【宽客学院】自定义运行模块使用简介

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hyper_run
标签: #<Tag:0x00007fcf616d2b48> #<Tag:0x00007fcf616d2a08>

(a1641181638) #22

直接克隆策略运行,无修改
很快就卡在某步自己停了


(lanchaiye) #23

怎么样分别做成模型,然后自己组合。
这个能再讲得详细点吗


(a1641181638) #24

没法并行【输入特征列表】这个模块啊


(woshisilvio) #25
克隆策略

    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多个特征,每行一个,可以包含基础特征和衍生特征\nrank_return_10/rank_return_20\navg_turn_15/turn_0\n#----动量反转因子\namount_0/deal_number_0\n#ta_cci_14_0\nrank_return_3\nrank_return_0\nreturn_0\n#return_3\n#turn_0/avg_turn_2\n#turn_1/avg_turn_2\n#swing_volatility_5_0/swing_volatility_10_0\n\n#rank_amount_1/rank_amount_2\n#list_days_0\n#ta_rsi_14_0\n#ta_macd(close_0,'long')\n#ta_bbands_upperband_14_0\n\n#mf_net_amount_xl_0\n#avg_turn_3\n#rank_avg_mf_net_amount_0\n#rank_sh_holder_avg_pct_0\n#rank_avg_mf_net_amount_3\n#turn_0/avg_turn_3\n#rank_return_20\n#rank_return_5/rank_return_20\n#avg_amount_0\n#rank_avg_amount_0/rank_avg_amount_1\n#rank_swing_volatility_5_0\n#rank_avg_turn_3","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","ModuleId":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","ModuleParameters":[{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"OutputPortsInternal":[{"Name":"predictions","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-01-02","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-03-31","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-86","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-86"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-86","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-215","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"360","V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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.portfolio.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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n \n # 获取今天和昨天的成交量\n volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')\n close_price = data.current(context.symbol(instrument), 'close') #当收盘价\n high_price = data.current(context.symbol(instrument), 'high') #当天最高价\n low_price = data.current(context.symbol(instrument), 'low') #当天最低价\n #turn_since_buy = data.history(context.symbol(instrument), 'turn', 3, '1d') #当天换手率\n if ((volume_since_buy[0] > volume_since_buy[2] > volume_since_buy[1] ) or 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    In [12]:
    # 本代码由可视化策略环境自动生成 2020年4月3日 11:08
    # 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。
    
    
    # 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 = 1
        # 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 = 1
        context.options['hold_days'] = 1
    
    # 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')]
        today = 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.portfolio.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.portfolio.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
    
            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:
                 
                    # 获取今天和昨天的成交量
                volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')
                close_price = data.current(context.symbol(instrument), 'close')  #当收盘价
                high_price = data.current(context.symbol(instrument), 'high')  #当天最高价
                low_price = data.current(context.symbol(instrument), 'low')  #当天最低价
                #turn_since_buy = data.history(context.symbol(instrument), 'turn', 3, '1d')  #当天换手率
                if ((volume_since_buy[0] > volume_since_buy[2] > volume_since_buy[1] ) or (volume_since_buy[2] < volume_since_buy[1] < volume_since_buy[0] )) and volume_since_buy[2]/volume_since_buy[0] < 1.05 :
                    current_price = data.current(context.symbol(instrument), 'price')
                    amount = math.floor(cash / current_price - cash / current_price % 100)
                    context.order_value(context.symbol(instrument), amount)
                    print('today = 我今天想买 ',today,'instrument = ',instrument)
                    return
                else:
                    print('today = 我今天没买成 ',today,'instrument = ',instrument)
                #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': '2015-05-23',
        '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/develop/datasource/deprecated/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/develop/bigexpr/usage.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)
    
    # 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)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': True,
    
        'm3': 'M.input_features.v1',
        'm3.features': """# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    rank_return_10/rank_return_20
    avg_turn_15/turn_0
    #----动量反转因子
    amount_0/deal_number_0
    #ta_cci_14_0
    rank_return_3
    rank_return_0
    return_0
    #return_3
    #turn_0/avg_turn_2
    #turn_1/avg_turn_2
    #swing_volatility_5_0/swing_volatility_10_0
    
