # Test PDF readability by checking file size and trying to read it + try: + file_size = os.path.getsize(pdf_file) + if file_size < 50000: # Less than 50KB might indicate issues + print(f"⚠️ Warning: PDF file size seems small ({file_size} bytes)") + else: + print(f"📈 PDF报告已保存: {pdf_file} (Size: {file_size:,} bytes)") + + # Quick validation - ensure we can open the file + with open(pdf_file, 'rb') as f: + header = f.read(10) + if not header.startswith(b'%PDF'): + print(f"⚠️ Warning: Generated file may not be a valid PDF") + else: + print(f"✅ PDF file validation passed") + + except Exception as e: + print(f"⚠️ Error validating PDF: {e}") + + print(f"📈 PDF报告已保存: {pdf_file}") + + +def main(): + """Main function to run strategy with config""" + config_file = 'config.json' + + if not os.path.exists(config_file): + print(f"❌ 配置文件未找到: {config_file}") + print("请确保config.json文件存在于当前目录") + return + + try: + # Initialize and run strategy + strategy = ConfigurableEmotionalDamageStrategy(config_file) + strategy.run_backtest() + + # Generate reports + generate_reports(strategy) + + except Exception as e: + print(f"❌ 运行失败: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/silly/归墟界面.html b/silly/归墟界面.html new file mode 100644 index 0000000..d2565cb --- /dev/null +++ b/silly/归墟界面.html @@ -0,0 +1,38 @@ + + + + + + 归墟 - 【梦星献作】 + + + +

+

归墟 - 【梦星献作】

+

这是一个测试页面，显示HTML正在正常渲染。

+
+ + \ No newline at end of file diff --git a/tokens.txt b/tokens.txt index cfddfa1..f8c16e5 100644 --- a/tokens.txt +++ b/tokens.txt @@ -12,7 +12,6 @@ chatgpt_model=gpt-4o-mini-2024-07-18 claude_API=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 alphavantage_API=H1TNEAN9JONTFCY6 VaultWarden_ADMIN_TOKEN=U2WwYJYRprFMpqxZdTpj6afU8VfBoGU0JSLvHE30WkbNMpAijHccDU1GPEI0/Bff -kimi_API=sk-xA6IE4AJTzXQh3MerrlAuriX3jhUDjRW4idMrIWfFteEahPe kimi_API_Base_URL=https://api.moonshot.ai/anthropic Gemeni_API=AIzaSyBZvm_cr5iHgPUpiDTZ7j_r4vYL8yW-tKw @@ -44,5 +43,5 @@ claude_invite_link=https://discord.com/api/oauth2/authorize?client_id=1390887125 #Set environment variables to use kimi: ``` export ANTHROPIC_BASE_URL=https://api.moonshot.ai/anthropic -export ANTHROPIC_AUTH_TOKEN=xA6IE4AJTzXQh3MerrlAuriX3jhUDjRW4idMrIWfFteEahPe +export ANTHROPIC_AUTH_TOKEN=sk-8IPjUk0pAv8AsLKaLgx0v1iuruEkJB9WQSLIA2u6Js27ZYgD ```

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