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克隆策略

DeepAlpha短周期因子系列研究：LSTM¶

回测¶

策略思想：基于模型的预测结果进行选股，选择当日排名靠前的50只股票买入，卖出其他持有的股票。
调仓周期：日频，每日换仓
资金管理：每只股票最大资金占用50%
手续费：买入0.0003，卖出0.0013

In [9]:

import tensorflow as tf

gpus = tf.config.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        
        logical_gpus = tf.config.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

1 Physical GPUs, 1 Logical GPUs

In [ ]:

# 本代码由可视化策略环境自动生成 2022年4月25日 10:09
# 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 m10_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    from sklearn.model_selection import train_test_split
    data = input_1.read()
    x_train, x_val, y_train, y_val = train_test_split(data["x"], data['y'], test_size=0.1)
    data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})
    data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})
    return Outputs(data_1=data_1, data_2=data_2, 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 m10_post_run_bigquant_run(outputs):
    return outputs

from tensorflow.keras.callbacks import EarlyStopping
m5_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=3)
# 用户的自定义层需要写到字典中，比如
# {
#   "MyLayer": MyLayer
# }
m5_custom_objects_bigquant_run = {
 
}

# 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 = 50
    # 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': '2011-01-01',
    'm1.end_date': '2013-12-31',
    '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,

    'm12': 'M.standardlize.v9',
    'm12.input_1': T.Graph.OutputPort('m2.data'),
    'm12.standard_func': 'ZScoreNorm',
    'm12.columns_input': 'label',

    'm3': 'M.input_features.v1',
    'm3.features': """close_0
open_0
high_0
low_0 
amount_0
turn_0 
return_0
 
close_1
open_1
high_1
low_1
return_1
amount_1
turn_1
 
close_2
open_2
high_2
low_2
amount_2
turn_2
return_2
 
close_3
open_3
high_3
low_3
amount_3
turn_3
return_3
 
close_4
open_4
high_4
low_4
amount_4
turn_4
return_4
 
mean(close_0, 5)
mean(low_0, 5)
mean(open_0, 5)
mean(high_0, 5)
mean(turn_0, 5)
mean(amount_0, 5)
mean(return_0, 5)
 
ts_max(close_0, 5)
ts_max(low_0, 5)
ts_max(open_0, 5)
ts_max(high_0, 5)
ts_max(turn_0, 5)
ts_max(amount_0, 5)
ts_max(return_0, 5)
 
ts_min(close_0, 5)
ts_min(low_0, 5)
ts_min(open_0, 5)
ts_min(high_0, 5)
ts_min(turn_0, 5)
ts_min(amount_0, 5)
ts_min(return_0, 5) 
 
std(close_0, 5)
std(low_0, 5)
std(open_0, 5)
std(high_0, 5)
std(turn_0, 5)
std(amount_0, 5)
std(return_0, 5)
 
ts_rank(close_0, 5)
ts_rank(low_0, 5)
ts_rank(open_0, 5)
ts_rank(high_0, 5)
ts_rank(turn_0, 5)
ts_rank(amount_0, 5)
ts_rank(return_0, 5)
 
decay_linear(close_0, 5)
decay_linear(low_0, 5)
decay_linear(open_0, 5)
decay_linear(high_0, 5)
decay_linear(turn_0, 5)
decay_linear(amount_0, 5)
decay_linear(return_0, 5)
 
correlation(volume_0, return_0, 5)
correlation(volume_0, high_0, 5)
correlation(volume_0, low_0, 5)
correlation(volume_0, close_0, 5)
correlation(volume_0, open_0, 5)
correlation(volume_0, turn_0, 5)
  
correlation(return_0, high_0, 5)
correlation(return_0, low_0, 5)
correlation(return_0, close_0, 5)
correlation(return_0, open_0, 5)
correlation(return_0, turn_0, 5)
 
correlation(high_0, low_0, 5)
correlation(high_0, close_0, 5)
correlation(high_0, open_0, 5)
correlation(high_0, turn_0, 5)
 
correlation(low_0, close_0, 5)
correlation(low_0, open_0, 5)
correlation(low_0, turn_0, 5)
 
correlation(close_0, open_0, 5)
correlation(close_0, turn_0, 5)

correlation(open_0, turn_0, 5)""",

    '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': True,
    'm16.remove_extra_columns': False,

    'm28': 'M.standardlize.v8',
    'm28.input_1': T.Graph.OutputPort('m16.data'),
    'm28.input_2': T.Graph.OutputPort('m3.data'),
    'm28.columns_input': '[]',

