克隆策略

In [2]:

from tensorflow.keras import optimizers

In [6]:

# 本代码由可视化策略环境自动生成 2021年10月12日 15:38
# 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 m4_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'], shuffle=False, test_size=0.2)
    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 m4_post_run_bigquant_run(outputs):
    return outputs

from tensorflow.keras.callbacks import EarlyStopping

m35_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=5)
# 用户的自定义层需要写到字典中，比如
# {
#   "MyLayer": MyLayer
# }
m35_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=0.0003, 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 = 5
    # 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'] = 2

# 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


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    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, -2) / 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)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=False
)

m13 = M.standardlize.v8(
    input_1=m2.data,
    columns_input='label'
)

m3 = M.input_features.v1(
    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(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m14 = M.standardlize.v8(
    input_1=m16.data,
    input_2=m3.data,
    columns_input=''
)

m21 = M.fillnan.v1(
    input_data=m14.data,
    features=m3.data,
    fill_value='0.0'
)

m7 = M.join.v3(
    data1=m13.data,
    data2=m21.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m26 = M.dl_convert_to_bin.v2(
    input_data=m7.data,
    features=m3.data,
    window_size=1,
    feature_clip=3,
    flatten=True,
    window_along_col='instrument'
)

m4 = M.cached.v3(
    input_1=m26.data,
    input_2=m3.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-10-08'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m25 = M.standardlize.v8(
    input_1=m18.data,
    input_2=m3.data,
    columns_input=''
)

m22 = M.fillnan.v1(
    input_data=m25.data,
    features=m3.data,
    fill_value='0.0'
)

m27 = M.dl_convert_to_bin.v2(
    input_data=m22.data,
    features=m3.data,
    window_size=1,
    feature_clip=3,
    flatten=True,
    window_along_col='instrument'
)

m6 = M.dl_layer_input.v1(
    shape='98,1',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m37 = M.dl_layer_batchnormalization.v1(
    inputs=m6.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m43 = M.dl_layer_conv1d.v1(
    inputs=m37.data,
    filters=128,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m44 = M.dl_layer_conv1d.v1(
    inputs=m43.data,
    filters=128,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m41 = M.dl_layer_maxpooling1d.v1(
    inputs=m44.data,
    pool_size=2,
    padding='valid',
    name=''
)

m45 = M.dl_layer_conv1d.v1(
    inputs=m41.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m47 = M.dl_layer_conv1d.v1(
    inputs=m45.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m48 = M.dl_layer_batchnormalization.v1(
    inputs=m47.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m50 = M.dl_layer_conv1d.v1(
    inputs=m48.data,
    filters=256,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m51 = M.dl_layer_batchnormalization.v1(
    inputs=m50.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m53 = M.dl_layer_conv1d.v1(
    inputs=m51.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m46 = M.dl_layer_batchnormalization.v1(
    inputs=m53.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m49 = M.dl_layer_conv1d.v1(
    inputs=m46.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m62 = M.dl_layer_add.v1(
    input1=m49.data,
    input2=m47.data,
    name=''
)

m38 = M.dl_layer_globalmaxpooling1d.v1(
    inputs=m62.data,
    name=''
)

m61 = M.dl_layer_dropout.v1(
    inputs=m38.data,
    rate=0.1,
    noise_shape='',
    name=''
)

m57 = M.dl_layer_dense.v1(
    inputs=m61.data,
    units=1,
    activation='linear',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m34 = M.dl_model_init.v1(
    inputs=m6.data,
    outputs=m57.data
)

m35 = M.dl_model_train.v1(
    input_model=m34.data,
    training_data=m4.data_1,
    validation_data=m4.data_2,
    optimizer='Adam',
    user_optimizer=optimizers.Adam(lr=0.002),
    loss='mean_squared_error',
    metrics='mse',
    batch_size=512,
    epochs=10000,
    earlystop=m35_earlystop_bigquant_run,
    custom_objects=m35_custom_objects_bigquant_run,
    n_gpus=1,
    verbose='2:每个epoch输出一行记录',
    m_cached=False
)

m11 = M.dl_model_predict.v1(
    trained_model=m35.data,
    input_data=m27.data,
    batch_size=512,
    n_gpus=1,
    verbose='2:每个epoch输出一行记录'
)

m24 = M.cached.v3(
    input_1=m11.data,
    input_2=m18.data,
    run=m24_run_bigquant_run,
    post_run=m24_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m24.data_1,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

