自定义模块出错如何解决?

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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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    In [16]:
    # 本代码由可视化策略环境自动生成 2019年10月27日 14:47
    # 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))
        
        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 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
    
    
    m1 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2017-06-01'),
        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/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)
    10*(shift(close, -5) / shift(open, -1) - shift(benchmark_close, -5) / shift(benchmark_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))
    
    # 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='2016-01-01',
        end_date='2017-01-01',
        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="""pb_lf_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))"""
    )
    
    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='2016-01-01',
        end_date='2017-06-01',
        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=False
    )
    
    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=''
    )
    
    m14 = M.standardlize.v8(
        input_1=m22.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    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=['上证主板', '深证主板'],
        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=30,
        feature_clip=7,
        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', '2017-06-02'),
        end_date=T.live_run_param('trading_date', '2019-04-16'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m21.data,
        start_date='2017-06-02',
        end_date='2019-04-16',
        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=False
    )
    
    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=''
    )
    
    m25 = M.standardlize.v8(
        input_1=m28.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m32 = M.chinaa_stock_filter.v1(
        input_data=m25.data,
        index_constituent_cond=['沪深300'],
        board_cond=['上证主板', '深证主板'],
        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=30,
        feature_clip=7,
        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='7,30',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_lstm.v1(
        inputs=m6.data,
        units=128,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='RandomUniform',
        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=0.2,
        recurrent_dropout=0.1,
        return_sequences=False,
        implementation='0',
        name=''
    )
    
    m12 = M.dl_layer_dense.v1(
        inputs=m10.data,
        units=64,
        activation='linear',
        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=''
    )
    
    m23 = M.dl_layer_dense.v1(
        inputs=m12.data,
        units=16,
        activation='linear',
        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=''
    )
    
    m29 = M.dl_layer_dense.v1(
        inputs=m23.data,
        units=1,
        activation='linear',
        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=m29.data,
        rate=0.2,
        noise_shape='',
        name=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m20.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        optimizer='Adam',
        loss='mean_squared_logarithmic_error',
        metrics='mse',
        batch_size=32,
        epochs=10,
        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=1000,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m18.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'
    )
    
    Epoch 1/10
     - 11s - loss: 0.1074 - mean_squared_error: 0.7209
    Epoch 2/10
     - 9s - loss: 0.1051 - mean_squared_error: 0.7178
    Epoch 3/10
     - 9s - loss: 0.1051 - mean_squared_error: 0.7176
    Epoch 4/10
     - 9s - loss: 0.1041 - mean_squared_error: 0.7139
    Epoch 5/10
     - 9s - loss: 0.1044 - mean_squared_error: 0.7135
    Epoch 6/10
     - 9s - loss: 0.1021 - mean_squared_error: 0.7068
    Epoch 7/10
     - 9s - loss: 0.1020 - mean_squared_error: 0.7050
    Epoch 8/10
     - 9s - loss: 0.1013 - mean_squared_error: 0.6995
    Epoch 9/10
     - 9s - loss: 0.1000 - mean_squared_error: 0.6952
    Epoch 10/10
     - 9s - loss: 0.0980 - mean_squared_error: 0.6837
    
    DataSource(d42fa4e6398f4b73800ea10d22a6389eT, v3)
    

    自定义Python模块(cached)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-16-92094b64191c> in <module>()
        443     input_ports='',
        444     params='{}',
    --> 445     output_ports=''
        446 )
        447 
    
    <ipython-input-16-92094b64191c> in m24_run_bigquant_run(input_1, input_2, input_3)
         40     pred_label = input_1.read_pickle()
         41     df = input_2.read_df()
    ---> 42     df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
         43     df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
         44     return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    ValueError: array length 17045 does not match index length 137675

