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attach.png (75581, 2018-01-31)
example.py (5942, 2018-01-31)

# 网格交易(期货) 基于网格交易方法的交易策略 ## 源码 ```python # coding=utf-8 from __future__ import print_function, absolute_import, unicode_literals import numpy as np import pandas as pd from gm.api import * ''' 本策略首先计算了过去300个价格数据的均值和标准差 并根据均值加减1和2个标准差得到网格的区间分界线, 并分别配以0.3和0.5的仓位权重 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): (-40,-3],(-3,-2],(-2,2],(2,3],(3,40](https://github.com/TruthHun/grid-trading/blob/master/具体价格等于均值+数字倍标准差) [-0.5, -0.3, 0.0, 0.3, 0.5](https://github.com/TruthHun/grid-trading/blob/master/资金比例,此处负号表示开空仓) 回测数据为:SHFE.rb1801的1min数据 回测时间为:2017-07-01 08:00:00到2017-10-01 16:00:00 ''' def init(context): context.symbol = 'SHFE.rb1801' # 订阅SHFE.rb1801, bar频率为1min subscribe(symbols=context.symbol, frequency='60s') # 获取过去300个价格数据 timeseries = history_n(symbol=context.symbol, frequency='60s', count=300, fields='close', fill_missing='Last', end_time='2017-07-01 08:00:00', df=True)['close'].values # 获取网格区间分界线 context.band = np.mean(timeseries) + np.array([-40, -3, -2, 2, 3, 40]) * np.std(timeseries) # 设置网格的仓位 context.weight = [0.5, 0.3, 0.0, 0.3, 0.5] def on_bar(context, bars): bar = bars[0] # 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 grid = pd.cut([bar.close], context.band, labels=[0, 1, 2, 3, 4])[0] # 获取多仓仓位 position_long = context.account().position(symbol=context.symbol, side=PositionSide_Long) # 获取空仓仓位 position_short = context.account().position(symbol=context.symbol, side=PositionSide_Short) # 若无仓位且价格突破则按照设置好的区间开仓 if not position_long and not position_short and grid != 2: # 大于3为在中间网格的上方,做多 if grid >= 3: order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Long) print(context.symbol, '以市价单开多仓到仓位', context.weight[grid]) if grid <= 1: order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Short) print(context.symbol, '以市价单开空仓到仓位', context.weight[grid]) # 持有多仓的处理 elif position_long: if grid >= 3: order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Long) print(context.symbol, '以市价单调多仓到仓位', context.weight[grid]) # 等于2为在中间网格,平仓 elif grid == 2: order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market, position_side=PositionSide_Long) print(context.symbol, '以市价单全平多仓') # 小于1为在中间网格的下方,做空 elif grid <= 1: order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market, position_side=PositionSide_Long) print(context.symbol, '以市价单全平多仓') order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Short) print(context.symbol, '以市价单开空仓到仓位', context.weight[grid]) # 持有空仓的处理 elif position_short: # 小于1为在中间网格的下方,做空 if grid <= 1: order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Short) print(context.symbol, '以市价单调空仓到仓位', context.weight[grid]) # 等于2为在中间网格,平仓 elif grid == 2: order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market, position_side=PositionSide_Short) print(context.symbol, '以市价单全平空仓') # 大于3为在中间网格的上方,做多 elif grid >= 3: order_target_percent(symbol=context.symbol, percent=0, order_type=OrderType_Market, position_side=PositionSide_Short) print(context.symbol, '以市价单全平空仓') order_target_percent(symbol=context.symbol, percent=context.weight[grid], order_type=OrderType_Market, position_side=PositionSide_Long) print(context.symbol, '以市价单开多仓到仓位', context.weight[grid]) if __name__ == '__main__': ''' strategy_id策略ID,由系统生成 filename文件名,请与本文件名保持一致 mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST token绑定计算机的ID,可在系统设置-密钥管理中生成 backtest_start_time回测开始时间 backtest_end_time回测结束时间 backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST backtest_initial_cash回测初始资金 backtest_commission_ratio回测佣金比例 backtest_slippage_ratio回测滑点比例 ''' run(strategy_id='strategy_id', filename='main.py', mode=MODE_BACKTEST, token='token_id', backtest_start_time='2017-07-01 08:00:00', backtest_end_time='2017-10-01 16:00:00', backtest_adjust=ADJUST_PREV, backtest_initial_cash=10000000, backtest_commission_ratio=0.0001, backtest_slippage_ratio=0.0001) ``` ## 绩效图 

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