关于默认可视化策略, 基础特征的"向前取数据天数"的微妙问题


(chaoskey) #1

关于默认可视化策略, 基础特征的"向前取数据天数"的微妙问题

改进前 对 跨4日的衍生因子. 取120 和 240相差很大.
改进后, 对 跨4日的衍生因子. 取120 和 240相差不大.

单因子可视化策略改进前

克隆策略

    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    In [12]:
    # 本代码由可视化策略环境自动生成 2017年12月12日 17:06
    # 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='2016-01-01',
        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
    rank(-1*delta((0.2*(high_0+low_0)/2)+(0.8*amount_0/volume_0*adjust_factor_0),4))
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=240
    )
    
    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=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2017-12-08'),
        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=120
    )
    
    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
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 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
    
    # 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=m8.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',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-12-12 16:58:04.996351] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-12 16:58:04.999610] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.000783] INFO: bigquant: instruments.v2 运行完成[0.004507s].
    [2017-12-12 16:58:05.015106] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-12-12 16:58:05.019751] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.021250] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.006182s].
    [2017-12-12 16:58:05.068008] INFO: bigquant: input_features.v1 开始运行..
    [2017-12-12 16:58:05.072355] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.073632] INFO: bigquant: input_features.v1 运行完成[0.005619s].
    [2017-12-12 16:58:05.171036] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-12 16:58:05.175275] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.176288] INFO: bigquant: general_feature_extractor.v6 运行完成[0.00526s].
    [2017-12-12 16:58:05.270657] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-12 16:58:05.275239] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.281707] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.011002s].
    [2017-12-12 16:58:05.370853] INFO: bigquant: join.v3 开始运行..
    [2017-12-12 16:58:05.377805] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.379149] INFO: bigquant: join.v3 运行完成[0.008343s].
    [2017-12-12 16:58:05.471650] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-12 16:58:05.475570] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.477378] INFO: bigquant: dropnan.v1 运行完成[0.005747s].
    [2017-12-12 16:58:05.570362] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2017-12-12 16:58:05.574093] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.575311] INFO: bigquant: stock_ranker_train.v5 运行完成[0.004973s].
    [2017-12-12 16:58:05.587051] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-12 16:58:05.590087] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.591186] INFO: bigquant: instruments.v2 运行完成[0.004134s].
    [2017-12-12 16:58:05.603214] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-12 16:58:05.668462] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.670090] INFO: bigquant: general_feature_extractor.v6 运行完成[0.066851s].
    [2017-12-12 16:58:05.683294] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-12 16:58:05.687922] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.689406] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.0061s].
    [2017-12-12 16:58:05.697944] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-12 16:58:05.701785] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.703409] INFO: bigquant: dropnan.v1 运行完成[0.005431s].
    [2017-12-12 16:58:05.772642] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2017-12-12 16:58:05.781414] INFO: bigquant: 命中缓存
    [2017-12-12 16:58:05.784439] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.011847s].
    [2017-12-12 16:58:05.985633] INFO: bigquant: backtest.v7 开始运行..
    [2017-12-12 16:58:05.989154] INFO: bigquant: 命中缓存
    
    • 收益率-2.51%
    • 年化收益率-1.35%
    • 基准收益率7.3%
    • 阿尔法-0.05
    • 贝塔1.07
    • 夏普比率-0.22
    • 收益波动率26.84%
    • 信息比率-0.27
    • 最大回撤22.15%
    [2017-12-12 16:58:08.171734] INFO: bigquant: backtest.v7 运行完成[2.186061s].
    

    -------------- 改进前(没有按起止时间进行截取) -------------------

    1) 新建默认可视化策略

    2) 只填写一个因子

    rank(-1*delta((0.2*(high_0+low_0)/2)+(0.8*amount_0/volume_0*adjust_factor_0),4))
    
    

    3) 修改基础数据多取的天数

    0 5 (训练数据基础多取天数, 预测回测数据多取天数) 下同

    收益率 -9.85% 年化收益率 -5.38% 基准收益率 7.3% 阿尔法 -0.09 贝塔 1.09 夏普比率 -0.36 收益波动率 27.47% 信息比率 -0.46 最大回撤 28.35%

    0 120

    收益率 -16.4% 年化收益率 -9.1% 基准收益率 7.3% 阿尔法 -0.13 贝塔 1.12 夏普比率 -0.51 收益波动率 26.56% 信息比率 -0.71 最大回撤 25.88%

