克隆策略

    {"Description":"实验创建于2018/10/16","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-1483:input_1","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-136:input_1","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-1442:instruments","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-1473:features","SourceOutputPortId":"-1468:data"},{"DestinationInputPortId":"-72:input_data","SourceOutputPortId":"-1473:data"},{"DestinationInputPortId":"-1473:input_data","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-1442:benchmark_ds","SourceOutputPortId":"-136:data_1"},{"DestinationInputPortId":"-1442:options_data","SourceOutputPortId":"-72:data"},{"DestinationInputPortId":"-79:input_1","SourceOutputPortId":"-72:data"},{"DestinationInputPortId":"-1442:trading_calendar","SourceOutputPortId":"-79:data_1"}],"ModuleNodes":[{"Id":"-25","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2021-05-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2021-05-15","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_FUTURE","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"RU2110.SHF","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-25"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-25","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"Comment":"","CommentCollapsed":true},{"Id":"-1442","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"2021-05-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2021-05-13","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 设置是否是结算模式\n context.set_need_settle(False)\n # 设置最大杠杆\n context.set_max_leverage(1, 'fill_amap')\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 获取当日指标数据\n today = data.current_dt # 当前交易日期\n print(\"trade.v4.log: \", today)\n \n # 跳过可能存在没有交易数据的交易时间，比如09:00:00\n try:\n buy_condition=context.buy_condition[today]\n sell_condition=context.sell_condition[today]\n except KeyError:\n return\n \n instrument = context.future_symbol(context.instruments[0]) # 交易标的\n curr_po=context.portfolio.positions[instrument] # 组合持仓\n curr_position = curr_po.amount # 持仓数量\n \n\n # 交易逻辑\n if buy_condition>0 and data.can_trade(instrument): # 开多，下单数量20手\n order_target(instrument, 20)\n print(today,'平空开多')\n \n elif sell_condition>0 and data.can_trade(instrument):# 开空，下单数量20手\n order_target(instrument, -20)\n print(today,'平多开空')","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n df = context.options['data'].read_df()\n# df['date']=df['date'].apply(lambda x:x.strftime('%Y-%m-%d'))\n df.set_index('date',inplace=True)\n context.buy_condition=df['buy_condition']\n context.sell_condition=df['sell_condition']\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。\ndef bigquant_run(context, data):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":"1000000","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"minute","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"真实价格","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"期货","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-1442"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-1442"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-1442","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":3,"Comment":"","CommentCollapsed":true},{"Id":"-1468","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\nbuy_condition=where((close-(mean(close,20)-2*std(close,20)))/(4*std(close,20))>=1,1,0)\nsell_condition=where((close-(mean(close,20)-2*std(close,20)))/(4*std(close,20))<=0,1,0)\n\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-1468"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1468","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"Comment":"","CommentCollapsed":true},{"Id":"-1473","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1473"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1473"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1473","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"Comment":"","CommentCollapsed":true},{"Id":"-1483","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3,before_days):\n # 示例代码如下。在这里编写您的代码\n start_date=(pd.to_datetime(input_1.read_pickle()['start_date']) - datetime.timedelta(days=before_days)).strftime('%Y-%m-%d')\n end_date=input_1.read_pickle()['end_date']\n instruments=input_1.read_pickle()['instruments']\n fields=['open','high','low','close']\n df = DataSource('bar1m_CN_FUTURE').read(instruments,start_date,end_date,fields)\n df['adjust_factor']=1.0\n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{'before_days':60}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-1483"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-1483"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-1483"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-1483","OutputType":null},{"Name":"data_2","NodeId":"-1483","OutputType":null},{"Name":"data_3","NodeId":"-1483","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"","CommentCollapsed":true},{"Id":"-136","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n start = input_1.read_pickle()['start_date']\n end = input_1.read_pickle()['end_date']\n bm = DataSource('bar1d_CN_FUTURE').read(instruments=['RU8888.SHF'],start_date=start,end_date=end)\n bm.index = range(len(bm)) \n data_1 = DataSource.write_df(bm)\n return Outputs(data_1=data_1)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-136"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-136"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-136"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-136","OutputType":null},{"Name":"data_2","NodeId":"-136","OutputType":null},{"Name":"data_3","NodeId":"-136","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"-72","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-72"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-72"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-72","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"Comment":"","CommentCollapsed":true},{"Id":"-79","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n from datetime import time\n trading_date = input_1.read()['date'].apply(lambda x: x.date()).unique().tolist() # 自定义的交易日历的日期\n trading_sections = [(time(9,0,0), time(11,30,0)), (time(13,30,0), time(15,0,0)), (time(21,0,0), time(23,0,0))] # 自定义的交易开、收盘时间\n trading_calendar = get_extend_calendar_with_tradingdays('MyFutCalendar', tradingdays=trading_date, trading_sections=trading_sections) # 生成自定义交易日历\n \n data_1 = DataSource.write_pickle(trading_calendar)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-79"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-79"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-79"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-79","OutputType":null},{"Name":"data_2","NodeId":"-79","OutputType":null},{"Name":"data_3","NodeId":"-79","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='-25' Position='-256,-107,200,200'/><NodePosition Node='-1442' Position='109.480712890625,465.98646545410156,200,200'/><NodePosition Node='-1468' Position='303,-135,200,200'/><NodePosition Node='-1473' Position='219,74,200,200'/><NodePosition Node='-1483' Position='-10,-16,200,200'/><NodePosition Node='-136' Position='-195.04826259613037,294.58880615234375,200,200'/><NodePosition Node='-72' Position='197,166,200,200'/><NodePosition Node='-79' Position='436.67376708984375,287.480712890625,200,200'/></NodePositions><NodeGroups /></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":true}
    In [261]:
    # 本代码由可视化策略环境自动生成 2021年5月29日13:02
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m4_run_bigquant_run(input_1, input_2, input_3,before_days):
    # 示例代码如下。在这里编写您的代码
    start_date=(pd.to_datetime(input_1.read_pickle()['start_date']) - datetime.timedelta(days=before_days)).strftime('%Y-%m-%d')
    end_date=input_1.read_pickle()['end_date']
    instruments=input_1.read_pickle()['instruments']
    fields=['open','high','low','close']
    df = DataSource('bar1m_CN_FUTURE').read(instruments,start_date,end_date,fields)
    df['adjust_factor']=1.0
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m4_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m1_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    start = input_1.read_pickle()['start_date']
    end = input_1.read_pickle()['end_date']
    bm = DataSource('bar1d_CN_FUTURE').read(instruments=['RU8888.SHF'],start_date=start,end_date=end)
    bm.index = range(len(bm)) 
    data_1 = DataSource.write_df(bm)
    return Outputs(data_1=data_1)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m1_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m9_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    from datetime import time
    trading_date = input_1.read()['date'].apply(lambda x: x.date()).unique().tolist() # 自定义的交易日历的日期
    trading_sections = [(time(9,0,0), time(11,30,0)), (time(13,30,0), time(15,0,0)), (time(21,0,0), time(23,0,0))]  # 自定义的交易开、收盘时间
    trading_calendar = get_extend_calendar_with_tradingdays('MyFutCalendar', tradingdays=trading_date, trading_sections=trading_sections) # 生成自定义交易日历
    
