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  1. # coding=utf-8
  2. from __future__ import print_function, absolute_import, unicode_literals
  3. import numpy as np
  4. from gm.api import *
  5. try:
  6. import statsmodels.tsa.stattools as ts
  7. except:
  8. print('请安装statsmodels库')
  9. '''
  10. 本策略根据EG两步法(1.序列同阶单整2.OLS残差平稳)判断序列具有协整关系之后(若无协整关系则全平仓位不进行操作)
  11. 通过计算两个真实价格序列回归残差的0.9个标准差上下轨,并在价差突破上轨的时候做空价差,价差突破下轨的时候做多价差
  12. 并在回归至标准差水平内的时候平仓
  13. 回测数据为:SHFE.rb1801和SHFE.rb1805的1min数据
  14. 回测时间为:2017-09-25 08:00:00到2017-10-01 15:00:00
  15. '''
  16. # 协整检验的函数
  17. def cointegration_test(series01, series02):
  18. urt_rb1801 = ts.adfuller(np.array(series01), 1)[1]
  19. urt_rb1805 = ts.adfuller(np.array(series01), 1)[1]
  20. # 同时平稳或不平稳则差分再次检验
  21. if (urt_rb1801 > 0.1 and urt_rb1805 > 0.1) or (urt_rb1801 < 0.1 and urt_rb1805 < 0.1):
  22. urt_diff_rb1801 = ts.adfuller(np.diff(np.array(series01)), 1)[1]
  23. urt_diff_rb1805 = ts.adfuller(np.diff(np.array(series01), 1))[1]
  24. # 同时差分平稳进行OLS回归的残差平稳检验
  25. if urt_diff_rb1801 < 0.1 and urt_diff_rb1805 < 0.1:
  26. matrix = np.vstack([series02, np.ones(len(series02))]).T
  27. beta, c = np.linalg.lstsq(matrix, series01)[0]
  28. resid = series01 - beta * series02 - c
  29. if ts.adfuller(np.array(resid), 1)[1] > 0.1:
  30. result = 0.0
  31. else:
  32. result = 1.0
  33. return beta, c, resid, result
  34. else:
  35. result = 0.0
  36. return 0.0, 0.0, 0.0, result
  37. else:
  38. result = 0.0
  39. return 0.0, 0.0, 0.0, result
  40. def init(context):
  41. context.goods = ['SHFE.rb1801', 'SHFE.rb1805']
  42. # 订阅品种
  43. subscribe(symbols=context.goods, frequency='60s', count=801, wait_group=True)
  44. def on_bar(context, bars):
  45. # 获取过去800个60s的收盘价数据
  46. close_01 = context.data(symbol=context.goods[0], frequency='60s', count=801, fields='close')['close'].values
  47. close_02 = context.data(symbol=context.goods[1], frequency='60s', count=801, fields='close')['close'].values
  48. # 展示两个价格序列的协整检验的结果
  49. beta, c, resid, result = cointegration_test(close_01, close_02)
  50. # 如果返回协整检验不通过的结果则全平仓位等待
  51. if not result:
  52. print('协整检验不通过,全平所有仓位')
  53. order_close_all()
  54. return
  55. # 计算残差的标准差上下轨
  56. mean = np.mean(resid)
  57. up = mean + 0.9 * np.std(resid)
  58. down = mean - 0.9 * np.std(resid)
  59. # 计算新残差
  60. resid_new = close_01[-1] - beta * close_02[-1] - c
  61. # 获取rb1801的多空仓位
  62. position_01_long = context.account().position(symbol=context.goods[0], side=PositionSide_Long)
  63. position_01_short = context.account().position(symbol=context.goods[0], side=PositionSide_Short)
  64. if not position_01_long and not position_01_short:
  65. # 上穿上轨时做空新残差
  66. if resid_new > up:
  67. order_target_volume(symbol=context.goods[0], volume=1, order_type=OrderType_Market,
  68. position_side=PositionSide_Short)
  69. print(context.goods[0] + '以市价单开空仓1手')
  70. order_target_volume(symbol=context.goods[1], volume=1, order_type=OrderType_Market,
  71. position_side=PositionSide_Long)
  72. print(context.goods[1] + '以市价单开多仓1手')
  73. # 下穿下轨时做多新残差
  74. if resid_new < down:
  75. order_target_volume(symbol=context.goods[0], volume=1, order_type=OrderType_Market,
  76. position_side=PositionSide_Long)
  77. print(context.goods[0], '以市价单开多仓1手')
  78. order_target_volume(symbol=context.goods[1], volume=1, order_type=OrderType_Market,
  79. position_side=PositionSide_Short)
  80. print(context.goods[1], '以市价单开空仓1手')
  81. # 新残差回归时平仓
  82. elif position_01_short:
  83. if resid_new <= up:
  84. order_close_all()
  85. print('价格回归,平掉所有仓位')
  86. # 突破下轨反向开仓
  87. if resid_new < down:
  88. order_target_volume(symbol=context.goods[0], volume=1, order_type=OrderType_Market,
  89. position_side=PositionSide_Long)
  90. print(context.goods[0], '以市价单开多仓1手')
  91. order_target_volume(symbol=context.goods[1], volume=1, order_type=OrderType_Market,
  92. position_side=PositionSide_Short)
  93. print(context.goods[1], '以市价单开空仓1手')
  94. elif position_01_long:
  95. if resid_new >= down:
  96. order_close_all()
  97. print('价格回归,平所有仓位')
  98. # 突破上轨反向开仓
  99. if resid_new > up:
  100. order_target_volume(symbol=context.goods[0], volume=1, order_type=OrderType_Market,
  101. position_side=PositionSide_Short)
  102. print(context.goods[0], '以市价单开空仓1手')
  103. order_target_volume(symbol=context.goods[1], volume=1, order_type=OrderType_Market,
  104. position_side=PositionSide_Long)
  105. print(context.goods[1], '以市价单开多仓1手')
  106. if __name__ == '__main__':
  107. '''
  108. strategy_id策略ID,由系统生成
  109. filename文件名,请与本文件名保持一致
  110. mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
  111. token绑定计算机的ID,可在系统设置-密钥管理中生成
  112. backtest_start_time回测开始时间
  113. backtest_end_time回测结束时间
  114. backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
  115. backtest_initial_cash回测初始资金
  116. backtest_commission_ratio回测佣金比例
  117. backtest_slippage_ratio回测滑点比例
  118. '''
  119. run(strategy_id='strategy_id',
  120. filename='main.py',
  121. mode=MODE_BACKTEST,
  122. token='token_id',
  123. backtest_start_time='2017-09-25 08:00:00',
  124. backtest_end_time='2017-10-01 16:00:00',
  125. backtest_adjust=ADJUST_PREV,
  126. backtest_initial_cash=500000,
  127. backtest_commission_ratio=0.0001,
  128. backtest_slippage_ratio=0.0001)

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