t1_converter.py 12 KB

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  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. CYB50 T+1 转换器 - 基于多空版本的做多交易转换为T+1规则
  5. 规则:
  6. 1. 提取多空版本中的所有做多交易
  7. 2. 买入当天不能卖出(T+1限制)
  8. 3. 如果原交易是T0(当天买卖),则延期到T+1开盘卖出
  9. 4. 重新计算延期后的盈亏(基于实际价格变化)
  10. """
  11. import pandas as pd
  12. import numpy as np
  13. from datetime import datetime, timedelta
  14. import sys
  15. sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
  16. from cyb50_30min_dual_direction import (
  17. ConfigManager, IntradayDataFetcher,
  18. DualDirectionSignalGenerator, DualDirectionExecutor
  19. )
  20. def get_next_trading_session_open(data_df, current_time):
  21. """获取下一个交易日的第一个开盘时间(注意:不是当天,是下一天)"""
  22. current_date = current_time.date()
  23. # 查找当前日期之后的所有数据
  24. future_data = data_df[data_df.index > current_time]
  25. if future_data.empty:
  26. return None, None
  27. # 获取所有日期,找到第一个不同于current_date的日期
  28. future_dates = future_data.index.date
  29. next_date = None
  30. for d in future_dates:
  31. if d != current_date:
  32. next_date = d
  33. break
  34. if next_date is None:
  35. return None, None
  36. # 获取下一个交易日的所有数据
  37. next_day_data = data_df[data_df.index.date == next_date]
  38. if next_day_data.empty:
  39. return None, None
  40. open_time = next_day_data.index[0]
  41. open_price = next_day_data.iloc[0]['Open'] # 使用开盘价
  42. return open_time, open_price
  43. def simulate_t1_trades(data_df, long_trades_df, initial_capital=1000000):
  44. """模拟T+1规则下的交易
  45. 规则:
  46. - 买入当天不能卖出
  47. - 如果原T0交易(当天买卖),延期到T+1开盘卖出
  48. - 卖出后当天可以再买(这是关键特性)
  49. """
  50. print("\n" + "="*80)
  51. print("T+1规则转换 - 基于多空版本做多交易")
  52. print("="*80)
  53. if len(long_trades_df) == 0:
  54. print("没有做多交易记录")
  55. return pd.DataFrame()
  56. # 按开仓时间排序
  57. long_trades_df = long_trades_df.sort_values('开仓时间').reset_index(drop=True)
  58. t1_trades = []
  59. capital = initial_capital
  60. for idx, trade in long_trades_df.iterrows():
  61. entry_time = trade['开仓时间']
  62. entry_price = trade['开仓价格']
  63. original_exit_time = trade['平仓时间']
  64. original_exit_price = trade['平仓价格']
  65. position_size = int(trade['仓位'])
  66. entry_signals = trade.get('入场信号', '')
  67. entry_date = entry_time.date()
  68. exit_date = original_exit_time.date()
  69. # 判断是否是T0交易
  70. is_t0 = (entry_date == exit_date)
  71. if is_t0:
  72. # T0交易需要延期到T+1开盘
  73. new_exit_time, new_exit_price = get_next_trading_session_open(data_df, entry_time)
  74. if new_exit_time is None:
  75. print(f"⚠️ 交易 #{idx+1}: 无法找到T+1开盘时间,使用原平仓价格")
  76. new_exit_time = original_exit_time
  77. new_exit_price = original_exit_price
  78. t1_adjusted = False
  79. else:
  80. # 计算新的盈亏
  81. # 假设使用开盘价卖出
  82. original_pnl = trade['盈亏金额']
  83. # 计算手续费(万分之一)
  84. commission_rate = 0.0001
  85. open_cost = position_size * entry_price * commission_rate
  86. close_cost = position_size * new_exit_price * commission_rate
  87. # 新的盈亏
  88. gross_pnl = (new_exit_price - entry_price) * position_size
  89. new_pnl = gross_pnl - open_cost - close_cost
  90. new_pnl_pct = (new_exit_price - entry_price) / entry_price * 100
  91. # 判断新的退出原因
  92. stop_loss = entry_price * 0.992 # 0.8%止损
