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    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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=0.00016, sell_cost=0.00116, 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 = 3\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 = [1, 0, 0]\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.5\n context.options['hold_days'] = 2","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 \n #------------------------------------------止损模块START--------------------------------------------\n date = data.current_dt.strftime('%Y-%m-%d')\n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stoploss_stock = [] 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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 \n #----------这里加入股票判断,如果已经止盈/止损了就跳过此股票,避免二次卖出--------\n if instrument in current_stoploss_stock:\n continue\n #----------------------------------------------------------------------------------------\n \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 price = data.current(context.symbol(instrument), 'price') # 最新价格\n stock_num = np.floor(cash/price/100)*100 # 向下取整\n context.order(context.symbol(instrument), stock_num) # 整百下单","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\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","type":"Literal","bound_global_parameter":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","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-2431"},{"name":"input_2","node_id":"-2431"},{"name":"input_3","node_id":"-2431"}],"output_ports":[{"name":"data_1","node_id":"-2431"},{"name":"data_2","node_id":"-2431"},{"name":"data_3","node_id":"-2431"}],"cacheable":true,"seq_num":33,"comment":"","comment_collapsed":true},{"node_id":"-243","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":"3","type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"False","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-243"},{"name":"features","node_id":"-243"}],"output_ports":[{"name":"data","node_id":"-243"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-436","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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    In [9]:
    # 本代码由可视化策略环境自动生成 2021年12月16日 10:48
    # 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 m30_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_2.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 m30_post_run_bigquant_run(outputs):
        return outputs
    
    from tensorflow.keras.callbacks import EarlyStopping
    m6_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=10)
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m6_custom_objects_bigquant_run = {
        
    }
    
    # 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 m33_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 m33_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m32_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=0.00016, sell_cost=0.00116, 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 = 3
        # 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 = [1, 0, 0]
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.5
        context.options['hold_days'] = 2
    # 回测引擎:每日数据处理函数,每天执行一次
    def m32_handle_data_bigquant_run(context, data):
        
       #------------------------------------------止损模块START--------------------------------------------
        date = data.current_dt.strftime('%Y-%m-%d')
        positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
        # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
        current_stoploss_stock = [] 
        if len(positions) > 0:
            for i in positions.keys():
                stock_cost = positions[i] 
                stock_market_price = data.current(context.symbol(i), 'price') 
                # 亏5%就止损
                if (stock_market_price - stock_cost) / stock_cost <= -0.05:   
                    context.order_target_percent(context.symbol(i),0)     
                    current_stoploss_stock.append(i)
                    print('日期:',date,'股票:',i,'出现止损状况')
        #-------------------------------------------止损模块END---------------------------------------------
        
        # 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:
                
               #----------这里加入股票判断,如果已经止盈/止损了就跳过此股票,避免二次卖出--------
                if instrument in current_stoploss_stock:
                    continue
               #----------------------------------------------------------------------------------------
                
                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:
                price = data.current(context.symbol(instrument), 'price')  # 最新价格
                stock_num = np.floor(cash/price/100)*100  # 向下取整
                context.order(context.symbol(instrument), stock_num) # 整百下单
    # 回测引擎:准备数据,只执行一次
    def m32_prepare_bigquant_run(context):
        pass
    
    
    m3 = M.dl_layer_input.v1(
        shape='3,4',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m4 = M.dl_layer_lstm.v1(
        inputs=m3.data,
        units=16,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='glorot_normal',
        recurrent_initializer='Orthogonal',
        bias_initializer='Zeros',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        recurrent_regularizer='None',
        recurrent_regularizer_l1=0,
        recurrent_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        dropout=0,
        recurrent_dropout=0,
        return_sequences=False,
        implementation='2',
        name=''
    )
    
    m19 = M.dl_layer_layernormalization.v1(
        inputs=m4.data,
        name=''
    )
    
    m10 = M.dl_layer_dense.v1(
        inputs=m19.data,
        units=16,
        activation='relu',
        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=''
    )
    
    m9 = M.dl_layer_dense.v1(
        inputs=m10.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=''
    )
    
    m5 = M.dl_model_init.v1(
        inputs=m3.data,
        outputs=m9.data
    )
    
    m8 = M.input_features.v1(
        features="""close_0
    low_0
    open_0
    turn_0
    """
    )
    
    m24 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2010-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m21 = M.advanced_auto_labeler.v2(
        instruments=m24.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:2日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -2) / shift(open, -1)-1
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    
    # 将分数映射到分类,这里使用20个分类
    #all_wbins(label, 20)
    
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False,
        user_functions={}
    )
    
    m2 = M.standardlize.v9(
        input_1=m21.data,
        standard_func='ZScoreNorm',
        columns_input=''
    )
    
    m22 = M.general_feature_extractor.v7(
        instruments=m24.data,
        features=m8.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m23 = M.derived_feature_extractor.v3(
        input_data=m22.data,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m12 = M.standardlize.v9(
        input_1=m23.data,
        input_2=m8.data,
        standard_func='ZScoreNorm',
        columns_input=''
    )
    
    m20 = M.dropnan.v2(
        input_data=m12.data,
        features=m8.data
    )
    
    m17 = M.join.v3(
        data1=m2.data,
        data2=m20.data,
        on='date,instrument',
        how='inner',
        sort=True
    )
    
    m1 = M.dl_convert_to_bin.v2(
        input_data=m17.data,
        features=m8.data,
        window_size=3,
        feature_clip=3,
        flatten=False,
        window_along_col='instrument'
    )
    
    m30 = M.cached.v3(
        input_2=m1.data,
        run=m30_run_bigquant_run,
        post_run=m30_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_model_train.v1(
        input_model=m5.data,
        training_data=m30.data_1,
        validation_data=m30.data_2,
        optimizer='Adam',
        loss='mean_squared_error',
        metrics='mse',
        batch_size=1024,
        epochs=5,
        earlystop=m6_earlystop_bigquant_run,
        custom_objects=m6_custom_objects_bigquant_run,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m28 = M.instruments.v2(
        start_date='2021-01-01',
        end_date='2021-11-30',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m16 = M.general_feature_extractor.v7(
        instruments=m28.data,
        features=m8.data,
        start_date='',
        end_date='',
        before_start_days=365
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m16.data,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m29 = M.standardlize.v9(
        input_1=m26.data,
        input_2=m8.data,
        standard_func='ZScoreNorm',
        columns_input=''
    )
    
    m18 = M.dropnan.v2(
        input_data=m29.data,
        features=m8.data
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m18.data,
        features=m8.data,
        window_size=3,
        feature_clip=3,
        flatten=False,
        window_along_col='instrument'
    )
    
    m7 = M.dl_model_predict.v1(
        trained_model=m6.data,
        input_data=m27.data,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m33 = M.cached.v3(
        input_1=m7.data,
        input_2=m18.data,
        run=m33_run_bigquant_run,
        post_run=m33_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m32 = M.trade.v4(
        instruments=m28.data,
        options_data=m33.data_1,
        start_date='',
        end_date='',
        initialize=m32_initialize_bigquant_run,
        handle_data=m32_handle_data_bigquant_run,
        prepare=m32_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=100000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    ---------------------------------------------------------------------------
    NotImplementedError                       Traceback (most recent call last)
    <ipython-input-9-208d43400d85> in <module>
        211 )
        212 
    --> 213 m5 = M.dl_model_init.v1(
        214     inputs=m3.data,
        215     outputs=m9.data
    
    NotImplementedError: Layer LayerNormalization has arguments in `__init__` and therefore must override `get_config`.