求助:老师您好,CNN超参数搜索报错,单独运行没问题,但是一旦传入多个参数就会报错

策略分享
标签: #<Tag:0x00007f8c5e5f76d8>

(sevencat) #1
克隆策略

策略简介

因子:样例因子(18个)

因子是否标准化:是

标注:未来5日收益(不做离散化)

算法:CNN

类型:回归问题

训练集:10-16年

测试集:16-20.03年

选股依据:根据预测值降序排序买入

持股数:30

持仓天数:5

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ull},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3872"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3872","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":33,"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":"# 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":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3907"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3907","OutputType":null},{"Name":"data_2","NodeId":"-3907","OutputType":null},{"Name":"data_3","NodeId":"-3907","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1114","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# #号开始的表示注释,注释需单独一行\n# 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征\n#cond1=st_status_0<1\n#过滤ST\ncond1=st_status_0<1\n#主力近流入排前10%\ncond2=rank(mf_net_pct_main_0)>0.9\n#三日内涨幅排前20%\ncond3=rank((close_0-close_3)/close_3)>0.8\n#成交金额2亿以上,成交量>10w\ncond4=(amount_0>200000000)&(volume_0>100000)\n\n\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-1114"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1114","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2393","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 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 = 5\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.hold_days = 5\n","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 # 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.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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":"# 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。\ndef bigquant_run(context, data):\n 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    In [3]:
    # 本代码由可视化策略环境自动生成 2020年9月25日 16:46
    # 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 m23_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 = 5
        # 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.hold_days = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m23_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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m23_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m23_before_trading_start_bigquant_run(context, data):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2019-01-01',
        '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, -3) / 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,
    
        'm13': 'M.standardlize.v8',
        'm13.input_1': T.Graph.OutputPort('m2.data'),
        'm13.columns_input': 'label',
    
        'm3': 'M.input_features.v1',
        'm3.features': """mean(mf_net_amount_m_0, 10)
    -avg_turn_3/rank_turn_3
    sign(mean(close_0, 5)-ta_bbands_m(close_0/adjust_factor_0, timeperiod=28))/return_10
    -1*sign(ta_stoch_slowk_5_3_0_3_0_0-ta_stoch_slowd_5_3_0_3_0_0)/price_limit_status_6
    ta_bbands_middleband_14_0/swing_volatility_10_0
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0""",
    
        'm20': 'M.input_features.v1',
        'm20.features_ds': T.Graph.OutputPort('m3.data'),
        'm20.features': """
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    #cond1=st_status_0<1
    #过滤ST
    cond1=st_status_0<1
    #主力近流入排前10%
    cond2=rank(mf_net_pct_main_0)>0.9
    #三日内涨幅排前20%
    cond3=rank((close_0-close_3)/close_3)>0.8
    #成交金额2亿以上,成交量>10w
    cond4=(amount_0>200000000)&(volume_0>100000)
    
    
    """,
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m20.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 90,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m20.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': True,
        'm16.remove_extra_columns': False,
    
        'm14': 'M.standardlize.v8',
        'm14.input_1': T.Graph.OutputPort('m16.data'),
        'm14.input_2': T.Graph.OutputPort('m3.data'),
        'm14.columns_input': '[]',
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m13.data'),
        'm7.data2': T.Graph.OutputPort('m14.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': 5,
        'm26.feature_clip': 1,
        'm26.flatten': True,
        'm26.window_along_col': 'instrument',
    
        'm4': 'M.cached.v3',
        'm4.input_1': T.Graph.OutputPort('m26.data'),
        'm4.input_2': T.Graph.OutputPort('m3.data'),
        'm4.run': m4_run_bigquant_run,
        'm4.post_run': m4_post_run_bigquant_run,
        'm4.input_ports': '',
        'm4.params': '{}',
        'm4.output_ports': '',
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2019-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2020-09-01'),
        '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('m20.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 90,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m20.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': '[]',
    
        'm27': 'M.dl_convert_to_bin.v2',
        'm27.input_data': T.Graph.OutputPort('m25.data'),
        'm27.features': T.Graph.OutputPort('m3.data'),
        'm27.window_size': 5,
        'm27.feature_clip': 1,
        'm27.flatten': True,
        'm27.window_along_col': 'instrument',
    
