用向导生成了策略,用了两因子, 为啥会 “模型训练失败” ?

策略分享
标签: #<Tag:0x00007f5b90609ad0>

(kkshsh) #1
克隆策略

    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    In [3]:
    # 本代码由可视化策略环境自动生成 2019年9月9日 10:38
    # 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。
    
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 {{web_host_url}}docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <{{web_host_url}}docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""ta_dma(close_0, 'golden_cross')
    ta_dma(close_0, 'long')"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    

    StockRanker训练(stock_ranker_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-3-ddb69e1c5b3e> in <module>()
        143     max_bins=1023,
        144     feature_fraction=1,
    --> 145     m_lazy_run=False
        146 )
        147 
    
    Exception: 模型训练失败:可能导致错误的原因是训练数据问题,请检查训练数据, err_code=1 (51dc919ad2ab11e9a16e0a580a8101b4)

    (iQuant) #2

    您好,收到您的提问,已提交至策略工程师,会尽快给您回复。


    (小Q) #3

    您好,你的两个因子都是布尔型变量,非0即1,在训练模型的时候,训练失败。


    建议使用更多的因子,因子取值更为丰富的因子。

    因子值非0即1 ,树模型在根据特征拟合模型的时候,无法找出模型,因此报错。


    (kkshsh) #4

    这肯定是0或1啊 , 如果我增加一个其他因子也不行啊。 修改成1 或 2 行吗?


    (kkshsh) #5

    这难道不是封装的问题? 这并不影响决策树判断啊。


    (kkshsh) #6

    最开始报错的时候不只有这两个因子,一共有几十个因子吧 , 是因为报错后我找到发现是这些因子的问题。


    (xgl891) #7

    您好,我们的模块并不是只是简单的封装了决策树,因此使用有一定的区别。如果您使用了多个因子也报错,您可以把使用了多个因子的策略也分享上来,我们再复现定位一下。


    (kkshsh) #8
    克隆策略

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dma(X,A):\n result=pd.DataFrame(np.zeros((len(X),X.columns.size)),index=list(X.index),columns=X.columns)\n result.iloc[0]=X.iloc[0]*A.iloc[0]\n for i in range(1,len(X)):\n result.iloc[i]=A.iloc[i]*X.iloc[i]+(1-A.iloc[i])*result.iloc[i-1] \n return result\ndef cal_cyc(df,N):\n hsl=pd.pivot_table(df,values='turn_0',index=['date'],columns=['instrument'])/100\n if N>0: \n AN=dma(N*hsl/(1+(N-1)*hsl),(1+(N-1)*hsl)/N)\n else:\n AN=hsl\n mclose=pd.pivot_table(df,values='close_0',index=['date'],columns=['instrument'])\n mopen=pd.pivot_table(df,values='open_0',index=['date'],columns=['instrument'])\n mid=(mclose+mopen)/2\n AN_1=AN.shift(1)\n AN_1.iloc[0]=0\n if N>0:\n CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)*4/5),1-(N-1)*AN_1*(1-hsl)/N/AN)\n else:\n CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)),1-AN_1*(1-hsl)/AN)\n cycn=CYCN.T.unstack().reset_index()\n cycn.columns=['date','instrument','cyc'+str(N)]\n df1=df.merge(cycn,on=['date','instrument']).copy()\n df1['close_0']=df1['cyc'+str(N)]\n return df1['close_0']\n# 按股票代码groupby计算个股超额收益率数据\ndef cyc(df,close_0,open_0,turn_0):\n return cal_cyc(df,5)\n\n\nbigquant_run = {\n 'cyc': cyc\n}\n\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1614"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1614"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1614","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1623","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-1623"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-1623"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1623","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1630","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":"def dma(X,A):\n result=pd.DataFrame(np.zeros((len(X),X.columns.size)),index=list(X.index),columns=X.columns)\n result.iloc[0]=X.iloc[0]*A.iloc[0]\n for i in range(1,len(X)):\n result.iloc[i]=A.iloc[i]*X.iloc[i]+(1-A.iloc[i])*result.iloc[i-1] \n return result\ndef cal_cyc(df,N):\n hsl=pd.pivot_table(df,values='turn_0',index=['date'],columns=['instrument'])/100\n if N>0: \n AN=dma(N*hsl/(1+(N-1)*hsl),(1+(N-1)*hsl)/N)\n else:\n AN=hsl\n mclose=pd.pivot_table(df,values='close_0',index=['date'],columns=['instrument'])\n mopen=pd.pivot_table(df,values='open_0',index=['date'],columns=['instrument'])\n mid=(mclose+mopen)/2\n AN_1=AN.shift(1)\n AN_1.iloc[0]=0\n if N>0:\n CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)*4/5),1-(N-1)*AN_1*(1-hsl)/N/AN)\n else:\n CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)),1-AN_1*(1-hsl)/AN)\n cycn=CYCN.T.unstack().reset_index()\n cycn.columns=['date','instrument','cyc'+str(N)]\n df1=df.merge(cycn,on=['date','instrument']).copy()\n df1['close_0']=df1['cyc'+str(N)]\n return df1['close_0']\n# 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系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n stock_count = 5\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\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 # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n 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      In [1]:
      # 本代码由可视化策略环境自动生成 2019年9月11日 10:10
      # 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。
      
