基准收益有点不太一样

策略分享
标签: #<Tag:0x00007fcc11ee8df8>

(laughing) #1
克隆策略

期货日线MACD策略

版本 v1.0

目录

  • ### MACD策略的交易规则

  • ### 策略构建步骤

  • ### 策略的实现

正文

一、MACD策略的交易规则

  • 相关指标定义如下:

    DIF=EMA(close,12)−EMA(close,26)

    DEM=EMA(DIF,9)

  • DIF从下而上穿过DEA,买入开仓;

  • DIF从上往下穿过DEA,卖出开仓;

二、策略构建步骤

1、确定期货合约和回测时间

  • 通过证券代码列表输入要回测的期货合约,以及回测的起止日期

2、确定买卖条件信号

  • 通过自定义Python模块m4获取合约基础数据,通过自定义Python模块m1获取DIF和DEA指标数据;
  • 在输入特征列表中通过表达式引擎定义 buy_condition=where((shift(DIF,1) > shift(DEA,1)) & (shift(DIF,2)<shift(DEA,2)),1,0),实现买入信号。
  • 在输入特征列表中通过表达式引擎定义 sell_condition=where((shift(DIF,1) < shift(DEA,1)) & (shift(DIF,2)>shift(DEA,2)),1,0),实现卖出信号。
  • 通过衍生特征抽取模块实现买卖条件指标 buy_condition 和 sell_condition 数据的抽取。
  • 通过缺失数据处理模块删去有缺失值的数据。

3、确定买卖原则

  • 如果当日 buy_condition > 0,执行平空开多操作;
  • 如果当日 sell_condition > 0,执行平多开空操作。

4、模拟回测

  • 通过 trade 模块中的初始化函数定义交易手续费、滑点、杠杆比例和是否逐日结算;
  • 通过 trade 模块中的准备函数定义 context.buy_condition 和 context.sell_condition 变量来获取并存放每日买卖交易信号;
  • 通过 trade 模块中的主函数(handle函数)查看每日的交易信号,按照买卖原则执行相应的交易操作。

三、策略的实现

可视化策略实现如下:

    {"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":"-327:input_1","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-1442:options_data","SourceOutputPortId":"-1632:data"},{"DestinationInputPortId":"-336:input_data","SourceOutputPortId":"-327:data_1"},{"DestinationInputPortId":"-1632:input_data","SourceOutputPortId":"-336:data"},{"DestinationInputPortId":"-336:features","SourceOutputPortId":"-342:data"}],"ModuleNodes":[{"Id":"-25","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-04-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-1-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_FUTURE","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"I1905.DCE","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 # 获取当日多头/空头信号数据\n today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期\n \n try:\n buy_condition = context.buy_condition[today]\n except:\n buy_condition = 0\n \n try:\n sell_condition = context.sell_condition[today]\n except:\n sell_condition = 0\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 # 交易逻辑\n if sell_condition>0: # 卖出开仓\n if curr_position >= 0 and data.can_trade(instrument):\n order_target(instrument, -20)\n print(today,'今日DIF下穿DEA,平多,卖空开仓')\n elif buy_condition>0:# 买入开仓\n if curr_position <= 0 and data.can_trade(instrument):\n order_target(instrument, 20)\n print(today,'今日DIF上穿DEA,平空,买多开仓')","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 # 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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":"-327","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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    In [12]:
    # 本代码由可视化策略环境自动生成 ‎2019‎年‎1月‎28‎日‎ ‎14‎:‎03
    # 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
        df = input_1.read_df()
        close = [float(x) for x in df['close']]
        import talib
        # 调用talib计算6日指数移动平均线的值
        df['EMA12'] = talib.EMA(np.array(close), timeperiod=6)  
        df['EMA26'] = talib.EMA(np.array(close), timeperiod=12)   
        # 调用talib计算MACD指标
        df['DIF'],df['DEA'],df['MACD'] = talib.MACD(np.array(close),
                                    fastperiod=6, slowperiod=12, signalperiod=9) 
        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 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):
        # 获取当日多头/空头信号数据
        today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期
        
        try:
            buy_condition = context.buy_condition[today]
        except:
            buy_condition = 0
        
        try:
            sell_condition = context.sell_condition[today]
        except:
            sell_condition = 0
        
