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克隆策略

Transformer在量化选股中的应用研究¶

Transformer:Attention is all you need¶

The naive transformer implemented here for financial time series prediction follows the paper "Attention is all you need":

Given the input (N, T, F),

An embedding layer that maps the input (N, T, F) to representation (N, T, F’);
A positional encoding layer that adds the positional sigmoid;
An encoder that consists of several encoding layers, each of which uses a self-attention layer as the computing module (function of query, key, and value).
A decoder that consists of an MLP (or a Linear layer) that maps the representation of the last time (N, 1, F') into output (N, 1).

In [1]:

import torch
import torch.nn as nn

from bigmodels.models.base import BaseModel
from bigmodels.models.transformer import Transformer
from bigmodels.schedule import get_cosine_schedule_with_warmup

In [2]:

class Transformer(BaseModel):
    """ Transformer: Attention is all you need
    paper: https://arxiv.org/abs/1706.03762

    Args:
        input_dim: 输入特征的数量
        output_dim: 输出特征的数量
        max_seq: 训练序列的最大窗口数
        enbed_dim: Transformer的d_model
        nhead: 多头的数量，默认8
        num_layers: Transformer中Encoder的层数，默认为4
        dropout: 默认为0.1
    """

    def __init__(self,
                 input_dim=98,
                 output_dim=1,
                 max_seq=5,
                 embed_dim=128,
                 nhead=8,
                 num_layers=4,
                 dropout=0.1):
        super(Transformer, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim

        self.feature_layer = nn.Linear(input_dim, embed_dim)
        # self.feature_pos = nn.Embedding(max_seq, embed_dim)
        # update position encoder with Positional
        self.pos_encoder = PositionalEncoding(embed_dim)

        encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=nhead, dropout=dropout)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        self.avgpoll1d = nn.AdaptiveAvgPool1d(1)

        # TODO, FNN for Decoder
        self.out = nn.Linear(embed_dim, output_dim)
        self._reset_parameters()

    def _reset_parameters(self):
        r"""Initiate parameters in the model."""
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def forward(self, x):
        x = self.feature_layer(x)

        # x = [bs, window, feature] -> [window, bs, feature]
        x = x.permute(1, 0, 2)

        x = self.pos_encoder(x)

        src_mask = None
        x = self.encoder(x, src_mask)
        x = x.permute(1, 0, 2)

        # x = self.avgpoll1d(x)
        # x = x.squeeze(-1)
        x = x[:, -1, :]

        x = self.out(x)
        return x.squeeze()
    
    def train_epoch(self, dataloader):
        model = self.train()
        optimizer = self.optimizer
        criterion = self.criterion

        losses = []
        for item in dataloader:
            feature = item[0].float().to(self.device)
            label = item[1].float().to(self.device)

            optimizer.zero_grad()
            output = model(feature)
            loss = criterion(output, label)

            loss.backward()
            # clip grade
            nn.utils.clip_grad_value_(self.parameters(), 3.0)  
            optimizer.step()
            for scheduler in self.schedulers:
                # transformer need warm up
                scheduler.step()

            losses.append(loss.item())

        return np.mean(losses)


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=1000):
        super(PositionalEncoding, self).__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer("pe", pe)

    def forward(self, x):
        # [T, N, F]
        return x + self.pe[: x.size(0), :]

In [3]:

# 本代码由可视化策略环境自动生成 2022年5月6日 11:40
# 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 m33_run_bigquant_run(input_1, input_2, input_3, embed_dim, nhead, num_layers, dropout):
    # 示例代码如下。在这里编写您的代码
    from sklearn.model_selection import train_test_split
    # train data
    train_data = input_1.read()
    x_train, x_val, y_train, y_val = train_test_split(train_data["x"], train_data['y'], test_size=0.1)
    # val data
    test_data = input_2.read()
    x_test = test_data["x"]
    
    model = Transformer(input_dim=98, embed_dim=embed_dim, nhead=nhead, num_layers=num_layers, dropout=dropout)
    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
    # transformer need warmup
    scheduler = get_cosine_schedule_with_warmup(optimizer=opt, num_warmup_steps=4000, num_training_steps=100000)
    loss = nn.MSELoss()
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    model.compile(optimizer=opt, loss=loss, device=device)
    model.fit(x_train, 
              y_train, 
              validation_data=(x_val, y_val), 
              batch_size=256, 
              epochs=10, 
              verbose=1,
              schedulers=[scheduler],
              num_workers=0)
    
    output = model.predict(x_test)
    
    data_1 = DataSource.write_pickle(output)
    return Outputs(data_1=data_1, 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 m33_post_run_bigquant_run(outputs):
    return outputs

# 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 m41_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[:], '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 m41_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m42_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 m42_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 m42_prepare_bigquant_run(context):
    pass


g = T.Graph({

    'm22': 'M.instruments.v2',
    'm22.start_date': '2011-01-01',
    'm22.end_date': '2013-12-31',
    'm22.market': 'CN_STOCK_A',
    'm22.instrument_list': '',
    'm22.max_count': 0,

    'm23': 'M.advanced_auto_labeler.v2',
    'm23.instruments': T.Graph.OutputPort('m22.data'),
    'm23.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)
""",
    'm23.start_date': '',
    'm23.end_date': '',
    'm23.benchmark': '000300.SHA',
    'm23.drop_na_label': True,
    'm23.cast_label_int': False,

    'm1': 'M.standardlize.v8',
    'm1.input_1': T.Graph.OutputPort('m23.data'),
    'm1.columns_input': 'label',

    'm24': 'M.input_features.v1',
    'm24.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)""",

    'm27': 'M.general_feature_extractor.v7',
    'm27.instruments': T.Graph.OutputPort('m22.data'),
    'm27.features': T.Graph.OutputPort('m24.data'),
    'm27.start_date': '',
    'm27.end_date': '',
    'm27.before_start_days': 10,

    'm28': 'M.derived_feature_extractor.v3',
    'm28.input_data': T.Graph.OutputPort('m27.data'),
    'm28.features': T.Graph.OutputPort('m24.data'),
    'm28.date_col': 'date',
    'm28.instrument_col': 'instrument',
    'm28.drop_na': True,
    'm28.remove_extra_columns': False,

    'm34': 'M.standardlize.v8',
    'm34.input_1': T.Graph.OutputPort('m28.data'),
    'm34.input_2': T.Graph.OutputPort('m24.data'),
    'm34.columns_input': '[]',