[2021-10-12 14:16:12.990919] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-12 14:16:13.016114] INFO: moduleinvoker: 命中缓存

[2021-10-12 14:16:13.018018] INFO: moduleinvoker: instruments.v2 运行完成[0.027128s].

[2021-10-12 14:16:13.028602] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-12 14:16:18.507751] INFO: 自动标注(股票): 加载历史数据: 2647809 行

[2021-10-12 14:16:18.510844] INFO: 自动标注(股票): 开始标注 ..

[2021-10-12 14:16:27.895098] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[14.866487s].

[2021-10-12 14:16:27.906343] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-10-12 14:16:57.233437] INFO: moduleinvoker: standardlize.v8 运行完成[29.327093s].

[2021-10-12 14:16:57.242947] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-12 14:16:57.252266] INFO: moduleinvoker: 命中缓存

[2021-10-12 14:16:57.254066] INFO: moduleinvoker: input_features.v1 运行完成[0.011137s].

[2021-10-12 14:16:57.274088] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-12 14:17:00.594399] INFO: 基础特征抽取: 年份 2017, 特征行数=19477

[2021-10-12 14:17:06.781327] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2021-10-12 14:17:13.633031] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2021-10-12 14:17:20.900843] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2021-10-12 14:17:21.095485] INFO: 基础特征抽取: 总行数: 2667292

[2021-10-12 14:17:21.193887] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[23.919794s].

[2021-10-12 14:17:21.208058] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-12 14:17:38.539768] INFO: derived_feature_extractor: 提取完成 mean(close_0, 5), 2.491s

[2021-10-12 14:17:40.850648] INFO: derived_feature_extractor: 提取完成 mean(low_0, 5), 2.308s

[2021-10-12 14:17:43.145670] INFO: derived_feature_extractor: 提取完成 mean(open_0, 5), 2.293s

[2021-10-12 14:17:45.462251] INFO: derived_feature_extractor: 提取完成 mean(high_0, 5), 2.314s

[2021-10-12 14:17:47.786525] INFO: derived_feature_extractor: 提取完成 mean(turn_0, 5), 2.323s

[2021-10-12 14:17:50.283908] INFO: derived_feature_extractor: 提取完成 mean(amount_0, 5), 2.495s

[2021-10-12 14:17:52.679980] INFO: derived_feature_extractor: 提取完成 mean(return_0, 5), 2.394s

[2021-10-12 14:17:55.263442] INFO: derived_feature_extractor: 提取完成 ts_max(close_0, 5), 2.581s

[2021-10-12 14:17:57.688087] INFO: derived_feature_extractor: 提取完成 ts_max(low_0, 5), 2.423s

[2021-10-12 14:18:00.181837] INFO: derived_feature_extractor: 提取完成 ts_max(open_0, 5), 2.490s

[2021-10-12 14:18:02.668848] INFO: derived_feature_extractor: 提取完成 ts_max(high_0, 5), 2.484s

[2021-10-12 14:18:05.038820] INFO: derived_feature_extractor: 提取完成 ts_max(turn_0, 5), 2.367s

[2021-10-12 14:18:07.499026] INFO: derived_feature_extractor: 提取完成 ts_max(amount_0, 5), 2.458s

[2021-10-12 14:18:09.929156] INFO: derived_feature_extractor: 提取完成 ts_max(return_0, 5), 2.428s

[2021-10-12 14:18:12.582335] INFO: derived_feature_extractor: 提取完成 ts_min(close_0, 5), 2.650s

