滚动训练在DNN策略上的应用分享

策略分享
标签: #<Tag:0x00007f5b9f093100>

(xgl891) #1

本文主要是分享一个滚动训练模块在DNN策略上的应用。

滚动训练模块是一个已经封装好的可视化模块,右侧属性栏里可以看见模块代码。
使用时主要需设置一下以下几个参数:

  • trading_days_market: 使用哪个市场的交易日历,如‘CN’
  • train_instruments_mid:训练数据 证券代码列表 模块id,如‘m1’
  • test_instruments_mid:测试数据 证券代码列表 模块id,如’m9’
  • predict_mid: 预测 模块id,如’m24’
  • trade_mid:回测 模块id,如’m19’
  • start_date:数据开始日期,如’2018-01-23’
  • end_date: 数据结束日期,如T.live_run_param(‘trading_date’, ‘2019-01-11’)
  • train_update_days: 更新周期,按交易日计算,每多少天更新一次
  • train_update_days_for_live: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: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: 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: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。

运行结束后,还可以使用以下代码查看相关的一些数据:

# 第一次训练的训练集
print(m10.result['rollings'][0]['m1'].data.read_pickle()['start_date'])
print(m10.result['rollings'][0]['m1'].data.read_pickle()['end_date'])
# 第一次训练的预测集
print(m10.result['rollings'][0]['m9'].data.read_pickle()['start_date'])
print(m10.result['rollings'][0]['m9'].data.read_pickle()['end_date'])
# 训练的第一个模型的预测结果
m10.result['rollings'][0]['m11'].data.read_pickle()

详细的滚动训练模块介绍见 文档-滚动训练(v1)

以下是一个模块在DNN上的应用实例,感兴趣的朋友可以克隆下来进行调参得到一个更好的回测结果。

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='m24', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2015-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2016-07-01'), # 数据结束日期\n train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=100, #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=100, # 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=100, # 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.read_df() 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': 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].data_1 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 [7]:
    # 本代码由可视化策略环境自动生成 2019年9月3日 09:57
    # 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 m24_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, 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 = 20
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2015-12-31',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': '',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.label_expr': """# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)-1
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': False,
    
        'm13': 'M.standardlize.v8',
        'm13.input_1': T.Graph.OutputPort('m2.data'),
        'm13.columns_input': 'label',
        'm13.m_cached': False,
    
        'm3': 'M.input_features.v1',
        'm3.features': """close_0/mean(close_0,5)
    close_0/mean(close_0,10)
    close_0/mean(close_0,20)
    close_0/open_0
    open_0/mean(close_0,5)
    open_0/mean(close_0,10)
    open_0/mean(close_0,20)""",
        'm3.m_cached': False,
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 0,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': True,
        'm16.remove_extra_columns': False,
    
        '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': 1,
        'm26.feature_clip': 5,
        'm26.flatten': True,
        'm26.window_along_col': 'instrument',
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2016-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2019-04-20'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 0,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': True,
        'm18.remove_extra_columns': False,
    
        'm25': 'M.standardlize.v8',
        'm25.input_1': T.Graph.OutputPort('m18.data'),
        'm25.input_2': T.Graph.OutputPort('m3.data'),
        'm25.columns_input': '[]',
    
        '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': 1,
        'm27.feature_clip': 5,
        'm27.flatten': True,
        'm27.window_along_col': 'instrument',
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '7',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm8': 'M.dl_layer_dense.v1',
        'm8.inputs': T.Graph.OutputPort('m6.data'),
        'm8.units': 256,
        'm8.activation': 'relu',
        'm8.use_bias': True,
        'm8.kernel_initializer': 'glorot_uniform',
        'm8.bias_initializer': 'Zeros',
        'm8.kernel_regularizer': 'None',
        'm8.kernel_regularizer_l1': 0,
        'm8.kernel_regularizer_l2': 0,
        'm8.bias_regularizer': 'None',
        'm8.bias_regularizer_l1': 0,
        'm8.bias_regularizer_l2': 0,
        'm8.activity_regularizer': 'None',
        'm8.activity_regularizer_l1': 0,
        'm8.activity_regularizer_l2': 0,
        'm8.kernel_constraint': 'None',
        'm8.bias_constraint': 'None',
        'm8.name': '',
    
        'm21': 'M.dl_layer_dropout.v1',
        'm21.inputs': T.Graph.OutputPort('m8.data'),
        'm21.rate': 0.1,
        'm21.noise_shape': '',
        'm21.name': '',
    
        'm20': 'M.dl_layer_dense.v1',
        'm20.inputs': T.Graph.OutputPort('m21.data'),
        'm20.units': 128,
        'm20.activation': 'relu',
        'm20.use_bias': True,
        'm20.kernel_initializer': 'glorot_uniform',
        'm20.bias_initializer': 'Zeros',
        'm20.kernel_regularizer': 'None',
        'm20.kernel_regularizer_l1': 0,
        'm20.kernel_regularizer_l2': 0,
        'm20.bias_regularizer': 'None',
        'm20.bias_regularizer_l1': 0,
        'm20.bias_regularizer_l2': 0,
        'm20.activity_regularizer': 'None',
        'm20.activity_regularizer_l1': 0,
        'm20.activity_regularizer_l2': 0,
        'm20.kernel_constraint': 'None',
        'm20.bias_constraint': 'None',
        'm20.name': '',
    
        'm22': 'M.dl_layer_dropout.v1',
        'm22.inputs': T.Graph.OutputPort('m20.data'),
        'm22.rate': 0.1,
        'm22.noise_shape': '',
        'm22.name': '',
    
