直播课大奖赛题目

策略分享
标签: #<Tag:0x00007f73dd8f3650>

(franklili) #2

这个比赛的截止时间是几日几点呀?


(a04512) #3

只用贴回测曲线还是自己的策略都要贴出来?


(yfx606) #4

大赛要求:“参选策略必须在给定模版基础上克隆,仅允许修改因子组合”
问题是:那就是只能改输入特征,这么理解对不对?也不允许加模块?


(iQuant) #5

时间截止3月22日23:59,之后提交的都不计算


(iQuant) #6

是的,只需要修改因子


(iQuant) #7

策略需要贴出来


(iQuant) #8

最终比赛排名结果将在3月23日晚上直播课上由老师公布,各位记得观看/回看23日直播。


(ahxdct) #11
克隆策略

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    In [7]:
    # 本代码由可视化策略环境自动生成 2020年3月17日 22:56
    # 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 = 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 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
    
    
    m1 = M.instruments.v2(
        start_date='2018-07-01',
        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/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, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2020-03-01'),
        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=0
    )
    
    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
    )
    
    m5 = M.decomposition_pca.v1(
        training_ds=m13.data,
        features=m3.data,
        predict_ds=m14.data,
        n_components=8,
        whiten=True,
        other_train_parameters={}
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.transform_trainds,
        features=m5.pca_features,
        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
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m5.transform_predictds,
        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'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1c32f0275c73497ca61c46dacd8318c9"}/bigcharts-data-end
    • 收益率24.03%
    • 年化收益率21.39%
    • 基准收益率30.87%
    • 阿尔法0.02
    • 贝塔0.71
    • 夏普比率0.85
    • 胜率0.52
    • 盈亏比1.18
    • 收益波动率22.23%
    • 信息比率-0.02
    • 最大回撤27.79%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a1e71f33b0644990815f49792e872d5c"}/bigcharts-data-end

    (公孙睿) #14

    可以使用大盘风控的吗?


    (my837563628) #17
    克隆策略

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      In [5]:
      # 本代码由可视化策略环境自动生成 2020年3月22日 20:03
      # 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 = 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 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
      
      
      m1 = M.instruments.v2(
          start_date='2018-01-01',
          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/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, -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="""rank_return_5
      avg_amount_0/avg_amount_5
      rank_avg_amount_0/rank_avg_amount_5
      rank_avg_amount_5/rank_avg_amount_10
      rank_return_10
      rank_return_0/rank_return_5
      rank_return_5/rank_return_10
      fs_net_profit_margin_ttm_0
      rank_fs_roe_0
      rank_fs_roa_0
      rank_fs_net_profit_yoy_0
      rank_fs_eps_yoy_0
      rank_market_cap_float_0
      rank(west_netprofit_ftm_0)
      rank(west_eps_ftm_0)
      rank(west_avgcps_ftm_0)
      rank(pb_lf_0)
      rank(avg_mf_net_amount_20)
      """
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      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
      )
      
      m4 = M.stock_ranker_train.v6(
          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,
          data_row_fraction=1,
          ndcg_discount_base=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', '2020-03-01'),
          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=0
      )
      
      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=m4.model,
          data=m14.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'
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-453beb4a138449c1aefdfb93c678ac46"}/bigcharts-data-end
      • 收益率57.45%
      • 年化收益率50.46%
      • 基准收益率30.87%
      • 阿尔法0.21
      • 贝塔0.85
      • 夏普比率1.67
      • 胜率0.52
      • 盈亏比1.12
      • 收益波动率24.54%
      • 信息比率0.06
      • 最大回撤15.25%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0ea1d6665b374ce49debca113f91bd54"}/bigcharts-data-end

      (buptleader) #18
      克隆策略

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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.hold_days = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 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.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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 instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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        In [2]:
        # 本代码由可视化策略环境自动生成 2020年3月20日 18:05
        # 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 m5_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.hold_days = 5
        
        # 回测引擎:每日数据处理函数,每天执行一次
        def m5_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 = 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 m5_prepare_bigquant_run(context):
            pass
        
        # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
        def m5_before_trading_start_bigquant_run(context, data):
            pass
        
        
        m1 = M.instruments.v2(
            start_date='2017-01-01',
            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/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, -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
        )
        
        m9 = M.instruments.v2(
            start_date=T.live_run_param('trading_date', '2019-01-01'),
            end_date=T.live_run_param('trading_date', '2020-03-01'),
            market='CN_STOCK_A',
            instrument_list='',
            max_count=0
        )
        
        m3 = M.input_features.v1(
            features="""
        # #号开始的表示注释,注释需单独一行
        # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
        ts_max(open_0,5)
        shift(low_0,5)
        ta_rsi(close_0,5)
        ta_mom(return_0,5)
        ta_bias(daily_return_0,5)
        decay_linear(volume_0,5)
        std(amount_0,5)
        ts_argmin(mf_net_pct_xl_0,5)
        ts_min(swing_volatility_5_0,5)
        product(sh_holder_avg_pct_3m_chng_0,5)
        group_sum(industry_sw_level1_0,ps_ttm_0)
        group_sum(industry_sw_level1_0,pb_lf_0)
        ta_macd_dea(close_0,26,12,9)
        beta_csi300_90_0"""
        )
        
        m15 = M.general_feature_extractor.v7(
            instruments=m1.data,
            features=m3.data,
            start_date='',
            end_date='',
            before_start_days=40
        )
        
        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
        )
        
        m17 = M.general_feature_extractor.v7(
            instruments=m9.data,
            features=m3.data,
            start_date='',
            end_date='',
            before_start_days=40
        )
        
        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
        )
        
        m4 = M.stock_ranker.v2(
            training_ds=m13.data,
            features=m3.data,
            predict_ds=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,
            slim_data=True
        )
        
        m5 = M.trade.v4(
            instruments=m9.data,
            options_data=m4.predictions,
            start_date='',
            end_date='',
            initialize=m5_initialize_bigquant_run,
            handle_data=m5_handle_data_bigquant_run,
            prepare=m5_prepare_bigquant_run,
            before_trading_start=m5_before_trading_start_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-1d49163d459a45d29c002dba2c8c765d"}/bigcharts-data-end
        • 收益率95.97%
        • 年化收益率83.22%
        • 基准收益率30.87%
        • 阿尔法0.42
        • 贝塔0.78
        • 夏普比率2.63
        • 胜率0.62
        • 盈亏比1.34
        • 收益波动率22.91%
        • 信息比率0.14
        • 最大回撤12.79%
        bigcharts-data-start/{"__type":"tabs","__id":"bigchart-b46b2310fe6642c29ce0ac363be84f1e"}/bigcharts-data-end

        (iQuant) #19

        只修改因子


        (luojinzh) #22
        克隆策略

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          In [10]:
          # 本代码由可视化策略环境自动生成 2020年3月21日 16:46
          # 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 = 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 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
          
          
          m1 = M.instruments.v2(
              start_date='2015-01-01',
              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/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, -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="""ta_sma_5_0
          fs_eqy_belongto_parcomsh_0
          rank_fs_roa_ttm_0
          fs_cash_ratio_0
          ta_mom_10_0
          fs_fixed_assets_disp_0
          ta_macd_macdsignal_12_26_9_0
          ta_bbands_upperband_14_0
          ta_wma_30_0
          ta_adx_14_0
          ta_trix_28_0
          ta_stoch_slowk_5_3_0_3_0_0
          ta_wma_60_0
          industry_sw_level1_0
          fs_quarter_index_0
          avg_mf_net_amount_1
          fs_total_equity_0
          ta_wma_20_0
          fs_operating_revenue_qoq_0
          market_cap_float_0
          """
          )
          
          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
          )
          
          m13 = M.dropnan.v1(
              input_data=m7.data
          )
          
          m4 = M.stock_ranker_train.v6(
              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,
              data_row_fraction=1,
              ndcg_discount_base=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', '2020-03-01'),
              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=90
          )
          
          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=m4.model,
              data=m14.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'
          )
          
