大连做网站需要多少钱,阿里云域名价格,导入wordpress 演示数据,做淘客网站要什么样服务器# codingutf-8from __future__ import print_function, absolute_import, unicode_literalsimport numpy as npimport pandas as pdfrom 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…# codingutf-8from __future__ import print_function, absolute_import, unicode_literalsimport numpy as npimport pandas as pdfrom 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](具体价格等于均值数字倍标准差)[-0.5, -0.3, 0.0, 0.3, 0.5](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 16:00:00def init(context):context.symbol SHFE.rb1801# 订阅SHFE.rb1801, bar频率为1minsubscribe(symbolscontext.symbol, frequency60s)# 获取过去300个价格数据timeseries history_n(symbolcontext.symbol, frequency60s, count300, fieldsclose, fill_missingLast,end_time2017-07-01 08:00:00, dfTrue)[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(symbolcontext.symbol, sidePositionSide_Long)# 获取空仓仓位position_short context.account().position(symbolcontext.symbol, sidePositionSide_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(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_Long)print(context.symbol, 以市价单开多仓到仓位, context.weight[grid])if grid 1:order_target_percent(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percent0, order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percent0, order_typeOrderType_Market,position_sidePositionSide_Long)print(context.symbol, 以市价单全平多仓)order_target_percent(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percent0, order_typeOrderType_Market,position_sidePositionSide_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(symbolcontext.symbol, percent0, order_typeOrderType_Market,position_sidePositionSide_Short)print(context.symbol, 以市价单全平空仓)order_target_percent(symbolcontext.symbol, percentcontext.weight[grid], order_typeOrderType_Market,position_sidePositionSide_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_BACKTESTtoken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_POSTbacktest_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_idstrategy_id,filenamemain.py,modeMODE_BACKTEST,tokentoken_id,backtest_start_time2017-07-01 08:00:00,backtest_end_time2017-10-01 16:00:00,backtest_adjustADJUST_PREV,backtest_initial_cash10000000,backtest_commission_ratio0.0001,backtest_slippage_ratio0.0001)
