一个很奇怪的问题---出现不存在的证券代码


(albertech) #1
克隆策略

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1\n else:\n context.sell_flag = 0\n \n if context.buy_flag==0 and context.sell_flag==0:\n return\n \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 %H:%M:%S')]\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 context.sell_flag>0:\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2020年9月1日 09:56
    # 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 m10_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
        context.buy_flag = 0
        context.sell_flag = 1
    # 回测引擎:每日数据处理函数,每天执行一次
    def m10_handle_data_bigquant_run(context, data):
        # 每天只在固定时间买入轮仓
        if data.current_dt.strftime('%H:%M:%S')=='09:40:00':
            context.buy_flag = 1
        else:
            context.buy_flag = 0
    
        # 每天只在固定时间卖出轮仓
        if data.current_dt.strftime('%H:%M:%S')=='14:50:00':
            context.sell_flag = 1
        else:
            context.sell_flag = 0
       
        if context.buy_flag==0 and context.sell_flag==0:
            return
        
        # 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 %H:%M:%S')]
    
        # 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 context.sell_flag>0:
            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
        if context.buy_flag>0:
            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 m10_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 m10_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2019-04-01',
        end_date='2020-04-30',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m6 = M.use_datasource.v1(
        instruments=m1.data,
        datasource_id='bar60m_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m13 = M.chinaa_stock_filter.v1(
        input_data=m6.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板', '创业板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m11 = M.auto_labeler_on_datasource.v1(
        input_data=m6.data,
        label_expr="""#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, -40) / 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)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        drop_na_label=True,
        cast_label_int=True,
        date_col='date',
        instrument_col='instrument',
        user_functions={}
    )
    
    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
    shift(ta_rsi(close,14),1)/60
    shift(ta_rsi(close,14),1)/100"""
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m13.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m11.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m12 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m12.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', '2020-05-01'),
        end_date=T.live_run_param('trading_date', '2020-08-28'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m15 = M.use_datasource.v1(
        instruments=m9.data,
        datasource_id='bar60m_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m14 = M.chinaa_stock_filter.v1(
        input_data=m15.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板', '创业板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m14.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m5.data,
        m_lazy_run=False
    )
    
    m10 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m10_initialize_bigquant_run,
        handle_data=m10_handle_data_bigquant_run,
        prepare=m10_prepare_bigquant_run,
        before_trading_start=m10_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000001,
        auto_cancel_non_tradable_orders=True,
        data_frequency='minute',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7fa006d4b7ea4a10b311f81dd513f557"}/bigcharts-data-end

    (adhaha111) #2

    您好,该股票在分钟数据中没有单独的表,您可以对其进行删除

    克隆策略

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      In [8]:
      # 本代码由可视化策略环境自动生成 2020年9月3日 08:48
      # 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。
      
      
      # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
      def m2_run_bigquant_run(input_1, input_2, input_3):
          # 示例代码如下。在这里编写您的代码
          data1 = instruments = input_1.read_pickle()
          instruments = data1["instruments"]
          instruments.remove("651002.SHA")
          data1["instruments"] = instruments
          
          return Outputs(data_1=DataSource.write_pickle(data1), data_2=None, data_3=None)
      
      # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
      def m2_post_run_bigquant_run(outputs):
          return outputs
      
      # 回测引擎:初始化函数,只执行一次
      def m10_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
          context.buy_flag = 0
          context.sell_flag = 1
      # 回测引擎:每日数据处理函数,每天执行一次
      def m10_handle_data_bigquant_run(context, data):
          # 每天只在固定时间买入轮仓
          if data.current_dt.strftime('%H:%M:%S')=='09:40:00':
              context.buy_flag = 1
          else:
              context.buy_flag = 0
      
          # 每天只在固定时间卖出轮仓
          if data.current_dt.strftime('%H:%M:%S')=='14:50:00':
              context.sell_flag = 1
          else:
              context.sell_flag = 0
         
          if context.buy_flag==0 and context.sell_flag==0:
              return
          
          # 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 %H:%M:%S')]
      
          # 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 context.sell_flag>0:
              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
          if context.buy_flag>0:
              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 m10_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 m10_before_trading_start_bigquant_run(context, data):
          pass
      
      
      m1 = M.instruments.v2(
          start_date='2019-04-01',
          end_date='2020-04-30',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m6 = M.use_datasource.v1(
          instruments=m1.data,
          datasource_id='bar60m_CN_STOCK_A',
          start_date='',
          end_date=''
      )
      
      m13 = M.chinaa_stock_filter.v1(
          input_data=m6.data,
          index_constituent_cond=['全部'],
          board_cond=['上证主板', '深证主板', '创业板'],
          industry_cond=['全部'],
          st_cond=['正常'],
          delist_cond=['非退市'],
          output_left_data=False
      )
      
      m11 = M.auto_labeler_on_datasource.v1(
          input_data=m6.data,
          label_expr="""#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, -40) / 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)
      where(shift(high, -1) == shift(low, -1), NaN, label)
      """,
          drop_na_label=True,
          cast_label_int=True,
          date_col='date',
          instrument_col='instrument',
          user_functions={}
      )
      
      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
      shift(ta_rsi(close,14),1)/60
      shift(ta_rsi(close,14),1)/100"""
      )
      
      m16 = M.derived_feature_extractor.v3(
          input_data=m13.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m7 = M.join.v3(
          data1=m11.data,
          data2=m16.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m12 = M.dropnan.v2(
          input_data=m7.data
      )
      
      m4 = M.stock_ranker_train.v6(
          training_ds=m12.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', '2020-05-01'),
          end_date=T.live_run_param('trading_date', '2020-08-28'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m15 = M.use_datasource.v1(
          instruments=m9.data,
          datasource_id='bar60m_CN_STOCK_A',
          start_date='',
          end_date=''
      )
      
      m14 = M.chinaa_stock_filter.v1(
          input_data=m15.data,
          index_constituent_cond=['全部'],
          board_cond=['上证主板', '深证主板', '创业板'],
          industry_cond=['全部'],
          st_cond=['正常'],
          delist_cond=['非退市'],
          output_left_data=False
      )
      
      m18 = M.derived_feature_extractor.v3(
          input_data=m14.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m5 = M.dropnan.v2(
          input_data=m18.data
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m4.model,
          data=m5.data,
          m_lazy_run=False
      )
      
      m2 = M.cached.v3(
          input_1=m9.data,
          run=m2_run_bigquant_run,
          post_run=m2_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m10 = M.trade.v4(
          instruments=m2.data_1,
          options_data=m8.predictions,
          start_date='',
          end_date='',
          initialize=m10_initialize_bigquant_run,
          handle_data=m10_handle_data_bigquant_run,
          prepare=m10_prepare_bigquant_run,
          before_trading_start=m10_before_trading_start_bigquant_run,
          volume_limit=0.025,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=1000001,
          auto_cancel_non_tradable_orders=True,
          data_frequency='minute',
          price_type='真实价格',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark=''
      )
      

      (albertech) #3

      跑不完整个流程就死掉了。
      请测试一下给一个完整的解决方案。

      删除了这个股票代码是可以,问题是:这个代码是怎么产生的?是不是平台的一个BUG?