    'm13': 'M.fillnan.v1',
    'm13.input_data': T.Graph.OutputPort('m28.data'),
    'm13.features': T.Graph.OutputPort('m3.data'),
    'm13.fill_value': '0.0',

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m12.data'),
    'm7.data2': T.Graph.OutputPort('m13.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': True,

    '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': 5,
    'm26.feature_clip': 3,
    'm26.flatten': False,
    'm26.window_along_col': 'instrument',

    'm10': 'M.cached.v3',
    'm10.input_1': T.Graph.OutputPort('m26.data'),
    'm10.run': m10_run_bigquant_run,
    'm10.post_run': m10_post_run_bigquant_run,
    'm10.input_ports': '',
    'm10.params': '{}',
    'm10.output_ports': '',

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2014-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2014-12-31'),
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('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': 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': '[]',

    'm14': 'M.fillnan.v1',
    'm14.input_data': T.Graph.OutputPort('m25.data'),
    'm14.features': T.Graph.OutputPort('m3.data'),
    'm14.fill_value': '0.0',

    'm27': 'M.dl_convert_to_bin.v2',
    'm27.input_data': T.Graph.OutputPort('m14.data'),
    'm27.features': T.Graph.OutputPort('m3.data'),
    'm27.window_size': 5,
    'm27.feature_clip': 3,
    'm27.flatten': False,
    'm27.window_along_col': 'instrument',

    'm6': 'M.dl_layer_input.v1',
    'm6.shape': '5,98',
    'm6.batch_shape': '',
    'm6.dtype': 'float32',
    'm6.sparse': False,
    'm6.name': '',

    'm8': 'M.dl_layer_lstm.v1',
    'm8.inputs': T.Graph.OutputPort('m6.data'),
    'm8.units': 64,
    'm8.activation': 'tanh',
    'm8.recurrent_activation': 'sigmoid',
    'm8.use_bias': True,
    'm8.kernel_initializer': 'glorot_uniform',
    'm8.recurrent_initializer': 'Orthogonal',
    'm8.bias_initializer': 'Zeros',
    'm8.unit_forget_bias': True,
    'm8.kernel_regularizer': 'None',
    'm8.kernel_regularizer_l1': 0,
    'm8.kernel_regularizer_l2': 0,
    'm8.recurrent_regularizer': 'None',
    'm8.recurrent_regularizer_l1': 0,
    'm8.recurrent_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.recurrent_constraint': 'None',
    'm8.bias_constraint': 'None',
    'm8.dropout': 0,
    'm8.recurrent_dropout': 0,
    'm8.return_sequences': False,
    'm8.implementation': '2',
    'm8.name': '',

    'm21': 'M.dl_layer_dropout.v1',
    'm21.inputs': T.Graph.OutputPort('m8.data'),
    'm21.rate': 0.5,
    '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': 0.5,
    '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('m10.data_1'),
    'm5.validation_data': T.Graph.OutputPort('m10.data_2'),
    'm5.optimizer': 'Adam',
    'm5.loss': 'mean_squared_error',
    'm5.metrics': 'mse',
    'm5.batch_size': 1024,
    'm5.epochs': 10,
    'm5.earlystop': m5_earlystop_bigquant_run,
    'm5.custom_objects': m5_custom_objects_bigquant_run,
    '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 m30_param_grid_builder_bigquant_run():
    param_grid = {}

    # 在这里设置需要调优的参数备选
    # param_grid['m3.features'] = ['close_1/close_0', 'close_2/close_0\nclose_3/close_0']
    param_grid['m8.units'] = [64, 128, 256]

    return param_grid

def m30_scoring_bigquant_run(result):
    score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]

    return {'score': score}


m30 = M.hyper_parameter_search.v1(
    param_grid_builder=m30_param_grid_builder_bigquant_run,
    scoring=m30_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=10,
    workers=1,
    worker_distributed_run=False,
    worker_silent=False,
    run_now=True,
    bq_graph=g
)

[2022-04-25 09:41:45.861073] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:41:46.042468] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.044128] INFO: moduleinvoker: instruments.v2 运行完成[0.183079s].

[2022-04-25 09:41:46.050807] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-25 09:41:46.068981] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.070880] INFO: moduleinvoker: input_features.v1 运行完成[0.020067s].

[2022-04-25 09:41:46.076279] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:41:46.097994] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.099824] INFO: moduleinvoker: instruments.v2 运行完成[0.02354s].