  93. take_profit = entry_price * 1.02 # 2%止盈
  94. if new_exit_price <= stop_loss:
  95. exit_reason = f"T+1延期止损(价格{new_exit_price:.2f}触及止损线{stop_loss:.2f},亏损{abs(new_pnl_pct):.2f}%)"
  96. elif new_exit_price >= take_profit:
  97. exit_reason = f"T+1延期止盈(价格{new_exit_price:.2f}触及止盈线{take_profit:.2f},盈利{new_pnl_pct:.2f}%)"
  98. else:
  99. exit_reason = f"T+1延期平仓(价格{new_exit_price:.2f},盈亏{new_pnl_pct:+.2f}%)"
  100. # 计算持仓时长(小时)
  101. hold_hours = (new_exit_time - entry_time).total_seconds() / 3600
  102. print(f"\n[T0→T1调整] 交易 #{idx+1}")
  103. print(f" 原交易: {entry_time.strftime('%m-%d %H:%M')} 买 → {original_exit_time.strftime('%m-%d %H:%M')} 卖")
  104. print(f" 新交易: {entry_time.strftime('%m-%d %H:%M')} 买 → {new_exit_time.strftime('%m-%d %H:%M')} 卖")
  105. print(f" 原盈亏: {original_pnl:+.2f}元")
  106. print(f" 新盈亏: {new_pnl:+.2f}元 (基于T+1开盘{new_exit_price:.2f})")
  107. print(f" 盈亏变化: {(new_pnl - original_pnl):+.2f}元")
  108. t1_adjusted = True
  109. # 更新交易记录
  110. trade_record = {
  111. '交易方向': '做多',
  112. '开仓时间': entry_time,
  113. '平仓时间': new_exit_time,
  114. '开仓价格': entry_price,
  115. '平仓价格': new_exit_price,
  116. '仓位': position_size,
  117. '盈亏金额': new_pnl,
  118. '盈亏百分比': new_pnl_pct,
  119. '退出原因': exit_reason,
  120. '持仓周期数': int(hold_hours * 2), # 30分钟周期数
  121. '持仓小时数': hold_hours,
  122. 'T+1调整': '是(T0→T1)',
  123. '原平仓时间': original_exit_time,
  124. '原平仓价格': original_exit_price,
  125. '原盈亏': original_pnl,
  126. '盈亏变化': new_pnl - original_pnl,
  127. '入场信号': entry_signals,
  128. '开仓市值': position_size * entry_price,
  129. }
  130. capital += new_pnl
  131. trade_record['平仓时资金'] = capital
  132. t1_trades.append(trade_record)
  133. continue
  134. # 非T0交易,使用原始盈亏(已包含手续费,无需重复计算)
  135. hold_hours = trade['持仓小时数']
  136. # 直接使用原始盈亏(原始交易已计算好手续费)
  137. pnl = trade['盈亏金额']
  138. pnl_pct = trade['盈亏百分比']
  139. capital += pnl
  140. trade_record = {
  141. '交易方向': '做多',
  142. '开仓时间': entry_time,
  143. '平仓时间': original_exit_time,
  144. '开仓价格': entry_price,
  145. '平仓价格': original_exit_price,
  146. '仓位': position_size,
  147. '盈亏金额': pnl,
  148. '盈亏百分比': pnl_pct,
  149. '退出原因': trade['退出原因'],
  150. '持仓周期数': trade['持仓周期数'],
  151. '持仓小时数': hold_hours,
  152. 'T+1调整': '否',
  153. '原平仓时间': original_exit_time,
  154. '原平仓价格': original_exit_price,
  155. '原盈亏': trade['盈亏金额'],
  156. '盈亏变化': 0,
  157. '入场信号': entry_signals,
  158. '开仓市值': position_size * entry_price,
  159. '平仓时资金': capital,
  160. }
  161. t1_trades.append(trade_record)
  162. t1_trades_df = pd.DataFrame(t1_trades)
  163. return t1_trades_df
  164. def compare_results(original_trades, t1_trades, initial_capital=1000000):
  165. """对比原始交易和T+1转换后的结果"""
  166. print("\n" + "="*80)
  167. print("T+1转换前后对比")
  168. print("="*80)
  169. # 原始统计
  170. original_total_pnl = original_trades['盈亏金额'].sum()
  171. original_final = initial_capital + original_total_pnl
  172. original_return = (original_final / initial_capital - 1) * 100
  173. original_win_rate = (original_trades['盈亏金额'] > 0).sum() / len(original_trades) * 100
  174. # T+1统计
  175. t1_total_pnl = t1_trades['盈亏金额'].sum()
  176. t1_final = initial_capital + t1_total_pnl
  177. t1_return = (t1_final / initial_capital - 1) * 100