    #rank_amount_1/rank_amount_2
    #list_days_0
    #ta_rsi_14_0
    #ta_macd(close_0,'long')
    #ta_bbands_upperband_14_0
    
    #mf_net_amount_xl_0
    #avg_turn_3
    #rank_avg_mf_net_amount_0
    #rank_sh_holder_avg_pct_0
    #rank_avg_mf_net_amount_3
    #turn_0/avg_turn_3
    #rank_return_20
    #rank_return_5/rank_return_20
    #avg_amount_0
    #rank_avg_amount_0/rank_avg_amount_1
    #rank_swing_volatility_5_0
    #rank_avg_turn_3""",
    
        'm5': 'M.input_features.v1',
        'm5.features_ds': T.Graph.OutputPort('m3.data'),
        'm5.features': """# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    close_0
    high_1
    open_0
    low_0
    st_status_0
    price_limit_status_0
    #-----------------
    #----------------------------------------------------------------
    #资金60日内 流出次数达到22次以上---1 #sum(mf_net_amount_0 < 0 ,45) > 25
    #cond1=where(sum(mf_net_amount_0 < 0 ,45) > 25,1,0)
    #---涨停当日均线多头排列向上突破----2
    #mean(close_0,5)>mean(close_0,10)
    #mean(close_0,10)>mean(close_0,20)
    #mean(close_0,20)>mean(close_0,40)
    #mean(close_0,40)>mean(close_0,120)
    #cond1=where(((mean(close_0,5)>mean(close_0,10))&(mean(close_0,10)>mean(close_0,20))&(mean(close_0,20)>mean(close_0,40))&(mean(close_0,40)>mean(close_0,120))),1,0)
    #----------涨停当日资金流入》0------3
    cond2=where(mf_net_amount_0>0,1,0)
    #mf_net_amount_0>0
    cond3=where(mf_net_amount_1<0,1,0)
    #mf_net_amount_1<0
    #------------成交量变化,涨停日放量---4
    #cond4=where(volume_1<volume_0,1,0)
    #----#ST状态---5
    cond5=where((st_status_0 == 0),1,0)
    #-------#破八日新高
    #close_1> ts_max(close_1, 8)
    #cond6=where(close_0> ts_max(close_0, 8),1,0)
    #----当日最低价 站稳60日线
    #low_1 > mean(close_1,60)
    #cond7=where((low_0 > mean(close_0,60)),1,0)
    #----换手率》5%
    #cond8=turn_0> 4.2
    #turn_0> 4.2
    turn_0 < 15
    #----当天涨跌停状态
    cond9=where((price_limit_status_0 == 3),1,0)
    #cond9= price_limit_status_0 == 3
    #cond10=where((sum(mf_net_amount_0 < 0 ,45) > 25),1,0)
    
    """,
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m5.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 360,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m5.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': False,
        'm16.remove_extra_columns': False,
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m2.data'),
        'm7.data2': T.Graph.OutputPort('m16.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m7.data'),
    
        'm4': 'M.stock_ranker_train.v6',
        'm4.training_ds': T.Graph.OutputPort('m13.data'),
        'm4.features': T.Graph.OutputPort('m3.data'),
        'm4.learning_algorithm': '排序',
        'm4.number_of_leaves': 30,
        'm4.minimum_docs_per_leaf': 1000,
        'm4.number_of_trees': 20,
        'm4.learning_rate': 0.1,
        'm4.max_bins': 1023,
        'm4.feature_fraction': 1,
        'm4.data_row_fraction': 1,
        'm4.ndcg_discount_base': 1,
        'm4.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2019-01-02'),
        'm9.end_date': T.live_run_param('trading_date', '2020-03-31'),
        '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('m5.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 360,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m5.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': False,
        'm18.remove_extra_columns': False,
    