[2022-04-25 09:41:46.108183] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.002074s].

[2022-04-25 09:41:46.117509] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-04-25 09:41:46.131110] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.132788] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.015279s].

[2022-04-25 09:41:46.150908] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:41:46.160964] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.162477] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011582s].

[2022-04-25 09:41:46.175373] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:41:46.189249] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.190958] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.015619s].

[2022-04-25 09:41:46.617565] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.419793s].

[2022-04-25 09:41:46.625997] INFO: moduleinvoker: standardlize.v9 开始运行..

[2022-04-25 09:41:46.639534] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.641797] INFO: moduleinvoker: standardlize.v9 运行完成[0.015798s].

[2022-04-25 09:41:46.650858] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:41:46.662264] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.664185] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013326s].

[2022-04-25 09:41:46.671319] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:41:46.683193] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.685591] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014253s].

[2022-04-25 09:41:46.698136] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005143s].

[2022-04-25 09:41:46.706700] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:41:46.719146] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.722480] INFO: moduleinvoker: standardlize.v8 运行完成[0.015773s].

[2022-04-25 09:41:46.736953] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:41:46.751540] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.754938] INFO: moduleinvoker: standardlize.v8 运行完成[0.017985s].

[2022-04-25 09:41:46.785568] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.021922s].

[2022-04-25 09:41:46.809959] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:41:46.829997] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.832100] INFO: moduleinvoker: fillnan.v1 运行完成[0.0222s].

[2022-04-25 09:41:46.840986] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:41:46.857195] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.859213] INFO: moduleinvoker: fillnan.v1 运行完成[0.018224s].

[2022-04-25 09:41:46.872979] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005296s].

[2022-04-25 09:41:46.898668] INFO: moduleinvoker: join.v3 开始运行..

[2022-04-25 09:41:46.918884] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.920680] INFO: moduleinvoker: join.v3 运行完成[0.022084s].

[2022-04-25 09:41:46.941449] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:41:46.960955] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:46.962782] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.021345s].

[2022-04-25 09:41:46.982272] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.011896s].

[2022-04-25 09:41:47.016200] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:41:47.049333] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:47.051322] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.035144s].

[2022-04-25 09:41:47.086356] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:41:47.170285] INFO: moduleinvoker: cached.v3 运行完成[0.08392s].

[2022-04-25 09:41:47.173678] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.116133s].

[2022-04-25 09:41:47.201571] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:41:47.231828] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:41:47.234086] INFO: moduleinvoker: cached.v3 运行完成[0.032518s].

[2022-04-25 09:41:47.247431] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2022-04-25 09:41:55.179111] INFO: dl_model_train: 准备训练，训练样本个数：1461443，迭代次数：10

[2022-04-25 09:43:08.918309] INFO: dl_model_train: 训练结束，耗时：73.74s

[2022-04-25 09:43:09.262153] INFO: moduleinvoker: dl_model_train.v1 运行完成[82.014759s].

[2022-04-25 09:43:09.269589] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2022-04-25 09:43:15.314532] INFO: moduleinvoker: dl_model_predict.v1 运行完成[6.044941s].

[2022-04-25 09:43:15.329875] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:45:31.374152] INFO: moduleinvoker: cached.v3 运行完成[136.044269s].

[2022-04-25 09:45:31.433839] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-25 09:45:31.441156] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-25 09:45:31.443469] INFO: backtest: product_type:stock by specified

[2022-04-25 09:45:31.535031] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-25 09:45:31.546804] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:45:31.549574] INFO: moduleinvoker: cached.v2 运行完成[0.014559s].

[2022-04-25 09:45:32.865550] INFO: algo: TradingAlgorithm V1.8.7

[2022-04-25 09:45:33.500166] INFO: algo: trading transform...

[2022-04-25 09:46:11.467491] INFO: Performance: Simulated 245 trading days out of 245.

[2022-04-25 09:46:11.469504] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-04-25 09:46:11.471106] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-04-25 09:46:28.898239] INFO: moduleinvoker: backtest.v8 运行完成[57.464408s].

[2022-04-25 09:46:28.900425] INFO: moduleinvoker: trade.v4 运行完成[57.517294s].

[2022-04-25 09:46:30.650434] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:46:30.659409] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.661537] INFO: moduleinvoker: instruments.v2 运行完成[0.011108s].

