为什么这两个回测,收益不一致,因子和回测时间都是一样的

策略分享
标签: #<Tag:0x00007f8c698fab90>

(侯) #1

唯一不一样的是输入特征的连线,训练出来的模型的gain也不一样,
参与训练的因子不应该只是stockRanker训练v6模块所连接的那个特征列表吗?

克隆策略

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    In [39]:
    # 本代码由可视化策略环境自动生成 2020年10月16日 20:59
    # 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 m19_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 = 1
        # 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 = 0.5
        context.options['hold_days'] = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_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.portfolio.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.portfolio.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
    
            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 m19_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m27_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 = 1
        # 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 = 0.5
        context.options['hold_days'] = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m27_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.portfolio.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.portfolio.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
    
            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 m27_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2019-01-01',
        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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -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="""close_1
    open_1
    high_1
    low_1"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    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
    )
    
    m5 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.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,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m11 = M.input_features.v1(
        features_ds=m3.data,
        features='jin4=ta_ma(close_4, derive=\'golden_cross\')'
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2020-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m11.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m11.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m10 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m12 = M.filter.v3(
        input_data=m10.data,
        expr='jin4>0',
        output_left_data=False
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m12.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_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='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    m6 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2019-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m13 = M.advanced_auto_labeler.v2(
        instruments=m6.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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -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
    )
    
    m14 = M.input_features.v1(
        features="""close_1
    open_1
    high_1
    low_1"""
    )
    
    m31 = M.input_features.v1(
        features_ds=m14.data,
        features='jin4=ta_ma(close_4, derive=\'golden_cross\')'
    )
    
    m23 = M.general_feature_extractor.v7(
        instruments=m6.data,
        features=m31.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m23.data,
        features=m31.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m20 = M.join.v3(
        data1=m13.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m29 = M.dropnan.v2(
        input_data=m20.data
    )
    
    m28 = M.stock_ranker_train.v6(
        training_ds=m29.data,
        features=m14.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,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m22 = M.instruments.v2(
        start_date='2019-01-01',
        end_date='2020-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m25 = M.general_feature_extractor.v7(
        instruments=m22.data,
        features=m31.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m25.data,
        features=m31.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m30 = M.dropnan.v2(
        input_data=m26.data
    )
    
    m32 = M.filter.v3(
        input_data=m30.data,
        expr='jin4>0',
        output_left_data=False
    )
    
    m21 = M.stock_ranker_predict.v5(
        model=m28.model,
        data=m32.data,
        m_lazy_run=False
    )
    
    m27 = M.trade.v4(
        instruments=m22.data,
        options_data=m21.predictions,
        start_date='',
        end_date='',
        initialize=m27_initialize_bigquant_run,
        handle_data=m27_handle_data_bigquant_run,
        prepare=m27_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='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4e1f4c2291ff4e0d8637923bef945aac"}/bigcharts-data-end
    • 收益率-31.68%
    • 年化收益率-32.53%
    • 基准收益率36.07%
    • 阿尔法-0.66
    • 贝塔0.94
    • 夏普比率-1.16
    • 胜率0.52
    • 盈亏比0.41
    • 收益波动率32.07%
    • 信息比率-0.16
    • 最大回撤50.89%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0e6005b4523f4417a728a8be7e9946ee"}/bigcharts-data-end
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3ce1491655d8425689ea684e4677e9dc"}/bigcharts-data-end
    • 收益率29.19%
    • 年化收益率30.28%
    • 基准收益率36.07%
    • 阿尔法-0.04
    • 贝塔1.07
    • 夏普比率0.87
    • 胜率0.55
    • 盈亏比1.05
    • 收益波动率33.26%
    • 信息比率-0.0
    • 最大回撤42.81%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-07cb6562c78a4bbe985e56c1aa662b3b"}/bigcharts-data-end

    (adhaha111) #2

    您好,两者的结果不一致的主要原因是训练集的数据量不同:
    image

    对于同种数据量,是可以跑出一样的效果的:

    克隆策略
    In [17]:
    m20.data.read().shape
    
    Out[17]:
    (806430, 14)
    In [18]:
    m29.data.read().shape
    
    Out[18]:
    (806382, 14)

      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      In [19]:
      # 本代码由可视化策略环境自动生成 2020年10月19日 09:25
      # 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 m19_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 = 1
          # 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 = 0.5
          context.options['hold_days'] = 1
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m19_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.portfolio.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.portfolio.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities)])))
      
              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 m19_prepare_bigquant_run(context):
          pass
      
      # 回测引擎:初始化函数,只执行一次
      def m27_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 = 1
          # 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 = 0.5
          context.options['hold_days'] = 1
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m27_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.portfolio.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.portfolio.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities)])))
      
              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 m27_prepare_bigquant_run(context):
          pass
      
      
      m1 = M.instruments.v2(
          start_date='2018-01-01',
          end_date='2019-01-01',
          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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -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="""close_1
      open_1
      high_1
      low_1"""
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=90
      )
      