          设置测试数据集,查看训练迭代过程的NDCG
          bigcharts-data-start/{"__type":"tabs","__id":"bigchart-ba7881cdce014acf98b32a4da8d696f7"}/bigcharts-data-end
          • 收益率69.9%
          • 年化收益率61.13%
          • 基准收益率30.87%
          • 阿尔法0.29
          • 贝塔0.8
          • 夏普比率1.91
          • 胜率0.53
          • 盈亏比2.24
          • 收益波动率25.07%
          • 信息比率0.08
          • 最大回撤13.35%
          bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c6497244aa7d49ea92eb75f42875864e"}/bigcharts-data-end

          (mubai_2014) #23

          训练集时间只要在18年12月31日之前,也可以自己修改吗


          (chris097) #26
          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 2020年3月21日 20:19
            # 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 = 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 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
            
            
            m1 = M.instruments.v2(
                start_date='2012-01-01',
                end_date='2016-12-31 ',
                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/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, -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
            market_cap_float_0"""
            )
            
            m15 = M.general_feature_extractor.v7(
                instruments=m1.data,
                features=m3.data,
                start_date='',
                end_date='',
                before_start_days=0
            )
            
            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
            )
            
            m4 = M.stock_ranker_train.v6(
                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,
                data_row_fraction=1,
                ndcg_discount_base=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', '2020-03-01'),
                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=0
            )
            
            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=m4.model,
                data=m14.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'
            )
            
            设置测试数据集,查看训练迭代过程的NDCG
            bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8f03af2275db4a629f9cdafe4c41dff3"}/bigcharts-data-end
            • 收益率67.04%
            • 年化收益率58.68%
            • 基准收益率30.87%
            • 阿尔法0.3
            • 贝塔0.66
            • 夏普比率2.01
            • 胜率0.56
            • 盈亏比1.29
            • 收益波动率22.84%
            • 信息比率0.07
            • 最大回撤14.72%
            bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d4545a798d1c4f7586619be21954fbed"}/bigcharts-data-end

            (my837563628) #30

            能修改向前取特征天数吗


            (公孙睿) #31
            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 2020年3月22日 19:12
              # 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 = 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 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
              
              
              m1 = M.instruments.v2(
                  start_date='2016-01-01',
                  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/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, -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_pb_lf_0
              rank_market_cap_0
              return_10/return_20
              turn_0
              ta_sma_10_0/ta_sma_20_0
              rank_turn_0
              return_10/return_0
              avg_turn_0/avg_turn_9
              rank_avg_turn_9
              std(volume_0, 20)
              ta_cci_14_0
              shift(ta_macd_dea(close_0),1)
              ta_roc(close_0, 5)
              ta_trix_28_0
              mean(abs(close_0-mean(close_0, 6)), 6)
              rank_fs_roa_ttm_0
              decay_linear(volume_0, 5)
              
              
              
              
              
              
              
              
              
              
              
              """
              )
              
              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
              )
              
              m13 = M.dropnan.v1(
                  input_data=m7.data
              )
              
              m4 = M.stock_ranker_train.v6(
                  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,
                  data_row_fraction=1,
                  ndcg_discount_base=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', '2020-03-01'),
                  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=90
              )
              
              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=m4.model,
                  data=m14.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'
              )
              
              设置测试数据集,查看训练迭代过程的NDCG
              bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1668f1c137374646bb7beeb66e6e58c4"}/bigcharts-data-end
              • 收益率105.73%
              • 年化收益率91.41%
              • 基准收益率30.87%
              • 阿尔法0.51
              • 贝塔0.55
              • 夏普比率3.03
              • 胜率0.57
              • 盈亏比1.48
              • 收益波动率21.21%
              • 信息比率0.13
              • 最大回撤8.43%
              bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0ee9188d4c4a4633962600e0711c4110"}/bigcharts-data-end

              (鲁鲁) #32
              克隆策略

                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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.hold_days = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 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.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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 instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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                In [2]:
                # 本代码由可视化策略环境自动生成 2020年3月22日 21:20
                # 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 m5_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.hold_days = 5
                
                # 回测引擎:每日数据处理函数,每天执行一次
                def m5_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 = 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 m5_prepare_bigquant_run(context):
                    pass
                
                # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
                def m5_before_trading_start_bigquant_run(context, data):
                    pass
                