        'm10': 'M.filter.v3',
        'm10.input_data': T.Graph.OutputPort('m18.data'),
        'm10.expr': 'st_status_0==0 and price_limit_status_0 == 3 ',
        'm10.output_left_data': False,
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m10.data'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m4.model'),
        'm8.data': T.Graph.OutputPort('m14.data'),
        'm8.m_lazy_run': False,
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m8.predictions'),
        '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,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 100000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '真实价格',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    #def m6_run_bigquant_run(bq_graph, inputs):
    #    g = bq_graph
    
        # 在这里做超参数优化,通过 T.paralle_map 计算框架和平台资源,可实现快速运行百万策略
        # parameters_list = []
        # parameters = {}
        # parameters['m3'] = 'M.input_features.v1' # 设置m3模块的模块类型,比如修改版本号
        # parameters['m3.features'] = 'close_1' # 设置m3模块的参数features
        # parameters['m5.features_ds'] = T.Graph.OutputPort('m3.data') # 设置m5模块端口连接
        # parameters['*.__enabled__'] = False # 禁用所有的模块
        # parameters['m3.__enabled__'] = True # 启用m3模块
        # for features in feature_groups:
        #     parameters['m3.features'] = '\n'.join(features)
        #     parameters_list.append({'parameters': parameters})
        # results = T.parallel_map(g.run, parameters_list, max_workers=100, remote_run=True)
    
    #    results = [g.run({})]
    
    #    return results
    #------
    def m6_run_bigquant_run(bq_graph, inputs):
    
        factor_list=features =['rank_return_10/rank_return_20',
    'avg_turn_15/turn_0',
    #----动量反转因子
    'amount_0/deal_number_0',
    'ta_cci_14_0',
    'rank_avg_amount_0/rank_avg_amount_1',
    'rank_swing_volatility_5_0',
    'rank_avg_turn_3',
    'rank_return_3',
    'rank_return_0',
    'return_0']
    
        
    #    for feature in features:
    #        parameters = {'m3.features':feature}
    #        parameters_list.append({'parameters': parameters})
        zuhe_list  = []
        for k in range(10):
              zuhe_list.append(random.sample(factor_list,7))
        parameters_list = []
        for zuhe in zuhe_list:
            parameters = {'m3.features':'\n'.join(zuhe)}
            parameters_list.append({'parameters': parameters})     
    #    for feature in features:
    #        parameters = {'m3.features':feature}
    #        parameters_list.append({'parameters': parameters})
    
        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=2, remote_run=True, silent=True)
    
        return results
    #---------
    
    
    m6 = M.hyper_run.v1(
        run=m6_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    [2020-04-03 11:05:41.186533] INFO: bigquant: T.parallel_map  开始并行运算..
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  1.9min
    [Parallel(n_jobs=2)]: Done   2 tasks      | elapsed:  1.9min
    [Parallel(n_jobs=2)]: Done   3 tasks      | elapsed:  1.9min
    [Parallel(n_jobs=2)]: Done   4 tasks      | elapsed:  1.9min
    

    自定义运行(hyper_run)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    TerminatedWorkerError                     Traceback (most recent call last)
    <ipython-input-12-0ddd98bcc23b> in <module>()
        359     run=m6_run_bigquant_run,
        360     run_now=True,
    --> 361     bq_graph=g
        362 )
    
    <ipython-input-12-0ddd98bcc23b> in m6_run_bigquant_run(bq_graph, inputs)
        350             return None
        351 
    --> 352     results = T.parallel_map(run, parameters_list, max_workers=2, remote_run=True, silent=True)
        353 
        354     return results
    
    TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker. The exit codes of the workers are {SIGKILL(-9)}

    老师您好,我想做个因子列表测试,从10个里面抽取7个做随机循环 并行计算,可是报错了,是什么原因呢?

    (a1641181638) #26

    自定义运行不稳定,哪怕是十几个的小任务量也经常跑断和各种BUG报错


    (mushroom) #27

    请问如何从回测模块获取特征重要度?文档中没有相关的数据字典。


    (侯) #28

    如何批量循环持仓时间呢


    (bigrzz) #29

    在回测模块设置持仓时间,传入到回测模块中;在自定义运行模块中批量运行不同时间的回测模块


    (侯) #30

    谢谢老师


    (berserk_wei) #31

    这里的 print(parameters)为什么看不到输出结果?