[2022-04-25 09:46:30.666000] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-25 09:46:30.676133] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.678125] INFO: moduleinvoker: input_features.v1 运行完成[0.012125s].

[2022-04-25 09:46:30.683414] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:46:30.690602] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.692557] INFO: moduleinvoker: instruments.v2 运行完成[0.009146s].

[2022-04-25 09:46:30.701129] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.002063s].

[2022-04-25 09:46:30.714787] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-04-25 09:46:30.726663] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.729200] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.014422s].

[2022-04-25 09:46:30.748461] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:46:30.761028] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.763253] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014801s].

[2022-04-25 09:46:30.778653] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:46:30.801223] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:30.803412] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.024756s].

[2022-04-25 09:46:31.372117] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.532804s].

[2022-04-25 09:46:31.396105] INFO: moduleinvoker: standardlize.v9 开始运行..

[2022-04-25 09:46:31.410777] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.413108] INFO: moduleinvoker: standardlize.v9 运行完成[0.016984s].

[2022-04-25 09:46:31.424291] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:46:31.436915] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.439196] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014902s].

[2022-04-25 09:46:31.445832] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:46:31.456054] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.457997] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012156s].

[2022-04-25 09:46:31.470322] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005521s].

[2022-04-25 09:46:31.475859] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:46:31.490099] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.491990] INFO: moduleinvoker: standardlize.v8 运行完成[0.016131s].

[2022-04-25 09:46:31.501434] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:46:31.511652] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.513325] INFO: moduleinvoker: standardlize.v8 运行完成[0.01189s].

[2022-04-25 09:46:31.530506] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.010817s].

[2022-04-25 09:46:31.538307] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:46:31.547321] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.549092] INFO: moduleinvoker: fillnan.v1 运行完成[0.010784s].

[2022-04-25 09:46:31.556307] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:46:31.564658] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.566562] INFO: moduleinvoker: fillnan.v1 运行完成[0.010243s].

[2022-04-25 09:46:31.578737] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005874s].

[2022-04-25 09:46:31.591259] INFO: moduleinvoker: join.v3 开始运行..

[2022-04-25 09:46:31.604384] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.606562] INFO: moduleinvoker: join.v3 运行完成[0.0153s].

[2022-04-25 09:46:31.627489] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:46:31.637132] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.639296] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.011821s].

[2022-04-25 09:46:31.656140] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.010709s].

[2022-04-25 09:46:31.672364] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:46:31.682742] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.684831] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.01248s].

[2022-04-25 09:46:31.728481] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:46:31.761582] INFO: moduleinvoker: cached.v3 运行完成[0.033106s].

[2022-04-25 09:46:31.764177] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.07395s].

[2022-04-25 09:46:31.782853] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:46:31.806503] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:46:31.808254] INFO: moduleinvoker: cached.v3 运行完成[0.025419s].

[2022-04-25 09:46:31.825586] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2022-04-25 09:46:40.223746] INFO: dl_model_train: 准备训练，训练样本个数：1461443，迭代次数：10

[2022-04-25 09:47:54.628108] INFO: dl_model_train: 训练结束，耗时：74.40s

[2022-04-25 09:47:55.012073] INFO: moduleinvoker: dl_model_train.v1 运行完成[83.186502s].

[2022-04-25 09:47:55.017408] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2022-04-25 09:48:05.643568] INFO: moduleinvoker: dl_model_predict.v1 运行完成[10.626167s].

[2022-04-25 09:48:05.656221] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:50:20.629416] INFO: moduleinvoker: cached.v3 运行完成[134.973183s].

[2022-04-25 09:50:20.683885] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-25 09:50:20.702558] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-25 09:50:20.704530] INFO: backtest: product_type:stock by specified

[2022-04-25 09:50:20.826650] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-25 09:50:20.836448] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:50:20.839304] INFO: moduleinvoker: cached.v2 运行完成[0.012668s].

[2022-04-25 09:51:00.284976] INFO: algo: TradingAlgorithm V1.8.7

[2022-04-25 09:51:00.951430] INFO: algo: trading transform...

[2022-04-25 09:51:36.389724] INFO: Performance: Simulated 245 trading days out of 245.

[2022-04-25 09:51:36.391159] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-04-25 09:51:36.392184] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-04-25 09:52:16.395811] INFO: moduleinvoker: backtest.v8 运行完成[115.711929s].

[2022-04-25 09:52:16.397545] INFO: moduleinvoker: trade.v4 运行完成[115.760837s].

