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

    [网格交易(期货)]

    原文: https://www.myquant.cn/docs/python_strategyies/104