        'm8': 'M.cached.v3',
        'm8.input_1': T.Graph.OutputPort('m27.data'),
        'm8.input_2': T.Graph.OutputPort('m3.data'),
        'm8.run': m8_run_bigquant_run,
        'm8.post_run': m8_post_run_bigquant_run,
        'm8.input_ports': '',
        'm8.params': '{}',
        'm8.output_ports': '',
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '18,5',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm10': 'M.dl_layer_conv1d.v1',
        'm10.inputs': T.Graph.OutputPort('m6.data'),
        'm10.filters': 32,
        'm10.kernel_size': '5',
        'm10.strides': '1',
        'm10.padding': 'valid',
        'm10.dilation_rate': 1,
        'm10.activation': 'relu',
        'm10.use_bias': True,
        'm10.kernel_initializer': 'glorot_uniform',
        'm10.bias_initializer': 'Zeros',
        'm10.kernel_regularizer': 'None',
        'm10.kernel_regularizer_l1': 0,
        'm10.kernel_regularizer_l2': 0,
        'm10.bias_regularizer': 'None',
        'm10.bias_regularizer_l1': 0,
        'm10.bias_regularizer_l2': 0,
        'm10.activity_regularizer': 'None',
        'm10.activity_regularizer_l1': 0,
        'm10.activity_regularizer_l2': 0,
        'm10.kernel_constraint': 'None',
        'm10.bias_constraint': 'None',
        'm10.name': '',
    
        'm12': 'M.dl_layer_maxpooling1d.v1',
        'm12.inputs': T.Graph.OutputPort('m10.data'),
        'm12.pool_size': 1,
        'm12.padding': 'valid',
        'm12.name': '',
    
        'm32': 'M.dl_layer_conv1d.v1',
        'm32.inputs': T.Graph.OutputPort('m12.data'),
        'm32.filters': 32,
        'm32.kernel_size': '3',
        'm32.strides': '1',
        'm32.padding': 'valid',
        'm32.dilation_rate': 1,
        'm32.activation': 'relu',
        'm32.use_bias': True,
        'm32.kernel_initializer': 'glorot_uniform',
        'm32.bias_initializer': 'Zeros',
        'm32.kernel_regularizer': 'None',
        'm32.kernel_regularizer_l1': 0,
        'm32.kernel_regularizer_l2': 0,
        'm32.bias_regularizer': 'None',
        'm32.bias_regularizer_l1': 0,
        'm32.bias_regularizer_l2': 0,
        'm32.activity_regularizer': 'None',
        'm32.activity_regularizer_l1': 0,
        'm32.activity_regularizer_l2': 0,
        'm32.kernel_constraint': 'None',
        'm32.bias_constraint': 'None',
        'm32.name': '',
    
        'm33': 'M.dl_layer_maxpooling1d.v1',
        'm33.inputs': T.Graph.OutputPort('m32.data'),
        'm33.pool_size': 1,
        'm33.padding': 'valid',
        'm33.name': '',
    
        'm28': 'M.dl_layer_globalmaxpooling1d.v1',
        'm28.inputs': T.Graph.OutputPort('m33.data'),
        'm28.name': '',
    
        'm30': 'M.dl_layer_dense.v1',
        'm30.inputs': T.Graph.OutputPort('m28.data'),
        'm30.units': 1,
        'm30.activation': 'linear',
        'm30.use_bias': True,
        'm30.kernel_initializer': 'glorot_uniform',
        'm30.bias_initializer': 'Zeros',
        'm30.kernel_regularizer': 'None',
        'm30.kernel_regularizer_l1': 0,
        'm30.kernel_regularizer_l2': 0,
        'm30.bias_regularizer': 'None',
        'm30.bias_regularizer_l1': 0,
        'm30.bias_regularizer_l2': 0,
        'm30.activity_regularizer': 'None',
        'm30.activity_regularizer_l1': 0,
        'm30.activity_regularizer_l2': 0,
        'm30.kernel_constraint': 'None',
        'm30.bias_constraint': 'None',
        'm30.name': '',
    
