515 lines
22 KiB
Python
515 lines
22 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from matplotlib.ticker import FuncFormatter
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import seaborn as sns
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import os
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class ChartGenerator:
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"""图表生成器，用于创建各种回测分析图表"""
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def __init__(self, output_dir='charts', strategy_name=None):
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"""初始化图表生成器"""
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# 根据策略名称创建子目录结构
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self.output_dir = output_dir
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if strategy_name:
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self.output_dir = os.path.join(output_dir, strategy_name)
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os.makedirs(self.output_dir, exist_ok=True)
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# 设置图表风格
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plt.style.use('ggplot')
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 支持中文
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plt.rcParams['axes.unicode_minus'] = False # 支持负号
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def _save_chart(self, fig, filename, dpi=300):
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"""保存图表"""
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filepath = os.path.join(self.output_dir, filename)
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fig.savefig(filepath, dpi=dpi, bbox_inches='tight')
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plt.close(fig)
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return filepath
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def plot_equity_curve(self, results, strategy_name, benchmark_data=None):
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"""1. 策略收益曲线图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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equity_df = results['equity_curve']
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# 检查权益曲线是否为空且包含'equity'列
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if equity_df is not None and not equity_df.empty and 'equity' in equity_df.columns:
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ax.plot(equity_df.index, equity_df['equity'], label=strategy_name)
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# 如果有基准数据，将基准收益曲线叠加到策略收益曲线上
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if benchmark_data is not None:
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# 计算基准收益率
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benchmark_returns = benchmark_data['close'] / benchmark_data['close'].iloc[0] - 1
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# 将基准收益率转换为权益曲线（基于初始资金）
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initial_capital = equity_df['equity'].iloc[0]
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benchmark_equity = initial_capital * (1 + benchmark_returns)
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# 确保基准数据与策略数据日期对齐
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benchmark_equity = benchmark_equity.reindex(equity_df.index).ffill().bfill()
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ax.plot(equity_df.index, benchmark_equity, label='上证指数 (000001.SH)', linestyle='--')
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else:
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# 如果没有有效的权益曲线数据，显示一条水平直线（初始资金）
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ax.plot([], [], label=strategy_name, color='blue')
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if benchmark_data is not None:
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# 计算基准收益率
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benchmark_returns = benchmark_data['close'] / benchmark_data['close'].iloc[0] - 1
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# 将基准收益率转换为权益曲线（基于初始资金）
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initial_capital = 1000000 # 默认初始资金
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benchmark_equity = initial_capital * (1 + benchmark_returns)
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ax.plot(benchmark_equity.index, benchmark_equity, label='上证指数 (000001.SH)', linestyle='--')
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ax.set_title(f'{strategy_name} 收益曲线与基准对比')
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ax.set_xlabel('日期')
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ax.set_ylabel('权益 (元)')
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ax.grid(True)
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ax.legend()
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# 格式化日期
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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fig.autofmt_xdate()
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return self._save_chart(fig, f'equity_curve_{strategy_name}.png')
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def plot_benchmark_comparison(self, results, benchmark_data, strategy_name):
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"""2. 基准收益曲线图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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# 计算策略收益率
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equity_df = results['equity_curve']
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# 检查权益曲线是否有效
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if equity_df is not None and not equity_df.empty and 'equity' in equity_df.columns:
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strategy_returns = equity_df['equity'] / equity_df['equity'].iloc[0] - 1
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ax.plot(strategy_returns.index, strategy_returns * 100, label=strategy_name)
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else:
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# 如果没有有效的权益曲线数据，不绘制策略曲线
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ax.plot([], [], label=strategy_name)
