{"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":"-1473:features","SourceOutputPortId":"-1468:data"},{"DestinationInputPortId":"-1632:input_data","SourceOutputPortId":"-1473:data"},{"DestinationInputPortId":"-1473:input_data","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-1442:benchmark_ds","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-1442:options_data","SourceOutputPortId":"-1632:data"}],"ModuleNodes":[{"Id":"-25","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-09-05 09:01:00","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2018-10-12 15:15:00","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"000001.SZA","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\n instrument_symbol = context.symbol(context.instruments[0]) # 交易标的\n curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量\n \n if context.index < 5: # 日内数据超过窗口范围后才开始交易\n context.index += 1\n return\n \n # 获取当前分钟的买卖信号\n today_date=data.current_dt.strftime('%Y-%m-%d %H:%M:%S')\n try:\n buy_condition = context.buy_condition[today_date]\n except:\n buy_condition = 0\n \n try: \n sell_condition=context.sell_condition[today_date]\n except:\n sell_condition = 0\n \n \n # 如果当前没有仓位，且大于通道价格上限，long开仓\n if curr_position == 0 and buy_condition>0:\n context.order_target_percent(instrument_symbol, 1)\n elif curr_position >= 0 and sell_condition>0:\n context.order_target(instrument_symbol, 0)","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 %H:%M:%S'))\n df.set_index('date',inplace=True)\n context.buy_condition=df['buy_condition']\n context.sell_condition=df['sell_condition']\n\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 context.set_commission(PerOrder(buy_cost=0.0013, sell_cost=0.0023, min_cost=5))\n # 当日分钟K线数量记录变量初始化\n context.index = 1\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":"50000","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":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","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(ta_rsi(close,14)>60,1,0)\nsell_condition=where(ta_rsi(close,14)<40,1,0)\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=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n ins=input_1.read_pickle()['instruments'][0]\n df = DataSource('bar1m_'+ins).read(start_date=start_date,end_date=end_date).set_index('date')\n df['adjust_factor']=1.0\n \n def resample(df,period):\n # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases\n Xmin_df=pd.DataFrame()\n Xmin_df['open'] = df['open'].resample(period, how='first')\n Xmin_df['high'] = df['high'].resample(period, how='max')\n Xmin_df['low'] = df['low'].resample(period, how='min')\n Xmin_df['close'] = df['close'].resample(period, how='last')\n Xmin_df['volume'] = df['volume'].resample(period, how='sum')\n Xmin_df['amount'] = df['amount'].resample(period, how='sum')\n Xmin_df.dropna(inplace=True)\n return Xmin_df\n df = df.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()\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':1}","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":false,"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}],"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='-55,0,200,200'/><NodePosition Node='-1442' Position='8,463,200,200'/><NodePosition Node='-1468' Position='551,32,200,200'/><NodePosition Node='-1473' Position='362,230,200,200'/><NodePosition Node='-1483' Position='95,131,200,200'/><NodePosition Node='-1632' Position='344,325,200,200'/></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}

# 本代码由可视化策略环境自动生成 2019年1月31日 09:37
# 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=input_1.read_pickle()['start_date']
    end_date=input_1.read_pickle()['end_date']
    ins=input_1.read_pickle()['instruments'][0]
    df = DataSource('bar1m_'+ins).read(start_date=start_date,end_date=end_date).set_index('date')
    df['adjust_factor']=1.0
    
    def resample(df,period):
        # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases
        Xmin_df=pd.DataFrame()
        Xmin_df['open'] = df['open'].resample(period, how='first')
        Xmin_df['high'] = df['high'].resample(period, how='max')
        Xmin_df['low'] = df['low'].resample(period, how='min')
        Xmin_df['close'] = df['close'].resample(period, how='last')
        Xmin_df['volume'] = df['volume'].resample(period, how='sum')
        Xmin_df['amount'] = df['amount'].resample(period, how='sum')
        Xmin_df.dropna(inplace=True)
        return Xmin_df
    df = df.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()
    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