[2022-04-25 09:52:18.749999] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:52:18.762148] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.764274] INFO: moduleinvoker: instruments.v2 运行完成[0.014281s].

[2022-04-25 09:52:18.769144] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-25 09:52:18.778917] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.780735] INFO: moduleinvoker: input_features.v1 运行完成[0.011585s].

[2022-04-25 09:52:18.786242] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 09:52:18.794699] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.796112] INFO: moduleinvoker: instruments.v2 运行完成[0.009872s].

[2022-04-25 09:52:18.803629] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.001602s].

[2022-04-25 09:52:18.811595] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-04-25 09:52:18.824115] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.826007] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.014407s].

[2022-04-25 09:52:18.859223] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:52:18.869735] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.871675] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.012479s].

[2022-04-25 09:52:18.891387] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 09:52:18.912614] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:18.914737] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.023366s].

[2022-04-25 09:52:19.568313] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.646587s].

[2022-04-25 09:52:19.589759] INFO: moduleinvoker: standardlize.v9 开始运行..

[2022-04-25 09:52:19.623369] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.625555] INFO: moduleinvoker: standardlize.v9 运行完成[0.035808s].

[2022-04-25 09:52:19.633696] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:52:19.658645] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.660909] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.027205s].

[2022-04-25 09:52:19.668570] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 09:52:19.680959] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.683077] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014504s].

[2022-04-25 09:52:19.693964] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004571s].

[2022-04-25 09:52:19.699557] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:52:19.711548] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.713799] INFO: moduleinvoker: standardlize.v8 运行完成[0.014229s].

[2022-04-25 09:52:19.722557] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 09:52:19.751286] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.753494] INFO: moduleinvoker: standardlize.v8 运行完成[0.03093s].

[2022-04-25 09:52:19.777102] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.012588s].

[2022-04-25 09:52:19.787629] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:52:19.797242] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.799569] INFO: moduleinvoker: fillnan.v1 运行完成[0.011948s].

[2022-04-25 09:52:19.808734] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 09:52:19.837030] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.838958] INFO: moduleinvoker: fillnan.v1 运行完成[0.030235s].

[2022-04-25 09:52:19.848665] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004162s].

[2022-04-25 09:52:19.857023] INFO: moduleinvoker: join.v3 开始运行..

[2022-04-25 09:52:19.912702] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.914860] INFO: moduleinvoker: join.v3 运行完成[0.057838s].

[2022-04-25 09:52:19.931994] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:52:19.946811] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:19.948925] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.016932s].

[2022-04-25 09:52:19.971044] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.014042s].

[2022-04-25 09:52:19.986678] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 09:52:19.998973] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:20.001005] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.014349s].

[2022-04-25 09:52:20.047417] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:52:20.201964] INFO: moduleinvoker: cached.v3 运行完成[0.154562s].

[2022-04-25 09:52:20.204437] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.198128s].

[2022-04-25 09:52:20.216599] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 09:52:20.241959] INFO: moduleinvoker: 命中缓存

[2022-04-25 09:52:20.244582] INFO: moduleinvoker: cached.v3 运行完成[0.027977s].

[2022-04-25 09:52:20.254195] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2022-04-25 09:52:33.078799] INFO: dl_model_train: 准备训练，训练样本个数：1461443，迭代次数：10

[2022-04-25 09:53:52.519518] INFO: dl_model_train: 训练结束，耗时：79.44s

[2022-04-25 09:53:53.335380] INFO: moduleinvoker: dl_model_train.v1 运行完成[93.081181s].

[2022-04-25 09:53:53.340707] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2022-04-25 09:54:03.451479] INFO: moduleinvoker: dl_model_predict.v1 运行完成[10.110768s].

[2022-04-25 09:54:03.465968] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 10:01:16.891194] INFO: moduleinvoker: cached.v3 运行完成[433.425216s].

[2022-04-25 10:01:16.950675] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-25 10:01:16.966069] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-25 10:01:16.969437] INFO: backtest: product_type:stock by specified

[2022-04-25 10:01:17.272162] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-25 10:01:17.309562] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:01:17.312012] INFO: moduleinvoker: cached.v2 运行完成[0.03987s].

[2022-04-25 10:02:08.185597] INFO: algo: TradingAlgorithm V1.8.7

[2022-04-25 10:02:08.794739] INFO: algo: trading transform...

[2022-04-25 10:02:59.924758] INFO: Performance: Simulated 245 trading days out of 245.