[2021-10-12 14:18:14.951910] INFO: derived_feature_extractor: 提取完成 ts_min(low_0, 5), 2.367s

[2021-10-12 14:18:17.371698] INFO: derived_feature_extractor: 提取完成 ts_min(open_0, 5), 2.418s

[2021-10-12 14:18:19.645019] INFO: derived_feature_extractor: 提取完成 ts_min(high_0, 5), 2.271s

[2021-10-12 14:18:22.035940] INFO: derived_feature_extractor: 提取完成 ts_min(turn_0, 5), 2.389s

[2021-10-12 14:18:24.423476] INFO: derived_feature_extractor: 提取完成 ts_min(amount_0, 5), 2.385s

[2021-10-12 14:18:26.822319] INFO: derived_feature_extractor: 提取完成 ts_min(return_0, 5), 2.397s

[2021-10-12 14:18:29.306489] INFO: derived_feature_extractor: 提取完成 std(close_0, 5), 2.482s

[2021-10-12 14:18:31.825024] INFO: derived_feature_extractor: 提取完成 std(low_0, 5), 2.516s

[2021-10-12 14:18:34.347484] INFO: derived_feature_extractor: 提取完成 std(open_0, 5), 2.520s

[2021-10-12 14:18:36.729408] INFO: derived_feature_extractor: 提取完成 std(high_0, 5), 2.380s

[2021-10-12 14:18:39.064897] INFO: derived_feature_extractor: 提取完成 std(turn_0, 5), 2.333s

[2021-10-12 14:18:41.506870] INFO: derived_feature_extractor: 提取完成 std(amount_0, 5), 2.440s

[2021-10-12 14:18:44.135487] INFO: derived_feature_extractor: 提取完成 std(return_0, 5), 2.626s

[2021-10-12 14:18:55.632665] INFO: derived_feature_extractor: 提取完成 ts_rank(close_0, 5), 11.494s

[2021-10-12 14:19:07.323679] INFO: derived_feature_extractor: 提取完成 ts_rank(low_0, 5), 11.689s

[2021-10-12 14:19:18.427169] INFO: derived_feature_extractor: 提取完成 ts_rank(open_0, 5), 11.101s

[2021-10-12 14:19:29.424899] INFO: derived_feature_extractor: 提取完成 ts_rank(high_0, 5), 10.995s

[2021-10-12 14:19:40.651409] INFO: derived_feature_extractor: 提取完成 ts_rank(turn_0, 5), 11.224s

[2021-10-12 14:19:51.554096] INFO: derived_feature_extractor: 提取完成 ts_rank(amount_0, 5), 10.901s

[2021-10-12 14:20:02.961522] INFO: derived_feature_extractor: 提取完成 ts_rank(return_0, 5), 11.406s

[2021-10-12 14:20:10.209429] INFO: derived_feature_extractor: 提取完成 decay_linear(close_0, 5), 7.246s

[2021-10-12 14:20:17.351266] INFO: derived_feature_extractor: 提取完成 decay_linear(low_0, 5), 7.139s

[2021-10-12 14:20:24.819808] INFO: derived_feature_extractor: 提取完成 decay_linear(open_0, 5), 7.466s

[2021-10-12 14:20:32.380983] INFO: derived_feature_extractor: 提取完成 decay_linear(high_0, 5), 7.559s

[2021-10-12 14:20:39.210979] INFO: derived_feature_extractor: 提取完成 decay_linear(turn_0, 5), 6.827s

[2021-10-12 14:20:46.454819] INFO: derived_feature_extractor: 提取完成 decay_linear(amount_0, 5), 7.241s

[2021-10-12 14:20:53.367849] INFO: derived_feature_extractor: 提取完成 decay_linear(return_0, 5), 6.911s

[2021-10-12 14:21:30.650867] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, return_0, 5), 37.281s

[2021-10-12 14:22:07.245767] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, high_0, 5), 36.592s

[2021-10-12 14:22:37.716750] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, low_0, 5), 30.469s