      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
      )
      
      m5 = M.dropnan.v2(
          input_data=m7.data
      )
      
      m4 = M.stock_ranker_train.v6(
          training_ds=m5.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,
          data_row_fraction=1,
          ndcg_discount_base=1,
          m_lazy_run=False
      )
      
      m11 = M.input_features.v1(
          features_ds=m3.data,
          features='jin4=close_0 - open_0'
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2019-01-01'),
          end_date=T.live_run_param('trading_date', '2020-01-01'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m17 = M.general_feature_extractor.v7(
          instruments=m9.data,
          features=m11.data,
          start_date='',
          end_date='',
          before_start_days=90
      )
      
      m18 = M.derived_feature_extractor.v3(
          input_data=m17.data,
          features=m11.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m10 = M.dropnan.v2(
          input_data=m18.data
      )
      
      m12 = M.filter.v3(
          input_data=m10.data,
          expr='jin4>0',
          output_left_data=False
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m4.model,
          data=m12.data,
          m_lazy_run=False
      )
      
      m19 = M.trade.v4(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date='',
          end_date='',
          initialize=m19_initialize_bigquant_run,
          handle_data=m19_handle_data_bigquant_run,
          prepare=m19_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='真实价格',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark='000300.SHA'
      )
      
      m6 = M.instruments.v2(
          start_date='2018-01-01',
          end_date='2019-01-01',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m13 = M.advanced_auto_labeler.v2(
          instruments=m6.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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -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
      )
      
      m14 = M.input_features.v1(
          features="""close_1
      open_1
      high_1
      low_1"""
      )
      
      m31 = M.input_features.v1(
          features_ds=m14.data,
          features='jin4=close_0 - open_0'
      )
      
      m23 = M.general_feature_extractor.v7(
          instruments=m6.data,
          features=m31.data,
          start_date='',
          end_date='',
          before_start_days=90
      )
      
      m24 = M.derived_feature_extractor.v3(
          input_data=m23.data,
          features=m31.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m20 = M.join.v3(
          data1=m13.data,
          data2=m24.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m29 = M.dropnan.v2(
          input_data=m20.data
      )
      
      m28 = M.stock_ranker_train.v6(
          training_ds=m29.data,
          features=m14.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,
          data_row_fraction=1,
          ndcg_discount_base=1,
          m_lazy_run=False
      )
      
      m22 = M.instruments.v2(
          start_date='2019-01-01',
          end_date='2020-01-01',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m25 = M.general_feature_extractor.v7(
          instruments=m22.data,
          features=m31.data,
          start_date='',
          end_date='',
          before_start_days=90
      )
      
      m26 = M.derived_feature_extractor.v3(
          input_data=m25.data,
          features=m31.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m30 = M.dropnan.v2(
          input_data=m26.data
      )
      
      m32 = M.filter.v3(
          input_data=m30.data,
          expr='jin4>0',
          output_left_data=False
      )
      
      m21 = M.stock_ranker_predict.v5(
          model=m28.model,
          data=m32.data,
          m_lazy_run=False
      )
      
      m27 = M.trade.v4(
          instruments=m22.data,
          options_data=m21.predictions,
          start_date='',
          end_date='',
          initialize=m27_initialize_bigquant_run,
          handle_data=m27_handle_data_bigquant_run,
          prepare=m27_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='真实价格',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark='000300.SHA'
      )
      
      设置评估测试数据集,查看训练曲线
      [视频教程]StockRanker训练曲线
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e91eb29e59964268a37b401950b11ea0"}/bigcharts-data-end
      • 收益率-22.78%
      • 年化收益率-23.43%
      • 基准收益率36.07%
      • 阿尔法-0.5
      • 贝塔0.94
      • 夏普比率-0.53
      • 胜率0.57
      • 盈亏比0.67
      • 收益波动率40.26%
      • 信息比率-0.09
      • 最大回撤43.26%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-25622311fc9644779ab504b6b04402aa"}/bigcharts-data-end
      设置评估测试数据集,查看训练曲线
      [视频教程]StockRanker训练曲线
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a39867bc6c084df286802e710e29661a"}/bigcharts-data-end
      • 收益率-22.78%
      • 年化收益率-23.43%
      • 基准收益率36.07%
      • 阿尔法-0.5
      • 贝塔0.94
      • 夏普比率-0.53
      • 胜率0.57
      • 盈亏比0.67
      • 收益波动率40.26%
      • 信息比率-0.09
      • 最大回撤43.26%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-07fa9608e88d41c28ae30a49d00fb9df"}/bigcharts-data-end

      (侯) #3

      老师,我发现您是把jin4=ta_ma(close_4, derive=‘golden_cross’)改成了jin4=close_0 - open_0,为什么ta_ma(close_4, derive=‘golden_cross’)会引起训练集数据的不同呢?


      (adhaha111) #4

      因为您两个策略中,一个是带了jin4,一个是没有带的,该字段中存在nan,在drop后,就会导致得到的数据量不一致


      (侯) #5

      非常感谢,现在换成了缺失值填充模块,结果就一样了