    'm35': 'M.fillnan.v1',
    'm35.input_data': T.Graph.OutputPort('m34.data'),
    'm35.features': T.Graph.OutputPort('m24.data'),
    'm35.fill_value': '0.0',

    'm25': 'M.join.v3',
    'm25.data1': T.Graph.OutputPort('m1.data'),
    'm25.data2': T.Graph.OutputPort('m35.data'),
    'm25.on': 'date,instrument',
    'm25.how': 'inner',
    'm25.sort': True,

    'm2': 'M.dl_convert_to_bin.v2',
    'm2.input_data': T.Graph.OutputPort('m25.data'),
    'm2.features': T.Graph.OutputPort('m24.data'),
    'm2.window_size': 5,
    'm2.feature_clip': 3,
    'm2.flatten': False,
    'm2.window_along_col': 'instrument',

    'm26': 'M.instruments.v2',
    'm26.start_date': '2014-01-01',
    'm26.end_date': '2014-12-31',
    'm26.market': 'CN_STOCK_A',
    'm26.instrument_list': '',
    'm26.max_count': 0,

    'm29': 'M.general_feature_extractor.v7',
    'm29.instruments': T.Graph.OutputPort('m26.data'),
    'm29.features': T.Graph.OutputPort('m24.data'),
    'm29.start_date': '',
    'm29.end_date': '',
    'm29.before_start_days': 10,

    'm30': 'M.derived_feature_extractor.v3',
    'm30.input_data': T.Graph.OutputPort('m29.data'),
    'm30.features': T.Graph.OutputPort('m24.data'),
    'm30.date_col': 'date',
    'm30.instrument_col': 'instrument',
    'm30.drop_na': True,
    'm30.remove_extra_columns': False,

    'm37': 'M.standardlize.v8',
    'm37.input_1': T.Graph.OutputPort('m30.data'),
    'm37.input_2': T.Graph.OutputPort('m24.data'),
    'm37.columns_input': '[]',

    'm36': 'M.fillnan.v1',
    'm36.input_data': T.Graph.OutputPort('m37.data'),
    'm36.features': T.Graph.OutputPort('m24.data'),
    'm36.fill_value': '0.0',

    'm32': 'M.dl_convert_to_bin.v2',
    'm32.input_data': T.Graph.OutputPort('m36.data'),
    'm32.features': T.Graph.OutputPort('m24.data'),
    'm32.window_size': 5,
    'm32.feature_clip': 3,
    'm32.flatten': False,
    'm32.window_along_col': 'instrument',

    'm33': 'M.cached.v3',
    'm33.input_1': T.Graph.OutputPort('m2.data'),
    'm33.input_2': T.Graph.OutputPort('m32.data'),
    'm33.run': m33_run_bigquant_run,
    'm33.post_run': m33_post_run_bigquant_run,
    'm33.input_ports': '',
    'm33.params': """{
    "embed_dim": 128,
    "nhead": 8,
    "num_layers": 2,
    "dropout": 0.3
}""",
    'm33.output_ports': '',

    'm41': 'M.cached.v3',
    'm41.input_1': T.Graph.OutputPort('m33.data_1'),
    'm41.input_2': T.Graph.OutputPort('m30.data'),
    'm41.run': m41_run_bigquant_run,
    'm41.post_run': m41_post_run_bigquant_run,
    'm41.input_ports': '',
    'm41.params': '{}',
    'm41.output_ports': '',

    'm42': 'M.trade.v4',
    'm42.instruments': T.Graph.OutputPort('m26.data'),
    'm42.options_data': T.Graph.OutputPort('m41.data_1'),
    'm42.benchmark_ds': T.Graph.OutputPort('m41.data_3'),
    'm42.start_date': '',
    'm42.end_date': '',
    'm42.initialize': m42_initialize_bigquant_run,
    'm42.handle_data': m42_handle_data_bigquant_run,
    'm42.prepare': m42_prepare_bigquant_run,
    'm42.volume_limit': 0.025,
    'm42.order_price_field_buy': 'open',
    'm42.order_price_field_sell': 'close',
    'm42.capital_base': 1000000,
    'm42.auto_cancel_non_tradable_orders': True,
    'm42.data_frequency': 'daily',
    'm42.price_type': '后复权',
    'm42.product_type': '股票',
    'm42.plot_charts': True,
    'm42.backtest_only': False,
    'm42.benchmark': '000300.SHA',
})

# g.run({})


def m4_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['m33.params'] = [
               """{"embed_dim": 64, "nhead": 8, "num_layers": 2, "dropout": 0.1}""",
               """{"embed_dim": 128, "nhead": 8, "num_layers": 2, "dropout": 0.1}""",
               """{"embed_dim": 256, "nhead": 8, "num_layers": 2, "dropout": 0.1}""",
               """{"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.1}""",
               """{"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.1}""",
               """{"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.1}""",
               """{"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.3}""",
               """{"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.3}""",
               """{"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.3}"""
              ]
    
    return param_grid

def m4_scoring_bigquant_run(result):
    score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]

    return {'score': score}


m4 = M.hyper_parameter_search.v1(
    param_grid_builder=m4_param_grid_builder_bigquant_run,
    scoring=m4_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
)

[2022-05-06 09:40:52.788677] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:40:52.812213] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:52.814239] INFO: moduleinvoker: instruments.v2 运行完成[0.025585s].

[2022-05-06 09:40:52.824654] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:40:52.834515] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:52.836376] INFO: moduleinvoker: input_features.v1 运行完成[0.011733s].

[2022-05-06 09:40:52.842378] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:40:52.850816] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:52.852635] INFO: moduleinvoker: instruments.v2 运行完成[0.010255s].

[2022-05-06 09:40:52.870397] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:40:52.882963] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:52.886949] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.016548s].

[2022-05-06 09:40:53.021170] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:40:53.033369] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.035184] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014041s].

[2022-05-06 09:40:53.051422] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:40:53.070201] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.072483] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.021084s].

[2022-05-06 09:40:53.082620] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:40:53.094042] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.096235] INFO: moduleinvoker: standardlize.v8 运行完成[0.013612s].

[2022-05-06 09:40:53.109717] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:40:53.122811] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.124973] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.01526s].

[2022-05-06 09:40:53.171341] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:40:53.184195] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.186674] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.015336s].

[2022-05-06 09:40:53.192741] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:40:53.202936] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.205010] INFO: moduleinvoker: standardlize.v8 运行完成[0.012266s].

[2022-05-06 09:40:53.210570] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:40:53.222049] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.224128] INFO: moduleinvoker: standardlize.v8 运行完成[0.01355s].

