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 (ac749eb8990311e9a6280a580a810486)

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    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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 cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n\n # 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\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 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    In [11]:
    # 本代码由可视化策略环境自动生成 2019年6月28日 01:51
    # 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.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='2014-01-01',
        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, -60) / 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="""# 多个特征,每行一个,可以包含基础特征和衍生特征
    #ROE
    fs_roe_0
    #dROE
    (fs_roe_0-shift(fs_roe_0,365))/shift(fs_roe_0,365)
    #CRS
    fs_net_cash_flow_ttm_0/fs_operating_revenue_0
    #dCFO(每股经营活动现金流同比增长率)
    (fs_net_cash_flow_0-shift(fs_net_cash_flow_0,365))/shift(fs_net_cash_flow_0,365)
    #dLEVER(权益比率同比增长率)
    (fs_common_equity_0/(fs_total_equity_0+fs_total_liability_0)-shift(fs_common_equity_0,365)/(shift(fs_total_equity_0,365)+shift(fs_total_liability_0,365)))/(shift(fs_common_equity_0,365)/(shift(fs_total_equity_0,365)+shift(fs_total_liability_0,365)))
    #dLIQUID(流动比率同比增长率)
    (fs_current_assets_0/fs_current_liabilities_0-shift(fs_current_assets_0,365)/shift(fs_current_liabilities_0,365))/(shift(fs_current_assets_0,365)/shift(fs_current_liabilities_0,365))
    # dMARGIN(销售毛利率同比增长率)、
    
    # fs_gross_profit_margin_yoy_0
    (fs_gross_profit_margin_0-shift(fs_gross_profit_margin_0,365))/shift(fs_gross_profit_margin_0,365)
    
    #dTURN(固定资产周转率同比增长率)
    ((fs_total_operating_costs_0- fs_selling_expenses_0)  /((fs_fixed_assets_0+shift(fs_fixed_assets_0,365))*0.5)-(shift(fs_total_operating_costs_0,365)- shift(fs_selling_expenses_0,365))  /((shift(fs_fixed_assets_0,365)+shift(fs_fixed_assets_0,730))*0.5))/(shift(fs_total_operating_costs_0,365)- shift(fs_selling_expenses_0,365))  /((shift(fs_fixed_assets_0,365)+shift(fs_fixed_assets_0,730))*0.5)""",
        m_cached=False
    )
    
    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,
        m_cached=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='2014-01-01',
        end_date='2019-01-01',
        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'
    )
    
    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-11-69100c5f2a5e> in <module>()
        162     max_bins=1023,
        163     feature_fraction=1,
    --> 164     m_lazy_run=False
        165 )
        166 
    
    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 (ac749eb8990311e9a6280a580a810486)

    (iQuant) #2

    收到您的提问,已提交给策略工程师,会尽快为您回复。


    (leegang) #3

    感谢感谢~


    (leegang) #4

    大概还需要多久可以回复呀?


    (达达) #5

    您好,您这个报错有几个原因

    1. 表达式过长导致训练不识别因子,用别名处理过长的表达式。

    2. 基础特征抽取的数据有个向前天数参数,您没修改,当您使用shift(xxx,365)这类因子的时候就无法获取训练集对应的因子计算值,因为这个因子的计算依赖于365个交易日前的数据,如果您不通过基础特征抽取模块向前抽取过去365个交易日的数据,是无法计算出来训练集起始日期当天shift(xxx,365)的因子值的。所以要设置向前天数参数,这里指的是自然天数,可以多取点比如填入500,这样就能覆盖到365天前的数据了。

    3. 训练集的起始日期应该和测试集的没有交集,您是用历史的数据训练一个选股模型,用来训练模型的数据不应该包括测试集的数据以避免模型提前知晓未来的数据。

    4. 验证级分支的基础特征抽取模块也要填写向前天数。

    给出您这个问题的策略修正后的结果,可以研究一下。

    克隆策略

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      In [6]:
      # 本代码由可视化策略环境自动生成 2019年6月29日 11:44
      # 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.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='2014-01-01',
          end_date='2017-01-01',
          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, -60) / 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="""# 多个特征,每行一个,可以包含基础特征和衍生特征
      #ROE
      f1=fs_roe_0
      #dROE
      f2=(fs_roe_0-shift(fs_roe_0,365))/shift(fs_roe_0,365)
      #CRS
      f3=fs_net_cash_flow_ttm_0/fs_operating_revenue_0
      #dCFO(每股经营活动现金流同比增长率)
      f4=(fs_net_cash_flow_0-shift(fs_net_cash_flow_0,365))/shift(fs_net_cash_flow_0,365)
      #dLEVER(权益比率同比增长率)
      f5=(fs_common_equity_0/(fs_total_equity_0+fs_total_liability_0)-shift(fs_common_equity_0,365)/(shift(fs_total_equity_0,365)+shift(fs_total_liability_0,365)))/(shift(fs_common_equity_0,365)/(shift(fs_total_equity_0,365)+shift(fs_total_liability_0,365)))
      #dLIQUID(流动比率同比增长率)
      f6=(fs_current_assets_0/fs_current_liabilities_0-shift(fs_current_assets_0,365)/shift(fs_current_liabilities_0,365))/(shift(fs_current_assets_0,365)/shift(fs_current_liabilities_0,365))
      # dMARGIN(销售毛利率同比增长率)、
      
      # fs_gross_profit_margin_yoy_0
      f7=(fs_gross_profit_margin_0-shift(fs_gross_profit_margin_0,365))/shift(fs_gross_profit_margin_0,365)
      
      #dTURN(固定资产周转率同比增长率)
      f8=((fs_total_operating_costs_0- fs_selling_expenses_0)  /((fs_fixed_assets_0+shift(fs_fixed_assets_0,365))*0.5)-(shift(fs_total_operating_costs_0,365)- shift(fs_selling_expenses_0,365))  /((shift(fs_fixed_assets_0,365)+shift(fs_fixed_assets_0,730))*0.5))/(shift(fs_total_operating_costs_0,365)- shift(fs_selling_expenses_0,365))  /((shift(fs_fixed_assets_0,365)+shift(fs_fixed_assets_0,730))*0.5)""",
          m_cached=False
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=500
      )
      
      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
      )
      
      m4 = M.features_short.v1(
          input_1=m3.data
      )
      
      m6 = M.stock_ranker_train.v5(
          training_ds=m13.data,
          features=m4.data_1,
          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', '2017-01-01'),
          end_date=T.live_run_param('trading_date', '2019-06-01'),
          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=500
      )
      
      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,
          m_cached=False
      )
      
      m19 = M.trade.v4(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date='2014-01-01',
          end_date='2019-01-01',
          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'
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c4416ccac7cd4a0ab03135d64053c8d9"}/bigcharts-data-end
      • 收益率2.39%
      • 年化收益率0.49%
      • 基准收益率29.21%
      • 阿尔法-0.03
      • 贝塔0.05
      • 夏普比率-0.37
      • 胜率0.56
      • 盈亏比0.83
      • 收益波动率6.09%
      • 信息比率-0.02
      • 最大回撤18.23%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a3392590a70c497bb0f0b2ada832f0c8"}/bigcharts-data-end

      (leegang) #6

      好的,多谢。
      对这个平台还不是很熟悉,不好意思~