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    In [3]:
    # 本代码由可视化策略环境自动生成 2020年3月18日 17:14
    # 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 m4_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 m4_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 m4_prepare_bigquant_run(context):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2015-01-01',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': '',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.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)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': True,
    
        'm3': 'M.input_features.v1',
        'm3.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
    """,
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 0,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': False,
        'm16.remove_extra_columns': False,
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m2.data'),
        'm7.data2': T.Graph.OutputPort('m16.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m7.data'),
    
        'm5': 'M.stock_ranker_train.v6',
        'm5.training_ds': T.Graph.OutputPort('m13.data'),
        'm5.features': T.Graph.OutputPort('m3.data'),
        'm5.learning_algorithm': '排序',
        'm5.number_of_leaves': 30,
        'm5.minimum_docs_per_leaf': 1000,
        'm5.number_of_trees': 20,
        'm5.learning_rate': 0.1,
        'm5.max_bins': 1023,
        'm5.feature_fraction': 1,
        'm5.data_row_fraction': 1,
        'm5.ndcg_discount_base': 1,
        'm5.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2015-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2017-01-01'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 60,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': False,
        'm18.remove_extra_columns': False,
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m18.data'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m5.model'),
        'm8.data': T.Graph.OutputPort('m14.data'),
        'm8.m_lazy_run': False,
    
        'm4': 'M.trade.v4',
        'm4.instruments': T.Graph.OutputPort('m9.data'),
        'm4.options_data': T.Graph.OutputPort('m8.predictions'),
        'm4.start_date': '',
        'm4.end_date': '',
        'm4.initialize': m4_initialize_bigquant_run,
        'm4.handle_data': m4_handle_data_bigquant_run,
        'm4.prepare': m4_prepare_bigquant_run,
        'm4.volume_limit': 0.025,
        'm4.order_price_field_buy': 'open',
        'm4.order_price_field_sell': 'close',
        'm4.capital_base': 1000000,
        'm4.auto_cancel_non_tradable_orders': True,
        'm4.data_frequency': 'daily',
        'm4.price_type': '后复权',
        'm4.product_type': '股票',
        'm4.plot_charts': True,
        'm4.backtest_only': False,
        'm4.benchmark': '',
    })
    
    # g.run({})
    
    
    def m10_param_grid_builder_bigquant_run():
        param_grid = {}
        param_grid['m5.number_of_trees'] = [30,50, 100, 200,300,500]
        param_grid['m5.number_of_leaves'] = [500,1000,1500,2000]
        param_grid['m5.minimum_docs_per_leaf'] = [1000,1500,2000]
        param_grid['m5.learning_rate'] = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
        param_grid['m5.feature_fraction'] = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
    
        return param_grid
    
    def m10_scoring_bigquant_run(result):
        score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]
    
        return score
    
    
    m10 = M.hyper_parameter_search.v1(
        param_grid_builder=m10_param_grid_builder_bigquant_run,
        scoring=m10_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=1,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    Fitting 1 folds for each of 7200 candidates, totalling 7200 fits
    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    [CV] m5.feature_fraction=0.1, m5.learning_rate=0.1, m5.minimum_docs_per_leaf=1000, m5.number_of_leaves=500, m5.number_of_trees=30 
    

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

    1.一键查看文档

    2.一键搜索答案

    超参搜索(hyper_parameter_search)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-ff1edf36e817> in <module>()
        235     worker_silent=True,
        236     run_now=True,
    --> 237     bq_graph=g
        238 )
    
    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 (cc54da7068f811ea96710a580a81033e)

    (达达) #2

    feature_fraction这个参数固定设置为1试一下
    另外评价指标是回测模块m4吧?