[2022-05-06 09:40:53.239328] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:40:53.251965] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.253782] INFO: moduleinvoker: fillnan.v1 运行完成[0.014465s].

[2022-05-06 09:40:53.264485] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:40:53.282011] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.284576] INFO: moduleinvoker: fillnan.v1 运行完成[0.020088s].

[2022-05-06 09:40:53.299668] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:40:53.313492] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.316118] INFO: moduleinvoker: join.v3 运行完成[0.01644s].

[2022-05-06 09:40:53.344249] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:40:53.354662] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.357163] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.012936s].

[2022-05-06 09:40:53.375401] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:40:53.388869] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.390995] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.015632s].

[2022-05-06 09:40:53.408387] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:40:53.419215] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.420807] INFO: moduleinvoker: cached.v3 运行完成[0.012406s].

[2022-05-06 09:40:53.431730] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:40:53.441480] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:40:53.443374] INFO: moduleinvoker: cached.v3 运行完成[0.011653s].

[2022-05-06 09:41:13.974814] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:41:13.981028] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:41:13.982600] INFO: backtest: product_type:stock by specified

[2022-05-06 09:41:14.060218] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:44:18.080222] INFO: backtest: 读取股票行情完成:1352832

[2022-05-06 09:45:43.483400] INFO: moduleinvoker: cached.v2 运行完成[269.423176s].

[2022-05-06 09:47:47.263148] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:48:12.724526] INFO: algo: trading transform...

[2022-05-06 09:48:55.665338] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 09:48:55.667460] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 09:48:55.668832] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 09:49:32.618558] INFO: moduleinvoker: backtest.v8 运行完成[498.643715s].

[2022-05-06 09:49:32.620573] INFO: moduleinvoker: trade.v4 运行完成[519.165085s].

[2022-05-06 09:49:32.636935] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:49:32.645869] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.647492] INFO: moduleinvoker: instruments.v2 运行完成[0.01057s].

[2022-05-06 09:49:32.651773] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:49:32.662099] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.663716] INFO: moduleinvoker: input_features.v1 运行完成[0.011938s].

[2022-05-06 09:49:32.669674] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:49:32.678480] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.680115] INFO: moduleinvoker: instruments.v2 运行完成[0.010442s].

[2022-05-06 09:49:32.688378] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:49:32.700434] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.702194] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.013818s].

[2022-05-06 09:49:32.717988] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:49:32.726598] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.728267] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010293s].

[2022-05-06 09:49:32.745738] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:49:32.756773] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.758575] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.012858s].

[2022-05-06 09:49:32.763513] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:49:32.779033] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.780728] INFO: moduleinvoker: standardlize.v8 运行完成[0.017215s].

[2022-05-06 09:49:32.787487] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:49:32.795413] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.796982] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009494s].

[2022-05-06 09:49:32.803639] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:49:32.813209] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.815218] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011578s].

[2022-05-06 09:49:32.820986] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:49:32.832153] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.833823] INFO: moduleinvoker: standardlize.v8 运行完成[0.012835s].

[2022-05-06 09:49:32.838853] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:49:32.852784] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.854538] INFO: moduleinvoker: standardlize.v8 运行完成[0.015682s].

[2022-05-06 09:49:32.862267] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:49:32.872541] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.873977] INFO: moduleinvoker: fillnan.v1 运行完成[0.011712s].

[2022-05-06 09:49:32.881767] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:49:32.890632] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.892015] INFO: moduleinvoker: fillnan.v1 运行完成[0.010251s].

[2022-05-06 09:49:32.899541] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:49:32.908618] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.910365] INFO: moduleinvoker: join.v3 运行完成[0.010821s].

[2022-05-06 09:49:32.923792] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:49:32.934477] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.936002] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.012226s].

[2022-05-06 09:49:32.949952] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:49:32.964709] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.966802] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.01685s].

[2022-05-06 09:49:32.980994] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:49:32.992839] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:32.994880] INFO: moduleinvoker: cached.v3 运行完成[0.013901s].

[2022-05-06 09:49:33.006759] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:49:33.015036] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:33.016776] INFO: moduleinvoker: cached.v3 运行完成[0.010032s].

[2022-05-06 09:49:33.064754] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:49:33.076174] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:49:33.077987] INFO: backtest: product_type:stock by specified

[2022-05-06 09:49:33.245276] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:49:33.254214] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:49:33.255935] INFO: moduleinvoker: cached.v2 运行完成[0.010682s].

[2022-05-06 09:50:25.373754] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:50:25.968065] INFO: algo: trading transform...

[2022-05-06 09:51:03.942871] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 09:51:03.944710] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 09:51:03.946437] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 09:51:52.884559] INFO: moduleinvoker: backtest.v8 运行完成[139.819777s].

[2022-05-06 09:51:52.887052] INFO: moduleinvoker: trade.v4 运行完成[139.864091s].

[2022-05-06 09:51:52.898779] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:51:52.907809] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:52.909679] INFO: moduleinvoker: instruments.v2 运行完成[0.010909s].

[2022-05-06 09:51:52.916541] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:51:52.925979] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:52.927691] INFO: moduleinvoker: input_features.v1 运行完成[0.011157s].

[2022-05-06 09:51:52.935255] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:51:52.972258] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:52.974513] INFO: moduleinvoker: instruments.v2 运行完成[0.039253s].

[2022-05-06 09:51:52.986017] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:51:52.993351] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:52.995202] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.009186s].

[2022-05-06 09:51:53.013782] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:51:53.021945] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.023851] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010105s].

[2022-05-06 09:51:53.045606] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:51:53.063612] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.066319] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.020725s].

[2022-05-06 09:51:53.196460] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:51:53.209028] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.211528] INFO: moduleinvoker: standardlize.v8 运行完成[0.015075s].

[2022-05-06 09:51:53.220611] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:51:53.245141] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.247576] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.026964s].

[2022-05-06 09:51:53.256285] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:51:53.289530] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.291832] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.035542s].

[2022-05-06 09:51:53.306289] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:51:53.320959] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.323122] INFO: moduleinvoker: standardlize.v8 运行完成[0.016842s].

[2022-05-06 09:51:53.349016] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:51:53.381768] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.383962] INFO: moduleinvoker: standardlize.v8 运行完成[0.034951s].

[2022-05-06 09:51:53.393181] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:51:53.401515] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.403538] INFO: moduleinvoker: fillnan.v1 运行完成[0.010355s].

