请教如何在滚动训练中查看模型ID和因子得分

新手专区
标签: #<Tag:0x00007fcf664692a8>

(runningpig) #1

在回测时打印似乎没有办法应用于滚动测试。

克隆策略

    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    In [2]:
    # 本代码由可视化策略环境自动生成 2018年3月8日 11:34
    # 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
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2012-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m15 = M.rolling_conf.v1(
        start_date='2010-01-01',
        end_date=T.live_run_param('trading_date', '2015-12-31'),
        rolling_update_days=365,
        rolling_min_days=730,
        rolling_max_days=0,
        rolling_count_for_live=1
    )
    
    m1 = M.instruments.v2(
        rolling_conf=m15.data,
        start_date='',
        end_date='',
        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/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 <https://bigquant.com/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
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m5.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=True
    )
    
    m16 = M.rolling_run.v1(
        run=m6.m_lazy_run,
        input_list=m15.data,
        param_name='rolling_input'
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m16.data,
        data=m14.data,
        m_lazy_run=True
    )
    
    m17 = M.rolling_run_predict.v1(
        predict=m8.m_lazy_run,
        model_param_name='model',
        data_param_name='data'
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_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StockRanker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StockRanker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 m12_prepare_bigquant_run(context):
        print('ID:', m6.model_id)
        print('因子得分:', m6.feature_gains.read_df())
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m17.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-03-08 11:33:31.040549] INFO: bigquant: input_features.v1 开始运行..
    [2018-03-08 11:33:31.189799] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.191708] INFO: bigquant: input_features.v1 运行完成[0.151215s].
    [2018-03-08 11:33:31.199315] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-08 11:33:31.203235] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.204767] INFO: bigquant: instruments.v2 运行完成[0.005444s].
    [2018-03-08 11:33:31.374952] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-03-08 11:33:31.378954] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.380438] INFO: bigquant: general_feature_extractor.v6 运行完成[0.005525s].
    [2018-03-08 11:33:31.411535] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-03-08 11:33:31.415584] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.416892] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.005363s].
    [2018-03-08 11:33:31.427679] INFO: bigquant: dropnan.v1 开始运行..
    [2018-03-08 11:33:31.431823] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.433214] INFO: bigquant: dropnan.v1 运行完成[0.005576s].
    [2018-03-08 11:33:31.442073] INFO: 滚动运行配置: 生成了 5 次滚动,第一次 {'end_date': '2011-12-31', 'start_date': '2010-01-01'},最后一次 {'end_date': '2015-12-30', 'start_date': '2010-01-01'}
    [2018-03-08 11:33:31.463463] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-08 11:33:31.468091] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.469714] INFO: bigquant: instruments.v2 运行完成[0.006301s].
    [2018-03-08 11:33:31.480932] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-03-08 11:33:31.485773] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.487126] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.006151s].
    [2018-03-08 11:33:31.645144] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-03-08 11:33:31.649141] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.650764] INFO: bigquant: general_feature_extractor.v6 运行完成[0.005673s].
    [2018-03-08 11:33:31.658875] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-03-08 11:33:31.662610] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.663879] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.005016s].
    [2018-03-08 11:33:31.672609] INFO: bigquant: join.v3 开始运行..
    [2018-03-08 11:33:31.676698] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.678065] INFO: bigquant: join.v3 运行完成[0.005439s].
    [2018-03-08 11:33:31.687089] INFO: bigquant: dropnan.v1 开始运行..
    [2018-03-08 11:33:31.690748] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.691828] INFO: bigquant: dropnan.v1 运行完成[0.004727s].
    [2018-03-08 11:33:31.705382] INFO: bigquant: 延迟运行 stock_ranker_train.v5
    [2018-03-08 11:33:31.713548] INFO: bigquant: rolling_run.v1 开始运行..
    [2018-03-08 11:33:31.717141] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.718528] INFO: bigquant: rolling_run.v1 运行完成[0.004965s].
    [2018-03-08 11:33:31.733178] INFO: bigquant: 延迟运行 stock_ranker_predict.v5
    [2018-03-08 11:33:31.742333] INFO: bigquant: rolling_run_predict.v1 开始运行..
    [2018-03-08 11:33:31.778838] INFO: bigquant: 命中缓存
    [2018-03-08 11:33:31.780267] INFO: bigquant: rolling_run_predict.v1 运行完成[0.037956s].
    [2018-03-08 11:33:31.812264] INFO: bigquant: backtest.v7 开始运行..
    