    0 240

    收益率 -2.51% 年化收益率 -1.35% 基准收益率 7.3% 阿尔法 -0.05 贝塔 1.07 夏普比率 -0.22 收益波动率 26.84% 信息比率 -0.27 最大回撤 22.15%

    In [ ]:
     
    

    单因子可视化策略改进后

    克隆策略

      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回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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.perf_tracker.position_tracker.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StockRanker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.perf_tracker.position_tracker.positions.items()}\n 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      In [65]:
      # 本代码由可视化策略环境自动生成 2017年12月12日 17:06
      # 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='2016-01-01',
          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
      rank(-1*delta((0.2*(high_0+low_0)/2)+(0.8*amount_0/volume_0*adjust_factor_0),4))
      """
      )
      
      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
      )
      
      # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
      def m15_run_bigquant_run(input_1, input_2, input_3):
          '''
              input_1 数据输入
              input_2 空
              input_3 参数输入
          '''
          
          # 参数
          params = input_3.read_pickle()
          
          # 输入
          df = input_1.read_df()
          # 过滤
          df = df[(df.date>=params["start_date"]) & (df.date<=params["end_date"])]
          # 输出
          data_1 = DataSource.write_df(df)
          return Outputs(data_1=data_1, data_2=None, data_3=None)
      
      m15 = M.cached.v3(
          input_1=m7.data,
          input_3=m1.data,
          run=m15_run_bigquant_run
      )
      
      m13 = M.dropnan.v1(
          input_data=m15.data_1
      )
      
      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', '2016-01-01'),
          end_date=T.live_run_param('trading_date', '2017-12-08'),
          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=5
      )
      
      m11 = M.derived_feature_extractor.v2(
          input_data=m10.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument'
      )
      
      # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
      def m16_run_bigquant_run(input_1, input_2, input_3):
          '''
              input_1 数据输入
              input_2 空
              input_3 参数输入
          '''
          
          # 参数
          params = input_3.read_pickle()
          
          # 输入
          df = input_1.read_df()
          # 过滤
          df = df[(df.date>=params["start_date"]) & (df.date<=params["end_date"])]
          # 输出
          data_1 = DataSource.write_df(df)
          return Outputs(data_1=data_1, data_2=None, data_3=None)
      
      m16 = M.cached.v3(
          input_1=m11.data,
          input_3=m9.data,
          run=m16_run_bigquant_run
      )
      
      m14 = M.dropnan.v1(
          input_data=m16.data_1
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m6.model,
          data=m14.data,
          m_lazy_run=False
      )
      
      # 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
      
      # 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=m8.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',
          plot_charts=True,
          backtest_only=False
      )
      