[2022-05-06 09:51:53.412820] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:51:53.421348] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.423530] INFO: moduleinvoker: fillnan.v1 运行完成[0.010708s].

[2022-05-06 09:51:53.434012] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:51:53.441711] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.443701] INFO: moduleinvoker: join.v3 运行完成[0.009698s].

[2022-05-06 09:51:53.460662] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:51:53.469355] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.471379] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.010745s].

[2022-05-06 09:51:53.487122] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:51:53.495094] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.496801] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.009706s].

[2022-05-06 09:51:53.511781] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:51:53.523154] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.525306] INFO: moduleinvoker: cached.v3 运行完成[0.013545s].

[2022-05-06 09:51:53.539215] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:51:53.549113] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.550651] INFO: moduleinvoker: cached.v3 运行完成[0.01145s].

[2022-05-06 09:51:53.605713] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:51:53.612243] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:51:53.613985] INFO: backtest: product_type:stock by specified

[2022-05-06 09:51:53.695321] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:51:53.703903] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:51:53.705987] INFO: moduleinvoker: cached.v2 运行完成[0.010684s].

[2022-05-06 09:52:40.761598] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:52:41.325259] INFO: algo: trading transform...

[2022-05-06 09:53:14.934927] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 09:53:14.936522] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 09:53:14.937679] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 09:53:22.900817] INFO: moduleinvoker: backtest.v8 运行完成[89.295082s].

[2022-05-06 09:53:22.902984] INFO: moduleinvoker: trade.v4 运行完成[89.345887s].

[2022-05-06 09:53:22.911429] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:53:22.920249] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:22.922320] INFO: moduleinvoker: instruments.v2 运行完成[0.010893s].

[2022-05-06 09:53:22.926973] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:53:22.936599] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:22.938124] INFO: moduleinvoker: input_features.v1 运行完成[0.01116s].

[2022-05-06 09:53:22.943208] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:53:22.955208] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:22.956583] INFO: moduleinvoker: instruments.v2 运行完成[0.013382s].

[2022-05-06 09:53:22.964969] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:53:22.981329] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:22.983018] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.018048s].

[2022-05-06 09:53:23.007115] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:53:23.042614] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.044287] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.037188s].

[2022-05-06 09:53:23.065852] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:53:23.086377] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.087835] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.021989s].

[2022-05-06 09:53:23.093444] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:53:23.111975] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.114179] INFO: moduleinvoker: standardlize.v8 运行完成[0.020718s].

[2022-05-06 09:53:23.122431] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:53:23.129607] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.131014] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.008586s].

[2022-05-06 09:53:23.137494] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:53:23.148693] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.150374] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012871s].

[2022-05-06 09:53:23.155826] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:53:23.164715] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.167155] INFO: moduleinvoker: standardlize.v8 运行完成[0.011326s].

[2022-05-06 09:53:23.173867] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:53:23.181832] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.183788] INFO: moduleinvoker: standardlize.v8 运行完成[0.009916s].

[2022-05-06 09:53:23.192231] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:53:23.223940] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.226264] INFO: moduleinvoker: fillnan.v1 运行完成[0.034026s].

[2022-05-06 09:53:23.234644] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:53:23.279691] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.282056] INFO: moduleinvoker: fillnan.v1 运行完成[0.047405s].

[2022-05-06 09:53:23.290620] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:53:23.299984] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.302226] INFO: moduleinvoker: join.v3 运行完成[0.011604s].

[2022-05-06 09:53:23.317514] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:53:23.326978] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.329324] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.011827s].

[2022-05-06 09:53:23.345149] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:53:23.353485] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.355666] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.010534s].

[2022-05-06 09:53:23.369399] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:53:23.379728] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.381838] INFO: moduleinvoker: cached.v3 运行完成[0.01246s].

[2022-05-06 09:53:23.393530] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:53:23.405137] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.407414] INFO: moduleinvoker: cached.v3 运行完成[0.013902s].

[2022-05-06 09:53:23.458007] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:53:23.464737] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:53:23.466430] INFO: backtest: product_type:stock by specified

[2022-05-06 09:53:23.549966] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:53:23.561471] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:53:23.563319] INFO: moduleinvoker: cached.v2 运行完成[0.013375s].

[2022-05-06 09:53:24.781773] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:53:25.299116] INFO: algo: trading transform...

[2022-05-06 09:53:56.834694] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 09:53:56.836170] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 09:53:56.837250] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 09:54:03.008787] INFO: moduleinvoker: backtest.v8 运行完成[39.550762s].

[2022-05-06 09:54:03.011007] INFO: moduleinvoker: trade.v4 运行完成[39.596616s].

[2022-05-06 09:54:03.021603] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:54:03.030883] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.032458] INFO: moduleinvoker: instruments.v2 运行完成[0.010876s].

[2022-05-06 09:54:03.035961] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:54:03.043713] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.044986] INFO: moduleinvoker: input_features.v1 运行完成[0.009025s].

[2022-05-06 09:54:03.049355] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:54:03.060334] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.061708] INFO: moduleinvoker: instruments.v2 运行完成[0.012352s].

[2022-05-06 09:54:03.069408] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:54:03.084225] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.086213] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.016799s].

[2022-05-06 09:54:03.105084] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:54:03.112415] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.114239] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009185s].

[2022-05-06 09:54:03.163787] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:54:03.171771] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.173642] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009883s].

[2022-05-06 09:54:03.179917] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:03.190611] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.192507] INFO: moduleinvoker: standardlize.v8 运行完成[0.012582s].

[2022-05-06 09:54:03.200127] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:54:03.219644] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.221503] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.021368s].

[2022-05-06 09:54:03.229493] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:54:03.244173] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.245898] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.0164s].

[2022-05-06 09:54:03.250763] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:03.264062] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.266017] INFO: moduleinvoker: standardlize.v8 运行完成[0.015238s].

[2022-05-06 09:54:03.271743] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:03.318272] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.320575] INFO: moduleinvoker: standardlize.v8 运行完成[0.048819s].

[2022-05-06 09:54:03.330311] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:54:03.350514] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.351826] INFO: moduleinvoker: fillnan.v1 运行完成[0.021523s].

[2022-05-06 09:54:03.363436] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:54:03.408254] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.410520] INFO: moduleinvoker: fillnan.v1 运行完成[0.047071s].

[2022-05-06 09:54:03.423245] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:54:03.433496] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.435264] INFO: moduleinvoker: join.v3 运行完成[0.012033s].

[2022-05-06 09:54:03.466498] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:54:03.475692] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.479234] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.012702s].