    (iQuant) #2

    您好,收到提问,已提交给策略工程师,会尽快给您回复。


    (达达) #3

    m24的第二个接口连线从m32的输出连过来,因为你过滤了股票因此数据的条目长度对不上了
    image


    (youknowwyq) #4
    克隆策略

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shift(benchmark_close, -5) / shift(benchmark_open, -1))\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, 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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 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 pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-141"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-141","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-160","ModuleId":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","ModuleParameters":[{"Name":"shape","Value":"7,30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dtype","Value":"float32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sparse","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-160"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-160","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"Adam","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_optimizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"loss","Value":"mean_squared_logarithmic_error","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"mse","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"10","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_model","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_data","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"validation_data","NodeId":"-1098"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1098","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"1000","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trained_model","NodeId":"-1540"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1540"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1540","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2431","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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 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":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-2431"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-2431","OutputType":null},{"Name":"data_2","NodeId":"-2431","OutputType":null},{"Name":"data_3","NodeId":"-2431","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-768","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"[]","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-768"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-768"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-768","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-773","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"label","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-773"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-773","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-778","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"[]","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-778"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-778"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-778","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-251","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":"7","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-251"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-251"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-251","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3880","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3880"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-3880"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3880","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":34,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3895","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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 df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle())\n \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n \n data_1 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1)\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 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,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3907","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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      In [7]:
      # 本代码由可视化策略环境自动生成 2019年10月28日 14:56
      # 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。
      
      
      # 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))
          
          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 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
      
      
      m1 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2016-01-01'),
          end_date=T.live_run_param('trading_date', '2017-06-01'),
          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/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)
      10*(shift(close, -5) / shift(open, -1) - shift(benchmark_close, -5) / shift(benchmark_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))
      
      # 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='2016-01-01',
          end_date='2017-01-01',
          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="""pb_lf_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))"""
      )
      
      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='2016-01-01',
          end_date='2017-06-01',
          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=False
      )
      
      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=''
      )
      
      m14 = M.standardlize.v8(
          input_1=m22.data,
          input_2=m3.data,
          columns_input='[]'
      )
      
      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=30,
          feature_clip=7,
          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', '2017-06-02'),
          end_date=T.live_run_param('trading_date', '2019-04-16'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m17 = M.general_feature_extractor.v7(
          instruments=m9.data,
          features=m21.data,
          start_date='2017-06-02',
          end_date='2019-04-16',
          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=False
      )
      
      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=''
      )
      
      m25 = M.standardlize.v8(
          input_1=m28.data,
          input_2=m3.data,
          columns_input='[]'
      )
      
      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=30,
          feature_clip=7,
          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='7,30',
          batch_shape='',
          dtype='float32',
          sparse=False,
          name=''
      )
      
      m10 = M.dl_layer_lstm.v1(
          inputs=m6.data,
          units=128,
          activation='tanh',
          recurrent_activation='hard_sigmoid',
          use_bias=True,
          kernel_initializer='RandomUniform',
          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=0.2,
          recurrent_dropout=0.1,
          return_sequences=False,
          implementation='0',
          name=''
      )
      
      m12 = M.dl_layer_dense.v1(
          inputs=m10.data,
          units=64,
          activation='linear',
          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=''
      )
      
      m23 = M.dl_layer_dense.v1(
          inputs=m12.data,
          units=16,
          activation='linear',
          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=''
      )
      
      m29 = M.dl_layer_dense.v1(
          inputs=m23.data,
          units=1,
          activation='linear',
          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=m29.data,
          rate=0.2,
          noise_shape='',
          name=''
      )
      
      m34 = M.dl_model_init.v1(
          inputs=m6.data,
          outputs=m20.data
      )
      
      m5 = M.dl_model_train.v1(
          input_model=m34.data,
          training_data=m4.data_1,
          optimizer='Adam',
          loss='mean_squared_logarithmic_error',
          metrics='mse',
          batch_size=32,
          epochs=10,
          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=m32.left_data,
          batch_size=1000,
          n_gpus=0,
          verbose='2:每个epoch输出一行记录'
      )
      
      m24 = M.cached.v3(
          input_1=m11.data,
          input_2=m8.data_1,
          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'
      )
      

      预测(深度学习)(dl_model_predict)使用错误,你可以:

      1.一键查看文档

      2.一键搜索答案

      ---------------------------------------------------------------------------
      AttributeError                            Traceback (most recent call last)
      <ipython-input-7-cb874eafd0a0> in <module>()
          433     batch_size=1000,
          434     n_gpus=0,
      --> 435     verbose='2:每个epoch输出一行记录'
          436 )
          437 
      
      AttributeError: 'NoneType' object has no attribute 'read_pickle'

      额 又出现了别的错误

      (达达) #5
      克隆策略

        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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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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 [2]:
        # 本代码由可视化策略环境自动生成 2019年10月30日 10:16
        # 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))
            
