如何在特征里把另一个特征值连续加


(nat_fit) #1

想实现如下功能:
特征A:判断5日均线>10日均线,记1,否则计-1
特征B:sum(‘A’,10) 记录10天内5日大于10日的天数

如果a用where(ta_sma_5_0>=ta_sma_10_0,1,-1) ,则B无法sum;
sum(int(‘A’),10), invalid function: int 转换也不让用

请问该如何实现这个特征呢?

多谢!


(iQuant) #2

收到您的提问,已提交给策略工程师,会尽快给您回复。


(达达) #3
克隆策略

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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29日 10:26
    # 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。
    
    
    m3 = 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
    conditionA = where(ta_sma_5_0>=ta_sma_10_0,1,-1)"""
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2019-09-01'),
        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
    )
    
    m4 = M.input_features.v1(
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    sum(conditionA,10)"""
    )
    
    m1 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    In [4]:
    m1.data.read_df().tail()
    
    Out[4]:
    date instrument ta_sma_10_0 ta_sma_5_0 conditionA sum(conditionA,10)
    585705 2019-08-30 688122.SHA 49.426998 47.202000 -1 -6.0
    585706 2019-08-30 688188.SHA 168.175003 157.326004 -1 -10.0
    585707 2019-08-30 688321.SHA 82.304001 77.720001 -1 -10.0
    585708 2019-08-30 688333.SHA 77.303001 74.676003 -1 -10.0
    585709 2019-08-30 688388.SHA 60.132999 57.961998 -1 -6.0

    (zhudan) #4

    sum (where (ta_sma_5_0>ta_sma_10_0, 1, -1), 5)
    可以吗


    (nat_fit) #5

    出错啊,max()我程序里应当是没用啊,不知道哪里出的错!https://i.bigquant.com/user/nat_fit/lab/share/%E6%B5%8B%E8%AF%95sum.ipynb


    (nat_fit) #6

    试过,会出错


    (nat_fit) #7

    到训练以前都是对的


    (达达) #8

    重新分享一下呢


    (nat_fit) #9

    请问怎么把策略内容分享上来呢?
    这个分享不对吗?https://i.bigquant.com/user/nat_fit/lab/share/%E6%B5%8B%E8%AF%95sum.ipynb


    (nat_fit) #10

    我感觉还是"conditionA=XXXX"这个认不到


    (达达) #11

    把策略克隆一个重新保存然后您把链接重新粘过来,链接单独另起一行


    (达达) #12

    condationA就是你定义的条件别名为conditionA


    (nat_fit) #13

    用这个吗?

    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30日 17:27
      # 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 = 2
          # 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([0.8,0.2])#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 = 0.98
          context.options['hold_days'] =1
      
      # 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.portfolio.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.portfolio.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities)])))
      
              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='2016-01-01',
          end_date='2019-01-01',
          market='CN_STOCK_A',
          instrument_list='600999.SHA',
          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 https://bigquant.com/docs/develop/datasource/deprecated/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 <https://bigquant.com/docs/develop/bigexpr/usage.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='conditionA=where(ta_sma_5_0>=ta_sma_10_0,1,-1)'
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=30
      )
      
      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
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2019-01-01'),
          end_date=T.live_run_param('trading_date', '2019-09-27'),
          market='CN_STOCK_A',
          instrument_list='600999.SHA',
          max_count=0
      )
      
      m17 = M.general_feature_extractor.v7(
          instruments=m9.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=30
      )
      
      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
      )
      
      m10 = M.input_features.v1(
          features="""
      # #号开始的表示注释,注释需单独一行
      # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
      sum(conditionA,8)"""
      )
      
      m4 = M.derived_feature_extractor.v3(
          input_data=m16.data,
          features=m10.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions={}
      )
      
      m7 = M.join.v3(
          data1=m2.data,
          data2=m4.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
      )
      
      m11 = M.input_features.v1(
          features="""
      # #号开始的表示注释,注释需单独一行
      # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
      sum(conditionA,8)"""
      )
      
      m5 = M.derived_feature_extractor.v3(
          input_data=m18.data,
          features=m11.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions={}
      )
      
      m14 = M.dropnan.v1(
          input_data=m5.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=100000,
          auto_cancel_non_tradable_orders=True,
          data_frequency='daily',
          price_type='真实价格',
          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

      ---------------------------------------------------------------------------
      ValueError                                Traceback (most recent call last)
      <ipython-input-3-e6b8230d1890> in <module>()
          184     max_bins=1023,
          185     feature_fraction=1,
      --> 186     m_lazy_run=False
          187 )
          188 
      
