打印交易明细
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@ -54,7 +54,7 @@ def get_processed_data():
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# 限制为最近一年数据
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end_date = smic_data.index.max()
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start_date = end_date - pd.Timedelta(days=360*3)
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start_date = end_date - pd.Timedelta(days=360)
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print(f"\n限制回测时间范围: {start_date} 到 {end_date}")
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@ -175,6 +175,209 @@ def print_statistics(strategy_data, portfolio):
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print(f"做空信号次数: {short_count}")
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print(f"总信号次数: {total_signals}")
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def print_trade_orders(strategy_data, stock_portfolio):
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"""打印所有交易订单信息"""
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if stock_portfolio is None:
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return
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print("\n" + "="*60)
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print("=== 详细交易订单信息 ===")
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print("="*60)
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try:
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# 获取订单记录
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orders_df = stock_portfolio.orders.records_readable
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if len(orders_df) > 0:
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print(f"\n总订单数量: {len(orders_df)}")
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# 按时间排序
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time_column = None
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possible_time_columns = ['Timestamp', 'Date', 'Time']
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for col in possible_time_columns:
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if col in orders_df.columns:
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time_column = col
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break
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if time_column:
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orders_sorted = orders_df.sort_values(time_column)
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else:
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orders_sorted = orders_df
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# 显示所有订单（按时间排序）
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pd.set_option('display.max_rows', None)
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pd.set_option('display.width', None)
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print("\n【所有交易订单（按时间排序）】")
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print(orders_sorted.to_string(index=False))
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# 按股票分组统计
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print(f"\n【按股票统计】")
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for column in ['SMIC', 'HHIC']:
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column_orders = orders_sorted[orders_sorted['Column'] == column]
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if len(column_orders) > 0:
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buy_orders = column_orders[column_orders['Side'] == 'Buy']
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sell_orders = column_orders[column_orders['Side'] == 'Sell']
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print(f"\n{column} 订单统计:")
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print(f" 总订单数: {len(column_orders)}")
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print(f" 买入订单: {len(buy_orders)}")
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print(f" 卖出订单: {len(sell_orders)}")
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print(f" 总交易金额: {column_orders['Size'].abs().sum():.2f} 股")
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print(f" 总手续费: {column_orders['Fees'].sum():.2f}")
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# 按日期分组统计
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if time_column:
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print(f"\n【按日期统计】")
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daily_orders = orders_sorted.groupby(time_column).agg({
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'Column': 'count',
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'Size': 'sum',
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'Fees': 'sum'
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}).rename(columns={'Column': '订单数'})
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print(daily_orders.to_string())
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else:
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print(f"\n【按日期统计】- 未找到时间列")
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# 重置pandas显示选项
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pd.reset_option('display.max_rows')
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pd.reset_option('display.width')
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else:
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print("没有找到交易订单")
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except Exception as e:
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print(f"打印订单信息时出错: {e}")
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import traceback
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traceback.print_exc()
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def print_detailed_trade_analysis(strategy_data, stock_portfolio):
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"""打印详细的交易分析"""
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if stock_portfolio is None:
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return
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print("\n" + "="*60)
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print("=== 详细交易分析 ===")
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print("="*60)
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try:
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# 获取交易记录
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trades_df = stock_portfolio.trades.records_readable
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if len(trades_df) > 0:
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print(f"\n总交易次数: {len(trades_df)}")
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# 按开仓时间排序
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trades_sorted = trades_df.sort_values('Entry Timestamp')
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# 显示所有交易（按时间排序）
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pd.set_option('display.max_rows', None)
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pd.set_option('display.width', None)
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print("\n【所有交易记录（按开仓时间排序）】")
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print(trades_sorted.to_string(index=False))
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# 按股票分组分析
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print(f"\n【按股票交易分析】")
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for column in ['SMIC', 'HHIC']:
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column_trades = trades_sorted[trades_sorted['Column'] == column]
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if len(column_trades) > 0:
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winning_trades = column_trades[column_trades['PnL'] > 0]
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losing_trades = column_trades[column_trades['PnL'] < 0]