            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 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
        
        
        m1 = M.instruments.v2(
            start_date=T.live_run_param('trading_date', '2016-01-01'),
            end_date=T.live_run_param('trading_date', '2017-06-01'),
            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/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)
        10*(shift(close, -5) / shift(open, -1) - shift(benchmark_close, -5) / shift(benchmark_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))
        
        # 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='2016-01-01',
            end_date='2017-01-01',
            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="""pb_lf_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))"""
        )
        
        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='2016-01-01',
            end_date='2017-06-01',
            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=False
        )
        
        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=''
        )
        
        m14 = M.standardlize.v8(
            input_1=m22.data,
            input_2=m3.data,
            columns_input='[]'
        )
        
        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=30,
            feature_clip=7,
            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', '2017-06-02'),
            end_date=T.live_run_param('trading_date', '2019-04-16'),
            market='CN_STOCK_A',
            instrument_list='',
            max_count=0
        )
        
        m17 = M.general_feature_extractor.v7(
            instruments=m9.data,
            features=m21.data,
            start_date='2017-06-02',
            end_date='2019-04-16',
            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=False
        )
        
        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=''
        )
        
        m25 = M.standardlize.v8(
            input_1=m28.data,
            input_2=m3.data,
            columns_input='[]'
        )
        
        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=30,
            feature_clip=7,
            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='7,30',
            batch_shape='',
            dtype='float32',
            sparse=False,
            name=''
        )
        
        m10 = M.dl_layer_lstm.v1(
            inputs=m6.data,
            units=128,
            activation='tanh',
            recurrent_activation='hard_sigmoid',
            use_bias=True,
            kernel_initializer='RandomUniform',
            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=0.2,
            recurrent_dropout=0.1,
            return_sequences=False,
            implementation='0',
            name=''
        )
        
        m12 = M.dl_layer_dense.v1(
            inputs=m10.data,
            units=64,
            activation='linear',
            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=''
        )
        
        m23 = M.dl_layer_dense.v1(
            inputs=m12.data,
            units=16,
            activation='linear',
            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=''
        )
        
        m29 = M.dl_layer_dense.v1(
            inputs=m23.data,
            units=1,
            activation='linear',
            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=m29.data,
            rate=0.2,
            noise_shape='',
            name=''
        )
        
        m34 = M.dl_model_init.v1(
            inputs=m6.data,
            outputs=m20.data
        )
        
        m5 = M.dl_model_train.v1(
            input_model=m34.data,
            training_data=m4.data_1,
            optimizer='Adam',
            loss='mean_squared_logarithmic_error',
            metrics='mse',
            batch_size=32,
            epochs=10,
            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=1000,
            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'
        )
        
        截面数据行业、市值中性化开始...
        中性化计算完成...
        
        截面数据行业、市值中性化开始...
        中性化计算完成...
        
        Using TensorFlow backend.
        
        Epoch 1/10
         - 15s - loss: 0.1057 - mean_squared_error: 0.7164
        Epoch 2/10
         - 14s - loss: 0.1047 - mean_squared_error: 0.7159
        Epoch 3/10
         - 11s - loss: 0.1042 - mean_squared_error: 0.7131
        Epoch 4/10
         - 12s - loss: 0.1039 - mean_squared_error: 0.7120
        Epoch 5/10
         - 10s - loss: 0.1023 - mean_squared_error: 0.7080
        Epoch 6/10
         - 10s - loss: 0.1019 - mean_squared_error: 0.7071
        Epoch 7/10
         - 10s - loss: 0.1009 - mean_squared_error: 0.6995
        Epoch 8/10
         - 9s - loss: 0.0995 - mean_squared_error: 0.6937
        Epoch 9/10
         - 9s - loss: 0.0980 - mean_squared_error: 0.6876
        Epoch 10/10
         - 10s - loss: 0.0970 - mean_squared_error: 0.6774
        
        DataSource(dca1e0b7eb1a4cd091e48e0052d48d2aT, v3)
        
        • 收益率1.7%
        • 年化收益率0.93%
        • 基准收益率16.81%
        • 阿尔法-0.06
        • 贝塔0.7
        • 夏普比率-0.05
        • 胜率0.38
        • 盈亏比0.93
        • 收益波动率15.87%
        • 信息比率-0.05
        • 最大回撤26.67%
        bigcharts-data-start/{"__id":"bigchart-22f14574f0fe4312b4125348f1867f31","__type":"tabs"}/bigcharts-data-end