        'm34': 'M.dl_model_init.v1',
        'm34.inputs': T.Graph.OutputPort('m6.data'),
        'm34.outputs': T.Graph.OutputPort('m30.data'),
    
        'm5': 'M.dl_model_train.v1',
        'm5.input_model': T.Graph.OutputPort('m34.data'),
        'm5.training_data': T.Graph.OutputPort('m4.data_1'),
        'm5.optimizer': 'RMSprop',
        'm5.loss': 'mean_squared_error',
        'm5.metrics': 'mae',
        'm5.batch_size': 10240,
        'm5.epochs': 5,
        'm5.n_gpus': 0,
        'm5.verbose': '2:每个epoch输出一行记录',
    
        'm11': 'M.dl_model_predict.v1',
        'm11.trained_model': T.Graph.OutputPort('m5.data'),
        'm11.input_data': T.Graph.OutputPort('m8.data_1'),
        'm11.batch_size': 10240,
        '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('m25.data'),
        'm24.run': m24_run_bigquant_run,
        'm24.post_run': m24_post_run_bigquant_run,
        'm24.input_ports': '',
        'm24.params': '{}',
        'm24.output_ports': '',
    
        'm23': 'M.trade.v4',
        'm23.instruments': T.Graph.OutputPort('m9.data'),
        'm23.options_data': T.Graph.OutputPort('m24.data_1'),
        'm23.start_date': '',
        'm23.end_date': '',
        'm23.initialize': m23_initialize_bigquant_run,
        'm23.handle_data': m23_handle_data_bigquant_run,
        'm23.prepare': m23_prepare_bigquant_run,
        'm23.before_trading_start': m23_before_trading_start_bigquant_run,
        'm23.volume_limit': 0.025,
        'm23.order_price_field_buy': 'open',
        'm23.order_price_field_sell': 'close',
        'm23.capital_base': 1000000,
        'm23.auto_cancel_non_tradable_orders': True,
        'm23.data_frequency': 'daily',
        'm23.price_type': '真实价格',
        'm23.product_type': '股票',
        'm23.plot_charts': True,
        'm23.backtest_only': False,
        'm23.benchmark': '',
    })
    
    # g.run({})
    
    
    def m19_param_grid_builder_bigquant_run():
        param_grid = {}
        param_grid['m27.feature_clip'] = [1,2,3]
        param_grid['m26.feature_clip'] = [1,2,3]
        # 在这里设置需要调优的参数备选
        # param_grid['m3.features'] = ['close_1/close_0', 'close_2/close_0\nclose_3/close_0']
        # param_grid['m6.number_of_trees'] = [5, 10, 20]
    
        return param_grid
    
    #'m27.feature_clip': 1,
    def m19_scoring_bigquant_run(result):
        score = result.get('m23').read_raw_perf()['sharpe'].tail(1)[0]
    
        return score
    
    
    m19 = M.hyper_parameter_search.v1(
        param_grid_builder=m19_param_grid_builder_bigquant_run,
        scoring=m19_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=1,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    Fitting 1 folds for each of 9 candidates, totalling 9 fits
    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    [CV] m26.feature_clip=1, m27.feature_clip=1 ..........................
    
    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-3-c810d13e6870> in <module>()
        448     worker_silent=True,
        449     run_now=True,
    --> 450     bq_graph=g
        451 )
    
    Exception: 任务运行失败, 8169891aff0811ea9de80a580a83000f Failed

    CNN超参数搜索报错,单独运行没问题,但是一旦传入多个参数就会报错,请教老师提供一个解决方法,是要重新写一个pickcle做参数循环麽?可是这边无法修改滚动窗口的代码块

    (adhaha111) #2

    您好,我们正在处理该问题


    (idelong) #3

    作者你好,可以加个vx交流吗,想订阅你的策略


    (idelong) #4

    大神你好,能加个微吗,希望和你交流策略相关问题


    (idelong) #5

    大神你好,希望能订阅你的策略,能加个微交流吗


    (idelong) #6

    大神你好,希望能订阅你的策略,能加个微交流吗