[2022-04-25 10:02:59.927262] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-04-25 10:02:59.929307] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-04-25 10:03:28.930115] INFO: moduleinvoker: backtest.v8 运行完成[131.979428s].

[2022-04-25 10:03:28.932277] INFO: moduleinvoker: trade.v4 运行完成[132.032402s].

[2022-04-25 10:03:29.848848] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 10:03:29.860713] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:29.863498] INFO: moduleinvoker: instruments.v2 运行完成[0.014639s].

[2022-04-25 10:03:29.869308] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-25 10:03:29.887134] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:29.889915] INFO: moduleinvoker: input_features.v1 运行完成[0.020616s].

[2022-04-25 10:03:29.896572] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-25 10:03:29.905880] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:29.908633] INFO: moduleinvoker: instruments.v2 运行完成[0.012034s].

[2022-04-25 10:03:29.920320] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.002809s].

[2022-04-25 10:03:29.933453] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-04-25 10:03:29.953849] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:29.961760] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.02787s].

[2022-04-25 10:03:29.992995] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 10:03:30.015732] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.018297] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.025345s].

[2022-04-25 10:03:30.037047] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-25 10:03:30.047384] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.050566] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.013538s].

[2022-04-25 10:03:30.653835] INFO: moduleinvoker: dl_layer_lstm.v1 运行完成[0.591353s].

[2022-04-25 10:03:30.660501] INFO: moduleinvoker: standardlize.v9 开始运行..

[2022-04-25 10:03:30.672383] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.674831] INFO: moduleinvoker: standardlize.v9 运行完成[0.014328s].

[2022-04-25 10:03:30.682907] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 10:03:30.692188] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.695560] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012646s].

[2022-04-25 10:03:30.707141] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-25 10:03:30.720066] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.722367] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.015225s].

[2022-04-25 10:03:30.734489] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005516s].

[2022-04-25 10:03:30.739652] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 10:03:30.748740] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.750152] INFO: moduleinvoker: standardlize.v8 运行完成[0.010511s].

[2022-04-25 10:03:30.755890] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-04-25 10:03:30.766022] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.768075] INFO: moduleinvoker: standardlize.v8 运行完成[0.012177s].

[2022-04-25 10:03:30.788866] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.014277s].

[2022-04-25 10:03:30.798696] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 10:03:30.812042] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.814152] INFO: moduleinvoker: fillnan.v1 运行完成[0.015452s].

[2022-04-25 10:03:30.823065] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-04-25 10:03:30.834886] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.836928] INFO: moduleinvoker: fillnan.v1 运行完成[0.013862s].

[2022-04-25 10:03:30.846970] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.003961s].

[2022-04-25 10:03:30.855067] INFO: moduleinvoker: join.v3 开始运行..

[2022-04-25 10:03:30.865792] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.867859] INFO: moduleinvoker: join.v3 运行完成[0.012783s].

[2022-04-25 10:03:30.884009] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 10:03:30.895794] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.897872] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.013879s].

[2022-04-25 10:03:30.919142] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.013711s].

[2022-04-25 10:03:30.936157] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-25 10:03:30.949958] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:30.951898] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.015746s].

[2022-04-25 10:03:30.996449] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 10:03:31.035006] INFO: moduleinvoker: cached.v3 运行完成[0.03859s].

[2022-04-25 10:03:31.037352] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.079392s].

[2022-04-25 10:03:31.050430] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 10:03:31.066876] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:03:31.069091] INFO: moduleinvoker: cached.v3 运行完成[0.018674s].

[2022-04-25 10:03:31.081230] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2022-04-25 10:03:39.177676] INFO: dl_model_train: 准备训练，训练样本个数：1461443，迭代次数：10

[2022-04-25 10:04:59.421625] INFO: dl_model_train: 训练结束，耗时：80.24s

[2022-04-25 10:04:59.933911] INFO: moduleinvoker: dl_model_train.v1 运行完成[88.852738s].

[2022-04-25 10:04:59.944023] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2022-04-25 10:05:08.301616] INFO: moduleinvoker: dl_model_predict.v1 运行完成[8.357591s].

[2022-04-25 10:05:08.313819] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-25 10:08:35.373286] INFO: moduleinvoker: cached.v3 运行完成[207.059461s].

[2022-04-25 10:08:35.431839] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-25 10:08:35.436770] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-25 10:08:35.438094] INFO: backtest: product_type:stock by specified

[2022-04-25 10:08:35.564921] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-25 10:08:35.575805] INFO: moduleinvoker: 命中缓存

[2022-04-25 10:08:35.577698] INFO: moduleinvoker: cached.v2 运行完成[0.012795s].

