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  1. # coding=utf-8
  2. from __future__ import print_function, absolute_import, unicode_literals
  3. import numpy as np
  4. import pandas as pd
  5. from gm.api import *
  6. '''
  7. 本策略首先计算了过去300个价格数据的均值和标准差
  8. 并根据均值加减1和2个标准差得到网格的区间分界线,
  9. 并分别配以0.3和0.5的仓位权重
  10. 然后根据价格所在的区间来配置仓位(+/-40为上下界,无实际意义):
  11. (-40,-3],(-3,-2],(-2,2],(2,3],(3,40](具体价格等于均值+数字倍标准差)
  12. [-0.5, -0.3, 0.0, 0.3, 0.5](资金比例,此处负号表示开空仓)
  13. 回测数据为:SHFE.rb1801的1min数据
  14. 回测时间为:2017-07-01 08:00:00到2017-10-01 16:00:00
  15. '''
  16. def init(context):
  17. context.symbol = 'SHFE.rb1801'
  18. # 订阅SHFE.rb1801, bar频率为1min
  19. subscribe(symbols=context.symbol, frequency='60s')
  20. # 获取过去300个价格数据
  21. timeseries = history_n(symbol=context.symbol, frequency='60s', count=300, fields='close', fill_missing='Last',
  22. end_time='2017-07-01 08:00:00', df=True)['close'].values
  23. # 获取网格区间分界线
  24. context.band = np.mean(timeseries) + np.array([-40, -3, -2, 2, 3, 40]) * np.std(timeseries)
  25. # 设置网格的仓位
  26. context.weight = [0.5, 0.3, 0.0, 0.3, 0.5]
  27. def on_bar(context, bars):
  28. bar = bars[0]
  29. # 根据价格落在(-40,-3],(-3,-2],(-2,2],(2,3],(3,40]的区间范围来获取最新收盘价所在的价格区间
  30. grid = pd.cut([bar.close], context.band, labels=[0, 1, 2, 3, 4])[0]
  31. # 获取多仓仓位
  32. position_long = context.account().position(symbol=context.symbol, side=PositionSide_Long)
  33. # 获取空仓仓位
  34. position_short = context.account().position(symbol=context.symbol, side=PositionSide_Short)
  35. # 若无仓位且价格突破则按照设置好的区间开仓
  36. if not position_long and not position_short and grid != 2:
  37. # 大于3为在中间网格的上方,做多
  38. if grid >= 3:
  39. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  40. position_side=PositionSide_Long)
  41. print(context.symbol, '以市价单开多仓到仓位', context.weight[grid])
  42. if grid <= 1:
  43. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  44. position_side=PositionSide_Short)
  45. print(context.symbol, '以市价单开空仓到仓位', context.weight[grid])
  46. # 持有多仓的处理
  47. elif position_long:
  48. if grid >= 3:
  49. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  50. position_side=PositionSide_Long)
  51. print(context.symbol, '以市价单调多仓到仓位', context.weight[grid])
  52. # 等于2为在中间网格,平仓
  53. elif grid == 2:
  54. order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market,
  55. position_side=PositionSide_Long)
  56. print(context.symbol, '以市价单全平多仓')
  57. # 小于1为在中间网格的下方,做空
  58. elif grid <= 1:
  59. order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market,
  60. position_side=PositionSide_Long)
  61. print(context.symbol, '以市价单全平多仓')
  62. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  63. position_side=PositionSide_Short)
  64. print(context.symbol, '以市价单开空仓到仓位', context.weight[grid])
  65. # 持有空仓的处理
  66. elif position_short:
  67. # 小于1为在中间网格的下方,做空
  68. if grid <= 1:
  69. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  70. position_side=PositionSide_Short)
  71. print(context.symbol, '以市价单调空仓到仓位', context.weight[grid])
  72. # 等于2为在中间网格,平仓
  73. elif grid == 2:
  74. order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market,
  75. position_side=PositionSide_Short)
  76. print(context.symbol, '以市价单全平空仓')
  77. # 大于3为在中间网格的上方,做多
  78. elif grid >= 3:
  79. order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market,
  80. position_side=PositionSide_Short)
  81. print(context.symbol, '以市价单全平空仓')
  82. order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market,
  83. position_side=PositionSide_Long)
  84. print(context.symbol, '以市价单开多仓到仓位', context.weight[grid])
  85. if __name__ == '__main__':
  86. '''
  87. strategy_id策略ID,由系统生成
  88. filename文件名,请与本文件名保持一致
  89. mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
  90. token绑定计算机的ID,可在系统设置-密钥管理中生成
  91. backtest_start_time回测开始时间
  92. backtest_end_time回测结束时间
  93. backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
  94. backtest_initial_cash回测初始资金
  95. backtest_commission_ratio回测佣金比例
  96. backtest_slippage_ratio回测滑点比例
  97. '''
  98. run(strategy_id='strategy_id',
  99. filename='main.py',
  100. mode=MODE_BACKTEST,
  101. token='token_id',
  102. backtest_start_time='2017-07-01 08:00:00',
  103. backtest_end_time='2017-10-01 16:00:00',
  104. backtest_adjust=ADJUST_PREV,
  105. backtest_initial_cash=10000000,
  106. backtest_commission_ratio=0.0001,
  107. backtest_slippage_ratio=0.0001)

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