[2021-10-12 14:23:07.879367] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, close_0, 5), 30.159s

[2021-10-12 14:23:38.698960] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, open_0, 5), 30.815s

[2021-10-12 14:24:08.898795] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, turn_0, 5), 30.197s

[2021-10-12 14:24:39.769990] INFO: derived_feature_extractor: 提取完成 correlation(return_0, high_0, 5), 30.869s

[2021-10-12 14:25:08.718177] INFO: derived_feature_extractor: 提取完成 correlation(return_0, low_0, 5), 28.944s

[2021-10-12 14:25:37.040360] INFO: derived_feature_extractor: 提取完成 correlation(return_0, close_0, 5), 28.321s

[2021-10-12 14:26:07.641443] INFO: derived_feature_extractor: 提取完成 correlation(return_0, open_0, 5), 30.599s

[2021-10-12 14:26:38.607277] INFO: derived_feature_extractor: 提取完成 correlation(return_0, turn_0, 5), 30.964s

[2021-10-12 14:27:09.739027] INFO: derived_feature_extractor: 提取完成 correlation(high_0, low_0, 5), 31.130s

[2021-10-12 14:27:40.852556] INFO: derived_feature_extractor: 提取完成 correlation(high_0, close_0, 5), 31.112s

[2021-10-12 14:28:10.542105] INFO: derived_feature_extractor: 提取完成 correlation(high_0, open_0, 5), 29.688s

[2021-10-12 14:28:41.069304] INFO: derived_feature_extractor: 提取完成 correlation(high_0, turn_0, 5), 30.526s

[2021-10-12 14:29:11.303899] INFO: derived_feature_extractor: 提取完成 correlation(low_0, close_0, 5), 30.232s

[2021-10-12 14:29:43.174912] INFO: derived_feature_extractor: 提取完成 correlation(low_0, open_0, 5), 31.868s

[2021-10-12 14:30:14.428031] INFO: derived_feature_extractor: 提取完成 correlation(low_0, turn_0, 5), 31.251s

[2021-10-12 14:30:49.374769] INFO: derived_feature_extractor: 提取完成 correlation(close_0, open_0, 5), 34.945s

[2021-10-12 14:31:26.152037] INFO: derived_feature_extractor: 提取完成 correlation(close_0, turn_0, 5), 36.775s

[2021-10-12 14:32:03.110391] INFO: derived_feature_extractor: 提取完成 correlation(open_0, turn_0, 5), 36.956s

[2021-10-12 14:32:05.531719] INFO: derived_feature_extractor: /y_2017, 19477

[2021-10-12 14:32:09.917691] INFO: derived_feature_extractor: /y_2018, 816987

[2021-10-12 14:32:20.476197] INFO: derived_feature_extractor: /y_2019, 884867

[2021-10-12 14:32:31.871800] INFO: derived_feature_extractor: /y_2020, 945961

[2021-10-12 14:32:38.941067] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[917.733017s].

[2021-10-12 14:32:38.950408] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-10-12 14:38:28.856833] INFO: moduleinvoker: standardlize.v8 运行完成[349.906339s].

[2021-10-12 14:38:28.876687] INFO: moduleinvoker: fillnan.v1 开始运行..

[2021-10-12 14:38:55.662071] INFO: moduleinvoker: fillnan.v1 运行完成[26.785392s].

[2021-10-12 14:38:55.677008] INFO: moduleinvoker: join.v3 开始运行..

[2021-10-12 14:39:49.379395] INFO: join: /data, 行数=2623538/2649327, 耗时=42.181922s

[2021-10-12 14:39:49.547525] INFO: join: 最终行数: 2623538

[2021-10-12 14:39:49.643546] INFO: moduleinvoker: join.v3 运行完成[53.966544s].

[2021-10-12 14:39:49.666968] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-10-12 14:40:10.786459] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[21.119486s].

[2021-10-12 14:40:10.812028] INFO: moduleinvoker: cached.v3 开始运行..