      [2017-12-12 16:21:06.326625] INFO: bigquant: instruments.v2 开始运行..
      [2017-12-12 16:21:06.330429] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.331375] INFO: bigquant: instruments.v2 运行完成[0.004788s].
      [2017-12-12 16:21:06.342827] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2017-12-12 16:21:06.346566] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.347640] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004859s].
      [2017-12-12 16:21:06.352873] INFO: bigquant: input_features.v1 开始运行..
      [2017-12-12 16:21:06.356236] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.357570] INFO: bigquant: input_features.v1 运行完成[0.004708s].
      [2017-12-12 16:21:06.370183] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2017-12-12 16:21:06.373214] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.374922] INFO: bigquant: general_feature_extractor.v6 运行完成[0.00472s].
      [2017-12-12 16:21:06.383835] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2017-12-12 16:21:06.387218] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.388654] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.004846s].
      [2017-12-12 16:21:06.398674] INFO: bigquant: join.v3 开始运行..
      [2017-12-12 16:21:06.401691] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.402699] INFO: bigquant: join.v3 运行完成[0.004063s].
      [2017-12-12 16:21:06.414157] INFO: bigquant: cached.v3 开始运行..
      [2017-12-12 16:21:06.417596] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.418971] INFO: bigquant: cached.v3 运行完成[0.004859s].
      [2017-12-12 16:21:06.430648] INFO: bigquant: dropnan.v1 开始运行..
      [2017-12-12 16:21:06.433867] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.434992] INFO: bigquant: dropnan.v1 运行完成[0.00436s].
      [2017-12-12 16:21:06.444606] INFO: bigquant: stock_ranker_train.v5 开始运行..
      [2017-12-12 16:21:06.448095] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.449114] INFO: bigquant: stock_ranker_train.v5 运行完成[0.004507s].
      [2017-12-12 16:21:06.454900] INFO: bigquant: instruments.v2 开始运行..
      [2017-12-12 16:21:06.459209] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.460164] INFO: bigquant: instruments.v2 运行完成[0.005274s].
      [2017-12-12 16:21:06.469711] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2017-12-12 16:21:06.473096] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.474071] INFO: bigquant: general_feature_extractor.v6 运行完成[0.004368s].
      [2017-12-12 16:21:06.482777] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2017-12-12 16:21:06.487526] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.488945] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.006211s].
      [2017-12-12 16:21:06.501445] INFO: bigquant: cached.v3 开始运行..
      [2017-12-12 16:21:06.505212] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.506719] INFO: bigquant: cached.v3 运行完成[0.005263s].
      [2017-12-12 16:21:06.516135] INFO: bigquant: dropnan.v1 开始运行..
      [2017-12-12 16:21:06.595031] INFO: bigquant: 命中缓存
      [2017-12-12 16:21:06.596727] INFO: bigquant: dropnan.v1 运行完成[0.080567s].
      [2017-12-12 16:21:06.740061] INFO: bigquant: stock_ranker_predict.v5 开始运行..
      [2017-12-12 16:21:10.845659] INFO: df2bin: prepare data: prediction ..
      [2017-12-12 16:21:23.880553] INFO: stock_ranker_predict: 准备预测: 1332505 行
      [2017-12-12 16:21:33.202489] INFO: bigquant: stock_ranker_predict.v5 运行完成[26.462372s].
      [2017-12-12 16:21:33.281677] INFO: bigquant: backtest.v7 开始运行..
      [2017-12-12 16:22:33.966446] INFO: Performance: Simulated 473 trading days out of 473.
      [2017-12-12 16:22:33.976586] INFO: Performance: first open: 2016-01-04 14:30:00+00:00
      [2017-12-12 16:22:33.983606] INFO: Performance: last close: 2017-12-08 20:00:00+00:00
      
      • 收益率-10.99%
      • 年化收益率-6.01%
      • 基准收益率7.3%
      • 阿尔法-0.1
      • 贝塔1.07
      • 夏普比率-0.4
      • 收益波动率25.98%
      • 信息比率-0.54
      • 最大回撤24.69%
      [2017-12-12 16:22:37.878046] INFO: bigquant: backtest.v7 运行完成[64.596315s].
      

      ----------------- 改进后: 自定义时间轴起始时间过滤 ----------------------

      测试因子(用到4天的数据):

      rank(-1*delta((0.2*(high_0+low_0)/2)+(0.8*amount_0/volume_0*adjust_factor_0),4))
      
      
      

      0 5

      收益率 -10.99% 年化收益率 -6.01% 基准收益率 7.3% 阿尔法 -0.1 贝塔 1.07 夏普比率 -0.4 收益波动率 25.98% 信息比率 -0.54 最大回撤 24.69%

      0 120

      收益率 -16.4% 年化收益率 -9.1% 基准收益率 7.3% 阿尔法 -0.13 贝塔 1.12 夏普比率 -0.51 收益波动率 26.56% 信息比率 -0.71 最大回撤 25.88%

      0 240

      收益率 -16.48% 年化收益率 -9.15% 基准收益率 7.3% 阿尔法 -0.13 贝塔 1.11 夏普比率 -0.51 收益波动率 26.63% 信息比率 -0.7 最大回撤 25.86%

      In [ ]:
       
      

      (iQuant) #2

      有个地方比较模糊,你三种情形下,训练集的 基础特征列表的“向前取数据天数”是一样的吗?如果这里不一样的话,表明训练集不一样,训练出来的模型不一样,回测结果有差异就是正常情形。


      (chaoskey) #3

      第一种情况 和 第二种情况有差异 属于正常. 但第二种和第三种情况的差异很大属于不应该.


      (chaoskey) #4

      并且经过我的改进后, 保证了第二种和第三种情况的差异很小.


      (chaoskey) #5

      无论 训练集的 基础特征列表的“向前取数据天数”是否一样,

      第二种和第三种情况的差异很大属于不应该相差很大.


      (chaoskey) #6

      就好比: 计算 mean(x, 20) 无论我多获取120天的数据, 还是 多获取240天的数据, 计算mean(x, 20)的结果都应该是一样.