克隆策略

    {"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":"2010-01-01","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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\n}\n","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":"0","type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_model","node_id":"-1098"},{"name":"training_data","node_id":"-1098"},{"name":"validation_data","node_id":"-1098"}],"output_ports":[{"name":"data","node_id":"-1098"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-1540","module_id":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","parameters":[{"name":"batch_size","value":"1024","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":0,"type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"trained_model","node_id":"-1540"},{"name":"input_data","node_id":"-1540"}],"output_ports":[{"name":"data","node_id":"-1540"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-2431","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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","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":24,"comment":"","comment_collapsed":true},{"node_id":"-243","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","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":26,"comment":"","comment_collapsed":true},{"node_id":"-251","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","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":"-251"},{"name":"features","node_id":"-251"}],"output_ports":[{"name":"data","node_id":"-251"}],"cacheable":true,"seq_num":27,"comment":"","comment_collapsed":true},{"node_id":"-436","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2021年10月31日 10:49
    # 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
    
    from tensorflow.keras.callbacks import EarlyStopping
    m5_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=3)
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m5_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 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=0.001, sell_cost=0.001, 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
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2017-12-31',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': '',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.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)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': False,
    
        'm29': 'M.standardlize.v8',
        'm29.input_1': T.Graph.OutputPort('m2.data'),
        'm29.columns_input': 'label',
    
        'm3': 'M.input_features.v1',
        'm3.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)
     
    ts_min(close_0, 5)
    ts_min(low_0, 5)
    ts_min(open_0, 5)
    ts_min(high_0, 5)
    ts_min(turn_0, 5)
    ts_min(amount_0, 5)
    ts_min(return_0, 5) 
     
    std(close_0, 5)
    std(low_0, 5)
    std(open_0, 5)
    std(high_0, 5)
    std(turn_0, 5)
    std(amount_0, 5)
    std(return_0, 5)
     
    ts_rank(close_0, 5)
    ts_rank(low_0, 5)
    ts_rank(open_0, 5)
    ts_rank(high_0, 5)
    ts_rank(turn_0, 5)
    ts_rank(amount_0, 5)
    ts_rank(return_0, 5)
     
    decay_linear(close_0, 5)
    decay_linear(low_0, 5)
    decay_linear(open_0, 5)
    decay_linear(high_0, 5)
    decay_linear(turn_0, 5)
    decay_linear(amount_0, 5)
    decay_linear(return_0, 5)
     
    correlation(volume_0, return_0, 5)
    correlation(volume_0, high_0, 5)
    correlation(volume_0, low_0, 5)
    correlation(volume_0, close_0, 5)
    correlation(volume_0, open_0, 5)
    correlation(volume_0, turn_0, 5)
      
    correlation(return_0, high_0, 5)
    correlation(return_0, low_0, 5)
    correlation(return_0, close_0, 5)
    correlation(return_0, open_0, 5)
    correlation(return_0, turn_0, 5)
     
    correlation(high_0, low_0, 5)
    correlation(high_0, close_0, 5)
    correlation(high_0, open_0, 5)
    correlation(high_0, turn_0, 5)
     
    correlation(low_0, close_0, 5)
    correlation(low_0, open_0, 5)
    correlation(low_0, turn_0, 5)
     
    correlation(close_0, open_0, 5)
    correlation(close_0, turn_0, 5)
    
    correlation(open_0, turn_0, 5)""",
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 0,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': True,
        'm16.remove_extra_columns': False,
    
        'm28': 'M.standardlize.v8',
        'm28.input_1': T.Graph.OutputPort('m16.data'),
        'm28.input_2': T.Graph.OutputPort('m3.data'),
        'm28.columns_input': '[]',
    
        'm13': 'M.fillnan.v1',
        'm13.input_data': T.Graph.OutputPort('m28.data'),
        'm13.features': T.Graph.OutputPort('m3.data'),
        'm13.fill_value': '0.0',
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m29.data'),
        'm7.data2': T.Graph.OutputPort('m13.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm26': 'M.dl_convert_to_bin.v2',
        'm26.input_data': T.Graph.OutputPort('m7.data'),
        'm26.features': T.Graph.OutputPort('m3.data'),
        'm26.window_size': 1,
        'm26.feature_clip': 3,
        'm26.flatten': True,
        'm26.window_along_col': 'instrument',
    
        'm10': 'M.cached.v3',
        'm10.input_1': T.Graph.OutputPort('m26.data'),
        'm10.run': m10_run_bigquant_run,
        'm10.post_run': m10_post_run_bigquant_run,
        'm10.input_ports': '',
        'm10.params': '{}',
        'm10.output_ports': '',
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2018-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2021-09-01'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 0,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': True,
        'm18.remove_extra_columns': False,
    
