强烈建议加一个逐天回测的模块

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标签: #<Tag:0x00007f9d5e8b2330>

(xie10) #1

目前的模拟交易只能是推到我的交易中,编写策略时只能使用固定模型回测,平台能不能添加一个逐天回测的模块,就是指定开始回测日期和结束日期,然后按照指定的日期自动运行代码一遍进行预测和回测,关键是解决动态模型回测难的问题。


(adhaha111) #2

您好,根据您的描述,滚动训练模块是否能满足您的要求呢:【宽客学院】滚动训练模块使用简介


(xie10) #3

您好,我一直在基于滚动训练模块去做动态模型的回测,然后把这个和推到我的交易中的交易记录进行对比,滚动训练得到的回测结果会出现以下的几种异常,1,动态模型的更新频率高,几天甚至每天,风控数据不足,手动强制修改滚动训练中回测开始时间和回测结束时间后,也会出现大盘风控数据缺失,从而没有任何回测输出,2,删除大盘风控后,奇偶交易日会和模拟交易中的情况错位,没办法真正了解模型的质量。因为动态模型几天甚至每天都要更新,跑一次都很需要时间,回测花了我大量的时间,主要是想问一下,平台能否提供一个和模拟交易一样的模块,主要是已有数据日历下动态模型做真实的回测用。谢谢


(xie10) #4
克隆策略

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= 1\n else:\n context.datecont = 0\n \n positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n #大盘风控模块,读取风控数据\n today = data.current_dt.strftime('%Y-%m-%d')\n \n #----------------大盘风控模块,读取风控数据------------------\n risk = 0\n today = data.current_dt.strftime('%Y-%m-%d')\n bm_ret0=ranker_prediction.bm_ret0.values[0]\n bm_ret1=ranker_prediction.bm_ret1.values[0]\n bm_ret2=ranker_prediction.bm_ret2.values[0]\n bm_ret3=ranker_prediction.bm_ret3.values[0]\n bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]\n bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]\n bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]\n \n if bm_ret0 < 0.001:\n if bm_risk_v0 > 0:\n print(today,'大盘放量下跌,全仓卖出')\n risk = 1\n elif bm_ret1 < 0.001 and bm_ret2 < 0.002:\n print(today,'大盘连续下跌,全仓卖出')\n risk = 1\n if bm_ret3 < -0.005:\n print(today,'大盘三日下跌超过2%,全仓卖出')\n risk = 1\n if bm_ret0 > 0.01:\n if (bm_risk_v0 + bm_risk_v1) < 0:\n print(today,'大盘缩量上涨,全仓卖出')\n risk = 1\n \n if risk == 1:\n \n if len(positions)>0:\n # 全部卖出后返回\n for instrument in positions:\n if data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行\n #---------------------大盘风控结束--------------------------------------\n \n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n #------------------------------------------卖出模块START--------------------------------------------\n if len(positions) > 0:\n for instrument in positions.keys():\n last_sale_date = positions[instrument].last_sale_date #上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days #持仓天数\n if hold_days >= 0:\n context.order_target(context.symbol(instrument), 0)\n #-------------------------------------------卖出模块END---------------------------------------------\n \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\n for i, instrument in enumerate(buy_instruments):\n try:\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 context.datecont == 1:\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 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bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m8', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2016-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-10-26'), # 数据结束日期\n train_update_days=10, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=None, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数\n train_data_min_days=1127, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=0, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 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    In [6]:
    # 本代码由可视化策略环境自动生成 2020年10月27日 21:07
    # 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 = (1,0)
        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
        #用于判断奇偶交易
        context.datecont = 0
    
    # 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')]
    
        if context.datecont == 0:
            context.datecont = 1
        else:
            context.datecont = 0
        
        positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        
        #大盘风控模块,读取风控数据
        today = data.current_dt.strftime('%Y-%m-%d')
        
         #----------------大盘风控模块,读取风控数据------------------
        risk = 0
        today = data.current_dt.strftime('%Y-%m-%d')
        bm_ret0=ranker_prediction.bm_ret0.values[0]
        bm_ret1=ranker_prediction.bm_ret1.values[0]
        bm_ret2=ranker_prediction.bm_ret2.values[0]
        bm_ret3=ranker_prediction.bm_ret3.values[0]
        bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]
        bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]
        bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]
        
        if bm_ret0 < 0.001:
            if bm_risk_v0 > 0:
                print(today,'大盘放量下跌,全仓卖出')
                risk = 1
            elif bm_ret1 < 0.001 and bm_ret2 < 0.002:
                print(today,'大盘连续下跌,全仓卖出')
                risk = 1
            if bm_ret3 < -0.005:
                print(today,'大盘三日下跌超过2%,全仓卖出')
                risk = 1
        if bm_ret0 > 0.01:
            if (bm_risk_v0 + bm_risk_v1) < 0:
                print(today,'大盘缩量上涨,全仓卖出')
                risk = 1
                
        if risk == 1:
            
            if len(positions)>0:
                # 全部卖出后返回
                for instrument in positions:
                    if data.can_trade(context.symbol(instrument)):
                        context.order_target_percent(context.symbol(instrument), 0)
            return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行
        #---------------------大盘风控结束--------------------------------------
        