    data_1 = DataSource.write_pickle(trading_calendar)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m9_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
   # 设置是否是结算模式
    context.set_need_settle(False)
    # 设置最大杠杆
    context.set_max_leverage(1, 'fill_amap')

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 获取当日指标数据
    today = data.current_dt # 当前交易日期
    print("trade.v4.log: ", today)
    
    # 跳过可能存在没有交易数据的交易时间，比如09:00:00
    try:
        buy_condition=context.buy_condition[today]
        sell_condition=context.sell_condition[today]
    except KeyError:
        return
    
    instrument = context.future_symbol(context.instruments[0]) # 交易标的
    curr_po=context.portfolio.positions[instrument] # 组合持仓
    curr_position = curr_po.amount  # 持仓数量
         

    # 交易逻辑
    if buy_condition>0 and data.can_trade(instrument): # 开多，下单数量20手
        order_target(instrument,  20)
        print(today,'平空开多')
            
    elif  sell_condition>0 and data.can_trade(instrument):# 开空，下单数量20手
        order_target(instrument, -20)
        print(today,'平多开空')
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    df = context.options['data'].read_df()
#     df['date']=df['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
    df.set_index('date',inplace=True)
    context.buy_condition=df['buy_condition']
    context.sell_condition=df['sell_condition']

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.instruments.v2(
    start_date='2021-05-01',
    end_date='2021-05-15',
    market='CN_FUTURE',
    instrument_list='RU2110.SHF',
    max_count=0
)

m4 = M.cached.v3(
    input_1=m2.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{\'before_days\':60}',
    output_ports=''
)

m1 = M.cached.v3(
    input_1=m2.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m5 = M.input_features.v1(
    features="""
# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
buy_condition=where((close-(mean(close,20)-2*std(close,20)))/(4*std(close,20))>=1,1,0)
sell_condition=where((close-(mean(close,20)-2*std(close,20)))/(4*std(close,20))<=0,1,0)

"""
)

m6 = M.derived_feature_extractor.v3(
    input_data=m4.data_1,
    features=m5.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m8 = M.dropnan.v2(
    input_data=m6.data
)

m9 = M.cached.v3(
    input_1=m8.data,
    run=m9_run_bigquant_run,
    post_run=m9_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m8.data,
    benchmark_ds=m1.data_1,
    trading_calendar=m9.data_1,
    start_date='2021-05-01',
    end_date='2021-05-13',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='minute',
    price_type='真实价格',
    product_type='期货',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

    trade.v4.log:  2021-05-06 09:00:00+00:00
trade.v4.log:  2021-05-06 09:01:00+00:00
2021-05-06 09:01:00+00:00 平空开多
trade.v4.log:  2021-05-06 09:02:00+00:00
2021-05-06 09:02:00+00:00 平空开多
trade.v4.log:  2021-05-06 09:03:00+00:00
2021-05-06 09:03:00+00:00 平空开多
trade.v4.log:  2021-05-06 09:04:00+00:00
trade.v4.log:  2021-05-06 09:05:00+00:00
trade.v4.log:  2021-05-06 09:06:00+00:00
trade.v4.log:  2021-05-06 09:07:00+00:00
trade.v4.log:  2021-05-06 09:08:00+00:00
trade.v4.log:  2021-05-06 09:09:00+00:00
trade.v4.log:  2021-05-06 09:10:00+00:00
trade.v4.log:  2021-05-06 09:11:00+00:00
trade.v4.log:  2021-05-06 09:12:00+00:00
trade.v4.log:  2021-05-06 09:13:00+00:00
trade.v4.log:  2021-05-06 09:14:00+00:00
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trade.v4.log:  2021-05-13 23:00:00+00:00
    • 收益率-2.46%
    • 年化收益率-64.94%
    • 基准收益率-4.59%
    • 阿尔法-2.61
    • 贝塔-0.84
    • 夏普比率-1.94
    • 胜率0.24
    • 盈亏比-21.37
    • 收益波动率50.13%
    • 信息比率0.08
    • 最大回撤4.21%
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