        'm25': 'M.standardlize.v8',
        'm25.input_1': T.Graph.OutputPort('m18.data'),
        'm25.input_2': T.Graph.OutputPort('m3.data'),
        'm25.columns_input': '[]',
    
        'm14': 'M.fillnan.v1',
        'm14.input_data': T.Graph.OutputPort('m25.data'),
        'm14.features': T.Graph.OutputPort('m3.data'),
        'm14.fill_value': '0.0',
    
        'm27': 'M.dl_convert_to_bin.v2',
        'm27.input_data': T.Graph.OutputPort('m14.data'),
        'm27.features': T.Graph.OutputPort('m3.data'),
        'm27.window_size': 1,
        'm27.feature_clip': 3,
        'm27.flatten': True,
        'm27.window_along_col': 'instrument',
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '98',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm8': 'M.dl_layer_dense.v1',
        'm8.inputs': T.Graph.OutputPort('m6.data'),
        'm8.units': 256,
        'm8.activation': 'relu',
        'm8.use_bias': True,
        'm8.kernel_initializer': 'glorot_uniform',
        'm8.bias_initializer': 'Zeros',
        'm8.kernel_regularizer': 'None',
        'm8.kernel_regularizer_l1': 0,
        'm8.kernel_regularizer_l2': 0,
        'm8.bias_regularizer': 'None',
        'm8.bias_regularizer_l1': 0,
        'm8.bias_regularizer_l2': 0,
        'm8.activity_regularizer': 'None',
        'm8.activity_regularizer_l1': 0,
        'm8.activity_regularizer_l2': 0,
        'm8.kernel_constraint': 'None',
        'm8.bias_constraint': 'None',
        'm8.name': '',
    
        'm21': 'M.dl_layer_dropout.v1',
        'm21.inputs': T.Graph.OutputPort('m8.data'),
        'm21.rate': 0.1,
        'm21.noise_shape': '',
        'm21.name': '',
    
        'm20': 'M.dl_layer_dense.v1',
        'm20.inputs': T.Graph.OutputPort('m21.data'),
        'm20.units': 128,
        'm20.activation': 'relu',
        'm20.use_bias': True,
        'm20.kernel_initializer': 'glorot_uniform',
        'm20.bias_initializer': 'Zeros',
        'm20.kernel_regularizer': 'None',
        'm20.kernel_regularizer_l1': 0,
        'm20.kernel_regularizer_l2': 0,
        'm20.bias_regularizer': 'None',
        'm20.bias_regularizer_l1': 0,
        'm20.bias_regularizer_l2': 0,
        'm20.activity_regularizer': 'None',
        'm20.activity_regularizer_l1': 0,
        'm20.activity_regularizer_l2': 0,
        'm20.kernel_constraint': 'None',
        'm20.bias_constraint': 'None',
        'm20.name': '',
    
        'm22': 'M.dl_layer_dropout.v1',
        'm22.inputs': T.Graph.OutputPort('m20.data'),
        'm22.rate': 0.1,
        'm22.noise_shape': '',
        'm22.name': '',
    
        'm23': 'M.dl_layer_dense.v1',
        'm23.inputs': T.Graph.OutputPort('m22.data'),
        'm23.units': 1,
        'm23.activation': 'linear',
        'm23.use_bias': True,
        'm23.kernel_initializer': 'glorot_uniform',
        'm23.bias_initializer': 'Zeros',
        'm23.kernel_regularizer': 'None',
        'm23.kernel_regularizer_l1': 0,
        'm23.kernel_regularizer_l2': 0,
        'm23.bias_regularizer': 'None',
        'm23.bias_regularizer_l1': 0,
        'm23.bias_regularizer_l2': 0,
        'm23.activity_regularizer': 'None',
        'm23.activity_regularizer_l1': 0,
        'm23.activity_regularizer_l2': 0,
        'm23.kernel_constraint': 'None',
        'm23.bias_constraint': 'None',
        'm23.name': '',
    
        'm4': 'M.dl_model_init.v1',
        'm4.inputs': T.Graph.OutputPort('m6.data'),
        'm4.outputs': T.Graph.OutputPort('m23.data'),
    