# 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):

    instrument_symbol = context.symbol(context.instruments[0]) # 交易标的
    curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量
    
    if context.index < 5: # 日内数据超过窗口范围后才开始交易
        context.index += 1
        return
    
    # 获取当前分钟的买卖信号
    today_date=data.current_dt.strftime('%Y-%m-%d %H:%M:%S')
    try:
        buy_condition = context.buy_condition[today_date]
    except:
        buy_condition = 0
    
    try:    
        sell_condition=context.sell_condition[today_date]
    except:
        sell_condition = 0
        
    
    # 如果当前没有仓位，且大于通道价格上限，long开仓
    if curr_position == 0 and buy_condition>0:
        context.order_target_percent(instrument_symbol, 1)
    elif curr_position >= 0 and sell_condition>0:
        context.order_target(instrument_symbol, 0)
# 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 %H:%M:%S'))
    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)
    context.set_commission(PerOrder(buy_cost=0.0013, sell_cost=0.0023, min_cost=5))
    # 当日分钟K线数量记录变量初始化
    context.index = 1

# 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='2018-09-05 09:01:00',
    end_date='2018-10-12 15:15:00',
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    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\':1}',
    output_ports='',
    m_cached=False
)

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(ta_rsi(close,14)>60,1,0)
sell_condition=where(ta_rsi(close,14)<40,1,0)
"""
)

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=m4.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=50000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='minute',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2019-01-31 09:37:20.175571] INFO: bigquant: instruments.v2 开始运行..
[2019-01-31 09:37:20.500163] INFO: bigquant: instruments.v2 运行完成[0.324591s].
[2019-01-31 09:37:20.503361] INFO: bigquant: cached.v3 开始运行..
[2019-01-31 09:37:21.297367] INFO: bigquant: cached.v3 运行完成[0.793962s].
[2019-01-31 09:37:21.300902] INFO: bigquant: input_features.v1 开始运行..
[2019-01-31 09:37:21.305314] INFO: bigquant: 命中缓存
[2019-01-31 09:37:21.306386] INFO: bigquant: input_features.v1 运行完成[0.005528s].
[2019-01-31 09:37:21.309574] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-01-31 09:37:21.345523] INFO: derived_feature_extractor: 提取完成 buy_condition=where(ta_rsi(close,14)>60,1,0), 0.015s
[2019-01-31 09:37:21.352616] INFO: derived_feature_extractor: 提取完成 sell_condition=where(ta_rsi(close,14)<40,1,0), 0.006s
[2019-01-31 09:37:21.707737] INFO: derived_feature_extractor: /data, 1100
[2019-01-31 09:37:21.808617] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.499009s].
[2019-01-31 09:37:21.812441] INFO: bigquant: dropnan.v1 开始运行..
[2019-01-31 09:37:22.225501] INFO: dropnan: /data, 1100/1100
[2019-01-31 09:37:22.232168] INFO: dropnan: 行数: 1100/1100
[2019-01-31 09:37:22.234170] INFO: bigquant: dropnan.v1 运行完成[0.421729s].
[2019-01-31 09:37:22.296470] INFO: bigquant: backtest.v8 开始运行..
[2019-01-31 09:37:22.299467] INFO: bigquant: biglearning backtest:V8.1.7
[2019-01-31 09:37:22.331892] INFO: bigquant: product_type:stock by specified
[2019-01-31 09:37:23.526106] INFO: bigquant: 读取股票行情完成:20400
[2019-01-31 09:37:23.547291] INFO: algo: TradingAlgorithm V1.4.5
[2019-01-31 09:37:24.450746] INFO: algo: trading transform...
[2019-01-31 09:37:46.110957] INFO: Performance: Simulated 22 trading days out of 22.
[2019-01-31 09:37:46.112048] INFO: Performance: first open: 2018-09-05 09:30:00+00:00
[2019-01-31 09:37:46.112764] INFO: Performance: last close: 2018-10-12 15:00:00+00:00

[2019-01-31 09:37:49.261127] INFO: bigquant: backtest.v8 运行完成[26.964628s].