      
      def dma(X,A):
          result=pd.DataFrame(np.zeros((len(X),X.columns.size)),index=list(X.index),columns=X.columns)
          result.iloc[0]=X.iloc[0]*A.iloc[0]
          for i in range(1,len(X)):
              result.iloc[i]=A.iloc[i]*X.iloc[i]+(1-A.iloc[i])*result.iloc[i-1]        
          return result
      def cal_cyc(df,N):
          hsl=pd.pivot_table(df,values='turn_0',index=['date'],columns=['instrument'])/100
          if N>0:    
              AN=dma(N*hsl/(1+(N-1)*hsl),(1+(N-1)*hsl)/N)
          else:
              AN=hsl
          mclose=pd.pivot_table(df,values='close_0',index=['date'],columns=['instrument'])
          mopen=pd.pivot_table(df,values='open_0',index=['date'],columns=['instrument'])
          mid=(mclose+mopen)/2
          AN_1=AN.shift(1)
          AN_1.iloc[0]=0
          if N>0:
              CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)*4/5),1-(N-1)*AN_1*(1-hsl)/N/AN)
          else:
              CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)),1-AN_1*(1-hsl)/AN)
          cycn=CYCN.T.unstack().reset_index()
          cycn.columns=['date','instrument','cyc'+str(N)]
          df1=df.merge(cycn,on=['date','instrument']).copy()
          df1['close_0']=df1['cyc'+str(N)]
          return df1['close_0']
      # 按股票代码groupby计算个股超额收益率数据
      def cyc(df,close_0,open_0,turn_0):
          return cal_cyc(df,5)
      
      
      m16_user_functions_bigquant_run = {
          'cyc':  cyc
      }
      
      
      def dma(X,A):
          result=pd.DataFrame(np.zeros((len(X),X.columns.size)),index=list(X.index),columns=X.columns)
          result.iloc[0]=X.iloc[0]*A.iloc[0]
          for i in range(1,len(X)):
              result.iloc[i]=A.iloc[i]*X.iloc[i]+(1-A.iloc[i])*result.iloc[i-1]        
          return result
      def cal_cyc(df,N):
          hsl=pd.pivot_table(df,values='turn_0',index=['date'],columns=['instrument'])/100
          if N>0:    
              AN=dma(N*hsl/(1+(N-1)*hsl),(1+(N-1)*hsl)/N)
          else:
              AN=hsl
          mclose=pd.pivot_table(df,values='close_0',index=['date'],columns=['instrument'])
          mopen=pd.pivot_table(df,values='open_0',index=['date'],columns=['instrument'])
          mid=(mclose+mopen)/2
          AN_1=AN.shift(1)
          AN_1.iloc[0]=0
          if N>0:
              CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)*4/5),1-(N-1)*AN_1*(1-hsl)/N/AN)
          else:
              CYCN=dma(mid*hsl/(AN-AN_1*(1-hsl)),1-AN_1*(1-hsl)/AN)
          cycn=CYCN.T.unstack().reset_index()
          cycn.columns=['date','instrument','cyc'+str(N)]
          df1=df.merge(cycn,on=['date','instrument']).copy()
          df1['close_0']=df1['cyc'+str(N)]
          return df1['close_0']
      # 按股票代码groupby计算个股超额收益率数据
      def cyc(df,close_0,open_0,turn_0):
          return cal_cyc(df,5)
      
      
      m18_user_functions_bigquant_run = {
          'cyc':  cyc
      }
      
      
      # 回测引擎:初始化函数,只执行一次
      def m19_initialize_bigquant_run(context):
          # 加载预测数据
          context.ranker_prediction = context.options['data'].read_df()
      
          # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
          context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
          # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
          # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
          stock_count = 5
          # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
          context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
          # 设置每只股票占用的最大资金比例
          context.max_cash_per_instrument = 0.2
          context.options['hold_days'] = 5
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m19_handle_data_bigquant_run(context, data):
          # 按日期过滤得到今日的预测数据
          ranker_prediction = context.ranker_prediction[
              context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
      
          # 1. 资金分配
          # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
          # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
          is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
          cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
          cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
          positions = {e.symbol: p.amount * p.last_sale_price
                       for e, p in context.perf_tracker.position_tracker.positions.items()}
      
          # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
          if not is_staging and cash_for_sell > 0:
              equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
              # print('rank order for sell %s' % instruments)
              for instrument in instruments:
                  context.order_target(context.symbol(instrument), 0)
                  cash_for_sell -= positions[instrument]
                  if cash_for_sell <= 0:
                      break
      
          # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
          buy_cash_weights = context.stock_weights
          buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
          max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
          for i, instrument in enumerate(buy_instruments):
              cash = cash_for_buy * buy_cash_weights[i]
              if cash > max_cash_per_instrument - positions.get(instrument, 0):
                  # 确保股票持仓量不会超过每次股票最大的占用资金量
                  cash = max_cash_per_instrument - positions.get(instrument, 0)
              if cash > 0:
                  context.order_value(context.symbol(instrument), cash)
      
      # 回测引擎:准备数据,只执行一次
      def m19_prepare_bigquant_run(context):
          pass
      
      
      m1 = M.instruments.v2(
          start_date='2010-01-01',
          end_date='2015-01-01',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m2 = M.advanced_auto_labeler.v2(
          instruments=m1.data,
          label_expr="""# #号开始的表示注释
      # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
      # 1. 可用数据字段见 {{web_host_url}}docs/data_history_data.html
      #   添加benchmark_前缀,可使用对应的benchmark数据
      # 2. 可用操作符和函数见 `表达式引擎 <{{web_host_url}}docs/big_expr.html>`_
      
      # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
      shift(close, -5) / shift(open, -1)
      
      # 极值处理:用1%和99%分位的值做clip
      clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
      
      # 将分数映射到分类,这里使用20个分类
      all_wbins(label, 20)
      
      # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
      where(shift(high, -1) == shift(low, -1), NaN, label)
      """,
          start_date='',
          end_date='',
          benchmark='000300.SHA',
          drop_na_label=True,
          cast_label_int=True
      )
      
      m3 = M.input_features.v1(
          features="""ks_ta_dma=where(ta_dma(close_0,'death_cross'),1,where(ta_dma(close_0,'short'),2,where(ta_dma(close_0,'long'),3,where(ta_dma(close_0,'golden_cross'),4,0))))
      ks_ta_ma=where(ta_ma(close_0,derive='death_cross'),1,where(ta_ma(close_0,derive='short'),2,where(ta_ma(close_0,derive='long'),3,where(ta_ma(close_0,derive='golden_cross'),4,0))))
      cyc(close_0,open_0,turn_0)
      
      return_0
      return_1
      return_2
      return_3
      return_4
      return_5
      turn_0
      turn_1
      turn_2
      turn_3
      turn_4
      turn_5"""
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      m16 = M.derived_feature_extractor.v3(
          input_data=m15.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions=m16_user_functions_bigquant_run
      )
      
      m7 = M.join.v3(
          data1=m2.data,
          data2=m16.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m13 = M.dropnan.v1(
          input_data=m7.data
      )
      
      m6 = M.stock_ranker_train.v5(
          training_ds=m13.data,
          features=m3.data,
          learning_algorithm='排序',
          number_of_leaves=1,
          minimum_docs_per_leaf=1,
          number_of_trees=2,
          learning_rate=0.1,
          max_bins=1,
          feature_fraction=1,
          m_lazy_run=False
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2015-01-01'),
          end_date=T.live_run_param('trading_date', '2017-01-01'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m17 = M.general_feature_extractor.v7(
          instruments=m9.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      m18 = M.derived_feature_extractor.v3(
          input_data=m17.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions=m18_user_functions_bigquant_run
      )
      
      m14 = M.dropnan.v1(
          input_data=m18.data
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m6.model,
          data=m14.data,
          m_lazy_run=False
      )
      
      m19 = M.trade.v4(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date='',
          end_date='',
          initialize=m19_initialize_bigquant_run,
          handle_data=m19_handle_data_bigquant_run,
          prepare=m19_prepare_bigquant_run,
          volume_limit=0.025,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=1000000,
          auto_cancel_non_tradable_orders=True,
          data_frequency='daily',
          price_type='后复权',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark='000300.SHA'
      )
      

      StockRanker训练(stock_ranker_train)使用错误,你可以:

      1.一键查看文档

      2.一键搜索答案

      ---------------------------------------------------------------------------
      Exception                                 Traceback (most recent call last)
      <ipython-input-1-49085182e81c> in <module>()
          230     max_bins=1,
          231     feature_fraction=1,
      --> 232     m_lazy_run=False
          233 )
          234 
      
      Exception: 模型训练失败:可能导致错误的原因是训练数据问题,请检查训练数据, err_code=1 (8af5eedcd43511e9a5ae0a580a810280)
      In [2]:
      m13.data.read().tail(15)
      