    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    <ipython-input-2-208db9649729> in <module>()
        231     plot_charts=True,
        232     backtest_only=False,
    --> 233     amount_integer=False
        234 )
    
    <ipython-input-2-208db9649729> in m12_prepare_bigquant_run(context)
        194 # 回测引擎:准备数据,只执行一次
        195 def m12_prepare_bigquant_run(context):
    --> 196         print('模型ID:', m6.model_id)
        197         print('模型因子得分:', m6.feature_gains.read_df())
        198 
    
    AttributeError: 'Outputs' object has no attribute 'model_id'

    (iQuant) #2

    你好,滚动训练是预先做好配置,真正运行完成后才知道每次训练的具体情形。你可以先跑一下样例策略,然后运行完毕后,看看m8、m16、m17这几个模块的结果,可以查询到每次训练的详情,包括模型ID和因子得分。


    (runningpig) #3

    多谢,还需要请教具体是哪一个模块的数据项。m17.predictions. 下面都不太像

    克隆策略

      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instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\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. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), 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      In [3]:
      # 本代码由可视化策略环境自动生成 2018年3月8日 15:07
      # 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
      return_5
      return_10
      return_20
      avg_amount_0/avg_amount_5
      avg_amount_5/avg_amount_20
      rank_avg_amount_0/rank_avg_amount_5
      rank_avg_amount_5/rank_avg_amount_10
      rank_return_0
      rank_return_5
      rank_return_10
      rank_return_0/rank_return_5
      rank_return_5/rank_return_10
      pe_ttm_0
      """
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2012-01-01'),
          end_date=T.live_run_param('trading_date', '2017-01-01'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m10 = M.general_feature_extractor.v6(
          instruments=m9.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      m11 = M.derived_feature_extractor.v2(
          input_data=m10.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument'
      )
      
      m14 = M.dropnan.v1(
          input_data=m11.data
      )
      
      m15 = M.rolling_conf.v1(
          start_date='2010-01-01',
          end_date=T.live_run_param('trading_date', '2015-12-31'),
          rolling_update_days=365,
          rolling_min_days=730,
          rolling_max_days=0,
          rolling_count_for_live=1
      )
      
      m1 = M.instruments.v2(
          rolling_conf=m15.data,
          start_date='',
          end_date='',
          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/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 <https://bigquant.com/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
      )
      
      m4 = M.general_feature_extractor.v6(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      m5 = M.derived_feature_extractor.v2(
          input_data=m4.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument'
      )
      
      m7 = M.join.v3(
          data1=m2.data,
          data2=m5.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=True
      )
      
      m16 = M.rolling_run.v1(
          run=m6.m_lazy_run,
          input_list=m15.data,
          param_name='rolling_input'
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m16.data,
          data=m14.data,
          m_lazy_run=True
      )
      
      m17 = M.rolling_run_predict.v1(
          predict=m8.m_lazy_run,
          model_param_name='model',
          data_param_name='data'
      )
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m12_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StockRanker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StockRanker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 m12_prepare_bigquant_run(context):
          pass
          #print('ID:', m6.model_id)
          #print('因子得分:', m6.feature_gains.read_df())
      
      # 回测引擎:初始化函数,只执行一次
      def m12_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
      
      m12 = M.trade.v3(
          instruments=m9.data,
          options_data=m17.predictions,
          start_date='',
          end_date='',
          handle_data=m12_handle_data_bigquant_run,
          prepare=m12_prepare_bigquant_run,
          initialize=m12_initialize_bigquant_run,
          volume_limit=0.025,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=1000000,
          benchmark='000300.SHA',
          auto_cancel_non_tradable_orders=True,
          data_frequency='daily',
          price_type='后复权',
          plot_charts=True,
          backtest_only=False,
          amount_integer=False
      )
      
