把排序算法改为了随机森林,主函数报错

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    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    In [4]:
    # 本代码由可视化策略环境自动生成 2017年12月6日 17:39
    # 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。
    
    
    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 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
    )
    
    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
    """
    )
    
    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'
    )
    
    m6 = M.join.v3(
        data1=m2.data,
        data2=m5.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m11 = M.dropnan.v1(
        input_data=m6.data
    )
    
    m13 = M.random_forest_train.v2(
        training_ds=m11.data,
        features=m3.data,
        n_estimators=10,
        max_features='auto',
        max_depth=30,
        min_samples_leaf=200,
        n_jobs=1,
        algo='classifier'
    )
    
    m7 = 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
    )
    
    m8 = M.general_feature_extractor.v6(
        instruments=m7.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m9 = M.derived_feature_extractor.v2(
        input_data=m8.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m12 = M.dropnan.v1(
        input_data=m9.data
    )
    
    m14 = M.random_forest_predict.v2(
        model=m13.model,
        data=m12.data,
        date_col='date',
        instrument_col='instrument',
        sort=True
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m10_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 m10_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m10_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
    
    m10 = M.trade.v3(
        instruments=m14.predictions,
        options_data=m7.data,
        start_date='',
        end_date='',
        handle_data=m10_handle_data_bigquant_run,
        prepare=m10_prepare_bigquant_run,
        initialize=m10_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',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-12-06 17:39:05.810494] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-06 17:39:05.818904] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:05.820462] INFO: bigquant: instruments.v2 运行完成[0.010014s].
    [2017-12-06 17:39:05.914085] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-12-06 17:39:05.925211] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:05.926479] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.012446s].
    [2017-12-06 17:39:06.010644] INFO: bigquant: input_features.v1 开始运行..
    [2017-12-06 17:39:06.036207] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.037749] INFO: bigquant: input_features.v1 运行完成[0.027117s].
    [2017-12-06 17:39:06.134373] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-06 17:39:06.139228] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.140673] INFO: bigquant: general_feature_extractor.v6 运行完成[0.006298s].
    [2017-12-06 17:39:06.212123] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-06 17:39:06.216503] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.217752] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.005641s].
    [2017-12-06 17:39:06.224729] INFO: bigquant: join.v3 开始运行..
    [2017-12-06 17:39:06.230200] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.231281] INFO: bigquant: join.v3 运行完成[0.006542s].
    [2017-12-06 17:39:06.311059] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-06 17:39:06.315280] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.316573] INFO: bigquant: dropnan.v1 运行完成[0.005545s].
    [2017-12-06 17:39:06.411941] INFO: bigquant: random_forest_train.v2 开始运行..
    [2017-12-06 17:39:06.416164] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.417123] INFO: bigquant: random_forest_train.v2 运行完成[0.005221s].
    [2017-12-06 17:39:06.456525] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-06 17:39:06.461716] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.462782] INFO: bigquant: instruments.v2 运行完成[0.006268s].
    [2017-12-06 17:39:06.495602] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-06 17:39:06.498705] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.499474] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003905s].
    [2017-12-06 17:39:06.507134] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-06 17:39:06.510920] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.513641] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.006504s].
    [2017-12-06 17:39:06.521682] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-06 17:39:06.526795] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.528052] INFO: bigquant: dropnan.v1 运行完成[0.006383s].
    [2017-12-06 17:39:06.610495] INFO: bigquant: random_forest_predict.v2 开始运行..
    [2017-12-06 17:39:06.613753] INFO: bigquant: 命中缓存
    [2017-12-06 17:39:06.614696] INFO: bigquant: random_forest_predict.v2 运行完成[0.004216s].
    
    ---------------------------------------------------------------------------
    UnpicklingError                           Traceback (most recent call last)
    <ipython-input-4-8122e1a96603> in <module>()
        206     data_frequency='daily',
        207     plot_charts=True,
    --> 208     backtest_only=False
        209 )
    
    UnpicklingError: invalid load key, 'H'.

