10年的训练数据只差2天,回测结果相差挺大。这个是什么引起的?

策略分享
标签: #<Tag:0x00007f5b8a029198>

(oversky2003) #1

训练时间从2009-01-01到2018-12-26,回测结果如下。

训练时间从2009-01-01到2018-12-31,回测结果如下。

10年的训练数据只差2天,回测结果相差挺大。这个是什么引起的?这个策略其实相差不算大,有些其它策略(如下面三楼的策略,收益相差50%),训练数据就差一点点,回测结果就完全不一样了。

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11日 22:33
    # 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=0.00015, sell_cost=0.00115, 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
        context.stock_count = 5
        context.hold_days = 1
    # 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.hold_days # 是否在建仓期间(前 hold_days 天)
        
        cash_avg = context.portfolio.portfolio_value / context.hold_days
        
        cash_for_buy = context.portfolio.cash
    
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
    
        to_buy_instruments = list(ranker_prediction.instrument[:context.stock_count])
        buy_instruments = [k for k in to_buy_instruments if k not in positions.keys()]#已有持仓不重复买入
        #----------------------------START:持有固定天数卖出---------------------------
        today = data.current_dt.strftime('%Y-%m-%d')
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
            for instrument in equities.keys():
                # 如果在买入列表中就不卖了
                if instrument in to_buy_instruments:
                    continue
                sid = equities[instrument].sid  # 交易标的
                # 今天和上次交易的时间相隔hold_days就全部卖出
                dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.hold_days].values[0])
                if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(sid, 0)
                    cash_for_buy += positions[instrument]
        #--------------------------------END:持有固定天数卖出---------------------------
        
        # 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
        # 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, ..]
        buy_stock_count=len(buy_instruments)
        buy_cash_weights = T.norm([1 / math.log(i + 2) for i in range(0, buy_stock_count)])
        # buy_cash_weights=[1/buy_stock_count]*buy_stock_count
    
        for i, instrument in enumerate(buy_instruments):
            if is_staging:
                cash =  min(cash_for_buy,cash_avg) * buy_cash_weights[i]
            else:
                cash =  cash_for_buy * buy_cash_weights[i]
            context.order_target_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
    
    
    m1 = M.instruments.v2(
        start_date='2009-01-01',
        end_date='2018-12-31',
        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(open, -2) / 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_0
    close_0 /ta_sma(close_0,55)
    amount_0/ta_sma(amount_0,55)
    turn_0
    market_cap_float_0/sh_holder_num_0
    pe_ttm_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=150
    )
    
    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', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2019-08-31'),
        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=150
    )
    
    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
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m4_initialize_bigquant_run,
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_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=''
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-22ed9875d5e2454c9565f2b48fba402b"}/bigcharts-data-end
    • 收益率5.78%
    • 年化收益率9.08%
    • 基准收益率26.2%
    • 阿尔法-0.24
    • 贝塔1.14
    • 夏普比率0.36
    • 胜率0.51
    • 盈亏比1.09
    • 收益波动率47.97%
    • 信息比率-0.03
    • 最大回撤39.15%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e1db1641d26e47cebf9da0a4595e985a"}/bigcharts-data-end
    In [4]:
    df=m13.data.read_df()
    print(df[df['date']=='2018-12-24'].head())
    
                amount_0      close_0       date  instrument  market_cap_float_0  \
    795827  4.771869e+08  1017.655762 2018-12-24  000001.SZA        1.617437e+11   
    795828  1.164771e+09  3406.911865 2018-12-24  000002.SZA        2.319983e+11   
    795829  3.776770e+06    66.850533 2018-12-24  000004.SZA        1.365567e+09   
    795830  5.119151e+06    26.319992 2018-12-24  000005.SZA        2.701472e+09   
    795831  5.548671e+07   182.425385 2018-12-24  000006.SZA        7.186482e+09   
    
              pe_ttm_0  return_0  sh_holder_num_0    turn_0  \
    795827    6.604004  0.996825         406242.0  0.296512   
    795828    8.518719  0.984742         261810.0  0.507680   
    795829  121.078865  1.011063           9541.0  0.277908   
    795830   79.000069  1.003534         127249.0  0.189230   
    795831    7.420006  0.988868          82728.0  0.773221   
    
            close_0 /ta_sma(close_0,55)  amount_0/ta_sma(amount_0,55)  \
    795827                     0.899323                      0.437902   
    795828                     0.987007                      1.007290   
    795829                     1.001661                      0.330863   
    795830                     0.986921                      0.183635   
    795831                     1.007458                      0.551536   
    
            market_cap_float_0/sh_holder_num_0        m:low       m:open  \
    795827                       398146.259461  1005.772339  1015.495178   
    795828                       886132.153852  3328.444336  3412.618408   
    795829                       143126.176292    65.346901    66.200310   
    795830                        21229.807260    26.134640    26.227316   
    795831                        86868.794894   180.371811   184.478943   
    
                m:amount       m:high  label  
    795827  4.771869e+08  1020.896729     10  
    795828  1.164771e+09  3435.445312     11  
    795829  3.776770e+06    67.053726      8  
    795830  5.119151e+06    26.505344      7  
    795831  5.548671e+07   185.163467      8  
    

    (polll) #2

    训练数据不一样的话,学习出来的模型是有差异的,不一样是正常现象。我的策略比你这俩差别更大呢,但其实实盘起来还好,不能对着这个日期调,只要前后稍微改动下,整体变动不大就行


    (oversky2003) #3

    下面的策略在之前的策略,加多了一个因子pe_ttm_0。10年的训练数据,2天的差别出来的回测结果就相差很大。这样是不是说明这个出来的模型是很不稳定的?!