        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  sell_condition>0: # 卖出开仓
            if curr_position >= 0 and data.can_trade(instrument):
                order_target(instrument, -20)
                print(today,'今日DIF下穿DEA,平多,卖空开仓')
        elif  buy_condition>0:# 买入开仓
            if curr_position <= 0 and data.can_trade(instrument):
                order_target(instrument, 20)
                print(today,'今日DIF上穿DEA,平空,买多开仓')
    # 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='2018-04-01',
        end_date='2019-1-01',
        market='CN_FUTURE',
        instrument_list='I1905.DCE',
        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=m4.data_1,
        run=m1_run_bigquant_run,
        post_run=m1_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = 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((shift(DIF,1)>shift(DEA,1))&(shift(DIF,2)<shift(DEA,2)),1,0)
    sell_condition=where((shift(DIF,1)<shift(DEA,1))&(shift(DIF,2)>shift(DEA,2)),1,0)"""
    )
    
    m5 = M.derived_feature_extractor.v3(
        input_data=m1.data_1,
        features=m6.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.dropnan.v1(
        input_data=m5.data
    )
    
    m3 = M.trade.v4(
        instruments=m2.data,
        options_data=m7.data,
        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=''
    )
    
    [2019-01-28 14:01:07.346931] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-28 14:01:07.368222] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.369960] INFO: bigquant: instruments.v2 运行完成[0.023037s].
    [2019-01-28 14:01:07.373714] INFO: bigquant: cached.v3 开始运行..
    [2019-01-28 14:01:07.382401] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.383833] INFO: bigquant: cached.v3 运行完成[0.010096s].
    [2019-01-28 14:01:07.392608] INFO: bigquant: cached.v3 开始运行..
    [2019-01-28 14:01:07.468303] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.469706] INFO: bigquant: cached.v3 运行完成[0.077063s].
    [2019-01-28 14:01:07.472428] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-28 14:01:07.477270] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.478310] INFO: bigquant: input_features.v1 运行完成[0.005884s].
    [2019-01-28 14:01:07.480954] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-28 14:01:07.485949] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.486861] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.00588s].
    [2019-01-28 14:01:07.491424] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-28 14:01:07.496031] INFO: bigquant: 命中缓存
    [2019-01-28 14:01:07.496938] INFO: bigquant: dropnan.v1 运行完成[0.005503s].
    [2019-01-28 14:01:07.513544] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-28 14:01:07.519580] INFO: bigquant: 命中缓存
    
    • 收益率-23.71%
    • 年化收益率-30.97%
    • 基准收益率-22.77%
    • 阿尔法-0.38
    • 贝塔-0.01
    • 夏普比率-1.95
    • 胜率0.35
    • 盈亏比0.45
    • 收益波动率19.52%
    • 信息比率-0.0
    • 最大回撤28.81%
    [2019-01-28 14:01:08.171716] INFO: bigquant: backtest.v8 运行完成[0.658153s].
    

    (iQuant) #2

    你的问题是:觉得基准收益率不对,对吧?
    这里的基准收益率如果不传入的话,应该是沪深300现货指数的收益率。


    (laughing) #3

    基准应该是该证券的实际走势吧,策略就是运用的策略所获得的收益。不然可比性就不强了。


    (laughing) #4

    不过好像还真是沪深300的


    (iQuant) #5

    基准您可以自己设置,指定任意的指数或者品种。
    如果用此合约作为基准,直接连一下就好了


    (laughing) #6

    好,谢谢~


    (iQuant) #7

    平台有期货各个品种的主力连续数据和指数数据,这俩都可以传入作为基准。


    (feynman0825) #8