衍生特征抽取(derived_feature_extractor)使用错误?

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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 = 30\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 = 0.9\n context.options['hold_days'] = 5","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 if context.trading_day_index % 20 != 0:\n return\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.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.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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outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3895"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3895","OutputType":null},{"Name":"data_2","NodeId":"-3895","OutputType":null},{"Name":"data_3","NodeId":"-3895","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"","CommentCollapsed":true},{"Id":"-3907","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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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):\n import matplotlib.pyplot as plt\n plt.style.use('seaborn')\n\n history = input_1.read()['history']\n loss = history['loss']\n val_loss = history['val_loss']\n mae = history['mean_absolute_error']\n val_mae = history['val_mean_absolute_error']\n epochs = range(1, len(loss) + 1)\n\n plt.figure(1)\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.legend()\n plt.show()\n\n plt.figure(2)\n plt.plot(epochs, mae, 'bo', label='Training mae')\n plt.plot(epochs, val_mae, 'b', label='Validation mae')\n plt.title('Training and validation mean absolute error')\n plt.xlabel('Epochs')\n plt.ylabel('MAE')\n plt.legend()\n plt.show()\n \n return Outputs()\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 [7]:
    # 本代码由可视化策略环境自动生成 2019年11月5日 11:33
    # 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 m4_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))
        flag = int(len(df['y'])/3)
        print("flag:", flag)
        val_x = df['x'][:flag]
        val_y = df['y'][:flag]
        partial_x = df['x'][flag:]
        partial_y = df['y'][flag:]
        val = {'x':val_x, 'y':val_y}
        train = {'x':partial_x, 'y':partial_y}
        
        # 划分2/3训练集和1/3验证集
        data_1 = DataSource.write_pickle(train)
        data_2 = DataSource.write_pickle(val)
        return Outputs(data_1=data_1, data_2=data_2)
    
    # 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 m4_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 m8_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 m8_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 m24_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 m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 = 30
        # 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 = 0.9
        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):
        if context.trading_day_index % 20 != 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')]
    
        # 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.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.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
    
    # 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 m31_run_bigquant_run(input_1):
        import matplotlib.pyplot as plt
        plt.style.use('seaborn')
    
        history = input_1.read()['history']
        loss = history['loss']
        val_loss = history['val_loss']
        mae = history['mean_absolute_error']
        val_mae = history['val_mean_absolute_error']
        epochs = range(1, len(loss) + 1)
    
        plt.figure(1)
        plt.plot(epochs, loss, 'bo', label='Training loss')
        plt.plot(epochs, val_loss, 'b', label='Validation loss')
        plt.title('Training and validation loss')
        plt.xlabel('Epochs')
        plt.ylabel('Loss')
        plt.legend()
        plt.show()
    
        plt.figure(2)
        plt.plot(epochs, mae, 'bo', label='Training mae')
        plt.plot(epochs, val_mae, 'b', label='Validation mae')
        plt.title('Training and validation mean absolute error')
        plt.xlabel('Epochs')
        plt.ylabel('MAE')
        plt.legend()
        plt.show()
        
        return Outputs()
    
    # 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 m31_post_run_bigquant_run(outputs):
        return outputs
    
    
    m1 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_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/data_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/big_expr.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)-shift(benchmark_close, -5)/shift(benchmark_close, -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))
    
    # 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='2014-01-01',
        end_date='2018-12-31',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False
    )
    
    m13 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""fs_total_operating_costs_0/fs_gross_revenues_0
    pb_lf_0
    fs_net_cash_flow_0/fs_operating_revenue_0
    fs_operating_revenue_qoq_0
    fs_operating_revenue_yoy_0
    fs_roe_0
    market_cap_0/(fs_net_income_0+fs_income_tax_0+fs_fixed_assets_disp_0)
    fs_net_cash_flow_0/fs_total_profit_0
    fs_deducted_profit_0/fs_net_income_0
    -1*correlation(rank(delta(log(volume_0),2)),rank(((close_0-open_0)/open_0)),6)
    sign(delta(volume_0,1))*(-1*delta(close_0,1))
    (-1*rank(delta(close_0/shift(close_0,1)-1,3)))*correlation(open_0,volume_0,10)
    -1*sum(rank(correlation(rank(high_0),rank(volume_0),3)),3)
    (ts_rank((volume_0/mean(amount_0,20)),20)*ts_rank((-1*delta(close_0,7)),8))"""
    )
    
