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    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-106:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-276:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"-106:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-113:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-122:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-129:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-243:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-251:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-266:input_2","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-288:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-298:input_2","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-293:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-243:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-122:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-141:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-113:input_data","from_node_id":"-106:data"},{"to_node_id":"-266:input_1","from_node_id":"-113:data"},{"to_node_id":"-129:input_data","from_node_id":"-122:data"},{"to_node_id":"-2431:input_2","from_node_id":"-129:data"},{"to_node_id":"-298:input_1","from_node_id":"-129:data"},{"to_node_id":"-168:inputs","from_node_id":"-160:data"},{"to_node_id":"-682:inputs","from_node_id":"-160:data"},{"to_node_id":"-224:inputs","from_node_id":"-168:data"},{"to_node_id":"-231:inputs","from_node_id":"-196:data"},{"to_node_id":"-196:inputs","from_node_id":"-224:data"},{"to_node_id":"-238:inputs","from_node_id":"-231:data"},{"to_node_id":"-682:outputs","from_node_id":"-238:data"},{"to_node_id":"-1098:input_model","from_node_id":"-682:data"},{"to_node_id":"-1540:trained_model","from_node_id":"-1098:data"},{"to_node_id":"-2431:input_1","from_node_id":"-1540:data"},{"to_node_id":"-141:options_data","from_node_id":"-2431:data_1"},{"to_node_id":"-436:input_1","from_node_id":"-243:data"},{"to_node_id":"-1540:input_data","from_node_id":"-251:data"},{"to_node_id":"-1098:training_data","from_node_id":"-436:data_1"},{"to_node_id":"-1098:validation_data","from_node_id":"-436:data_2"},{"to_node_id":"-288:input_data","from_node_id":"-266:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-288:data"},{"to_node_id":"-251:input_data","from_node_id":"-293:data"},{"to_node_id":"-293:input_data","from_node_id":"-298:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"-276:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2017-06-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2017-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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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 = 20\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.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 [62]:
    # 本代码由可视化策略环境自动生成 2022年6月30日 14:45
    # 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 m10_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        from sklearn.model_selection import train_test_split
        data = input_1.read()
        x_train, x_val, y_train, y_val = train_test_split(data["x"], data['y'])
        data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})
        data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})
        return Outputs(data_1=data_1, data_2=data_2, 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 m10_post_run_bigquant_run(outputs):
        return outputs
    
    import tensorflow as tf 
    
    def swish(x):
        return x*tf.atan(x)
    
    m8_user_activation_bigquant_run=swish
    import tensorflow as tf 
    
    def swish(x):
        return x*tf.atan(x)
    
    m20_user_activation_bigquant_run=swish
    from tensorflow.keras.callbacks import EarlyStopping
    m5_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=5)
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m5_custom_objects_bigquant_run = {'swish':swish
     
    }
    
    # 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 = 20
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.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='2017-06-01',
        end_date='2017-12-31',
        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)-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='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False
    )
    
    m29 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""close_0
    open_0
    high_0
    low_0 
    amount_0
    turn_0 
    return_0
     
    close_1
    open_1
    high_1
    low_1
    return_1
    amount_1
    turn_1
     
    close_2
    open_2
    high_2
    low_2
    amount_2
    turn_2
    return_2
     
    close_3
    open_3
    high_3
    low_3
    amount_3
    turn_3
    return_3
     
    close_4
    open_4
    high_4
    low_4
    amount_4
    turn_4
    return_4
     
    mean(close_0, 5)
    mean(low_0, 5)
    mean(open_0, 5)
    mean(high_0, 5)
    mean(turn_0, 5)
    mean(amount_0, 5)
    mean(return_0, 5)
     
    ts_max(close_0, 5)
    ts_max(low_0, 5)
    ts_max(open_0, 5)
    ts_max(high_0, 5)
    ts_max(turn_0, 5)
    ts_max(amount_0, 5)
    ts_max(return_0, 5)
      """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m28 = M.standardlize.v8(
        input_1=m16.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m13 = M.fillnan.v1(
        input_data=m28.data,
        features=m3.data,
        fill_value='0.0'
    )
    
