zhutoutoutousan
213 lines
7.5 KiB
Python
213 lines
7.5 KiB
Python
"""
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Performance Analysis and Reporting
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This module provides tools for analyzing backtest results and generating reports.
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"""
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from typing import Dict, Any, List
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from datetime import datetime
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class PerformanceAnalyzer:
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"""
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Analyzes backtest performance and generates reports.
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"""
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def __init__(self, backtest_results: Dict[str, Any]):
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"""
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Initialize with backtest results.
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Args:
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backtest_results: Results dictionary from BacktestEngine.run()
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"""
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self.results = backtest_results
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self.metrics = backtest_results['metrics']
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self.trades = backtest_results['trades']
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self.strategy_name = backtest_results['strategy_name']
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def print_summary(self):
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"""Print a summary of the backtest results."""
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print("\n" + "="*60)
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print(f"BACKTEST SUMMARY: {self.strategy_name}")
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print("="*60)
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print(f"\nInitial Balance: ${self.metrics['initial_balance']:,.2f}")
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print(f"Final Balance: ${self.metrics['final_balance']:,.2f}")
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print(f"Total Return: {self.metrics['total_return_pct']:.2f}%")
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print(f"\nTotal Trades: {self.metrics['total_trades']}")
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print(f"Winning Trades: {self.metrics['winning_trades']}")
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print(f"Losing Trades: {self.metrics['losing_trades']}")
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print(f"Win Rate: {self.metrics['win_rate_pct']:.2f}%")
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print(f"\nTotal Profit: ${self.metrics['total_profit']:,.2f}")
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print(f"Total Loss: ${self.metrics['total_loss']:,.2f}")
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print(f"Profit Factor: {self.metrics['profit_factor']:.2f}")
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print(f"\nAverage Win: ${self.metrics['avg_win']:,.2f}")
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print(f"Average Loss: ${self.metrics['avg_loss']:,.2f}")
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print(f"Max Drawdown: {self.metrics['max_drawdown_pct']:.2f}%")
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if self.metrics.get('parameters'):
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print(f"\nStrategy Parameters:")
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for key, value in self.metrics['parameters'].items():
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print(f" {key}: {value}")
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print("="*60 + "\n")
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def get_trades_dataframe(self) -> pd.DataFrame:
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"""Convert trades list to pandas DataFrame."""
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if not self.trades:
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return pd.DataFrame()
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df = pd.DataFrame(self.trades)
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df['open_time'] = pd.to_datetime(df['open_time'])
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df['close_time'] = pd.to_datetime(df['close_time'])
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df['duration'] = df['close_time'] - df['open_time']
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return df
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def plot_equity_curve(self, save_path: str = None):
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"""
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Plot equity curve over time.
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Args:
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save_path: Optional path to save the plot
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"""
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if not self.trades:
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print("No trades to plot")
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return
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df = self.get_trades_dataframe()
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df = df.sort_values('close_time')
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# Calculate cumulative equity
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cumulative_profit = df['profit'].cumsum()
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equity_curve = self.metrics['initial_balance'] + cumulative_profit
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plt.figure(figsize=(12, 6))
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plt.plot(df['close_time'], equity_curve, linewidth=2, label='Equity')
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plt.axhline(y=self.metrics['initial_balance'], color='r', linestyle='--', label='Initial Balance')
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plt.xlabel('Time')
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plt.ylabel('Equity ($)')
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plt.title(f'Equity Curve - {self.strategy_name}')
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Equity curve saved to {save_path}")
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else:
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plt.show()
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def plot_drawdown(self, save_path: str = None):
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"""
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Plot drawdown over time.
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Args:
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save_path: Optional path to save the plot
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"""
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if not self.trades:
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print("No trades to plot")
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return
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df = self.get_trades_dataframe()
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df = df.sort_values('close_time')
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# Calculate cumulative equity
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cumulative_profit = df['profit'].cumsum()
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equity_curve = self.metrics['initial_balance'] + cumulative_profit
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# Calculate running maximum
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running_max = equity_curve.expanding().max()
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drawdown = (equity_curve - running_max) / running_max * 100
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plt.figure(figsize=(12, 6))
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plt.fill_between(df['close_time'], drawdown, 0, alpha=0.3, color='red', label='Drawdown')
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plt.plot(df['close_time'], drawdown, linewidth=1, color='darkred')
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plt.xlabel('Time')
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plt.ylabel('Drawdown (%)')
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plt.title(f'Drawdown Chart - {self.strategy_name}')
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Drawdown chart saved to {save_path}")
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else:
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plt.show()
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def plot_monthly_returns(self, save_path: str = None):
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"""
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Plot monthly returns.
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Args:
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save_path: Optional path to save the plot
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"""
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if not self.trades:
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print("No trades to plot")
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return
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df = self.get_trades_dataframe()
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df = df.sort_values('close_time')
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# Group by month
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df['month'] = df['close_time'].dt.to_period('M')
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monthly_returns = df.groupby('month')['profit'].sum()
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monthly_returns_pct = (monthly_returns / self.metrics['initial_balance']) * 100
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plt.figure(figsize=(12, 6))
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colors = ['green' if x > 0 else 'red' for x in monthly_returns_pct]
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plt.bar(range(len(monthly_returns_pct)), monthly_returns_pct, color=colors, alpha=0.7)
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plt.xlabel('Month')
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plt.ylabel('Return (%)')
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plt.title(f'Monthly Returns - {self.strategy_name}')
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plt.xticks(range(len(monthly_returns_pct)), [str(x) for x in monthly_returns_pct.index], rotation=45)
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plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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plt.grid(True, alpha=0.3, axis='y')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Monthly returns chart saved to {save_path}")
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else:
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plt.show()
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def export_trades_csv(self, filepath: str):
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"""
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Export trades to CSV file.
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Args:
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filepath: Path to save CSV file
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"""
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df = self.get_trades_dataframe()
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df.to_csv(filepath, index=False)
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print(f"Trades exported to {filepath}")
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def generate_report(self, output_dir: str = "backtest_results"):
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"""
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Generate a comprehensive report with all charts and data.
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Args:
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output_dir: Directory to save report files
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"""
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import os
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os.makedirs(output_dir, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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prefix = f"{self.strategy_name}_{timestamp}"
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# Print summary
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self.print_summary()
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# Generate plots
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self.plot_equity_curve(os.path.join(output_dir, f"{prefix}_equity_curve.png"))
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self.plot_drawdown(os.path.join(output_dir, f"{prefix}_drawdown.png"))
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self.plot_monthly_returns(os.path.join(output_dir, f"{prefix}_monthly_returns.png"))
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# Export trades
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self.export_trades_csv(os.path.join(output_dir, f"{prefix}_trades.csv"))
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print(f"\nReport generated in {output_dir}/")
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