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# 计算基准收益率
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benchmark_returns = benchmark_data['close'] / benchmark_data['close'].iloc[0] - 1
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ax.plot(benchmark_returns.index, benchmark_returns * 100, label='基准')
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ax.set_title(f'{strategy_name} 与基准收益对比')
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ax.set_xlabel('日期')
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ax.set_ylabel('累计收益率 (%)')
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ax.grid(True)
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ax.legend()
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# 格式化日期
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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fig.autofmt_xdate()
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return self._save_chart(fig, f'benchmark_comparison_{strategy_name}.png')
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def plot_drawdown_curve(self, results, strategy_name):
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"""3. 回撤曲线图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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equity_df = results['equity_curve']
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# 检查权益曲线是否有效
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if equity_df is not None and not equity_df.empty and 'equity' in equity_df.columns:
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equity_df['cummax'] = equity_df['equity'].cummax()
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equity_df['drawdown'] = (equity_df['equity'] - equity_df['cummax']) / equity_df['cummax'] * 100
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ax.fill_between(equity_df.index, equity_df['drawdown'], 0, alpha=0.5, color='red')
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ax.plot(equity_df.index, equity_df['drawdown'], color='red', linewidth=1)
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ax.set_ylim(bottom=equity_df['drawdown'].min() * 1.1)
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else:
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# 如果没有有效的权益曲线数据，显示一个空图表
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ax.set_ylim(bottom=-20)
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ax.set_title(f'{strategy_name} 回撤曲线')
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ax.set_xlabel('日期')
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ax.set_ylabel('回撤 (%)')
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ax.grid(True)
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# 格式化日期
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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fig.autofmt_xdate()
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return self._save_chart(fig, f'drawdown_curve_{strategy_name}.png')
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def plot_return_distribution(self, results, strategy_name):
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"""4. 收益分布直方图"""
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fig, ax = plt.subplots(figsize=(10, 6))
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equity_df = results['equity_curve']
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# 检查权益曲线是否有效
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if equity_df is not None and not equity_df.empty and 'returns' in equity_df.columns:
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daily_returns = equity_df['returns'].dropna() * 100
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if not daily_returns.empty:
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sns.histplot(daily_returns, bins=50, kde=True, ax=ax)
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else:
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ax.text(0.5, 0.5, '无收益率数据', ha='center', va='center', transform=ax.transAxes)
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else:
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# 如果没有有效的权益曲线数据，显示提示
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ax.text(0.5, 0.5, '无收益率数据', ha='center', va='center', transform=ax.transAxes)
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ax.set_title(f'{strategy_name} 日收益率分布')
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ax.set_xlabel('日收益率 (%)')
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ax.set_ylabel('频率')
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ax.grid(True)
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return self._save_chart(fig, f'return_distribution_{strategy_name}.png')
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def plot_monthly_returns(self, results, strategy_name):
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"""5. 月度收益柱状图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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equity_df = results['equity_curve']
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# 检查权益曲线是否有效
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if equity_df is not None and not equity_df.empty and 'equity' in equity_df.columns:
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monthly_returns = equity_df['equity'].resample('M').last().pct_change() * 100
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monthly_returns = monthly_returns.dropna()
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if not monthly_returns.empty:
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colors = ['green' if ret > 0 else 'red' for ret in monthly_returns]
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ax.bar(monthly_returns.index, monthly_returns, color=colors)
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else:
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ax.text(0.5, 0.5, '无月度收益数据', ha='center', va='center', transform=ax.transAxes)
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else:
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# 如果没有有效的权益曲线数据，显示提示
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ax.text(0.5, 0.5, '无月度收益数据', ha='center', va='center', transform=ax.transAxes)