      [2018-03-08 15:06:22.512648] INFO: bigquant: input_features.v1 开始运行..
      [2018-03-08 15:06:22.656246] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.657761] INFO: bigquant: input_features.v1 运行完成[0.145169s].
      [2018-03-08 15:06:22.664627] INFO: bigquant: instruments.v2 开始运行..
      [2018-03-08 15:06:22.667401] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.668417] INFO: bigquant: instruments.v2 运行完成[0.00377s].
      [2018-03-08 15:06:22.839527] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2018-03-08 15:06:22.844861] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.846516] INFO: bigquant: general_feature_extractor.v6 运行完成[0.006989s].
      [2018-03-08 15:06:22.856288] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2018-03-08 15:06:22.860185] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.861599] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.005311s].
      [2018-03-08 15:06:22.869703] INFO: bigquant: dropnan.v1 开始运行..
      [2018-03-08 15:06:22.873038] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.874440] INFO: bigquant: dropnan.v1 运行完成[0.00471s].
      [2018-03-08 15:06:22.882378] INFO: 滚动运行配置: 生成了 5 次滚动,第一次 {'end_date': '2011-12-31', 'start_date': '2010-01-01'},最后一次 {'end_date': '2015-12-30', 'start_date': '2010-01-01'}
      [2018-03-08 15:06:22.900383] INFO: bigquant: instruments.v2 开始运行..
      [2018-03-08 15:06:22.903860] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.905214] INFO: bigquant: instruments.v2 运行完成[0.004855s].
      [2018-03-08 15:06:22.915170] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2018-03-08 15:06:22.918969] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:22.920113] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.00492s].
      [2018-03-08 15:06:23.085779] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2018-03-08 15:06:23.089849] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.091245] INFO: bigquant: general_feature_extractor.v6 运行完成[0.005499s].
      [2018-03-08 15:06:23.101199] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2018-03-08 15:06:23.106420] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.108175] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.006974s].
      [2018-03-08 15:06:23.117661] INFO: bigquant: join.v3 开始运行..
      [2018-03-08 15:06:23.121603] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.122804] INFO: bigquant: join.v3 运行完成[0.005134s].
      [2018-03-08 15:06:23.130596] INFO: bigquant: dropnan.v1 开始运行..
      [2018-03-08 15:06:23.133731] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.135213] INFO: bigquant: dropnan.v1 运行完成[0.00461s].
      [2018-03-08 15:06:23.147127] INFO: bigquant: 延迟运行 stock_ranker_train.v5
      [2018-03-08 15:06:23.155214] INFO: bigquant: rolling_run.v1 开始运行..
      [2018-03-08 15:06:23.158447] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.159464] INFO: bigquant: rolling_run.v1 运行完成[0.004253s].
      [2018-03-08 15:06:23.169984] INFO: bigquant: 延迟运行 stock_ranker_predict.v5
      [2018-03-08 15:06:23.177021] INFO: bigquant: rolling_run_predict.v1 开始运行..
      [2018-03-08 15:06:23.233386] INFO: bigquant: 命中缓存
      [2018-03-08 15:06:23.234913] INFO: bigquant: rolling_run_predict.v1 运行完成[0.057853s].
      [2018-03-08 15:06:23.269247] INFO: bigquant: backtest.v7 开始运行..
      [2018-03-08 15:06:23.272943] INFO: bigquant: 命中缓存
      
      • 收益率2801.43%
      • 年化收益率101.19%
      • 基准收益率41.11%
      • 阿尔法0.94
      • 贝塔0.96
      • 夏普比率2.73
      • 胜率0.623
      • 盈亏比0.95
      • 收益波动率35.89%
      • 信息比率3.61
      • 最大回撤53.27%
      [2018-03-08 15:06:27.609720] INFO: bigquant: backtest.v7 运行完成[4.340455s].
      
      In [ ]:
      m8.m_lazy_run.id
      


      (iQuant) #4

      希望对你有所帮助!


      (runningpig) #5

      非常感谢!