      Out[2]:
      close_0 date instrument open_0 return_0 return_1 return_2 return_3 return_4 return_5 ... turn_5 ks_ta_dma ks_ta_ma cyc(close_0,open_0,turn_0) m:low m:high m:amount m:close m:open label
      489784 24.392860 2011-01-28 601088.SHA 24.466080 0.994881 1.009087 1.017452 1.017452 1.005172 1.007343 ... 0.115515 2 2 24.574613 24.246420 24.560221 2.926906e+08 24.392860 24.466080 10
      489785 24.832182 2011-01-31 601088.SHA 24.455620 1.018010 1.012799 1.027261 1.035777 1.035777 1.023276 ... 0.084989 2 2 24.587700 24.403320 24.863564 3.368724e+08 24.832182 24.455620 11
      489786 24.905403 2011-02-01 601088.SHA 24.999544 1.002949 1.021012 1.015785 1.030290 1.038831 1.038831 ... 0.084726 2 3 24.644519 24.685741 25.104143 2.662818e+08 24.905403 24.999544 11
      489787 24.539301 2011-02-09 601088.SHA 24.915863 0.985300 0.988206 1.006003 1.000853 1.015145 1.023560 ... 0.060372 2 3 24.661622 24.445160 24.968163 3.517532e+08 24.539301 24.915863 13
      489788 24.947243 2011-02-10 601088.SHA 24.466080 1.016624 1.001680 1.004634 1.022727 1.017491 1.032021 ... 0.113502 2 3 24.669536 24.466080 25.051844 2.904754e+08 24.947243 24.466080 12
      489789 25.030924 2011-02-11 601088.SHA 24.947243 1.003354 1.020034 1.005040 1.008003 1.026158 1.020904 ... 0.077063 2 3 24.737435 24.863564 25.166904 3.615336e+08 25.030924 24.947243 10
      489790 26.035089 2011-02-14 601088.SHA 25.093683 1.040117 1.043606 1.060955 1.045359 1.048442 1.067324 ... 0.087716 2 3 25.045357 25.083223 26.150150 8.324524e+08 26.035089 25.093683 8
      489791 26.014170 2011-02-15 601088.SHA 26.097849 0.999196 1.039281 1.042767 1.060102 1.044519 1.047599 ... 0.068500 2 3 25.349742 25.951408 26.558092 7.797372e+08 26.014170 26.097849 8
      489792 26.338430 2011-02-16 601088.SHA 25.982790 1.012465 1.011651 1.052236 1.055765 1.073316 1.057539 ... 0.091140 3 3 25.558913 25.679447 26.359352 7.163435e+08 26.338430 25.982790 8
      489793 26.338430 2011-02-17 601088.SHA 26.401192 1.000000 1.012465 1.011651 1.052236 1.055765 1.073316 ... 0.075420 3 3 25.734523 26.024630 26.484873 6.157569e+08 26.338430 26.401192 10
      489794 25.867729 2011-02-18 601088.SHA 26.296591 0.982129 0.982129 0.994371 0.993572 1.033431 1.036897 ... 0.092571 3 3 25.790816 25.836348 26.307051 4.351444e+08 25.867729 26.296591 11
      489795 26.223370 2011-02-21 601088.SHA 25.794508 1.013748 0.995631 0.995631 1.008042 1.007232 1.047639 ... 0.206344 3 3 25.826073 25.731747 26.307051 4.157807e+08 26.223370 25.794508 9
      489796 26.035089 2011-02-22 601088.SHA 26.547632 0.992820 1.006470 0.988483 0.988483 1.000804 1.000000 ... 0.190696 3 3 25.977362 25.993250 26.934654 1.000465e+09 26.035089 26.547632 11
      489797 26.432571 2011-02-23 601088.SHA 25.993250 1.015267 1.007978 1.021836 1.003574 1.003574 1.016084 ... 0.175781 3 3 26.029405 25.784048 26.516253 6.927648e+08 26.432571 25.993250 8
      489798 26.976494 2011-02-24 601088.SHA 26.505793 1.020578 1.036159 1.028720 1.042863 1.024226 1.024226 ... 0.150465 3 3 26.231316 26.505793 27.353056 1.019045e+09 26.976494 26.505793 8

      15 rows × 25 columns


      (kkshsh) #9

      这个是我随便加了几个因子


      (kkshsh) #10

      不能训练 可能的原因是树比较少? 那这个需要符合什么条件呢?


      (kkshsh) #11

      https://i.bigquant.com/user/kkshsh/lab/share/AI选股策略4.ipynb?_t=1568167994836

      前两个因子已经不再是0 1 而是多个值了 ,但是依然不会进入决策树判断 。 是因为这个因子无法拟合 完全无效??