                
                m1 = M.instruments.v2(
                    start_date='2017-01-01',
                    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/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, -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
                )
                
                m9 = M.instruments.v2(
                    start_date=T.live_run_param('trading_date', '2019-01-01'),
                    end_date=T.live_run_param('trading_date', '2020-03-01'),
                    market='CN_STOCK_A',
                    instrument_list='',
                    max_count=0
                )
                
                m3 = M.input_features.v1(
                    features="""
                # #号开始的表示注释,注释需单独一行
                # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
                ts_max(low_0,5)
                shift(open_0,5)
                ta_roc(return_0,5)
                ta_beta(daily_return_0,return_0,5)
                ts_argmin(return_0,5)
                ta_ema(mf_net_pct_m_0,5)
                group_rank(industry_sw_level1_0,sh_holder_avg_pct_0)
                group_sum(industry_sw_level1_0,pe_lyr_0)
                beta_csi800_180_0
                beta_csi100_180_0
                beta_csi300_60_0"""
                )
                
                m15 = M.general_feature_extractor.v7(
                    instruments=m1.data,
                    features=m3.data,
                    start_date='',
                    end_date='',
                    before_start_days=40
                )
                
                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
                )
                
                m17 = M.general_feature_extractor.v7(
                    instruments=m9.data,
                    features=m3.data,
                    start_date='',
                    end_date='',
                    before_start_days=40
                )
                
                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
                )
                
                m4 = M.stock_ranker.v2(
                    training_ds=m13.data,
                    features=m3.data,
                    predict_ds=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,
                    slim_data=True
                )
                
                m5 = M.trade.v4(
                    instruments=m9.data,
                    options_data=m4.predictions,
                    start_date='',
                    end_date='',
                    initialize=m5_initialize_bigquant_run,
                    handle_data=m5_handle_data_bigquant_run,
                    prepare=m5_prepare_bigquant_run,
                    before_trading_start=m5_before_trading_start_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-73f6ce32c62249388ec2e2c39cd1b517"}/bigcharts-data-end
                • 收益率65.99%
                • 年化收益率57.79%
                • 基准收益率30.87%
                • 阿尔法0.27
                • 贝塔0.77
                • 夏普比率2.08
                • 胜率0.56
                • 盈亏比1.41
                • 收益波动率21.62%
                • 信息比率0.09
                • 最大回撤17.04%
                bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5ec7e6d84bea4b5c8676b8f4878b36c0"}/bigcharts-data-end

                (hardsum) #33
                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 2020年3月22日 21:36
                  # 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 m5_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.hold_days = 5
                  
                  # 回测引擎:每日数据处理函数,每天执行一次
                  def m5_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 = 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 m5_prepare_bigquant_run(context):
                      pass
                  
                  # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
                  def m5_before_trading_start_bigquant_run(context, data):
                      pass
                  
                  
                  m1 = M.instruments.v2(
                      start_date='2017-01-01',
                      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/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, -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
                  )
                  
                  m9 = M.instruments.v2(
                      start_date=T.live_run_param('trading_date', '2019-01-01'),
                      end_date=T.live_run_param('trading_date', '2020-03-01'),
                      market='CN_STOCK_A',
                      instrument_list='',
                      max_count=0
                  )
                  
                  m3 = M.input_features.v1(
                      features="""
                  # #号开始的表示注释,注释需单独一行
                  # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
                  ta_beta(low_0,return_0,5)
                  ta_ma(close_0,5)
                  ts_argmin(close_0,5)
                  ta_ema(return_0,5)
                  ts_rank(return_0,5)
                  ta_rsi(amount_0,5)
                  ta_sma(deal_number_0,5)
                  ta_bias(swing_volatility_5_0,5)
                  rank(swing_volatility_5_0)
                  rank(volatility_5_0)
                  rank(sh_holder_avg_pct_3m_chng_0)
                  decay_linear(sh_holder_avg_pct_3m_chng_0,5)
                  beta_sse50_120_0
                  beta_industry_10_0"""
                  )
                  
                  m15 = M.general_feature_extractor.v7(
                      instruments=m1.data,
                      features=m3.data,
                      start_date='',
                      end_date='',
                      before_start_days=40
                  )
                  