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ax.set_title(f'{strategy_name} 月度收益率')
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ax.set_xlabel('月份')
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ax.set_ylabel('月度收益率 (%)')
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ax.grid(True, axis='y')
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# 格式化日期
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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fig.autofmt_xdate()
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return self._save_chart(fig, f'monthly_returns_{strategy_name}.png')
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def plot_holding_value(self, results, strategy_name):
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"""6. 持仓市值曲线图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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equity_df = results['equity_curve']
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# 检查权益曲线是否有效
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if equity_df is not None and not equity_df.empty:
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# 绘制持仓市值
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if 'positions_value' in equity_df.columns:
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ax.plot(equity_df.index, equity_df['positions_value'], label='持仓市值')
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else:
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ax.plot([], [], label='持仓市值')
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# 绘制现金
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if 'cash' in equity_df.columns:
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ax.plot(equity_df.index, equity_df['cash'], label='现金')
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else:
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ax.plot([], [], label='现金')
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else:
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# 如果没有有效的权益曲线数据，绘制空图表
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ax.plot([], [], label='持仓市值')
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ax.plot([], [], label='现金')
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ax.set_title(f'{strategy_name} 持仓市值变化')
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ax.set_xlabel('日期')
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ax.set_ylabel('金额 (元)')
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ax.grid(True)
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ax.legend()
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# 格式化日期
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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fig.autofmt_xdate()
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return self._save_chart(fig, f'holding_value_{strategy_name}.png')
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def plot_turnover(self, results, strategy_name):
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"""7. 换手率柱状图"""
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# 换手率需要从交易数据中计算
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fig, ax = plt.subplots(figsize=(12, 6))
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trades_df = results['trades']
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equity_df = results['equity_curve']
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if trades_df.empty or equity_df is None or equity_df.empty or 'positions_value' not in equity_df.columns:
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ax.text(0.5, 0.5, '无换手率数据', ha='center', va='center', transform=ax.transAxes)
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ax.set_title(f'{strategy_name} 月度换手率')
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ax.set_xlabel('月份')
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ax.set_ylabel('换手率 (%)')
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ax.grid(True, axis='y')
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return self._save_chart(fig, f'turnover_{strategy_name}.png')
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# 按月份分组，计算每个月的交易金额
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trades_df['month'] = trades_df['date'].dt.to_period('M')
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monthly_trades = trades_df.groupby('month')['price'].sum() * trades_df.groupby('month')['shares'].sum()
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# 计算平均持仓市值作为分母
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monthly_holding = equity_df['positions_value'].resample('M').mean()
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# 计算换手率
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turnover = monthly_trades / monthly_holding * 100
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turnover = turnover.dropna()
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if not turnover.empty:
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ax.bar(turnover.index.astype(str), turnover)
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else:
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ax.text(0.5, 0.5, '无换手率数据', ha='center', va='center', transform=ax.transAxes)
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ax.set_title(f'{strategy_name} 月度换手率')
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ax.set_xlabel('月份')
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ax.set_ylabel('换手率 (%)')
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ax.grid(True, axis='y')
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fig.autofmt_xdate()
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return self._save_chart(fig, f'turnover_{strategy_name}.png')
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def plot_sector_contribution(self, results, strategy_name):
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"""8. 行业/板块收益贡献图"""
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# 行业贡献需要股票的行业信息，这里假设trades_df包含sector列
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if 'sector' not in results['trades'].columns:
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return None