    克隆策略

    StockRanker多因子选股策略

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      In [17]:
      # 本代码由可视化策略环境自动生成 2020年3月19日 14:33
      # 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 m4_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 m4_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 m4_prepare_bigquant_run(context):
          pass
      
      
      g = T.Graph({
      
          'm1': 'M.instruments.v2',
          'm1.start_date': '2010-01-01',
          'm1.end_date': '2015-01-01',
          'm1.market': 'CN_STOCK_A',
          'm1.instrument_list': '',
          'm1.max_count': 0,
      
          'm2': 'M.advanced_auto_labeler.v2',
          'm2.instruments': T.Graph.OutputPort('m1.data'),
          'm2.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)
      """,
          'm2.start_date': '',
          'm2.end_date': '',
          'm2.benchmark': '000300.SHA',
          'm2.drop_na_label': True,
          'm2.cast_label_int': True,
      
          'm3': 'M.input_features.v1',
          'm3.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""",
      
          'm15': 'M.general_feature_extractor.v7',
          'm15.instruments': T.Graph.OutputPort('m1.data'),
          'm15.features': T.Graph.OutputPort('m3.data'),
          'm15.start_date': '',
          'm15.end_date': '',
          'm15.before_start_days': 0,
      
          'm16': 'M.derived_feature_extractor.v3',
          'm16.input_data': T.Graph.OutputPort('m15.data'),
          'm16.features': T.Graph.OutputPort('m3.data'),
          'm16.date_col': 'date',
          'm16.instrument_col': 'instrument',
          'm16.drop_na': False,
          'm16.remove_extra_columns': False,
      
          'm7': 'M.join.v3',
          'm7.data1': T.Graph.OutputPort('m2.data'),
          'm7.data2': T.Graph.OutputPort('m16.data'),
          'm7.on': 'date,instrument',
          'm7.how': 'inner',
          'm7.sort': False,
      
          'm13': 'M.dropnan.v1',
          'm13.input_data': T.Graph.OutputPort('m7.data'),
      
          'm5': 'M.stock_ranker_train.v6',
          'm5.training_ds': T.Graph.OutputPort('m13.data'),
          'm5.features': T.Graph.OutputPort('m3.data'),
          'm5.learning_algorithm': '排序',
          'm5.number_of_leaves': 30,
          'm5.minimum_docs_per_leaf': 1000,
          'm5.number_of_trees': 20,
          'm5.learning_rate': 0.1,
          'm5.max_bins': 1023,
          'm5.feature_fraction': 1,
          'm5.data_row_fraction': 1,
          'm5.ndcg_discount_base': 1,
          'm5.m_lazy_run': False,
      
          'm9': 'M.instruments.v2',
          'm9.start_date': T.live_run_param('trading_date', '2015-01-01'),
          'm9.end_date': T.live_run_param('trading_date', '2017-01-01'),
          'm9.market': 'CN_STOCK_A',
          'm9.instrument_list': '',
          'm9.max_count': 0,
      
          'm17': 'M.general_feature_extractor.v7',
          'm17.instruments': T.Graph.OutputPort('m9.data'),
          'm17.features': T.Graph.OutputPort('m3.data'),
          'm17.start_date': '',
          'm17.end_date': '',
          'm17.before_start_days': 60,
      
          'm18': 'M.derived_feature_extractor.v3',
          'm18.input_data': T.Graph.OutputPort('m17.data'),
          'm18.features': T.Graph.OutputPort('m3.data'),
          'm18.date_col': 'date',
          'm18.instrument_col': 'instrument',
          'm18.drop_na': False,
          'm18.remove_extra_columns': False,
      
          'm14': 'M.dropnan.v1',
          'm14.input_data': T.Graph.OutputPort('m18.data'),
      
          'm8': 'M.stock_ranker_predict.v5',
          'm8.model': T.Graph.OutputPort('m5.model'),
          'm8.data': T.Graph.OutputPort('m14.data'),
          'm8.m_lazy_run': False,
      