        'm5': 'M.dl_model_train.v1',
        'm5.input_model': T.Graph.OutputPort('m4.data'),
        'm5.training_data': T.Graph.OutputPort('m10.data_1'),
        'm5.validation_data': T.Graph.OutputPort('m10.data_2'),
        'm5.optimizer': 'RMSprop',
        'm5.loss': 'mean_squared_error',
        'm5.metrics': 'mse',
        'm5.batch_size': 1024,
        'm5.epochs': 20,
        'm5.earlystop': m5_earlystop_bigquant_run,
        'm5.custom_objects': m5_custom_objects_bigquant_run,
        'm5.n_gpus': 0,
        'm5.verbose': '2:每个epoch输出一行记录',
        'm5.m_cached': False,
    
        'm11': 'M.dl_model_predict.v1',
        'm11.trained_model': T.Graph.OutputPort('m5.data'),
        'm11.input_data': T.Graph.OutputPort('m27.data'),
        'm11.batch_size': 1024,
        'm11.n_gpus': 0,
        'm11.verbose': '2:每个epoch输出一行记录',
    
        'm24': 'M.cached.v3',
        'm24.input_1': T.Graph.OutputPort('m11.data'),
        'm24.input_2': T.Graph.OutputPort('m18.data'),
        'm24.run': m24_run_bigquant_run,
        'm24.post_run': m24_post_run_bigquant_run,
        'm24.input_ports': '',
        'm24.params': '{}',
        'm24.output_ports': '',
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m24.data_1'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0.025,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 1000000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '后复权',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    def m31_run_bigquant_run(bq_graph, inputs):
         
        test_years = ['2014','2015','2016','2017','2018','2019','2020','2021']
        parameters_list = []
         
        for i in test_years:
            train_start_date =  str(int(i) -3)+'-01'+'-01'
            train_end_date =  str(int(i) - 1)+'-12'+'-31'
            test_start_date = i+'-01'+'-01'
            if i == test_years[-1]:
                test_end_date = i+'-09'+'-01'
            else:
                test_end_date  =  i+'-12'+'-31'
            
            parameters = {'m1.start_date':train_start_date,
                          'm1.end_date':train_end_date,
                          'm9.start_date':test_start_date,
                          'm9.end_date':test_end_date,
                         }
            
            parameters_list.append({'parameters': parameters})
        print(len(parameters_list), parameters_list)
    
        def run(parameters):
            try:
                print(parameters)
                return g.run(parameters)
            except Exception as e:
                print('ERROR --------', e)
                return None
            
        results = T.parallel_map(run, parameters_list, max_workers=8, remote_run=True, silent=True)
    
        return results
    
    
    m31 = M.hyper_run.v1(
        run=m31_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    In [ ]:
    print(len(m31.result))
    for i in range(len(m31.result)):
        print('==='*15, i)
        perf = m31.result[i]['m19'].raw_perf.read()
        T.render_perf(perf, buy_moment="buy", sell_moment="sell")
    
    In [3]:
    # T.render_perf(m31.result[0]['m19'].raw_perf.read(), buy_moment = "buy", sell_moment = "sell")
    df = pd.DataFrame() 
    for i in range(len(m31.result)):
        tmp = m31.result[i]['m19'].raw_perf.read()
        df = df.append(tmp[['returns','benchmark_period_return']])
    
    In [4]:
    import empyrical
    def get_stats(returns, benchmark_period_return):
        return_ratio  = empyrical.cum_returns_final(returns)
        annual_return_ratio  = empyrical.annual_return(returns)
        sharp_ratio = empyrical.sharpe_ratio(returns,0.035/252)
        return_volatility = empyrical.annual_volatility(returns)
        max_drawdown  = empyrical.max_drawdown(returns)
        benchmark_returns = (benchmark_period_return+1)/(benchmark_period_return+1).shift(1)-1
        alpha, beta =empyrical.alpha_beta_aligned(returns, benchmark_returns)
        
        return {
          'return_ratio': return_ratio,
          'annual_return_ratio': annual_return_ratio,
          'beta': beta,
          'alpha': alpha,
          'sharp_ratio': sharp_ratio,
          'return_volatility': return_volatility,
          'max_drawdown': max_drawdown,
        }
    get_stats(df['returns'], df['benchmark_period_return'])
    
    Out[4]:
    {'return_ratio': 63.120698592428724,
     'annual_return_ratio': 0.7519024660961879,
     'beta': 0.48546565304668354,
     'alpha': 0.8158119966625121,
     'sharp_ratio': 1.7219246312810157,
     'return_volatility': 0.3392824400837007,
     'max_drawdown': -0.5532899611410996}
    In [5]:
    T.plot((df['returns']+1).cumprod())