用了别名,还是有错误

策略分享
标签: #<Tag:0x00007fc575b8cf30>

(人人富足) #1
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.options['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.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. 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2020年4月6日 13:18
    # 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="""alpha151=return_9/return_5
    alpha152=avg_turn_6/turn_0
    alpha153=return_3/return_0
    alpha154=ta_atr(high_0,low_0,close_0,28)/close_0
    alpha155=close_0/mean(close_0,10)
    alpha156=return_1/return_5
    alpha157=return_0/return_3
    alpha158=mean(close_0,30)/close_0
    alpha159=return_1/return_0
    alpha160=return_9/return_3
    alpha161=ta_ema(close_0,30)/close_0
    alpha162=avg_turn_3/turn_0
    alpha163=return_1/return_3
    alpha164=close_0/mean(close_0,30)
    alpha165=return_6/return_5
    alpha166=return_6/return_0
    alpha167=close_0/mean(close_0,20)
    alpha168=return_0/return_5
    alpha169=return_6/return_3
    alpha170=fs_net_profit_yoy_0
    alpha171=fs_net_profit_qoq_0
    alpha172=return_90/return_5
    alpha173=return_15/return_0
    alpha174=avg_turn_15/turn_0
    alpha175=return_20/return_5
    alpha176=return_50/return_5
    alpha177=rank_sh_holder_num_0
    alpha178=return_30/return_5
    alpha179=avg_turn_20/turn_0
    alpha180=return_30/return_0
    alpha181=return_30/return_3
    alpha182=return_20/return_0
    alpha183=return_20/return_3
    alpha184=return_15/return_5
    alpha185=rank_fs_cash_ratio_0
    alpha186=return_70/return_5
    alpha187=return_60/return_5
    alpha188=return_80/return_5
    alpha189=return_15/return_3
    alpha190=return_30/return_10
    alpha191=return_70/return_10
    alpha192=amount_0/avg_amount_5
    alpha193=return_80/return_10
    alpha194=return_50/return_10
    alpha195=return_20/return_10
    alpha196=return_90/return_10
    alpha197=amount_0/avg_amount_3
    alpha198=return_120/return_5
    alpha199=return_60/return_10
    alpha200=fs_net_profit_margin_0
    alpha201=(high_0-low_0)/close_0
    alpha202=return_120/return_10
    alpha203=mean(close_0,20)/mean(close_0,30)
    alpha204=mean(close_0,30)/mean(close_0,60)
    alpha205=mean(close_0,10)/mean(close_0,60)
    alpha206=(low_1-close_0)/close_0
    alpha207=rank_market_cap_float_0
    alpha208=mean(close_0,10)/mean(close_0,20)
    alpha209=(low_1-close_1)/close_0
    alpha210=(close_1-low_0)/close_0
    alpha211=(low_0-close_1)/close_0
    alpha212=mean(close_0,10)/mean(close_0,30)
    alpha213=rank_fs_net_profit_qoq_0
    alpha214=rank_sh_holder_avg_pct_0
    alpha215=fs_gross_profit_margin_0
    alpha216=(high_0-close_1)/close_0
    alpha217=(high_1-close_0)/close_0