    m21 = M.input_features.v1(
        features_ds=m3.data,
        features="""industry_sw_level1_0
    market_cap_float_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m21.data,
        start_date='2015-01-01',
        end_date='2018-12-31',
        before_start_days=30
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m21.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=True
    )
    
    m22 = M.neutralize.v13(
        input_1=m16.data,
        input_2=m3.data,
        market_value_key=True,
        industry_output_key=True,
        market_col_name='market_cap_float_0',
        industry_sw_col_name='industry_sw_level1_0',
        columns_input="""fs_total_operating_costs_0/fs_gross_revenues_0
    pb_lf_0
    fs_net_cash_flow_0/fs_operating_revenue_0
    fs_operating_revenue_qoq_0
    fs_operating_revenue_yoy_0
    fs_roe_0
    market_cap_0/(fs_net_income_0+fs_income_tax_0+fs_fixed_assets_disp_0)
    fs_net_cash_flow_0/fs_total_profit_0
    fs_deducted_profit_0/fs_net_income_0
    -1*correlation(rank(delta(log(volume_0),2)),rank(((close_0-open_0)/open_0)),6)
    sign(delta(volume_0,1))*(-1*delta(close_0,1))
    (-1*rank(delta(close_0/shift(close_0,1)-1,3)))*correlation(open_0,volume_0,10)
    -1*sum(rank(correlation(rank(high_0),rank(volume_0),3)),3)
    (ts_rank((volume_0/mean(amount_0,20)),20)*ts_rank((-1*delta(close_0,7)),8))"""
    )
    
    m14 = M.standardlize.v8(
        input_1=m22.data,
        input_2=m3.data,
        columns_input="""fs_total_operating_costs_0/fs_gross_revenues_0
    pb_lf_0
    fs_net_cash_flow_0/fs_operating_revenue_0
    fs_operating_revenue_qoq_0
    fs_operating_revenue_yoy_0
    fs_roe_0
    market_cap_0/(fs_net_income_0+fs_income_tax_0+fs_fixed_assets_disp_0)
    fs_net_cash_flow_0/fs_total_profit_0
    fs_deducted_profit_0/fs_net_income_0
    -1*correlation(rank(delta(log(volume_0),2)),rank(((close_0-open_0)/open_0)),6)
    sign(delta(volume_0,1))*(-1*delta(close_0,1))
    (-1*rank(delta(close_0/shift(close_0,1)-1,3)))*correlation(open_0,volume_0,10)
    -1*sum(rank(correlation(rank(high_0),rank(volume_0),3)),3)
    (ts_rank((volume_0/mean(amount_0,20)),20)*ts_rank((-1*delta(close_0,7)),8))"""
    )
    
    m7 = M.join.v3(
        data1=m13.data,
        data2=m14.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m30 = M.chinaa_stock_filter.v1(
        input_data=m7.data,
        index_constituent_cond=['沪深300'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        output_left_data=False
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m30.data,
        features=m3.data,
        window_size=10,
        feature_clip=11,
        flatten=True,
        window_along_col='instrument'
    )
    
    m4 = M.cached.v3(
        input_1=m26.data,
        input_2=m3.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-01-01'),
        end_date=T.live_run_param('trading_date', '2019-08-30'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m21.data,
        start_date='2018-01-01',
        end_date='2019-08-30',
        before_start_days=30
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m21.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=True
    )
    
    m28 = M.neutralize.v13(
        input_1=m18.data,
        input_2=m3.data,
        market_value_key=True,
        industry_output_key=True,
        market_col_name='market_cap_float_0',
        industry_sw_col_name='industry_sw_level1_0',
        columns_input="""fs_total_operating_costs_0/fs_gross_revenues_0
    pb_lf_0
    fs_net_cash_flow_0/fs_operating_revenue_0
    fs_operating_revenue_qoq_0
    fs_operating_revenue_yoy_0
    fs_roe_0
    market_cap_0/(fs_net_income_0+fs_income_tax_0+fs_fixed_assets_disp_0)
    fs_net_cash_flow_0/fs_total_profit_0
    fs_deducted_profit_0/fs_net_income_0
    -1*correlation(rank(delta(log(volume_0),2)),rank(((close_0-open_0)/open_0)),6)
    sign(delta(volume_0,1))*(-1*delta(close_0,1))
    (-1*rank(delta(close_0/shift(close_0,1)-1,3)))*correlation(open_0,volume_0,10)
    -1*sum(rank(correlation(rank(high_0),rank(volume_0),3)),3)
    (ts_rank((volume_0/mean(amount_0,20)),20)*ts_rank((-1*delta(close_0,7)),8))"""
    )
    
