滚动训练参数设置导致的【模型训练失败:可能导致错误的原因是训练数据问题,请检查训练数据】

报错
问题反馈
标签: #<Tag:0x00007fcf690f5510> #<Tag:0x00007fcf690f53d0>

(a1641181638) #1

基本确定了是【滚动训练】模块里,最小时间、最大时间和更新周期的设置问题。
当按教程里的250,250,250是没事的,但是当我想设置成用3个月训练1个月,每个月滚动一次。即更新周期60,最大最小90的时候就会出现这个问题
参考了一些帖子和做了一些实验,还是没搞明白

克隆策略

    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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\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='2017-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-02-07'), # 数据结束日期\n train_update_days=30, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=True, #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=60, # 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=60, # 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:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\n rollings.append({\n 'train_start_date': 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    In [23]:
    # 本代码由可视化策略环境自动生成 2020年2月7日 19:04
    # 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 = 3
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = 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': '2017-01-01',
        '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 {{web_host_url}}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 <{{web_host_url}}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)
    
    # 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': """market_cap_0  #总市值
    pe_lyr_0   #市盈率
    pb_lf_0    #市净率
    ps_ttm_0   #市销率""",
    
        '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': 10,
    
        '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,
    
        'm11': 'M.filter_concept.v4',
        'm11.input_1': T.Graph.OutputPort('m7.data'),
        'm11.concept_str': '人工智能',
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m11.data_1'),
    
        'm6': 'M.stock_ranker_train.v5',
        'm6.training_ds': T.Graph.OutputPort('m13.data'),
        'm6.features': T.Graph.OutputPort('m3.data'),
        'm6.learning_algorithm': '排序',
        'm6.number_of_leaves': 30,
        'm6.minimum_docs_per_leaf': 1000,
        'm6.number_of_trees': 20,
        'm6.learning_rate': 0.1,
        'm6.max_bins': 1023,
        'm6.feature_fraction': 1,
        'm6.m_lazy_run': False,
    
        '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', '2020-01-01'),
        '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': 10,
    
        '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,
    
        'm4': 'M.filter_concept.v4',
        'm4.input_1': T.Graph.OutputPort('m18.data'),
        'm4.concept_str': '人工智能',
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m4.data_1'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m6.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': 0.025,
        '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 m5_run_bigquant_run(
        bq_graph,
        inputs,
        trading_days_market='CN', # 使用那个市场的交易日历
        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='2017-01-01', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2020-02-07'), # 数据结束日期
        train_update_days=30, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=True, #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=60, # 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=60, # 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:
                    train_start_date = max(train_end_date - train_data_max_days, 0)
                else:
                    train_start_date = start_date
                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}
    
    
    m5 = M.hyper_rolling_train.v1(
        run=m5_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    

    StockRanker训练(stock_ranker_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    滚动训练(hyper_rolling_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-23-cdb53a8e759d> in <module>()
        295     run=m5_run_bigquant_run,
        296     run_now=True,
    --> 297     bq_graph=g
        298 )
    
    <ipython-input-23-cdb53a8e759d> in m5_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)
        277         parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        278         # print('------ rolling_train:', parameters)
    --> 279         results.append(g.run(parameters))
        280 
        281     # 合并预测结果并回测
    
    Exception: 模型训练失败:可能导致错误的原因是训练数据问题,请检查训练数据, err_code=1 (00968ac0499811eaae3d0a580a810760)

    (iQuant) #3
    克隆策略

      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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.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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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='2019-09-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2020-02-23'), # 数据结束日期\n train_update_days=60, # 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=90, # 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=90, # 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 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n 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\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n 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])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': 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      In [12]:
      # 本代码由可视化策略环境自动生成 2020年2月26日 10:05
      # 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 = 3
          # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
          context.stock_weights = 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': '2017-01-01',
          '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 {{web_host_url}}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 <{{web_host_url}}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)
      
      # 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': """market_cap_0
      pe_lyr_0
      pb_lf_0
      ps_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': 10,
      
          '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,
      
          'm4': 'M.filter_concept.v4',
          'm4.input_1': T.Graph.OutputPort('m7.data'),
          'm4.concept_str': '人工智能',
      
          'm13': 'M.dropnan.v1',
          'm13.input_data': T.Graph.OutputPort('m4.data_1'),
      
          'm10': 'M.stock_ranker_train.v6',
          'm10.training_ds': T.Graph.OutputPort('m13.data'),
          'm10.features': T.Graph.OutputPort('m3.data'),
          'm10.learning_algorithm': '排序',
          'm10.number_of_leaves': 30,
          'm10.minimum_docs_per_leaf': 1000,
          'm10.number_of_trees': 20,
          'm10.learning_rate': 0.1,
          'm10.max_bins': 1023,
          'm10.feature_fraction': 1,
          'm10.data_row_fraction': 1,
          'm10.ndcg_discount_base': 1,
          'm10.m_lazy_run': False,
      
          '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', '2020-01-01'),
          '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': 10,
      
          '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,
      
          'm11': 'M.filter_concept.v4',
          'm11.input_1': T.Graph.OutputPort('m18.data'),
          'm11.concept_str': '人工智能',
      
          'm14': 'M.dropnan.v1',
          'm14.input_data': T.Graph.OutputPort('m11.data_1'),
      
          'm8': 'M.stock_ranker_predict.v5',
          'm8.model': T.Graph.OutputPort('m10.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': 0.025,
          '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 m5_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='2019-09-01', # 数据开始日期
          end_date=T.live_run_param('trading_date', '2020-02-23'), # 数据结束日期
          train_update_days=60, # 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=90, # 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=90, # 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}
      
      
      m5 = M.hyper_rolling_train.v1(
          run=m5_run_bigquant_run,
          run_now=True,
          bq_graph=g
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d7535d5efb5240dea4adddd657d4f401"}/bigcharts-data-end
      • 收益率15.01%
      • 年化收益率396.43%
      • 基准收益率-0.96%
      • 阿尔法1.73
      • 贝塔0.86
      • 夏普比率4.1
      • 胜率0.5
      • 盈亏比2.73
      • 收益波动率40.34%
      • 信息比率0.4
      • 最大回撤8.36%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2c5f9feab6e6476ab2a4ba503c82ec9e"}/bigcharts-data-end