[2022-05-06 09:54:03.500574] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:54:03.510471] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.512735] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.012189s].

[2022-05-06 09:54:03.526724] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:54:03.542736] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.544496] INFO: moduleinvoker: cached.v3 运行完成[0.017794s].

[2022-05-06 09:54:03.555034] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:54:03.574829] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.576431] INFO: moduleinvoker: cached.v3 运行完成[0.021428s].

[2022-05-06 09:54:03.631291] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:54:03.636658] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:54:03.637904] INFO: backtest: product_type:stock by specified

[2022-05-06 09:54:03.718460] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:54:03.758734] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:03.760553] INFO: moduleinvoker: cached.v2 运行完成[0.042114s].

[2022-05-06 09:54:04.990396] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:54:05.593605] INFO: algo: trading transform...

[2022-05-06 09:54:45.816805] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 09:54:45.818567] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 09:54:45.819947] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 09:54:54.858924] INFO: moduleinvoker: backtest.v8 运行完成[51.227606s].

[2022-05-06 09:54:54.861064] INFO: moduleinvoker: trade.v4 运行完成[51.279024s].

[2022-05-06 09:54:54.870528] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:54:54.884404] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:54.887042] INFO: moduleinvoker: instruments.v2 运行完成[0.016525s].

[2022-05-06 09:54:54.892563] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:54:54.901024] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:54.903414] INFO: moduleinvoker: input_features.v1 运行完成[0.010872s].

[2022-05-06 09:54:54.909436] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:54:54.917779] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:54.919804] INFO: moduleinvoker: instruments.v2 运行完成[0.010367s].

[2022-05-06 09:54:54.973271] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 09:54:54.984903] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:54.987255] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.013995s].

[2022-05-06 09:54:55.003588] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:54:55.015479] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.017325] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.013748s].

[2022-05-06 09:54:55.034562] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:54:55.047047] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.048622] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014085s].

[2022-05-06 09:54:55.057063] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:55.086977] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.089343] INFO: moduleinvoker: standardlize.v8 运行完成[0.032274s].

[2022-05-06 09:54:55.098531] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:54:55.110942] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.113468] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.01493s].

[2022-05-06 09:54:55.122117] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:54:55.131888] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.133690] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011606s].

[2022-05-06 09:54:55.139109] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:55.163160] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.165555] INFO: moduleinvoker: standardlize.v8 运行完成[0.02645s].

[2022-05-06 09:54:55.172246] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 09:54:55.201659] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.204223] INFO: moduleinvoker: standardlize.v8 运行完成[0.031976s].

[2022-05-06 09:54:55.213700] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:54:55.230061] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.231691] INFO: moduleinvoker: fillnan.v1 运行完成[0.018005s].

[2022-05-06 09:54:55.238882] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 09:54:55.246125] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.247303] INFO: moduleinvoker: fillnan.v1 运行完成[0.008421s].

[2022-05-06 09:54:55.254946] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:54:55.262857] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.264970] INFO: moduleinvoker: join.v3 运行完成[0.01002s].

[2022-05-06 09:54:55.283353] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:54:55.291275] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.293492] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.010165s].

[2022-05-06 09:54:55.312571] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 09:54:55.360990] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:54:55.363107] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.050536s].

[2022-05-06 09:54:55.377087] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 10:35:36.253827] INFO: moduleinvoker: cached.v3 运行完成[2440.87675s].

[2022-05-06 10:35:36.268771] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 10:35:40.077802] INFO: moduleinvoker: cached.v3 运行完成[3.809053s].

[2022-05-06 10:35:40.146640] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 10:35:40.168217] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 10:35:40.170064] INFO: backtest: product_type:stock by specified

[2022-05-06 10:35:40.252251] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 10:35:40.270549] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:35:40.272972] INFO: moduleinvoker: cached.v2 运行完成[0.02072s].

[2022-05-06 10:35:41.779251] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 10:35:42.499177] INFO: algo: trading transform...

[2022-05-06 10:36:20.659093] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 10:36:20.660599] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 10:36:20.661776] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 10:36:28.961346] INFO: moduleinvoker: backtest.v8 运行完成[48.81469s].

[2022-05-06 10:36:28.963583] INFO: moduleinvoker: trade.v4 运行完成[48.877679s].

[2022-05-06 10:36:28.975931] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 10:36:28.990478] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:28.993179] INFO: moduleinvoker: instruments.v2 运行完成[0.017249s].

[2022-05-06 10:36:28.999638] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 10:36:29.009136] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.011346] INFO: moduleinvoker: input_features.v1 运行完成[0.011717s].

[2022-05-06 10:36:29.017414] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 10:36:29.027650] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.030226] INFO: moduleinvoker: instruments.v2 运行完成[0.012808s].

[2022-05-06 10:36:29.040932] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 10:36:29.083389] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.087287] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.046347s].

[2022-05-06 10:36:29.107641] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 10:36:29.118093] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.120718] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01309s].

[2022-05-06 10:36:29.152794] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 10:36:29.175521] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.177336] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.024559s].

[2022-05-06 10:36:29.182956] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:36:29.207376] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.209582] INFO: moduleinvoker: standardlize.v8 运行完成[0.026633s].

[2022-05-06 10:36:29.217805] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 10:36:29.231059] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.233172] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.015377s].

[2022-05-06 10:36:29.239446] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 10:36:29.286603] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.288920] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.04946s].

[2022-05-06 10:36:29.296133] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:36:29.308899] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.311534] INFO: moduleinvoker: standardlize.v8 运行完成[0.015401s].

[2022-05-06 10:36:29.317848] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:36:29.371478] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.373991] INFO: moduleinvoker: standardlize.v8 运行完成[0.056137s].

[2022-05-06 10:36:29.413579] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 10:36:29.434781] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.439922] INFO: moduleinvoker: fillnan.v1 运行完成[0.026346s].

[2022-05-06 10:36:29.450826] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 10:36:29.466800] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.469210] INFO: moduleinvoker: fillnan.v1 运行完成[0.01838s].

[2022-05-06 10:36:29.478556] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 10:36:29.490501] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.492881] INFO: moduleinvoker: join.v3 运行完成[0.01433s].

[2022-05-06 10:36:29.508842] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 10:36:29.518748] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.520482] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.011664s].

[2022-05-06 10:36:29.535850] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 10:36:29.557567] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:36:29.559589] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.023758s].

[2022-05-06 10:36:29.573830] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 10:55:32.912317] INFO: moduleinvoker: cached.v3 运行完成[1143.338504s].