    (iQuant) #2

    原始是这样的:
    Trade(回测/模拟交易)模块有两个输入端,该两个接口线连错位置了,把两个线位置互换一下就好了。
    因此建议:在连线的时候,一定注意端口对应什么样的输入。如果连错了的话,就会有UnpicklingError: invalid load key, ‘H’.的报错。
    image

    修改后的策略:

    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 2017年12月7日 14:35
      # 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。
      
      
      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 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
      )
      
      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
      """
      )
      
      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'
      )
      
      m6 = M.join.v3(
          data1=m2.data,
          data2=m5.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m11 = M.dropnan.v1(
          input_data=m6.data
      )
      
      m13 = M.random_forest_train.v2(
          training_ds=m11.data,
          features=m3.data,
          n_estimators=10,
          max_features='auto',
          max_depth=30,
          min_samples_leaf=200,
          n_jobs=1,
          algo='classifier'
      )
      
      m7 = 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
      )
      
      m8 = M.general_feature_extractor.v6(
          instruments=m7.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      m9 = M.derived_feature_extractor.v2(
          input_data=m8.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument'
      )
      
      m12 = M.dropnan.v1(
          input_data=m9.data
      )
      
      m14 = M.random_forest_predict.v2(
          model=m13.model,
          data=m12.data,
          date_col='date',
          instrument_col='instrument',
          sort=True
      )
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m10_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 m10_prepare_bigquant_run(context):
          pass
      
      # 回测引擎:初始化函数,只执行一次
      def m10_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
      
      m10 = M.trade.v3(
          instruments=m7.data,
          options_data=m14.predictions,
          start_date='',
          end_date='',
          handle_data=m10_handle_data_bigquant_run,
          prepare=m10_prepare_bigquant_run,
          initialize=m10_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',
          plot_charts=True,
          backtest_only=False
      )
      
      [2017-12-07 14:37:39.088051] INFO: bigquant: instruments.v2 开始运行..
      [2017-12-07 14:37:39.095360] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.096170] INFO: bigquant: instruments.v2 运行完成[0.008148s].
      [2017-12-07 14:37:39.103201] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2017-12-07 14:37:39.109166] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.110015] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.006803s].
      [2017-12-07 14:37:39.113742] INFO: bigquant: input_features.v1 开始运行..
      [2017-12-07 14:37:39.135352] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.136252] INFO: bigquant: input_features.v1 运行完成[0.022506s].
      [2017-12-07 14:37:39.162045] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2017-12-07 14:37:39.166412] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.167216] INFO: bigquant: general_feature_extractor.v6 运行完成[0.005198s].
      [2017-12-07 14:37:39.173401] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2017-12-07 14:37:39.176656] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.177394] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003991s].
      [2017-12-07 14:37:39.183749] INFO: bigquant: join.v3 开始运行..
      [2017-12-07 14:37:39.187455] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.188199] INFO: bigquant: join.v3 运行完成[0.00444s].
      [2017-12-07 14:37:39.193764] INFO: bigquant: dropnan.v1 开始运行..
      [2017-12-07 14:37:39.196948] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.197679] INFO: bigquant: dropnan.v1 运行完成[0.003914s].
      [2017-12-07 14:37:39.203797] INFO: bigquant: random_forest_train.v2 开始运行..
      [2017-12-07 14:37:39.206560] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.207523] INFO: bigquant: random_forest_train.v2 运行完成[0.003721s].
      [2017-12-07 14:37:39.211893] INFO: bigquant: instruments.v2 开始运行..
      [2017-12-07 14:37:39.216076] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.216804] INFO: bigquant: instruments.v2 运行完成[0.004908s].
      [2017-12-07 14:37:39.242265] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2017-12-07 14:37:39.245464] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.246321] INFO: bigquant: general_feature_extractor.v6 运行完成[0.004076s].
      [2017-12-07 14:37:39.252760] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2017-12-07 14:37:39.255497] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.256470] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003694s].
      [2017-12-07 14:37:39.262211] INFO: bigquant: dropnan.v1 开始运行..
      [2017-12-07 14:37:39.264857] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.265682] INFO: bigquant: dropnan.v1 运行完成[0.003481s].
      [2017-12-07 14:37:39.272964] INFO: bigquant: random_forest_predict.v2 开始运行..
      [2017-12-07 14:37:39.275373] INFO: bigquant: 命中缓存
      [2017-12-07 14:37:39.276228] INFO: bigquant: random_forest_predict.v2 运行完成[0.00326s].
      [2017-12-07 14:37:39.300638] INFO: bigquant: backtest.v7 开始运行..
      [2017-12-07 14:37:39.303902] INFO: bigquant: 命中缓存
      
      • 收益率114.95%
      • 年化收益率48.46%
      • 基准收益率-6.33%
      • 阿尔法0.52
      • 贝塔1.0
      • 夏普比率1.06
      • 收益波动率42.4%
      • 信息比率1.85
      • 最大回撤51.22%
      [2017-12-07 14:37:40.970885] INFO: bigquant: backtest.v7 运行完成[1.670203s].