[2022-04-25 10:08:55.732344] INFO: algo: TradingAlgorithm V1.8.7

[2022-04-25 10:08:56.321440] INFO: algo: trading transform...

[2022-04-25 10:09:33.820300] INFO: Performance: Simulated 245 trading days out of 245.

[2022-04-25 10:09:33.822113] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-04-25 10:09:33.823680] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

Fitting 1 folds for each of 3 candidates, totalling 3 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV 1/1; 1/3] START m8.units=64.................................................

Epoch 1/10
1428/1428 - 12s - loss: 0.9923 - mse: 0.9923 - val_loss: 0.9853 - val_mse: 0.9853
Epoch 2/10
1428/1428 - 6s - loss: 0.9846 - mse: 0.9846 - val_loss: 0.9817 - val_mse: 0.9817
Epoch 3/10
1428/1428 - 6s - loss: 0.9813 - mse: 0.9813 - val_loss: 0.9788 - val_mse: 0.9788
Epoch 4/10
1428/1428 - 6s - loss: 0.9788 - mse: 0.9788 - val_loss: 0.9759 - val_mse: 0.9759
Epoch 5/10
1428/1428 - 6s - loss: 0.9764 - mse: 0.9764 - val_loss: 0.9733 - val_mse: 0.9733
Epoch 6/10
1428/1428 - 6s - loss: 0.9740 - mse: 0.9740 - val_loss: 0.9724 - val_mse: 0.9724
Epoch 7/10
1428/1428 - 6s - loss: 0.9719 - mse: 0.9719 - val_loss: 0.9714 - val_mse: 0.9714
Epoch 8/10
1428/1428 - 6s - loss: 0.9702 - mse: 0.9702 - val_loss: 0.9711 - val_mse: 0.9711
Epoch 9/10
1428/1428 - 6s - loss: 0.9679 - mse: 0.9679 - val_loss: 0.9680 - val_mse: 0.9680
Epoch 10/10
1428/1428 - 6s - loss: 0.9662 - mse: 0.9662 - val_loss: 0.9680 - val_mse: 0.9680

563/563 - 2s
DataSource(c1088728ccce411f924b1b4ee5963c71T)

收益率146.06%
年化收益率152.47%
基准收益率51.66%
阿尔法0.95
贝塔0.63
夏普比率3.61
胜率0.65
盈亏比1.28
收益波动率25.83%
信息比率0.14
最大回撤12.6%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-151724d558764c06aacaa3a92b91cc24"}/bigcharts-data-end

[CV 1/1; 1/3] END ...........m8.units=64; score: (test=3.608) total time= 4.7min
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.7min remaining:    0.0s
[CV 1/1; 2/3] START m8.units=128................................................

Epoch 1/10
1428/1428 - 12s - loss: 0.9919 - mse: 0.9919 - val_loss: 0.9842 - val_mse: 0.9842
Epoch 2/10
1428/1428 - 6s - loss: 0.9840 - mse: 0.9840 - val_loss: 0.9803 - val_mse: 0.9803
Epoch 3/10
1428/1428 - 6s - loss: 0.9797 - mse: 0.9797 - val_loss: 0.9758 - val_mse: 0.9758
Epoch 4/10
1428/1428 - 6s - loss: 0.9760 - mse: 0.9760 - val_loss: 0.9730 - val_mse: 0.9730
Epoch 5/10
1428/1428 - 6s - loss: 0.9726 - mse: 0.9726 - val_loss: 0.9699 - val_mse: 0.9699
Epoch 6/10
1428/1428 - 6s - loss: 0.9688 - mse: 0.9688 - val_loss: 0.9677 - val_mse: 0.9677
Epoch 7/10
1428/1428 - 6s - loss: 0.9653 - mse: 0.9653 - val_loss: 0.9662 - val_mse: 0.9662
Epoch 8/10
1428/1428 - 7s - loss: 0.9613 - mse: 0.9613 - val_loss: 0.9643 - val_mse: 0.9643
Epoch 9/10
1428/1428 - 6s - loss: 0.9573 - mse: 0.9573 - val_loss: 0.9610 - val_mse: 0.9610
Epoch 10/10
1428/1428 - 7s - loss: 0.9524 - mse: 0.9524 - val_loss: 0.9580 - val_mse: 0.9580

563/563 - 2s
DataSource(23573293481d4bd4a644fe6ae0a98e6cT)

收益率126.89%
年化收益率132.26%
基准收益率51.66%
阿尔法0.8
贝塔0.62
夏普比率3.36
胜率0.64
盈亏比1.32
收益波动率25.18%
信息比率0.12
最大回撤13.4%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f5ccbe429ef449cba15616aa9777a449"}/bigcharts-data-end

[CV 1/1; 2/3] END ..........m8.units=128; score: (test=3.362) total time= 5.8min
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 10.5min remaining:    0.0s
[CV 1/1; 3/3] START m8.units=256................................................