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print(f"\n{column} 交易分析:")
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print(f" 总交易次数: {len(column_trades)}")
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print(f" 盈利交易: {len(winning_trades)}")
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print(f" 亏损交易: {len(losing_trades)}")
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print(f" 胜率: {len(winning_trades)/len(column_trades)*100:.1f}%")
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print(f" 总盈亏: {column_trades['PnL'].sum():.2f}")
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print(f" 平均每笔盈亏: {column_trades['PnL'].mean():.2f}")
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print(f" 最大盈利: {column_trades['PnL'].max():.2f}")
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print(f" 最大亏损: {column_trades['PnL'].min():.2f}")
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if len(winning_trades) > 0:
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print(f" 平均盈利: {winning_trades['PnL'].mean():.2f}")
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if len(losing_trades) > 0:
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print(f" 平均亏损: {losing_trades['PnL'].mean():.2f}")
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# 正确的配对交易分析
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print(f"\n【配对交易分析（修正）】")
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# 按开仓时间分组，找出同一天开仓的SMIC和HHIC交易
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entry_groups = trades_sorted.groupby('Entry Timestamp').agg({
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'Column': list,
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'PnL': list,
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'Size': list,
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'Direction': list,
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'Exit Timestamp': list
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})
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pair_trades = []
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for entry_date, group_data in entry_groups.iterrows():
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columns = group_data['Column']
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pnls = group_data['PnL']
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directions = group_data['Direction']
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exit_times = group_data['Exit Timestamp']
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# 检查是否同时有SMIC和HHIC的交易
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if 'SMIC' in columns and 'HHIC' in columns:
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smic_idx = columns.index('SMIC')
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hhic_idx = columns.index('HHIC')
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# 检查交易方向是否配对（一个做多，一个做空）
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if directions[smic_idx] != directions[hhic_idx]:
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pair_pnl = pnls[smic_idx] + pnls[hhic_idx]
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pair_trades.append({
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'entry_date': entry_date,
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'exit_date_smic': exit_times[smic_idx],
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'exit_date_hhic': exit_times[hhic_idx],
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'smic_pnl': pnls[smic_idx],
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'hhic_pnl': pnls[hhic_idx],
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'total_pnl': pair_pnl,
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'smic_direction': directions[smic_idx],
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'hhic_direction': directions[hhic_idx]
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})
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if pair_trades:
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# 按开仓时间排序配对交易
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pair_trades_sorted = sorted(pair_trades, key=lambda x: x['entry_date'])
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print(f"配对交易次数: {len(pair_trades_sorted)}")
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total_pair_pnl = sum(trade['total_pnl'] for trade in pair_trades_sorted)
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avg_pair_pnl = total_pair_pnl / len(pair_trades_sorted)
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winning_pairs = [t for t in pair_trades_sorted if t['total_pnl'] > 0]
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losing_pairs = [t for t in pair_trades_sorted if t['total_pnl'] < 0]
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print(f"配对交易总盈亏: {total_pair_pnl:.2f}")
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print(f"平均每对盈亏: {avg_pair_pnl:.2f}")
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print(f"盈利配对: {len(winning_pairs)}")
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print(f"亏损配对: {len(losing_pairs)}")
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print(f"配对胜率: {len(winning_pairs)/len(pair_trades_sorted)*100:.1f}%")
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# 显示每对交易的详细信息（按时间排序）
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print(f"\n【各配对交易详情（按开仓时间排序）】")
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for i, trade in enumerate(pair_trades_sorted, 1):
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status = "盈利" if trade['total_pnl'] > 0 else "亏损"
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print(f"第{i}对 - 开仓: {trade['entry_date']}")
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print(f" SMIC({trade['smic_direction']}): {trade['smic_pnl']:.2f} (平仓: {trade['exit_date_smic']})")
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print(f" HHIC({trade['hhic_direction']}): {trade['hhic_pnl']:.2f} (平仓: {trade['exit_date_hhic']})")
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print(f" 总盈亏: {trade['total_pnl']:.2f} [{status}]")
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print()
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else:
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print("没有找到配对交易")
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# 重置pandas显示选项
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pd.reset_option('display.max_rows')
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pd.reset_option('display.width')
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else:
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print("没有找到交易记录")
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except Exception as e:
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print(f"打印交易分析时出错: {e}")
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import traceback
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traceback.print_exc()
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def main():
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"""主函数"""
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@ -197,8 +400,13 @@ def main():
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# 绘制基于比率的结果
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plot_results(strategy_data, ratio_portfolio)
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# 打印详细交易订单信息
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print_trade_orders(strategy_data, stock_portfolio)
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# 打印详细交易分析
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print_detailed_trade_analysis(strategy_data, stock_portfolio)
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print("程序执行完成!")
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if __name__ == "__main__":
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main()
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