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fig, ax = plt.subplots(figsize=(10, 6))
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trades_df = results['trades']
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sector_returns = {} # 这里需要根据实际数据计算行业贡献
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# 示例实现，需要根据实际数据调整
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if not trades_df.empty:
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# 按行业分组，计算每个行业的总盈亏
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sector_pnl = trades_df.groupby('sector')['pnl'].sum()
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# 绘制饼图
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fig, ax = plt.subplots(figsize=(10, 8))
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ax.pie(sector_pnl, labels=sector_pnl.index, autopct='%1.1f%%', startangle=90)
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ax.axis('equal') # 保证饼图是圆形
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ax.set_title(f'{strategy_name} 行业收益贡献')
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return self._save_chart(fig, f'sector_contribution_{strategy_name}.png')
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return None
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def plot_factor_contribution(self, factor_data, strategy_name):
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"""9. 因子收益贡献图"""
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# 因子贡献需要因子数据
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if not factor_data:
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return None
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fig, ax = plt.subplots(figsize=(10, 6))
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# 示例实现，需要根据实际因子数据调整
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factors = list(factor_data.keys())
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contributions = list(factor_data.values())
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colors = ['green' if contrib > 0 else 'red' for contrib in contributions]
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ax.bar(factors, contributions, color=colors)
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ax.set_title(f'{strategy_name} 因子收益贡献')
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ax.set_xlabel('因子')
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ax.set_ylabel('贡献度 (%)')
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ax.grid(True, axis='y')
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return self._save_chart(fig, f'factor_contribution_{strategy_name}.png')
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def plot_signal_distribution(self, results, strategy_name):
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"""10. 交易信号分布图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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trades_df = results['trades']
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if trades_df.empty:
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return None
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# 计算每个月的买入和卖出信号数量
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trades_df['month'] = trades_df['date'].dt.to_period('M')
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signal_counts = trades_df.groupby(['month', 'action']).size().unstack(fill_value=0)
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signal_counts.plot(kind='bar', ax=ax)
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ax.set_title(f'{strategy_name} 交易信号分布')
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ax.set_xlabel('月份')
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ax.set_ylabel('信号数量')
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ax.grid(True, axis='y')
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ax.legend()
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fig.autofmt_xdate()
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return self._save_chart(fig, f'signal_distribution_{strategy_name}.png')
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def plot_all_trades(self, results, strategy_name):
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"""11. 所有交易记录表格"""
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trades_df = results['trades']
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fig, ax = plt.subplots(figsize=(14, max(8, len(trades_df) * 0.3)))
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ax.axis('tight')
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ax.axis('off')
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if not trades_df.empty:
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# 选择需要显示的列
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display_columns = ['date', 'ts_code', 'action', 'price', 'shares', 'commission',
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'pnl', 'pnl_pct', 'reason']
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# 确保所有需要的列都存在
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available_columns = [col for col in display_columns if col in trades_df.columns]
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trades_data = trades_df[available_columns].copy()
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# 格式化数据
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if 'price' in trades_data.columns:
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trades_data['price'] = trades_data['price'].round(2)
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if 'commission' in trades_data.columns:
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trades_data['commission'] = trades_data['commission'].round(2)
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if 'pnl' in trades_data.columns:
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trades_data['pnl'] = trades_data['pnl'].round(2)
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if 'pnl_pct' in trades_data.columns:
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trades_data['pnl_pct'] = (trades_data['pnl_pct'] * 100).round(2)
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# 创建表格