    训练时间从2009-01-01到2018-12-26,回测结果如下。

    训练时间从2009-01-01到2018-12-31,回测结果如下。

    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11日 22:28
      # 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=0.00015, sell_cost=0.00115, 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
          context.stock_count = 5
          context.hold_days = 1
      # 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.hold_days # 是否在建仓期间(前 hold_days 天)
          
          cash_avg = context.portfolio.portfolio_value / context.hold_days
          
          cash_for_buy = context.portfolio.cash
      
          positions = {e.symbol: p.amount * p.last_sale_price
                       for e, p in context.portfolio.positions.items()}
      
          to_buy_instruments = list(ranker_prediction.instrument[:context.stock_count])
          buy_instruments = [k for k in to_buy_instruments if k not in positions.keys()]#已有持仓不重复买入
          #----------------------------START:持有固定天数卖出---------------------------
          today = data.current_dt.strftime('%Y-%m-%d')
          # 不是建仓期(在前hold_days属于建仓期)
          if not is_staging:
              equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
              for instrument in equities.keys():
                  # 如果在买入列表中就不卖了
                  if instrument in to_buy_instruments:
                      continue
                  sid = equities[instrument].sid  # 交易标的
                  # 今天和上次交易的时间相隔hold_days就全部卖出
                  dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.hold_days].values[0])
                  if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                      context.order_target_percent(sid, 0)
                      cash_for_buy += positions[instrument]
          #--------------------------------END:持有固定天数卖出---------------------------
          
          # 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
          # 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, ..]
          buy_stock_count=len(buy_instruments)
          buy_cash_weights = T.norm([1 / math.log(i + 2) for i in range(0, buy_stock_count)])
          # buy_cash_weights=[1/buy_stock_count]*buy_stock_count
      
          for i, instrument in enumerate(buy_instruments):
              if is_staging:
                  cash =  min(cash_for_buy,cash_avg) * buy_cash_weights[i]
              else:
                  cash =  cash_for_buy * buy_cash_weights[i]
              context.order_target_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
      
      
      m1 = M.instruments.v2(
          start_date='2009-01-01',
          end_date='2018-12-26',
          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(open, -2) / 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_0
      close_0 /ta_sma(close_0,55)
      amount_0/ta_sma(amount_0,55)
      turn_0
      market_cap_float_0/sh_holder_num_0
      pe_ttm_0
      """
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=150
      )
      
      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', '2019-01-01'),
          end_date=T.live_run_param('trading_date', '2019-08-31'),
          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=150
      )
      
      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
      )
      
      m4 = M.trade.v4(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date='',
          end_date='',
          initialize=m4_initialize_bigquant_run,
          handle_data=m4_handle_data_bigquant_run,
          prepare=m4_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=''
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-b7668c7262804adcbd694d2f1eb54169"}/bigcharts-data-end
      • 收益率55.36%
      • 年化收益率97.61%
      • 基准收益率26.2%
      • 阿尔法0.32
      • 贝塔1.43
      • 夏普比率1.39
      • 胜率0.53
      • 盈亏比1.09
      • 收益波动率59.39%
      • 信息比率0.06
      • 最大回撤58.17%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3b8053945b364c3093f1f1289150f0e4"}/bigcharts-data-end
      In [2]:
      df=m13.data.read_df()
      print(df[df['date']=='2018-12-24'].head())
      
                  amount_0      close_0       date  instrument  market_cap_float_0  \
      795823  4.771869e+08  1017.655762 2018-12-24  000001.SZA        1.617437e+11   
      795824  1.164771e+09  3406.911865 2018-12-24  000002.SZA        2.319983e+11   
      795825  3.776770e+06    66.850533 2018-12-24  000004.SZA        1.365567e+09   
      795826  5.119151e+06    26.319992 2018-12-24  000005.SZA        2.701472e+09   
      795827  5.548671e+07   182.425385 2018-12-24  000006.SZA        7.186482e+09   
      
                pe_ttm_0  return_0  sh_holder_num_0    turn_0  \
      795823    6.604004  0.996825         406242.0  0.296512   
      795824    8.518719  0.984742         261810.0  0.507680   
      795825  121.078865  1.011063           9541.0  0.277908   
      795826   79.000069  1.003534         127249.0  0.189230   
      795827    7.420006  0.988868          82728.0  0.773221   
      
              close_0 /ta_sma(close_0,55)  amount_0/ta_sma(amount_0,55)  \
      795823                     0.899323                      0.437902   
      795824                     0.987007                      1.007290   
      795825                     1.001661                      0.330863   
      795826                     0.986921                      0.183635   
      795827                     1.007458                      0.551536   
      
              market_cap_float_0/sh_holder_num_0        m:low       m:open  \
      795823                       398146.259461  1005.772339  1015.495178   
      795824                       886132.153852  3328.444336  3412.618408   
      795825                       143126.176292    65.346901    66.200310   
      795826                        21229.807260    26.134640    26.227316   
      795827                        86868.794894   180.371811   184.478943   
      
                  m:amount       m:high  label  
      795823  4.771869e+08  1020.896729     10  
      795824  1.164771e+09  3435.445312     11  
      795825  3.776770e+06    67.053726      8  
      795826  5.119151e+06    26.505344      7  
      795827  5.548671e+07   185.163467      8  
      

      (alexanderjs) #4

      失之毫厘,缪以千里


      (达达) #5

      加了因子估计模型多半要变化较大,同样的一组因子下可以进行年度滚动训练,或者测试一下起止时间的敏感度,好的模型通常对训练集不会太敏感。