        # 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)
        
       #------------------------------------------卖出模块START--------------------------------------------
        if len(positions) > 0:
            for instrument in positions.keys():
                last_sale_date = positions[instrument].last_sale_date   #上次交易日期
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days #持仓天数
                if hold_days >= 0:
                    context.order_target(context.symbol(instrument), 0)
        #-------------------------------------------卖出模块END---------------------------------------------
        
        
        # 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):
            try:
                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 context.datecont == 1:
                    # 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
                    if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 0.80:
                        current_price = data.current(context.symbol(instrument), 'price')
                        amount = math.floor(cash / current_price - cash / current_price % 100)
                        context.order(context.symbol(instrument), amount)
                        return
                    else:
                        print('today = ',today,'instrument = ',instrument)
            except:
                print('today = ',today,'instrument = ',instrument)
    # 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': '2015-01-01',
        '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
    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
    """,
    
        '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': 90,
    
        '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': 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,
    
        'm5': 'M.dropnan.v2',
        'm5.input_data': T.Graph.OutputPort('m7.data'),
    
        'm4': 'M.stock_ranker_train.v6',
        'm4.training_ds': T.Graph.OutputPort('m5.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': 2,
        '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', '2015-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2017-01-01'),
        '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': 90,
    
        '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': False,
        'm18.remove_extra_columns': False,
    
        'm10': 'M.dropnan.v2',
        'm10.input_data': T.Graph.OutputPort('m18.data'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m4.model'),
        'm8.data': T.Graph.OutputPort('m10.data'),
        'm8.m_lazy_run': False,
    
        'm6': 'M.input_features.v1',
        'm6.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
    ret_1=close/shift(close,1)
    ret_3=close/shift(close,3)
    volumepct_1=volume/shift(volume,1)
    bm_ret0=ret_1
    bm_ret1=shift(bm_ret0,1)
    bm_ret2=shift(bm_ret0,2)
    bm_ret3=ret_3
    bm_risk_v0=volumepct_1
    bm_risk_v1=shift(bm_risk_v0,1)
    bm_risk_v2=shift(bm_risk_v0,2)""",
    
        'm11': 'M.index_feature_extract.v3',
        'm11.input_1': T.Graph.OutputPort('m9.data'),
        'm11.input_2': T.Graph.OutputPort('m6.data'),
        'm11.before_days': 100,
        'm11.index': '000001.HIX',
    
        'm12': 'M.join.v3',
        'm12.data1': T.Graph.OutputPort('m8.predictions'),
        'm12.data2': T.Graph.OutputPort('m11.data_1'),
        'm12.on': 'date',
        'm12.how': 'left',
        'm12.sort': False,
    
        'm13': 'M.filtet_st_stock_tomo.v3',
        'm13.input_1': T.Graph.OutputPort('m12.data'),
    
        'm14': 'M.sort.v4',
        'm14.input_ds': T.Graph.OutputPort('m13.data_1'),
        'm14.sort_by': 'date,position',
        'm14.group_by': '--',
        'm14.keep_columns': '--',
        'm14.ascending': True,
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m14.sorted_data'),
        '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': '真实价格',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    def m20_run_bigquant_run(
        bq_graph,
        inputs,
        trading_days_market='CN', # 使用那个市场的交易日历, TODO
        train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
        test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
        predict_mid='m8', # 预测 模块id
        trade_mid='m19', # 回测 模块id
        start_date='2016-01-01', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2020-10-26'), # 数据结束日期
        train_update_days=10, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=None, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数
        train_data_min_days=1127, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
        train_data_max_days=0, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
        rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制
    ):
        def merge_datasources(input_1):
            df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
            df = pd.concat(df_list)
            instrument_data = {
                'start_date': df['date'].min().strftime('%Y-%m-%d'),
                'end_date': df['date'].max().strftime('%Y-%m-%d'),
                'instruments': list(set(df['instrument'])),
            }
            return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))
    
        def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
            # 是否实盘模式
            tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
            is_live_run = T.live_run_param('trading_date', None) is not None
    
            if is_live_run and train_update_days_for_live:
                train_update_days = train_update_days_for_live
    
            rollings = []
            train_end_date = train_data_min_days
            while train_end_date < len(tdays):
                if train_data_max_days is not None and train_data_max_days > 0:
                    train_start_date = max(train_end_date - train_data_max_days, 0)
                else:
                    train_start_date = 0
                rollings.append({
                    'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                    'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                    'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                    'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
                })
                train_end_date += train_update_days
    
            if not rollings:
                raise Exception('没有滚动需要执行,请检查配置')
    
            if is_live_run and rolling_count_for_live:
                rollings = rollings[-rolling_count_for_live:]
    
            return rollings
    
        g = bq_graph
    
        rolling_dates = gen_rolling_dates(
            trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
    