    alpha218=rank_fs_net_profit_yoy_0
    alpha219=(open_0-close_0)/close_0
    alpha220=(close_1-high_0)/close_0
    alpha221=(high_1-close_1)/close_0
    alpha222=(high_0-low_0)/(close_0-open_0)
    alpha223=rank_fs_operating_revenue_yoy_0
    alpha224=rank_fs_operating_revenue_qoq_0
    alpha225=(open_0-close_0)/(high_0-low_0)
    alpha226=rank_sh_holder_avg_pct_6m_chng_0
    alpha227=rank_sh_holder_avg_pct_3m_chng_0
    alpha228=mean(close_0,3)/close_0
    alpha229=mean(amount_0,3)/amount_0
    alpha230=mean(volume_0,3)/volume_0
    alpha231=avg_mf_net_amount_6/mf_net_amount_0
    alpha232=avg_mf_net_amount_9/mf_net_amount_0
    alpha233=avg_mf_net_amount_3/mf_net_amount_0
    alpha234=avg_mf_net_amount_20/mf_net_amount_0
    alpha235=avg_mf_net_amount_15/mf_net_amount_0
    alpha236=avg_mf_net_amount_12/mf_net_amount_0
    alpha237=avg_mf_net_amount_9/avg_mf_net_amount_3
    alpha238=avg_mf_net_amount_6/avg_mf_net_amount_3
    alpha239=close_0/mean(close_0,3)
    alpha240=avg_mf_net_amount_20/avg_mf_net_amount_3
    alpha241=avg_mf_net_amount_12/avg_mf_net_amount_3
    alpha242=avg_mf_net_amount_15/avg_mf_net_amount_3
    alpha243=amount_0/mean(amount_0,3)
    alpha244=((close_0-low_0)-(high_0-close_0))/(high_0-close_0)
    alpha245=(high_0-low_0+high_1-low_1+high_2-low_2)/close_0
    alpha246=mean(close_0,6)/close_0
    alpha247=mean(amount_0,6)/amount_0
    alpha248=mean(volume_0,6)/volume_0
    alpha249=3/1*(high_0-low_0)/(high_0-low_0+high_1-low_1+high_2-low_2)
    alpha250=mean(close_0,6)/mean(close_0,3)
    alpha251=mean(close_0,9)/close_0
    alpha252=mean(amount_0,6)/mean(amount_0,3)
    alpha253=mean(amount_0,9)/amount_0
    alpha254=mean(volume_0,9)/volume_0
    alpha255=(mean(high_0,6)-mean(low_0,6))/close_0
    alpha256=mean(close_0,9)/mean(close_0,3)
    alpha257=mean(amount_0,9)/mean(amount_0,3)
    alpha258=mean(close_0,15)/close_0
    alpha259=(mean(high_0,9)-mean(low_0,9))/close_0
    alpha260=mean(amount_0,15)/amount_0
    alpha261=mean(volume_0,15)/volume_0
    alpha262=(mean(high_0,6)-mean(low_0,6))/(mean(high_0,3)-mean(low_0,3))
    alpha263=mean(close_0,15)/mean(close_0,3)
    alpha264=mean(amount_0,15)/mean(amount_0,3)
    alpha265=mean(close_0,20)/close_0
    alpha266=mean(amount_0,20)/amount_0
    alpha267=mean(volume_0,20)/volume_0
    alpha268=mean(close_0,20)/mean(close_0,3)
    alpha269=(mean(high_0,9)-mean(low_0,9))/(mean(high_0,3)-mean(low_0,3))
    alpha270=mean(amount_0,20)/mean(amount_0,3)
    alpha271=(sum(high_0,15)-sum(low_0,15))/close_0
    alpha272=(mean(high_0,15)-mean(low_0,15))/(mean(high_0,3)-mean(low_0,3))
    alpha273=(sum(high_0,20)-sum(low_0,20))/close_0
    alpha274=(mean(high_0,20)-mean(low_0,20))/(mean(high_0,3)-mean(low_0,3))
    