        'm23': 'M.dl_layer_dense.v1',
        'm23.inputs': T.Graph.OutputPort('m22.data'),
        'm23.units': 1,
        'm23.activation': 'linear',
        'm23.use_bias': True,
        'm23.kernel_initializer': 'glorot_uniform',
        'm23.bias_initializer': 'Zeros',
        'm23.kernel_regularizer': 'None',
        'm23.kernel_regularizer_l1': 0,
        'm23.kernel_regularizer_l2': 0,
        'm23.bias_regularizer': 'None',
        'm23.bias_regularizer_l1': 0,
        'm23.bias_regularizer_l2': 0,
        'm23.activity_regularizer': 'None',
        'm23.activity_regularizer_l1': 0,
        'm23.activity_regularizer_l2': 0,
        'm23.kernel_constraint': 'None',
        'm23.bias_constraint': 'None',
        'm23.name': '',
    
        'm4': 'M.dl_model_init.v1',
        'm4.inputs': T.Graph.OutputPort('m6.data'),
        'm4.outputs': T.Graph.OutputPort('m23.data'),
    
        'm5': 'M.dl_model_train.v1',
        'm5.input_model': T.Graph.OutputPort('m4.data'),
        'm5.training_data': T.Graph.OutputPort('m26.data'),
        'm5.optimizer': 'Adam',
        'm5.loss': 'mean_squared_error',
        'm5.metrics': 'mse',
        'm5.batch_size': 1024,
        '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('m27.data'),
        'm11.batch_size': 1024,
        'm11.n_gpus': 0,
        'm11.verbose': '2:每个epoch输出一行记录',
    
        'm24': 'M.cached.v3',
        'm24.input_1': T.Graph.OutputPort('m11.data'),
        'm24.input_2': T.Graph.OutputPort('m18.data'),
        'm24.run': m24_run_bigquant_run,
        'm24.post_run': m24_post_run_bigquant_run,
        'm24.input_ports': '',
        'm24.params': '{}',
        'm24.output_ports': '',
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m24.data_1'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0.025,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 1000000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '后复权',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    def m10_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='m24', # 预测 模块id
        trade_mid='m19', # 回测 模块id
        start_date='2015-01-01', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2016-07-01'), # 数据结束日期
        train_update_days=90, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=100, #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=100, # 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=100, # 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.read_df() 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].data_1 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}
    
    
    m10 = M.hyper_rolling_train.v1(
        run=m10_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    Epoch 1/5
     - 4s - loss: 0.9815 - mean_squared_error: 0.9815
    Epoch 2/5
     - 4s - loss: 0.9759 - mean_squared_error: 0.9759
    Epoch 3/5
     - 4s - loss: 0.9751 - mean_squared_error: 0.9751
    Epoch 4/5
     - 4s - loss: 0.9743 - mean_squared_error: 0.9743
    Epoch 5/5
     - 4s - loss: 0.9738 - mean_squared_error: 0.9738
    
    DataSource(65ee60d9a4e14897881e13d213351849T, v3)
    
    Epoch 1/5
     - 4s - loss: 0.9687 - mean_squared_error: 0.9687
    Epoch 2/5
     - 4s - loss: 0.9613 - mean_squared_error: 0.9613
    Epoch 3/5
     - 5s - loss: 0.9602 - mean_squared_error: 0.9602
    Epoch 4/5
     - 6s - loss: 0.9579 - mean_squared_error: 0.9579
    Epoch 5/5
     - 5s - loss: 0.9565 - mean_squared_error: 0.9565
    
    DataSource(b01372df3ac4464fadd25e56581b3eccT, v3)
    
    Epoch 1/5
     - 8s - loss: 0.9732 - mean_squared_error: 0.9732
    Epoch 2/5
     - 7s - loss: 0.9677 - mean_squared_error: 0.9677
    Epoch 3/5
     - 6s - loss: 0.9665 - mean_squared_error: 0.9665
    Epoch 4/5
     - 6s - loss: 0.9653 - mean_squared_error: 0.9653
    Epoch 5/5
     - 6s - loss: 0.9642 - mean_squared_error: 0.9642
    
    DataSource(eea8b6306cf648809d3a7b942bf6c696T, v3)
    
    • 收益率29.49%
    • 年化收益率30.31%
    • 基准收益率-29.48%
    • 阿尔法0.71
    • 贝塔1.09
    • 夏普比率0.72
    • 胜率0.57
    • 盈亏比0.94
    • 收益波动率50.81%
    • 信息比率0.14
    • 最大回撤36.03%
    bigcharts-data-start/{"__id":"bigchart-ddbb936c9a3e4988bc8f673de76f842a","__type":"tabs"}/bigcharts-data-end
    In [9]:
    # 第一次训练的训练集
    print(m10.result['rollings'][0]['m1'].data.read_pickle()['start_date'])
    print(m10.result['rollings'][0]['m1'].data.read_pickle()['end_date'])
    # 第一次训练的预测集
    print(m10.result['rollings'][0]['m9'].data.read_pickle()['start_date'])
    print(m10.result['rollings'][0]['m9'].data.read_pickle()['end_date'])
    # 训练的第一个模型的预测结果
    m10.result['rollings'][0]['m11'].data.read_pickle()
    
    2015-01-05
    2015-06-02
    2015-06-03
    2015-10-16
    
    Out[9]:
    array([[-0.04863386],
           [-0.0616019 ],
           [ 0.0240671 ],
           ...,
           [ 0.07101997],
           [ 0.06661613],
           [ 0.04292194]], dtype=float32)

    使用深度学习DNN构建选股模型
    使用深度学习DNN构建选股模型
    使用深度学习DNN构建选股模型