为什么会出现array length does not match index length?

策略分享
标签: #<Tag:0x00007fc0660bfa30>

(oversky2003) #1

前几天跑还没有问题,我什么都没有改,就有问题了。

https://i.bigquant.com/user/oversky2003/lab/share/MA-long_10-dp-lstm.ipynb?_t=1564985827425


(iQuant) #2

您好,收到您的提问,已将问题提交给策略工程师,会尽快给您回复。


(达达) #3

模块连接有问题,应该在过滤模块后连线

克隆策略

    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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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return 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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年8月6日 14:25
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # 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 m14_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 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 m14_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m25_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 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 m25_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m7_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), 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 m7_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m15_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=0.00015, sell_cost=0.00115, 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
        context.stock_count = 10
        # 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 = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m15_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 = context.portfolio.cash
    
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
    
        to_buy_instruments = list(ranker_prediction.instrument[:context.stock_count])
        buy_instruments = [k for k in to_buy_instruments if k not in positions.keys()]#已有持仓不重复买入
        #----------------------------START:持有固定天数卖出---------------------------
        today = data.current_dt.strftime('%Y-%m-%d')
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
            for instrument in equities.keys():
                # 如果在买入列表中就不卖了
                if instrument in buy_instruments:
                    continue
                sid = equities[instrument].sid  # 交易标的
                # 今天和上次交易的时间相隔hold_days就全部卖出
                dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.hold_days].values[0])
                if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(sid, 0)
                    cash_for_buy += positions[instrument]
        #--------------------------------END:持有固定天数卖出---------------------------
        
        # 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
        # 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, ..]
        buy_stock_count=len(buy_instruments)
        buy_cash_weights = T.norm([1 / math.log(i + 2) for i in range(0, buy_stock_count)])
        # buy_cash_weights=[1/buy_stock_count]*buy_stock_count
    
        for i, instrument in enumerate(buy_instruments):
            if is_staging:
                cash =  min(cash_for_buy,cash_avg) * buy_cash_weights[i]
            else:
                cash =  cash_for_buy * buy_cash_weights[i]
            context.order_target_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m15_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 m15_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2009-01-01',
        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(open, -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,
        user_functions={}
    )
    
    m11 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""close_0/mean(close_0,5)
    close_0/mean(close_0,10)
    close_0/mean(close_0,20)
    close_0/open_0
    open_0/mean(close_0,5)
    open_0/mean(close_0,10)
    open_0/mean(close_0,20)"""
    )
    
    m4 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=150
    )
    
    m5 = M.derived_feature_extractor.v3(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m27 = M.standardlize.v8(
        input_1=m5.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m6 = M.join.v3(
        data1=m11.data,
        data2=m27.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m28 = M.chinaa_stock_filter.v1(
        input_data=m6.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m22 = M.dl_convert_to_bin.v2(
        input_data=m28.data,
        features=m3.data,
        window_size=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m14 = M.cached.v3(
        input_1=m22.data,
        input_2=m3.data,
        run=m14_run_bigquant_run,
        post_run=m14_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m8 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2019-07-26'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m9 = M.general_feature_extractor.v7(
        instruments=m8.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=150
    )
    
    m10 = M.derived_feature_extractor.v3(
        input_data=m9.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m26 = M.standardlize.v8(
        input_1=m10.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m29 = M.chinaa_stock_filter.v1(
        input_data=m26.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m12 = M.dl_convert_to_bin.v2(
        input_data=m29.data,
        features=m3.data,
        window_size=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m25 = M.cached.v3(
        input_1=m12.data,
        input_2=m3.data,
        run=m25_run_bigquant_run,
        post_run=m25_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m13 = M.dl_layer_input.v1(
        shape='7,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m17 = M.dl_layer_lstm.v1(
        inputs=m13.data,
        units=5,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        recurrent_initializer='Orthogonal',
        bias_initializer='Zeros',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        recurrent_regularizer='None',
        recurrent_regularizer_l1=0,
        recurrent_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        dropout=0,
        recurrent_dropout=0,
        return_sequences=False,
        implementation='0',
        name=''
    )
    
    m18 = M.dl_layer_dropout.v1(
        inputs=m17.data,
        rate=0.2,
        noise_shape='',
        name=''
    )
    
    m19 = M.dl_layer_dense.v1(
        inputs=m18.data,
        units=5,
        activation='tanh',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m20 = M.dl_layer_dropout.v1(
        inputs=m19.data,
        rate=0.2,
        noise_shape='',
        name=''
    )
    
    m21 = M.dl_layer_dense.v1(
        inputs=m20.data,
        units=1,
        activation='tanh',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m16 = M.dl_model_init.v1(
        inputs=m13.data,
        outputs=m21.data
    )
    
    m23 = M.dl_model_train.v1(
        input_model=m16.data,
        training_data=m14.data_1,
        optimizer='RMSprop',
        loss='mean_squared_error',
        metrics='mae',
        batch_size=256,
        epochs=5,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.dl_model_predict.v1(
        trained_model=m23.data,
        input_data=m25.data_1,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m7 = M.cached.v3(
        input_1=m24.data,
        input_2=m29.data,
        run=m7_run_bigquant_run,
        post_run=m7_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m15 = M.trade.v4(
        instruments=m8.data,
        options_data=m7.data_1,
        start_date='',
        end_date='',
        initialize=m15_initialize_bigquant_run,
        handle_data=m15_handle_data_bigquant_run,
        prepare=m15_prepare_bigquant_run,
        before_trading_start=m15_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='open',
        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.HIX'
    )
    
    Using TensorFlow backend.
    
    Epoch 1/5
     - 326s - loss: 0.9851 - mean_absolute_error: 0.8160
    Epoch 2/5
     - 360s - loss: 0.9839 - mean_absolute_error: 0.8154
    Epoch 3/5
     - 371s - loss: 0.9838 - mean_absolute_error: 0.8153
    Epoch 4/5
     - 389s - loss: 0.9836 - mean_absolute_error: 0.8152
    Epoch 5/5
     - 374s - loss: 0.9836 - mean_absolute_error: 0.8152
    
    DataSource(84caecbdf8a1466a985caf5a19611954T, v3)
    
    • 收益率31.19%
    • 年化收益率64.16%
    • 基准收益率28.16%
    • 阿尔法0.1
    • 贝塔0.99
    • 夏普比率1.37
    • 胜率0.53
    • 盈亏比1.15
    • 收益波动率39.93%
    • 信息比率0.02
    • 最大回撤21.88%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-88155cdc39f3486bb7c974e1830eb131"}/bigcharts-data-end