          'm4': 'M.trade.v4',
          'm4.instruments': T.Graph.OutputPort('m9.data'),
          'm4.options_data': T.Graph.OutputPort('m8.predictions'),
          'm4.start_date': '',
          'm4.end_date': '',
          'm4.initialize': m4_initialize_bigquant_run,
          'm4.handle_data': m4_handle_data_bigquant_run,
          'm4.prepare': m4_prepare_bigquant_run,
          'm4.volume_limit': 0.025,
          'm4.order_price_field_buy': 'open',
          'm4.order_price_field_sell': 'close',
          'm4.capital_base': 1000000,
          'm4.auto_cancel_non_tradable_orders': True,
          'm4.data_frequency': 'daily',
          'm4.price_type': '后复权',
          'm4.product_type': '股票',
          'm4.plot_charts': True,
          'm4.backtest_only': False,
          'm4.benchmark': '',
      })
      
      # g.run({})
      
      
      def m10_param_grid_builder_bigquant_run():
          param_grid = {}
          param_grid['m5.number_of_trees'] = [30]
          param_grid['m5.number_of_leaves'] = [200]
          param_grid['m5.minimum_docs_per_leaf'] = [1000]
          param_grid['m5.learning_rate'] = [0.1,0.2]
          param_grid['m5.feature_fraction'] = [1]
      
          return param_grid
      def m10_scoring_bigquant_run(result):
          score = result['m4'].read_raw_perf()['sharpe'].values[-1]
      
          return score
      
      
      m10 = M.hyper_parameter_search.v1(
          param_grid_builder=m10_param_grid_builder_bigquant_run,
          scoring=m10_scoring_bigquant_run,
          search_algorithm='网格搜索',
          search_iterations=2,
          workers=1,
          worker_distributed_run=True,
          worker_silent=True,
          run_now=True,
          bq_graph=g
      )
      
      Fitting 1 folds for each of 2 candidates, totalling 2 fits
      [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
      [CV] m5.feature_fraction=1, m5.learning_rate=0.1, m5.minimum_docs_per_leaf=1000, m5.number_of_leaves=200, m5.number_of_trees=30 
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-bd6e032e06c14833bd752660a1d3349b"}/bigcharts-data-end
      • 收益率188.67%
      • 年化收益率72.88%
      • 基准收益率-6.33%
      • 阿尔法0.63
      • 贝塔1.05
      • 夏普比率1.38
      • 胜率0.61
      • 盈亏比0.88
      • 收益波动率45.11%
      • 信息比率0.13
      • 最大回撤59.85%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-514fc094d27a46ffae2769d2ac5fc0b3"}/bigcharts-data-end
      [CV]  m5.feature_fraction=1, m5.learning_rate=0.1, m5.minimum_docs_per_leaf=1000, m5.number_of_leaves=200, m5.number_of_trees=30, score=1.3777374693526747, total=   2.5s
      [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.6s remaining:    0.0s
      [CV] m5.feature_fraction=1, m5.learning_rate=0.2, m5.minimum_docs_per_leaf=1000, m5.number_of_leaves=200, m5.number_of_trees=30 
      
      • 收益率273.87%
      • 年化收益率97.58%
      • 基准收益率-6.33%
      • 阿尔法0.78
      • 贝塔1.09
      • 夏普比率1.63
      • 胜率0.64
      • 盈亏比0.82
      • 收益波动率46.82%
      • 信息比率0.15
      • 最大回撤59.54%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-add47504107d4adcb12bdc590a80f74a"}/bigcharts-data-end
      [CV]  m5.feature_fraction=1, m5.learning_rate=0.2, m5.minimum_docs_per_leaf=1000, m5.number_of_leaves=200, m5.number_of_trees=30, score=1.6300004697469055, total= 8.7min
      [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  8.8min remaining:    0.0s
      [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  8.8min finished
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-24c89b76ac8e4eec89d1d6094353a80e"}/bigcharts-data-end
      • 收益率273.87%
      • 年化收益率97.58%
      • 基准收益率-6.33%
      • 阿尔法0.78
      • 贝塔1.09
      • 夏普比率1.63
      • 胜率0.64
      • 盈亏比0.82
      • 收益波动率46.82%
      • 信息比率0.15
      • 最大回撤59.54%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-b3052765ceb7497482965be645312cf5"}/bigcharts-data-end

      (冰柠檬) #3

      好的 谢谢老师