  178. t1_win_rate = (t1_trades['盈亏金额'] > 0).sum() / len(t1_trades) * 100
  179. # T0交易统计
  180. t0_adjusted = t1_trades[t1_trades['T+1调整'] == '是(T0→T1)']
  181. print(f"\n【原始交易(T0规则)】")
  182. print(f" 交易次数: {len(original_trades)}")
  183. print(f" 总盈亏: {original_total_pnl:+,.2f}元")
  184. print(f" 最终资金: {original_final:,.2f}元")
  185. print(f" 收益率: {original_return:+.2f}%")
  186. print(f" 胜率: {original_win_rate:.1f}%")
  187. print(f"\n【T+1转换后】")
  188. print(f" 交易次数: {len(t1_trades)}")
  189. print(f" 总盈亏: {t1_total_pnl:+,.2f}元")
  190. print(f" 最终资金: {t1_final:,.2f}元")
  191. print(f" 收益率: {t1_return:+.2f}%")
  192. print(f" 胜率: {t1_win_rate:.1f}%")
  193. print(f"\n【T+1调整统计】")
  194. print(f" T0→T1调整交易数: {len(t0_adjusted)}笔")
  195. if len(t0_adjusted) > 0:
  196. print(f" 调整后盈亏变化: {t0_adjusted['盈亏变化'].sum():+,.2f}元")
  197. print(f" 平均每笔变化: {t0_adjusted['盈亏变化'].mean():+,.2f}元")
  198. print(f" 调整交易明细:")
  199. for _, row in t0_adjusted.iterrows():
  200. print(f" {row['开仓时间'].strftime('%m-%d %H:%M')} - "
  201. f"原盈亏{row['原盈亏']:+.0f} → 新盈亏{row['盈亏金额']:+.0f} "
  202. f"({row['盈亏变化']:+.0f})")
  203. print(f"\n【收益差异】")
  204. print(f" 收益率变化: {(t1_return - original_return):+.2f}%")
  205. print(f" 绝对盈亏差: {(t1_total_pnl - original_total_pnl):+,.2f}元")
  206. def main():
  207. """主程序"""
  208. print("="*80)
  209. print("CYB50 T+1 交易转换器")
  210. print("基于多空版本的做多交易,应用T+1规则")
  211. print("="*80)
  212. initial_capital = 1000000
  213. # 1. 运行多空版本获取原始交易数据
  214. print("\n【步骤1】运行多空版本获取原始交易...")
  215. config_manager = ConfigManager('config.json')
  216. fetcher = IntradayDataFetcher(config_manager)
  217. end_date = datetime.now()
  218. start_date = end_date - timedelta(days=70)
  219. raw_data = fetcher.fetch_30min_data(start_date, end_date)
  220. data_with_indicators = fetcher.calculate_intraday_indicators(raw_data)
  221. signal_generator = DualDirectionSignalGenerator()
  222. signals_df = signal_generator.generate_dual_direction_signals(data_with_indicators)
  223. executor = DualDirectionExecutor(initial_capital=initial_capital)
  224. results_df, trades_df = executor.execute_dual_direction_trades(signals_df)
  225. # 提取做多交易
  226. long_trades = trades_df[trades_df['交易方向'] == '做多'].copy()
  227. print(f"✅ 获取到 {len(long_trades)} 笔做多交易")
  228. # 2. 应用T+1规则
  229. print("\n【步骤2】应用T+1规则转换...")
  230. t1_trades = simulate_t1_trades(data_with_indicators, long_trades, initial_capital)
  231. # 3. 对比结果
  232. print("\n【步骤3】对比分析...")
  233. compare_results(long_trades, t1_trades, initial_capital)
  234. # 4. 导出结果
  235. if len(t1_trades) > 0:
  236. print("\n【步骤4】导出T+1交易记录...")
  237. # 格式化时间
  238. export_df = t1_trades.copy()
  239. for col in ['开仓时间', '平仓时间', '原平仓时间']:
  240. if col in export_df.columns:
  241. export_df[col] = export_df[col].dt.strftime('%Y-%m-%d %H:%M:%S')
  242. timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
  243. output_file = f'cyb50_t1_converted_trades_{timestamp}.csv'
  244. export_df.to_csv(output_file, index=False, encoding='utf-8-sig')
  245. print(f"✅ T+1交易记录已保存: {output_file}")
  246. print("\n" + "="*80)
  247. print("转换完成!")
  248. print("="*80)
  249. if __name__ == "__main__":
  250. main()