[2022-05-06 10:55:32.929268] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 10:55:37.582538] INFO: moduleinvoker: cached.v3 运行完成[4.653286s].

[2022-05-06 10:55:37.646332] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 10:55:37.659799] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 10:55:37.660940] INFO: backtest: product_type:stock by specified

[2022-05-06 10:55:37.730337] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 10:55:37.741887] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:55:37.743463] INFO: moduleinvoker: cached.v2 运行完成[0.013148s].

[2022-05-06 10:55:38.918495] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 10:55:39.429802] INFO: algo: trading transform...

[2022-05-06 10:56:10.706365] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 10:56:10.707990] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 10:56:10.709125] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 10:56:27.748853] INFO: moduleinvoker: backtest.v8 运行完成[50.102488s].

[2022-05-06 10:56:27.750469] INFO: moduleinvoker: trade.v4 运行完成[50.1607s].

[2022-05-06 10:56:27.759154] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 10:56:27.771207] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.772513] INFO: moduleinvoker: instruments.v2 运行完成[0.013372s].

[2022-05-06 10:56:27.775924] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 10:56:27.786499] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.787619] INFO: moduleinvoker: input_features.v1 运行完成[0.011699s].

[2022-05-06 10:56:27.793753] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 10:56:27.806653] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.807792] INFO: moduleinvoker: instruments.v2 运行完成[0.01404s].

[2022-05-06 10:56:27.815003] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 10:56:27.825121] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.827166] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.012162s].

[2022-05-06 10:56:27.841684] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 10:56:27.850757] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.852045] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010368s].

[2022-05-06 10:56:27.865859] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 10:56:27.875649] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.876977] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011134s].

[2022-05-06 10:56:27.881318] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:56:27.891994] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.893239] INFO: moduleinvoker: standardlize.v8 运行完成[0.011919s].

[2022-05-06 10:56:27.899481] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 10:56:27.908166] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.909355] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009873s].

[2022-05-06 10:56:27.915184] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 10:56:27.923674] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.924931] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009746s].

[2022-05-06 10:56:27.929381] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:56:27.940094] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.941370] INFO: moduleinvoker: standardlize.v8 运行完成[0.011986s].

[2022-05-06 10:56:27.945339] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 10:56:27.955417] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.956626] INFO: moduleinvoker: standardlize.v8 运行完成[0.011286s].

[2022-05-06 10:56:27.963882] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 10:56:27.974960] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.976222] INFO: moduleinvoker: fillnan.v1 运行完成[0.012338s].

[2022-05-06 10:56:27.983242] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 10:56:27.995253] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:27.996419] INFO: moduleinvoker: fillnan.v1 运行完成[0.013176s].

[2022-05-06 10:56:28.004004] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 10:56:28.011880] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:28.012999] INFO: moduleinvoker: join.v3 运行完成[0.008994s].

[2022-05-06 10:56:28.026413] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 10:56:28.037278] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:28.038456] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.012048s].

[2022-05-06 10:56:28.069056] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 10:56:28.083945] INFO: moduleinvoker: 命中缓存

[2022-05-06 10:56:28.085647] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.016611s].

[2022-05-06 10:56:28.097034] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:17:10.333699] INFO: moduleinvoker: cached.v3 运行完成[1242.236637s].

[2022-05-06 11:17:10.379986] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:17:15.262853] INFO: moduleinvoker: cached.v3 运行完成[4.882876s].

[2022-05-06 11:17:15.373384] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 11:17:15.386211] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 11:17:15.387711] INFO: backtest: product_type:stock by specified

[2022-05-06 11:17:15.475119] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 11:17:15.488023] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:17:15.490356] INFO: moduleinvoker: cached.v2 运行完成[0.015259s].

[2022-05-06 11:17:16.823555] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 11:17:17.481222] INFO: algo: trading transform...

[2022-05-06 11:17:54.342608] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 11:17:54.344291] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 11:17:54.345695] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 11:18:03.777839] INFO: moduleinvoker: backtest.v8 运行完成[48.40443s].

[2022-05-06 11:18:03.780101] INFO: moduleinvoker: trade.v4 运行完成[48.501276s].

[2022-05-06 11:18:03.791785] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 11:18:03.801674] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.803842] INFO: moduleinvoker: instruments.v2 运行完成[0.012075s].

[2022-05-06 11:18:03.808567] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 11:18:03.823269] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.825809] INFO: moduleinvoker: input_features.v1 运行完成[0.017244s].

[2022-05-06 11:18:03.831168] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 11:18:03.838209] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.839573] INFO: moduleinvoker: instruments.v2 运行完成[0.008409s].

[2022-05-06 11:18:03.847593] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 11:18:03.861271] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.863626] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.016021s].

[2022-05-06 11:18:03.886358] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 11:18:03.894708] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.896944] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010611s].

[2022-05-06 11:18:03.913416] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 11:18:03.926903] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.929084] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.015663s].

[2022-05-06 11:18:03.934297] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:18:03.945141] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.946338] INFO: moduleinvoker: standardlize.v8 运行完成[0.012038s].

[2022-05-06 11:18:03.952138] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 11:18:03.995247] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:03.997804] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.045673s].

[2022-05-06 11:18:04.005881] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 11:18:04.015796] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.017958] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012071s].

[2022-05-06 11:18:04.023426] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:18:04.058550] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.060923] INFO: moduleinvoker: standardlize.v8 运行完成[0.037485s].

[2022-05-06 11:18:04.066632] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:18:04.090776] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.093035] INFO: moduleinvoker: standardlize.v8 运行完成[0.026399s].

[2022-05-06 11:18:04.103298] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 11:18:04.113227] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.115171] INFO: moduleinvoker: fillnan.v1 运行完成[0.01187s].

[2022-05-06 11:18:04.125947] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 11:18:04.165165] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.167342] INFO: moduleinvoker: fillnan.v1 运行完成[0.041388s].

[2022-05-06 11:18:04.176095] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 11:18:04.187885] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.189738] INFO: moduleinvoker: join.v3 运行完成[0.013647s].

[2022-05-06 11:18:04.205336] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 11:18:04.215625] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.217567] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.01224s].

[2022-05-06 11:18:04.234939] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 11:18:04.247555] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:18:04.249010] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.014099s].

[2022-05-06 11:18:04.259739] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:39:06.793286] INFO: moduleinvoker: cached.v3 运行完成[1262.533541s].

[2022-05-06 11:39:06.805318] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:39:10.544669] INFO: moduleinvoker: cached.v3 运行完成[3.739355s].