    m25 = M.standardlize.v8(
        input_1=m28.data,
        input_2=m3.data,
        columns_input="""fs_total_operating_costs_0/fs_gross_revenues_0
    pb_lf_0
    fs_net_cash_flow_0/fs_operating_revenue_0
    fs_operating_revenue_qoq_0
    fs_operating_revenue_yoy_0
    fs_roe_0
    market_cap_0/(fs_net_income_0+fs_income_tax_0+fs_fixed_assets_disp_0)
    fs_net_cash_flow_0/fs_total_profit_0
    fs_deducted_profit_0/fs_net_income_0
    -1*correlation(rank(delta(log(volume_0),2)),rank(((close_0-open_0)/open_0)),6)
    sign(delta(volume_0,1))*(-1*delta(close_0,1))
    (-1*rank(delta(close_0/shift(close_0,1)-1,3)))*correlation(open_0,volume_0,10)
    -1*sum(rank(correlation(rank(high_0),rank(volume_0),3)),3)
    (ts_rank((volume_0/mean(amount_0,20)),20)*ts_rank((-1*delta(close_0,7)),8))"""
    )
    
    m32 = M.chinaa_stock_filter.v1(
        input_data=m25.data,
        index_constituent_cond=['沪深300'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        output_left_data=False
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m32.data,
        features=m3.data,
        window_size=10,
        feature_clip=11,
        flatten=True,
        window_along_col='instrument'
    )
    
    m8 = M.cached.v3(
        input_1=m27.data,
        input_2=m3.data,
        run=m8_run_bigquant_run,
        post_run=m8_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_layer_input.v1(
        shape='14,10',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_lstm.v1(
        inputs=m6.data,
        units=8,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='he_normal',
        recurrent_initializer='Orthogonal',
        bias_initializer='he_normal',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0.1,
        kernel_regularizer_l2=0.1,
        recurrent_regularizer='None',
        recurrent_regularizer_l1=0,
        recurrent_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='L1L2',
        activity_regularizer_l1=0.1,
        activity_regularizer_l2=0.2,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        return_sequences=True,
        implementation='0',
        name=''
    )
    
    m35 = M.dl_layer_dropout.v1(
        inputs=m10.data,
        rate=0.5,
        noise_shape='',
        name=''
    )
    
    m12 = M.dl_layer_lstm.v1(
        inputs=m35.data,
        units=8,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='he_normal',
        recurrent_initializer='Orthogonal',
        bias_initializer='Zeros',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        recurrent_regularizer='L1L2',
        recurrent_regularizer_l1=0.1,
        recurrent_regularizer_l2=0.1,
        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',
        return_sequences=False,
        implementation='0',
        name=''
    )
    
    m33 = M.dl_layer_dropout.v1(
        inputs=m12.data,
        rate=0.5,
        noise_shape='',
        name=''
    )
    
    m29 = M.dl_layer_dense.v1(
        inputs=m33.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=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m29.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        validation_data=m4.data_2,
        optimizer='RMSprop',
        loss='mean_squared_error',
        metrics='mae',
        batch_size=100,
        epochs=500,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m8.data_1,
        batch_size=100,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m32.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m24.data_1,
        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='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    m31 = M.cached.v3(
        input_1=m5.data,
        run=m31_run_bigquant_run,
        post_run=m31_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    

    衍生特征抽取(derived_feature_extractor)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-7-d8356973c5ef> in <module>()
        229     instrument_col='instrument',
        230     drop_na=True,
    --> 231     remove_extra_columns=True
        232 )
        233 
    
    TypeError: 'NoneType' object is not iterable

    (iQuant) #2

    收到提问,已提交至策略工程师,会尽快给您回复。


    (youknowwyq) #3

    你好 啥时候可以修复