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table = ax.table(cellText=trades_data.values,
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colLabels=trades_data.columns,
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cellLoc='center',
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loc='center')
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# 设置表格样式
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table.auto_set_font_size(False)
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table.set_fontsize(9)
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table.scale(1, 1.5)
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# 设置表头背景色
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for i, key in enumerate(trades_data.columns):
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table[(0, i)].set_facecolor('#40466e')
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table[(0, i)].set_text_props(color='white')
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table[(0, i)].set_edgecolor('black')
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else:
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ax.text(0.5, 0.5, '无交易记录', ha='center', va='center', transform=ax.transAxes, fontsize=12)
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ax.set_title(f'{strategy_name} 所有交易记录', fontsize=14, pad=20)
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return self._save_chart(fig, f'all_trades_{strategy_name}.png')
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def plot_daily_trades(self, results, strategy_name):
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"""12. 每日买卖记录表格"""
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trades_df = results['trades']
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fig, ax = plt.subplots(figsize=(14, max(8, len(trades_df) * 0.3)))
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ax.axis('tight')
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ax.axis('off')
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if not trades_df.empty:
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# 按日期分组
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daily_trades = trades_df.groupby('date')
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# 准备数据
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daily_data = []
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for date, group in daily_trades:
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# 分别统计买入和卖出
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buys = group[group['action'] == 'BUY']
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sells = group[group['action'] == 'SELL']
|
||
|
||
# 买入信息
|
||
buy_count = len(buys)
|
||
buy_volume = buys['shares'].sum() if 'shares' in buys.columns else 0
|
||
buy_amount = (buys['price'] * buys['shares']).sum() if 'price' in buys.columns and 'shares' in buys.columns else 0
|
||
|
||
# 卖出信息
|
||
sell_count = len(sells)
|
||
sell_volume = sells['shares'].sum() if 'shares' in sells.columns else 0
|
||
sell_amount = (sells['price'] * sells['shares']).sum() if 'price' in sells.columns and 'shares' in sells.columns else 0
|
||
sell_pnl = sells['pnl'].sum() if 'pnl' in sells.columns else 0
|
||
|
||
daily_data.append({
|
||
'日期': date,
|
||
'买入次数': buy_count,
|
||
'买入股数': buy_volume,
|
||
'买入金额': buy_amount,
|
||
'卖出次数': sell_count,
|
||
'卖出股数': sell_volume,
|
||
'卖出金额': sell_amount,
|
||
'当日盈亏': sell_pnl
|
||
})
|
||
|
||
# 创建DataFrame
|
||
daily_df = pd.DataFrame(daily_data)
|
||
|
||
# 格式化数据
|
||
daily_df['买入金额'] = daily_df['买入金额'].round(2)
|
||
daily_df['卖出金额'] = daily_df['卖出金额'].round(2)
|
||
daily_df['当日盈亏'] = daily_df['当日盈亏'].round(2)
|
||
|
||
# 创建表格
|
||
table = ax.table(cellText=daily_df.values,
|
||
colLabels=daily_df.columns,
|
||
cellLoc='center',
|
||
loc='center')
|
||
|
||
# 设置表格样式
|
||
table.auto_set_font_size(False)
|
||
table.set_fontsize(9)
|
||
table.scale(1, 1.5)
|
||
|
||
# 设置表头背景色
|
||
for i, key in enumerate(daily_df.columns):
|
||
table[(0, i)].set_facecolor('#40466e')
|
||
table[(0, i)].set_text_props(color='white')
|
||
table[(0, i)].set_edgecolor('black')
|
||
else:
|
||
ax.text(0.5, 0.5, '无交易记录', ha='center', va='center', transform=ax.transAxes, fontsize=12)
|
||
|
||
ax.set_title(f'{strategy_name} 每日买卖记录', fontsize=14, pad=20)
|
||
|
||
return self._save_chart(fig, f'daily_trades_{strategy_name}.png')
|
||
|
||
def generate_all_charts(self, results, strategy_name, benchmark_data=None, factor_data=None):
|
||
"""生成所有图表"""
|
||
charts = []
|
||
|
||
# 生成策略收益曲线图（包含基准对比）
|
||
charts.append(('收益曲线', self.plot_equity_curve(results, strategy_name, benchmark_data)))
|
||
|
||
# 生成基准对比图（如果有基准数据）- 保持独立的基准对比图
|
||
if benchmark_data is not None:
|
||
charts.append(('基准对比', self.plot_benchmark_comparison(results, benchmark_data, strategy_name)))
|
||
|
||
# 生成回撤曲线图
|
||
charts.append(('回撤曲线', self.plot_drawdown_curve(results, strategy_name)))
|
||
|
||
# 生成收益分布直方图
|
||
charts.append(('收益分布', self.plot_return_distribution(results, strategy_name)))
|
||
|
||
# 生成月度收益柱状图
|
||
charts.append(('月度收益', self.plot_monthly_returns(results, strategy_name)))
|
||
|
||
# 生成持仓市值曲线图
|
||
charts.append(('持仓市值', self.plot_holding_value(results, strategy_name)))
|
||
|
||
# 生成换手率柱状图
|
||
turnover_chart = self.plot_turnover(results, strategy_name)
|
||
if turnover_chart:
|
||
charts.append(('换手率', turnover_chart))
|
||
|
||
# 生成行业贡献图
|
||
sector_chart = self.plot_sector_contribution(results, strategy_name)
|
||
if sector_chart:
|
||
charts.append(('行业贡献', sector_chart))
|
||
|
||
# 生成因子贡献图（如果有因子数据）
|
||
if factor_data is not None:
|
||
factor_chart = self.plot_factor_contribution(factor_data, strategy_name)
|
||
if factor_chart:
|
||
charts.append(('因子贡献', factor_chart))
|
||
|
||
# 生成交易信号分布图
|
||
signal_chart = self.plot_signal_distribution(results, strategy_name)
|
||
if signal_chart:
|
||
charts.append(('信号分布', signal_chart))
|
||
|
||
# 生成所有交易记录图表
|
||
all_trades_chart = self.plot_all_trades(results, strategy_name)
|
||
charts.append(('所有交易记录', all_trades_chart))
|
||
|
||
# 生成每日买卖记录图表
|
||
daily_trades_chart = self.plot_daily_trades(results, strategy_name)
|
||
charts.append(('每日买卖记录', daily_trades_chart))
|
||
|
||
return charts |