克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-106:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-773:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-106:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-113:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-122:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-129:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-251:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-768:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-778:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-122:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-141:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-113:input_data","SourceOutputPortId":"-106:data"},{"DestinationInputPortId":"-768:input_1","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-778:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-168:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-682:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-224:inputs","SourceOutputPortId":"-168:data"},{"DestinationInputPortId":"-231:inputs","SourceOutputPortId":"-196:data"},{"DestinationInputPortId":"-196:inputs","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-238:inputs","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-682:outputs","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-682:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-768:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-773:data"},{"DestinationInputPortId":"-251:input_data","SourceOutputPortId":"-778:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-251:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-12-31","ValueType":"Literal","LinkedGlobalParameter":null},{"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-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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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\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)