[2022-05-06 11:39:10.603374] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 11:39:10.610845] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 11:39:10.612112] INFO: backtest: product_type:stock by specified

[2022-05-06 11:39:10.690393] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 11:39:10.703391] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:39:10.704868] INFO: moduleinvoker: cached.v2 运行完成[0.014496s].

[2022-05-06 11:39:11.974378] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 11:39:12.475823] INFO: algo: trading transform...

[2022-05-06 11:39:51.173469] INFO: Performance: Simulated 245 trading days out of 245.

[2022-05-06 11:39:51.175312] INFO: Performance: first open: 2014-01-02 09:30:00+00:00

[2022-05-06 11:39:51.176405] INFO: Performance: last close: 2014-12-31 15:00:00+00:00

[2022-05-06 11:40:03.373475] INFO: moduleinvoker: backtest.v8 运行完成[52.770075s].

[2022-05-06 11:40:03.375914] INFO: moduleinvoker: trade.v4 运行完成[52.823059s].

[2022-05-06 11:40:03.392915] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 11:40:03.402343] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.403764] INFO: moduleinvoker: instruments.v2 运行完成[0.010863s].

[2022-05-06 11:40:03.407546] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 11:40:03.416674] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.417947] INFO: moduleinvoker: input_features.v1 运行完成[0.010404s].

[2022-05-06 11:40:03.423868] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 11:40:03.431600] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.432834] INFO: moduleinvoker: instruments.v2 运行完成[0.008969s].

[2022-05-06 11:40:03.440203] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-05-06 11:40:03.448546] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.449780] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.009578s].

[2022-05-06 11:40:03.462713] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 11:40:03.470433] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.471724] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009017s].

[2022-05-06 11:40:03.490745] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 11:40:03.500646] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.502010] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011276s].

[2022-05-06 11:40:03.506390] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:40:03.517402] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.518668] INFO: moduleinvoker: standardlize.v8 运行完成[0.012277s].

[2022-05-06 11:40:03.524894] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 11:40:03.530685] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.531904] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.00701s].

[2022-05-06 11:40:03.537841] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 11:40:03.544484] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.545701] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.00786s].

[2022-05-06 11:40:03.550366] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:40:03.557649] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.558894] INFO: moduleinvoker: standardlize.v8 运行完成[0.008529s].

[2022-05-06 11:40:03.563286] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-05-06 11:40:03.574507] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.575818] INFO: moduleinvoker: standardlize.v8 运行完成[0.012531s].

[2022-05-06 11:40:03.583026] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 11:40:03.590444] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.591679] INFO: moduleinvoker: fillnan.v1 运行完成[0.008653s].

[2022-05-06 11:40:03.598540] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-05-06 11:40:03.606727] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.607950] INFO: moduleinvoker: fillnan.v1 运行完成[0.009409s].

[2022-05-06 11:40:03.614948] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 11:40:03.624312] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.625536] INFO: moduleinvoker: join.v3 运行完成[0.010588s].

[2022-05-06 11:40:03.638917] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 11:40:03.648886] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.650245] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.011331s].

[2022-05-06 11:40:03.700845] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-05-06 11:40:03.712910] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.714255] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.013428s].

[2022-05-06 11:40:03.725802] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:40:03.735491] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.736725] INFO: moduleinvoker: cached.v3 运行完成[0.010926s].

[2022-05-06 11:40:03.745920] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 11:40:03.754723] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:03.755928] INFO: moduleinvoker: cached.v3 运行完成[0.010012s].

[2022-05-06 11:40:03.818502] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 11:40:03.835005] INFO: moduleinvoker: 命中缓存

[2022-05-06 11:40:09.950389] INFO: moduleinvoker: backtest.v8 运行完成[6.131866s].

[2022-05-06 11:40:09.952204] INFO: moduleinvoker: trade.v4 运行完成[6.19088s].

[2022-05-06 11:40:09.955404] INFO: moduleinvoker: hyper_parameter_search.v1 运行完成[7157.17981s].

Fitting 1 folds for each of 9 candidates, totalling 9 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV 1/1; 1/9] START m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 2, "dropout": 0.1}

收益率157.8%
年化收益率164.87%
基准收益率51.66%
阿尔法1.01
贝塔0.67
夏普比率3.86
胜率0.65
盈亏比1.31
收益波动率25.34%
信息比率0.16
最大回撤9.19%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1b31e81034bd4ee7b4ea327ad78dc873"}/bigcharts-data-end

[CV 1/1; 1/9] END m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 2, "dropout": 0.1}; total time= 8.7min
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  8.7min remaining:    0.0s
[CV 1/1; 2/9] START m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 2, "dropout": 0.1}

收益率115.09%
年化收益率119.85%
基准收益率51.66%
阿尔法0.73
贝塔0.57
夏普比率3.39
胜率0.65
盈亏比1.28
收益波动率23.22%
信息比率0.11
最大回撤10.85%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9446d1b57f18479aa6ff74a104dfb1a8"}/bigcharts-data-end

[CV 1/1; 2/9] END m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 2, "dropout": 0.1}; total time= 2.3min
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 11.0min remaining:    0.0s
[CV 1/1; 3/9] START m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 2, "dropout": 0.1}

收益率92.65%
年化收益率96.29%
基准收益率51.66%
阿尔法0.53
贝塔0.6
夏普比率2.67
胜率0.64
盈亏比1.22
收益波动率25.38%
信息比率0.07
最大回撤16.12%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6f6fb20f185147c585027bde41004347"}/bigcharts-data-end

[CV 1/1; 3/9] END m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 2, "dropout": 0.1}; total time= 1.5min
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 12.5min remaining:    0.0s
[CV 1/1; 4/9] START m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.1}

收益率137.44%
年化收益率143.38%
基准收益率51.66%
阿尔法0.89
贝塔0.62
夏普比率3.46
胜率0.65
盈亏比1.29
收益波动率25.84%
信息比率0.13
最大回撤11.21%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c350bdccadf749d6a120fccd12a2cee2"}/bigcharts-data-end

[CV 1/1; 4/9] END m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.1}; total time=  40.1s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed: 13.2min remaining:    0.0s
[CV 1/1; 5/9] START m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.1}

收益率30.73%
年化收益率31.74%
基准收益率51.66%
阿尔法-0.07
贝塔0.81
夏普比率1.21
胜率0.51
盈亏比1.38
收益波动率22.46%
信息比率-0.06
最大回撤17.08%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-dc284b9ac6484a78b8ab5d39c10a77ba"}/bigcharts-data-end