                  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
                  )
                  
                  m17 = M.general_feature_extractor.v7(
                      instruments=m9.data,
                      features=m3.data,
                      start_date='',
                      end_date='',
                      before_start_days=40
                  )
                  
                  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
                  )
                  
                  m4 = M.stock_ranker.v2(
                      training_ds=m13.data,
                      features=m3.data,
                      predict_ds=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,
                      slim_data=True
                  )
                  
                  m5 = M.trade.v4(
                      instruments=m9.data,
                      options_data=m4.predictions,
                      start_date='',
                      end_date='',
                      initialize=m5_initialize_bigquant_run,
                      handle_data=m5_handle_data_bigquant_run,
                      prepare=m5_prepare_bigquant_run,
                      before_trading_start=m5_before_trading_start_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-8b086ce980b94ff0b94f236bc97bd0de"}/bigcharts-data-end
                  设置测试数据集,查看训练迭代过程的NDCG
                  bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0f64eaaf893646fcb144da68d2941e03"}/bigcharts-data-end
                  • 收益率73.22%
                  • 年化收益率63.96%
                  • 基准收益率30.87%
                  • 阿尔法0.3
                  • 贝塔0.82
                  • 夏普比率2.09
                  • 胜率0.57
                  • 盈亏比1.34
                  • 收益波动率23.64%
                  • 信息比率0.1
                  • 最大回撤12.02%
                  bigcharts-data-start/{"__type":"tabs","__id":"bigchart-428bb94928174d93b09ee42ddcc9d4a1"}/bigcharts-data-end

                  (Arrow flowing) #36
                  克隆策略

                    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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.portfolio.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,按机器学习算法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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n 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                    In [43]:
                    # 本代码由可视化策略环境自动生成 2020年3月22日 23:43
                    # 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 = 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 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
                    
                    
                    m1 = M.instruments.v2(
                        start_date='2018-07-01',
                        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/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, -5) / shift(open, -1)
                    
                    # 极值处理:用1%和99%分位的值做clip
                    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
                    
                    # 将分数映射到分类,这里使用20个分类
                    all_wbins(label, 20)
                    
                    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
                    where(shift(high, -1) == shift(low, -1), NaN, label)
                    """,
                        start_date='',
                        end_date='',
                        benchmark='000300.SHA',
                        drop_na_label=True,
                        cast_label_int=True
                    )
                    
                    m3 = M.input_features.v1(
                        features="""# #号开始的表示注释
                    # 多个特征,每行一个,可以包含基础特征和衍生特征
                    return_5
                    return_10
                    return_20
                    avg_amount_0/avg_amount_5
                    avg_amount_5/avg_amount_20
                    ts_min(swing_volatility_5_0,5)
                    rank_avg_amount_0/rank_avg_amount_5
                    rank_return_0
                    rank_return_5
                    rank_return_10
                    rank_return_0/rank_return_5
                    rank_return_5/rank_return_10
                    pe_ttm_0
                    std(amount_0,5)
                    rank_turn_0
                    """
                    )
                    
                    m15 = M.general_feature_extractor.v7(
                        instruments=m1.data,
                        features=m3.data,
                        start_date='',
                        end_date='',
                        before_start_days=0
                    )
                    
                    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
                    )
                    
                    m4 = M.stock_ranker_train.v6(
                        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,
                        data_row_fraction=1,
                        ndcg_discount_base=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', '2020-03-01'),
                        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=0
                    )
                    
                    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=m4.model,
                        data=m14.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'
                    )
                    
                    设置测试数据集,查看训练迭代过程的NDCG
                    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-b9a2db088724414cbb491fa9c3dab0a7"}/bigcharts-data-end
                    • 收益率82.51%
                    • 年化收益率71.86%
                    • 基准收益率30.87%
                    • 阿尔法0.36
                    • 贝塔0.78
                    • 夏普比率2.28
                    • 胜率0.56
                    • 盈亏比1.39
                    • 收益波动率23.72%
                    • 信息比率0.11
                    • 最大回撤13.5%
                    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-bfad1ca4e9bd418faf5097d7df3a8111"}/bigcharts-data-end