      ValueError: max() arg is an empty sequence

      (达达) #14

      问题在于:

      1. 股票池只有1个股票,你可以查看证券代码列表,默认为空表示全市场。这是个选股模型,用的是排序算法。只有一个票怎么排序?没意义的。

      2. 输入给训练模块和预测模块可以简化用v2的版本,然后传递的参数是因子数据包括训练集、预测集以及指定训练的因子列名,如果你用conditionA=xxx别名处理后,因子的列名就是conditionA,这时候你传一个conditionA=xxx的字符串过去给训练器是找不到叫conditionA=xxx这个列的,你可以自己右键查看一下传入训练之前模块的结果数据,列名只有conditionA的。

      最后修改的策略如下:

      克隆策略

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        In [26]:
        # 本代码由可视化策略环境自动生成 2019年9月30日 17:37
        # 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。
        
        
        # 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 = 2
            # 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([0.8,0.2])#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 = 0.98
            context.options['hold_days'] =1
        
        # 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.portfolio.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.portfolio.positions.items()}
                instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                        lambda x: x in equities)])))
        
                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='2016-01-01',
            end_date='2019-01-01',
            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 https://bigquant.com/docs/develop/datasource/deprecated/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 <https://bigquant.com/docs/develop/bigexpr/usage.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='conditionA=where(ta_sma_5_0>=ta_sma_10_0,1,0)'
        )
        
        m15 = M.general_feature_extractor.v7(
            instruments=m1.data,
            features=m3.data,
            start_date='',
            end_date='',
            before_start_days=8
        )
        
        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
        )
        
        m9 = M.instruments.v2(
            start_date=T.live_run_param('trading_date', '2019-01-01'),
            end_date=T.live_run_param('trading_date', '2019-09-27'),
            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=8
        )
        
        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
        )
        
        m10 = M.input_features.v1(
            features="""
        # #号开始的表示注释,注释需单独一行
        # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
        sum(conditionA,8)"""
        )
        
        m4 = M.derived_feature_extractor.v3(
            input_data=m16.data,
            features=m10.data,
            date_col='date',
            instrument_col='instrument',
            drop_na=False,
            remove_extra_columns=False,
            user_functions={}
        )
        
        m7 = M.join.v3(
            data1=m2.data,
            data2=m4.data,
            on='date,instrument',
            how='inner',
            sort=True
        )
        
        m13 = M.dropnan.v1(
            input_data=m7.data
        )
        
        m5 = M.derived_feature_extractor.v3(
            input_data=m18.data,
            features=m10.data,
            date_col='date',
            instrument_col='instrument',
            drop_na=False,
            remove_extra_columns=False,
            user_functions={}
        )
        
        m14 = M.dropnan.v1(
            input_data=m5.data
        )
        
        m11 = M.input_features.v1(
            features_ds=m10.data,
            features="""
        # #号开始的表示注释,注释需单独一行
        # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
        conditionA
        """
        )
        
        m12 = M.stock_ranker.v2(
            training_ds=m13.data,
            features=m11.data,
            predict_ds=m14.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,
            slim_data=True
        )
        
        m19 = M.trade.v4(
            instruments=m9.data,
            options_data=m12.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=100000,
            auto_cancel_non_tradable_orders=True,
            data_frequency='daily',
            price_type='真实价格',
            product_type='股票',
            plot_charts=True,
            backtest_only=False,
            benchmark='000300.SHA'
        )
        
        设置测试数据集,查看训练迭代过程的NDCG
        bigcharts-data-start/{"__type":"tabs","__id":"bigchart-fd256e42ba5c46beaa995f7df9dd4cfd"}/bigcharts-data-end
        设置测试数据集,查看训练迭代过程的NDCG
        bigcharts-data-start/{"__type":"tabs","__id":"bigchart-cb0c6a5610ad43b8b100d932a48b8b43"}/bigcharts-data-end
        • 收益率7.03%
        • 年化收益率9.86%
        • 基准收益率27.97%
        • 阿尔法-0.16
        • 贝塔0.79
        • 夏普比率0.37
        • 胜率0.45
        • 盈亏比1.48
        • 收益波动率28.24%
        • 信息比率-0.06
        • 最大回撤22.44%
        bigcharts-data-start/{"__type":"tabs","__id":"bigchart-fef810ecf6c1491a83d96cbe42f4ae8e"}/bigcharts-data-end

        (nat_fit) #15

        终于出来了,谢谢!!!