    m7 = M.join.v3(
        data1=m29.data,
        data2=m13.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m7.data,
        features=m3.data,
        window_size=1,
        feature_clip=3,
        flatten=True,
        window_along_col='instrument'
    )
    
    m10 = M.cached.v3(
        input_1=m26.data,
        run=m10_run_bigquant_run,
        post_run=m10_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', '2021-08-20'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m25 = M.standardlize.v8(
        input_1=m18.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m14 = M.fillnan.v1(
        input_data=m25.data,
        features=m3.data,
        fill_value='0.0'
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m14.data,
        features=m3.data,
        window_size=1,
        feature_clip=3,
        flatten=True,
        window_along_col='instrument'
    )
    
    m6 = M.dl_layer_input.v1(
        shape='49',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m8 = M.dl_layer_dense.v1(
        inputs=m6.data,
        units=256,
        activation='自定义',
        user_activation=m8_user_activation_bigquant_run,
        use_bias=False,
        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=''
    )
    
    m21 = M.dl_layer_dropout.v1(
        inputs=m8.data,
        rate=0.1,
        noise_shape='',
        name=''
    )
    
    m20 = M.dl_layer_dense.v1(
        inputs=m21.data,
        units=128,
        activation='自定义',
        user_activation=m20_user_activation_bigquant_run,
        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=''
    )
    
    m22 = M.dl_layer_dropout.v1(
        inputs=m20.data,
        rate=0.1,
        noise_shape='',
        name=''
    )
    
    m23 = M.dl_layer_dense.v1(
        inputs=m22.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=''
    )
    
    m4 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m23.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m4.data,
        training_data=m10.data_1,
        validation_data=m10.data_2,
        optimizer='Adam',
        loss='自定义',
        user_loss=tf.keras.losses.Huber(
            delta=1,
            reduction=tf.keras.losses.Reduction.SUM,
            name='huber_loss'
        ),
        metrics='mse',
        batch_size=1024,
        epochs=30,
        earlystop=m5_earlystop_bigquant_run,
        custom_objects=m5_custom_objects_bigquant_run,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录',
        m_cached=False
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m27.data,
        batch_size=1024,
        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 1/30
    314/314 - 5s - loss: 385.3247 - mse: 1.0073 - val_loss: 382.1423 - val_mse: 0.9960
    Epoch 2/30
    314/314 - 3s - loss: 383.0572 - mse: 1.0001 - val_loss: 381.8679 - val_mse: 0.9946
    Epoch 3/30
    314/314 - 3s - loss: 382.5898 - mse: 0.9985 - val_loss: 381.3745 - val_mse: 0.9947
    Epoch 4/30
    314/314 - 1s - loss: 382.3132 - mse: 0.9981 - val_loss: 381.4762 - val_mse: 0.9971
    Epoch 5/30
    314/314 - 1s - loss: 382.0121 - mse: 0.9969 - val_loss: 380.9921 - val_mse: 0.9939
    Epoch 6/30
    314/314 - 1s - loss: 381.7484 - mse: 0.9965 - val_loss: 381.3236 - val_mse: 0.9896
    Epoch 7/30
    314/314 - 1s - loss: 381.4542 - mse: 0.9958 - val_loss: 381.4939 - val_mse: 0.9942
    Epoch 8/30
    314/314 - 1s - loss: 381.5289 - mse: 0.9961 - val_loss: 380.4246 - val_mse: 0.9916
    Epoch 9/30
    314/314 - 1s - loss: 381.0350 - mse: 0.9950 - val_loss: 381.0897 - val_mse: 1.0026
    Epoch 10/30
    314/314 - 1s - loss: 380.9453 - mse: 0.9949 - val_loss: 381.2305 - val_mse: 0.9898
    Epoch 11/30
    314/314 - 1s - loss: 380.8914 - mse: 0.9944 - val_loss: 380.4555 - val_mse: 0.9942
    
    3214/3214 - 2s
    DataSource(44512aa76e514f06aef6d6a84383a69eT)