{"Description":"实验创建于2018/10/16","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-1442:instruments","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-1483:input_1","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-136:input_1","SourceOutputPortId":"-25:data"},{"DestinationInputPortId":"-1473:features","SourceOutputPortId":"-1468:data"},{"DestinationInputPortId":"-1632:input_data","SourceOutputPortId":"-1473:data"},{"DestinationInputPortId":"-1473:input_data","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-1442:options_data","SourceOutputPortId":"-1632:data"},{"DestinationInputPortId":"-1442:benchmark_ds","SourceOutputPortId":"-136:data_1"}],"ModuleNodes":[{"Id":"-25","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2017-11-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2018-05-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_FUTURE","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"RU1809.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,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1442","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","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.strftime('%Y-%m-%d') # 当前交易日期\n buy_condition=context.buy_condition[today]\n sell_condition=context.sell_condition[today]\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":"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')","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":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","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,"IsPartOfPartialRun":null,"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,"IsPartOfPartialRun":null,"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,"IsPartOfPartialRun":null,"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('bar1d_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,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1632","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1632"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1632","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"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 # 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# 本代码由可视化策略环境自动生成 2019年3月9日 16:10
# 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('bar1d_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

# 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.strftime('%Y-%m-%d') # 当前交易日期
    buy_condition=context.buy_condition[today]
    sell_condition=context.sell_condition[today]
    
    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：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='2017-11-01',
    end_date='2018-05-01',
    market='CN_FUTURE',
    instrument_list='RU1809.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={}
)

m7 = M.dropnan.v1(
    input_data=m6.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m7.data,
    benchmark_ds=m1.data_1,
    start_date='',
    end_date='',
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    initialize=m3_initialize_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='daily',
    price_type='后复权',
    product_type='期货',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2019-03-09 16:06:02.222671] INFO: bigquant: instruments.v2 开始运行..

[2019-03-09 16:06:02.229560] INFO: bigquant: 命中缓存

[2019-03-09 16:06:02.231405] INFO: bigquant: instruments.v2 运行完成[0.008732s].

[2019-03-09 16:06:02.236410] INFO: bigquant: cached.v3 开始运行..

[2019-03-09 16:06:02.241849] INFO: bigquant: 命中缓存

[2019-03-09 16:06:02.243259] INFO: bigquant: cached.v3 运行完成[0.006846s].

[2019-03-09 16:06:02.246818] INFO: bigquant: cached.v3 开始运行..

[2019-03-09 16:06:02.477100] INFO: bigquant: cached.v3 运行完成[0.23027s].

[2019-03-09 16:06:02.480137] INFO: bigquant: input_features.v1 开始运行..

[2019-03-09 16:06:02.484523] INFO: bigquant: 命中缓存

[2019-03-09 16:06:02.485871] INFO: bigquant: input_features.v1 运行完成[0.005728s].

[2019-03-09 16:06:02.488940] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-03-09 16:06:02.493502] INFO: bigquant: 命中缓存

[2019-03-09 16:06:02.494877] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.005952s].

[2019-03-09 16:06:02.497337] INFO: bigquant: dropnan.v1 开始运行..

[2019-03-09 16:06:02.501807] INFO: bigquant: 命中缓存

[2019-03-09 16:06:02.503071] INFO: bigquant: dropnan.v1 运行完成[0.005721s].

[2019-03-09 16:06:02.515925] INFO: bigquant: backtest.v8 开始运行..

[2019-03-09 16:06:02.519219] INFO: bigquant: biglearning backtest:V8.1.11

[2019-03-09 16:06:02.549881] INFO: bigquant: product_type:future by specified

[2019-03-09 16:06:03.085132] INFO: bigquant: 读取期货行情完成:147

[2019-03-09 16:06:03.093608] INFO: algo: TradingAlgorithm V1.4.7

[2019-03-09 16:06:03.172198] INFO: algo: trading transform...

[2019-03-09 16:06:03.567635] INFO: Performance: Simulated 120 trading days out of 120.

[2019-03-09 16:06:03.570212] INFO: Performance: first open: 2017-10-31 21:00:00+00:00

[2019-03-09 16:06:03.571665] INFO: Performance: last close: 2018-04-27 15:00:00+00:00

[2019-03-09 16:06:04.163973] INFO: bigquant: backtest.v8 运行完成[1.648038s].

2017-11-02 平空开多
2017-11-06 平空开多
2017-11-07 平空开多
2017-11-13 平空开多
2017-12-04 平空开多
2018-01-23 平多开空
2018-01-24 平多开空
2018-01-30 平多开空
2018-01-31 平多开空
2018-02-01 平多开空
2018-02-06 平多开空
2018-03-20 平多开空
2018-03-21 平多开空
2018-03-22 平多开空
2018-03-23 平多开空
2018-03-26 平多开空
2018-03-27 平多开空