[CV 1/1; 5/9] END m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.1}; total time=  51.8s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed: 14.0min remaining:    0.0s
[CV 1/1; 6/9] START m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.1}

epoch 0   |  train_loss 1.00927|  vall_loss 0.98584|  0:01:46s
epoch 1   |  train_loss 0.99702|  vall_loss 0.98999|  0:03:35s
epoch 2   |  train_loss 0.99861|  vall_loss 0.99061|  0:05:23s
epoch 3   |  train_loss 0.99856|  vall_loss 0.99055|  0:07:11s
epoch 4   |  train_loss 0.99851|  vall_loss 0.99012|  0:09:01s
epoch 5   |  train_loss 0.99848|  vall_loss 0.99056|  0:10:52s
epoch 6   |  train_loss 0.99845|  vall_loss 0.99084|  0:12:51s
epoch 7   |  train_loss 0.99845|  vall_loss 0.99093|  0:14:46s
epoch 8   |  train_loss 0.99844|  vall_loss 0.99034|  0:16:46s
epoch 9   |  train_loss 0.99841|  vall_loss 0.99038|  0:18:33s

收益率53.03%
年化收益率54.9%
基准收益率51.66%
阿尔法0.19
贝塔0.59
夏普比率2.38
胜率0.57
盈亏比1.4
收益波动率17.82%
信息比率0.0
最大回撤7.77%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e429713083db43e4a707e84219dfac7d"}/bigcharts-data-end

[CV 1/1; 6/9] END m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.1}; total time=41.6min
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed: 55.6min remaining:    0.0s
[CV 1/1; 7/9] START m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.3}

epoch 0   |  train_loss 1.01678|  vall_loss 0.98507|  0:01:52s
epoch 1   |  train_loss 0.98982|  vall_loss 0.98561|  0:03:47s
epoch 2   |  train_loss 0.98915|  vall_loss 0.98324|  0:05:37s
epoch 3   |  train_loss 0.98919|  vall_loss 0.98176|  0:07:34s
epoch 4   |  train_loss 0.98824|  vall_loss 0.98072|  0:09:33s
epoch 5   |  train_loss 0.98794|  vall_loss 0.98108|  0:11:36s
epoch 6   |  train_loss 0.98922|  vall_loss 0.98322|  0:13:24s
epoch 7   |  train_loss 0.98725|  vall_loss 0.98317|  0:15:13s
epoch 8   |  train_loss 0.98611|  vall_loss 0.98139|  0:16:58s
epoch 9   |  train_loss 0.98467|  vall_loss 0.98176|  0:18:46s

收益率132.67%
年化收益率138.35%
基准收益率51.66%
阿尔法0.83
贝塔0.64
夏普比率3.28
胜率0.66
盈亏比1.19
收益波动率26.68%
信息比率0.12
最大回撤15.57%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f23619359b464c79b32bff6ac1948143"}/bigcharts-data-end

[CV 1/1; 7/9] END m33.params={"embed_dim": 64, "nhead": 8, "num_layers": 4, "dropout": 0.3}; total time=20.0min
[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed: 75.6min remaining:    0.0s
[CV 1/1; 8/9] START m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.3}

epoch 0   |  train_loss 1.02812|  vall_loss 0.99750|  0:01:46s
epoch 1   |  train_loss 0.99159|  vall_loss 1.00375|  0:03:43s
epoch 2   |  train_loss 0.99581|  vall_loss 1.00686|  0:05:30s
epoch 3   |  train_loss 0.99681|  vall_loss 1.00710|  0:07:21s
epoch 4   |  train_loss 0.99675|  vall_loss 1.00694|  0:09:12s
epoch 5   |  train_loss 0.99670|  vall_loss 1.00741|  0:11:02s
epoch 6   |  train_loss 0.99669|  vall_loss 1.00606|  0:12:56s
epoch 7   |  train_loss 0.99666|  vall_loss 1.00602|  0:14:44s
epoch 8   |  train_loss 0.99666|  vall_loss 1.00648|  0:16:35s
epoch 9   |  train_loss 0.99664|  vall_loss 1.00657|  0:18:37s

收益率56.09%
年化收益率58.09%
基准收益率51.66%
阿尔法0.24
贝塔0.57
夏普比率2.06
胜率0.61
盈亏比1.13
收益波动率21.99%
信息比率0.01
最大回撤12.15%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e383dab0eda8471381472c4f0cd6077a"}/bigcharts-data-end

[CV 1/1; 8/9] END m33.params={"embed_dim": 128, "nhead": 8, "num_layers": 4, "dropout": 0.3}; total time=21.6min
[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed: 97.2min remaining:    0.0s
[CV 1/1; 9/9] START m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.3}

epoch 0   |  train_loss 1.03854|  vall_loss 0.99538|  0:01:59s
epoch 1   |  train_loss 0.99559|  vall_loss 0.99731|  0:03:53s
epoch 2   |  train_loss 0.99814|  vall_loss 0.99709|  0:05:47s
epoch 3   |  train_loss 0.99793|  vall_loss 0.99797|  0:07:43s
epoch 4   |  train_loss 0.99786|  vall_loss 0.99746|  0:09:41s
epoch 5   |  train_loss 0.99779|  vall_loss 0.99708|  0:11:44s
epoch 6   |  train_loss 0.99776|  vall_loss 0.99771|  0:13:37s
epoch 7   |  train_loss 0.99775|  vall_loss 0.99720|  0:15:35s
epoch 8   |  train_loss 0.99770|  vall_loss 0.99688|  0:17:32s
epoch 9   |  train_loss 0.99771|  vall_loss 0.99709|  0:19:20s

收益率37.35%
年化收益率38.6%
基准收益率51.66%
阿尔法0.07
贝塔0.58
夏普比率1.77
胜率0.58
盈亏比1.25
收益波动率17.66%
信息比率-0.04
最大回撤8.71%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d3aa43423f1e4c0aae0bddd293ad7d97"}/bigcharts-data-end

[CV 1/1; 9/9] END m33.params={"embed_dim": 256, "nhead": 8, "num_layers": 4, "dropout": 0.3}; total time=22.0min
[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed: 119.2min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed: 119.2min finished

收益率157.8%
年化收益率164.87%
基准收益率51.66%
阿尔法1.01
贝塔0.67
夏普比率3.86
胜率0.65
盈亏比1.31
收益波动率25.34%
信息比率0.16
最大回撤9.19%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7ae8a5ced1cf41fa89557efebf072d20"}/bigcharts-data-end