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=120
    )
    
    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', '2020-03-27'),
        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=120
    )
    
    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'
    )
    

    (达达) #2

    你需要用简称处理模块处理一下

    克隆策略

      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      In [ ]:
      # 本代码由可视化策略环境自动生成 2020年3月24日 17:49
      # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
      
      
      # 回测引擎:初始化函数,只执行一次
      def m19_initialize_bigquant_run(context):
          # 加载预测数据
          context.ranker_prediction = context.options['data'].read_df()
      
          # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
          context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
          # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
          # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
          stock_count = 1
          # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
          context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
          # 设置每只股票占用的最大资金比例
          context.max_cash_per_instrument =0.5
          context.options['hold_days'] = 1
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m19_handle_data_bigquant_run(context, data):
          # 按日期过滤得到今日的预测数据
          ranker_prediction = context.ranker_prediction[
              context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
      
          # 1. 资金分配
          # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
          # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
          is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
          cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
          cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
          positions = {e.symbol: p.amount * p.last_sale_price
                       for e, p in context.portfolio.positions.items()}
      
          # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
          if not is_staging and cash_for_sell > 0:
              equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
              instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities)])))
      
              for instrument in instruments:
                  context.order_target(context.symbol(instrument), 0)
                  cash_for_sell -= positions[instrument]
                  if cash_for_sell <= 0:
                      break
      
          # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
          buy_cash_weights = context.stock_weights
          buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
          max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
          for i, instrument in enumerate(buy_instruments):
              cash = cash_for_buy * buy_cash_weights[i]
              if cash > max_cash_per_instrument - positions.get(instrument, 0):
                  # 确保股票持仓量不会超过每次股票最大的占用资金量
                  cash = max_cash_per_instrument - positions.get(instrument, 0)
              if cash>0:
                  current_price = data.current(context.symbol(instrument), 'price')
                  amount = int(cash / current_price/100)*100
                  context.order(context.symbol(instrument), amount)
      
      # 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='2015-02-01',
          end_date='2019-05-01',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m2 = M.advanced_auto_labeler.v2(
          instruments=m1.data,
          label_expr="""# #号开始的表示注释
      # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
      # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
      #   添加benchmark_前缀,可使用对应的benchmark数据
      # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
      
      # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
      shift(close, -2) / shift(open, -1)
      
      # 极值处理:用1%和99%分位的值做clip
      clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
      
      # 将分数映射到分类,这里使用20个分类
      #all_wbins(label, 20)
      
      # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
      where(shift(high, -1) == shift(low, -1), NaN, label)
      """,
          start_date='',
          end_date='',
          benchmark='000300.SHA',
          drop_na_label=True,
          cast_label_int=True
      )
      
      m3 = M.input_features.v1(
          features="""# #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
      zhuli=(ta_sma2( ta_sma2( (close_0-ts_min(low_0, 21))/(ts_max(high_0, 21)-ts_min(low_0, 21))*100, 18, 8), 13, 8))/(ta_ema(3*ta_sma2( (close_0-ts_min(low_0, 30))/(ts_max(high_0, 30)-ts_min(low_0, 30))*100, 5, 1)-2*ta_sma2( ta_sma2( (close_0-ts_min(low_0, 30))/(ts_max(high_0, 30)-ts_min(low_0, 30))*100, 5, 1), 3, 1), 6))
      
      sanhu=20/(100*(ts_max(high_0,60)-close_0)/(ts_max(high_0,60)-ts_min(low_0,60)))
      """
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=60
      )
      
      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
      )
      
      m5 = M.features_short.v1(
          input_1=m3.data
      )
      
      m4 = M.stock_ranker_train.v6(
          training_ds=m13.data,
          features=m5.data_1,
          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', '2020-01-01'),
          end_date=T.live_run_param('trading_date', '2020-03-19'),
          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=100000,
          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'
      )
      

      (人人富足) #3

      谢谢大神!


      (人人富足) #4

      克隆后运行,新的错误又来了,请指教!

      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24日 18:20
        # 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 m19_initialize_bigquant_run(context):
            # 加载预测数据
            context.ranker_prediction = context.options['data'].read_df()
        
            # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
            context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
            # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
            # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
            stock_count = 1
            # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
            context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
            # 设置每只股票占用的最大资金比例
            context.max_cash_per_instrument =0.5
            context.options['hold_days'] = 1
        
        # 回测引擎:每日数据处理函数,每天执行一次
        def m19_handle_data_bigquant_run(context, data):
            # 按日期过滤得到今日的预测数据
            ranker_prediction = context.ranker_prediction[
                context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
        
            # 1. 资金分配
            # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
            # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
            is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
            cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
            cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
            cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
            positions = {e.symbol: p.amount * p.last_sale_price
                         for e, p in context.portfolio.positions.items()}
        
            # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
            if not is_staging and cash_for_sell > 0:
                equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
                instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                        lambda x: x in equities)])))
        
                for instrument in instruments:
                    context.order_target(context.symbol(instrument), 0)
                    cash_for_sell -= positions[instrument]
                    if cash_for_sell <= 0:
                        break
        
            # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
            buy_cash_weights = context.stock_weights
            buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
            max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
            for i, instrument in enumerate(buy_instruments):
                cash = cash_for_buy * buy_cash_weights[i]
                if cash > max_cash_per_instrument - positions.get(instrument, 0):
                    # 确保股票持仓量不会超过每次股票最大的占用资金量
                    cash = max_cash_per_instrument - positions.get(instrument, 0)
                if cash>0:
                    current_price = data.current(context.symbol(instrument), 'price')
                    amount = int(cash / current_price/100)*100
                    context.order(context.symbol(instrument), amount)
        
        # 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='2015-02-01',
            end_date='2019-05-01',
            market='CN_STOCK_A',
            instrument_list='',
            max_count=0
        )
        
        m2 = M.advanced_auto_labeler.v2(
            instruments=m1.data,
            label_expr="""# #号开始的表示注释
        # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
        # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
        #   添加benchmark_前缀,可使用对应的benchmark数据
        # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
        
        # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
        shift(close, -2) / shift(open, -1)
        
        # 极值处理:用1%和99%分位的值做clip
        clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
        
        # 将分数映射到分类,这里使用20个分类
        #all_wbins(label, 20)
        
        # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
        where(shift(high, -1) == shift(low, -1), NaN, label)
        """,
            start_date='',
            end_date='',
            benchmark='000300.SHA',
            drop_na_label=True,
            cast_label_int=True
        )
        
        m3 = M.input_features.v1(
            features="""# #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
        zhuli=(ta_sma2( ta_sma2( (close_0-ts_min(low_0, 21))/(ts_max(high_0, 21)-ts_min(low_0, 21))*100, 18, 8), 13, 8))/(ta_ema(3*ta_sma2( (close_0-ts_min(low_0, 30))/(ts_max(high_0, 30)-ts_min(low_0, 30))*100, 5, 1)-2*ta_sma2( ta_sma2( (close_0-ts_min(low_0, 30))/(ts_max(high_0, 30)-ts_min(low_0, 30))*100, 5, 1), 3, 1), 6))
        
        sanhu=20/(100*(ts_max(high_0,60)-close_0)/(ts_max(high_0,60)-ts_min(low_0,60)))
        """
        )
        
        m15 = M.general_feature_extractor.v7(
            instruments=m1.data,
            features=m3.data,
            start_date='',
            end_date='',
            before_start_days=60
        )
        
        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
        )
        
        m5 = M.features_short.v1(
            input_1=m3.data
        )
        
        m4 = M.stock_ranker_train.v6(
            training_ds=m13.data,
            features=m5.data_1,
            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', '2020-01-01'),
            end_date=T.live_run_param('trading_date', '2020-03-19'),
            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=100000,
            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-613a8dc90aaf455fb4d8ad014da71e9b"}/bigcharts-data-end

        缺失数据处理(dropnan)使用错误,你可以:

        1.一键查看文档

        2.一键搜索答案

        ---------------------------------------------------------------------------
        Exception                                 Traceback (most recent call last)
        <ipython-input-1-0847b2e6354c> in <module>()
            184 
            185 m14 = M.dropnan.v1(
        --> 186     input_data=m18.data
            187 )
            188 
        
        Exception: no data left after dropnan

        (人人富足) #5

        有没有一个文档或贴子专门讲解因子简称问题


        (达达) #6

        这回不是因子简称的问题了,是因为你的预测集的抽取天数少了,用的因子是用到了好几天的数据。需要修改基础特征抽取模块中的参数,向前取天数这个参数写大一些。


        (人人富足) #7

        非常感谢!