Epoch 1/10
1428/1428 - 14s - loss: 0.9911 - mse: 0.9911 - val_loss: 0.9842 - val_mse: 0.9842
Epoch 2/10
1428/1428 - 7s - loss: 0.9833 - mse: 0.9833 - val_loss: 0.9793 - val_mse: 0.9793
Epoch 3/10
1428/1428 - 7s - loss: 0.9785 - mse: 0.9785 - val_loss: 0.9750 - val_mse: 0.9750
Epoch 4/10
1428/1428 - 7s - loss: 0.9741 - mse: 0.9741 - val_loss: 0.9700 - val_mse: 0.9700
Epoch 5/10
1428/1428 - 6s - loss: 0.9690 - mse: 0.9690 - val_loss: 0.9670 - val_mse: 0.9670
Epoch 6/10
1428/1428 - 7s - loss: 0.9634 - mse: 0.9634 - val_loss: 0.9631 - val_mse: 0.9631
Epoch 7/10
1428/1428 - 7s - loss: 0.9560 - mse: 0.9560 - val_loss: 0.9593 - val_mse: 0.9593
Epoch 8/10
1428/1428 - 7s - loss: 0.9475 - mse: 0.9475 - val_loss: 0.9550 - val_mse: 0.9550
Epoch 9/10
1428/1428 - 7s - loss: 0.9379 - mse: 0.9379 - val_loss: 0.9507 - val_mse: 0.9507
Epoch 10/10
1428/1428 - 6s - loss: 0.9262 - mse: 0.9262 - val_loss: 0.9452 - val_mse: 0.9452

563/563 - 2s
DataSource(dc734962ca354cc68ba3d8b2d55a1ba6T)

收益率106.35%
年化收益率110.66%
基准收益率51.66%
阿尔法0.6
贝塔0.66
夏普比率3.01
胜率0.62
盈亏比1.33
收益波动率24.85%
信息比率0.09
最大回撤12.15%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-17c4f28c05514940afc08d82fcb5e4aa"}/bigcharts-data-end

[CV 1/1; 3/3] END ..........m8.units=256; score: (test=3.008) total time=11.2min
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 21.7min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 21.7min finished

Epoch 1/10
1428/1428 - 15s - loss: 0.9923 - mse: 0.9923 - val_loss: 0.9853 - val_mse: 0.9853
Epoch 2/10
1428/1428 - 7s - loss: 0.9845 - mse: 0.9845 - val_loss: 0.9820 - val_mse: 0.9820
Epoch 3/10
1428/1428 - 7s - loss: 0.9813 - mse: 0.9813 - val_loss: 0.9789 - val_mse: 0.9789
Epoch 4/10
1428/1428 - 7s - loss: 0.9789 - mse: 0.9789 - val_loss: 0.9762 - val_mse: 0.9762
Epoch 5/10
1428/1428 - 7s - loss: 0.9764 - mse: 0.9764 - val_loss: 0.9734 - val_mse: 0.9734
Epoch 6/10
1428/1428 - 7s - loss: 0.9740 - mse: 0.9740 - val_loss: 0.9721 - val_mse: 0.9721
Epoch 7/10
1428/1428 - 7s - loss: 0.9719 - mse: 0.9719 - val_loss: 0.9719 - val_mse: 0.9719
Epoch 8/10
1428/1428 - 7s - loss: 0.9702 - mse: 0.9702 - val_loss: 0.9706 - val_mse: 0.9706
Epoch 9/10
1428/1428 - 6s - loss: 0.9679 - mse: 0.9679 - val_loss: 0.9679 - val_mse: 0.9679
Epoch 10/10
1428/1428 - 6s - loss: 0.9661 - mse: 0.9661 - val_loss: 0.9674 - val_mse: 0.9674

563/563 - 4s
DataSource(4edf625549014d2eb5d574525cb23330T)