        # 训练和预测
        results = []
        for rolling in rolling_dates:
            parameters = {}
            # 先禁用回测
            parameters[trade_mid + '.__enabled__'] = False
            parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
            parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
            parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
            parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
            print('------ rolling_train:', parameters)
            results.append(g.run(parameters))
    
        # 合并预测结果并回测
        mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
        parameters = {}
        parameters['*.__enabled__'] = False
        parameters[trade_mid + '.__enabled__'] = True
        parameters[trade_mid + '.instruments'] = mx.instrument_data
        parameters[trade_mid + '.options_data'] = mx.data
    
        trade = g.run(parameters)
    
        return {'rollings': results, 'trade': trade}
    
    
    m20 = M.hyper_rolling_train.v1(
        run=m20_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    ------ rolling_train: {'m19.__enabled__': False, 'm1.start_date': '2016-01-04', 'm1.end_date': '2020-08-18', 'm9.start_date': '2020-08-19', 'm9.end_date': '2020-09-01'}
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-aee6fb7410b043fd96abbb496258de72"}/bigcharts-data-end
    ------ rolling_train: {'m19.__enabled__': False, 'm1.start_date': '2016-01-04', 'm1.end_date': '2020-09-01', 'm9.start_date': '2020-09-02', 'm9.end_date': '2020-09-15'}
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c86ce5f5479444acbad542cdf989147a"}/bigcharts-data-end
    ------ rolling_train: {'m19.__enabled__': False, 'm1.start_date': '2016-01-04', 'm1.end_date': '2020-09-15', 'm9.start_date': '2020-09-16', 'm9.end_date': '2020-09-29'}
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-121228dd0fff46a6b10927700e10d14c"}/bigcharts-data-end
    ------ rolling_train: {'m19.__enabled__': False, 'm1.start_date': '2016-01-04', 'm1.end_date': '2020-09-29', 'm9.start_date': '2020-09-30', 'm9.end_date': '2020-10-21'}
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-613d8ba974ac49e888e5315ed515b412"}/bigcharts-data-end
    ------ rolling_train: {'m19.__enabled__': False, 'm1.start_date': '2016-01-04', 'm1.end_date': '2020-10-21', 'm9.start_date': '2020-10-22', 'm9.end_date': '2020-10-26'}
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d881fa22b27e4008b5da78c0a3c655ea"}/bigcharts-data-end
    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    <ipython-input-6-d91e72cd7d7b> in <module>()
        397     run=m20_run_bigquant_run,
        398     run_now=True,
    --> 399     bq_graph=g
        400 )
    
    <ipython-input-6-d91e72cd7d7b> in m20_run_bigquant_run(bq_graph, inputs, trading_days_market, train_instruments_mid, test_instruments_mid, predict_mid, trade_mid, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
        389     parameters[trade_mid + '.options_data'] = mx.data
        390 
    --> 391     trade = g.run(parameters)
        392 
        393     return {'rollings': results, 'trade': trade}
    
    <ipython-input-6-d91e72cd7d7b> in m19_handle_data_bigquant_run(context, data)
         41     risk = 0
         42     today = data.current_dt.strftime('%Y-%m-%d')
    ---> 43     bm_ret0=ranker_prediction.bm_ret0.values[0]
         44     bm_ret1=ranker_prediction.bm_ret1.values[0]
         45     bm_ret2=ranker_prediction.bm_ret2.values[0]
    
    AttributeError: 'DataFrame' object has no attribute 'bm_ret0'

    (xie10) #5

    带风控的滚动训练就出这个问题


    (xie120) #6

    你试着把指数的数据不用外部采集合并,而是直接写到回测的数据准备函数中,给你个例子
    start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime(’%Y-%m-%d’)
    df=DataSource(‘bar1d_index_CN_STOCK_A’).read(start_date=start_date,end_date=context.end_date,fields=[‘close’])
    benckmark_data=df[df.instrument==‘000001.HIX’]
    #计算上证指数1日涨幅
    benckmark_data[‘ret_1’]=benckmark_data[‘close’]/benckmark_data[‘close’].shift(1)


    (xie10) #7

    好的,原来需要写到数据准备函数中啊,我还在滚动中强制启动指数特征数据采集模块也无效0_0