[2021-10-12 14:40:18.427001] INFO: moduleinvoker: cached.v3 运行完成[7.614976s].

[2021-10-12 14:40:18.435523] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-12 14:40:18.444162] INFO: moduleinvoker: 命中缓存

[2021-10-12 14:40:18.447135] INFO: moduleinvoker: instruments.v2 运行完成[0.011614s].

[2021-10-12 14:40:18.464627] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-12 14:40:22.323364] INFO: 基础特征抽取: 年份 2020, 特征行数=32852

[2021-10-12 14:40:28.359000] INFO: 基础特征抽取: 年份 2021, 特征行数=786470

[2021-10-12 14:40:28.470474] INFO: 基础特征抽取: 总行数: 819322

[2021-10-12 14:40:28.540331] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.07574s].

[2021-10-12 14:40:28.553489] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-12 14:40:34.008081] INFO: derived_feature_extractor: 提取完成 mean(close_0, 5), 1.077s

[2021-10-12 14:40:35.007269] INFO: derived_feature_extractor: 提取完成 mean(low_0, 5), 0.995s

[2021-10-12 14:40:36.002583] INFO: derived_feature_extractor: 提取完成 mean(open_0, 5), 0.992s

[2021-10-12 14:40:36.979519] INFO: derived_feature_extractor: 提取完成 mean(high_0, 5), 0.975s

[2021-10-12 14:40:38.017278] INFO: derived_feature_extractor: 提取完成 mean(turn_0, 5), 1.035s

[2021-10-12 14:40:39.062215] INFO: derived_feature_extractor: 提取完成 mean(amount_0, 5), 1.043s

[2021-10-12 14:40:40.123547] INFO: derived_feature_extractor: 提取完成 mean(return_0, 5), 1.059s

[2021-10-12 14:40:41.123200] INFO: derived_feature_extractor: 提取完成 ts_max(close_0, 5), 0.997s

[2021-10-12 14:40:42.142524] INFO: derived_feature_extractor: 提取完成 ts_max(low_0, 5), 1.017s

[2021-10-12 14:40:43.219847] INFO: derived_feature_extractor: 提取完成 ts_max(open_0, 5), 1.075s

[2021-10-12 14:40:44.301676] INFO: derived_feature_extractor: 提取完成 ts_max(high_0, 5), 1.079s

[2021-10-12 14:40:45.350532] INFO: derived_feature_extractor: 提取完成 ts_max(turn_0, 5), 1.046s

[2021-10-12 14:40:46.353741] INFO: derived_feature_extractor: 提取完成 ts_max(amount_0, 5), 1.000s

[2021-10-12 14:40:47.357197] INFO: derived_feature_extractor: 提取完成 ts_max(return_0, 5), 1.001s

[2021-10-12 14:40:48.407412] INFO: derived_feature_extractor: 提取完成 ts_min(close_0, 5), 1.047s

[2021-10-12 14:40:49.436089] INFO: derived_feature_extractor: 提取完成 ts_min(low_0, 5), 1.027s

[2021-10-12 14:40:50.433853] INFO: derived_feature_extractor: 提取完成 ts_min(open_0, 5), 0.996s

[2021-10-12 14:40:51.463781] INFO: derived_feature_extractor: 提取完成 ts_min(high_0, 5), 1.028s

[2021-10-12 14:40:52.475808] INFO: derived_feature_extractor: 提取完成 ts_min(turn_0, 5), 1.010s

[2021-10-12 14:40:53.564446] INFO: derived_feature_extractor: 提取完成 ts_min(amount_0, 5), 1.086s

[2021-10-12 14:40:54.555544] INFO: derived_feature_extractor: 提取完成 ts_min(return_0, 5), 0.989s

[2021-10-12 14:40:55.590255] INFO: derived_feature_extractor: 提取完成 std(close_0, 5), 1.033s

[2021-10-12 14:40:56.667783] INFO: derived_feature_extractor: 提取完成 std(low_0, 5), 1.074s

[2021-10-12 14:40:57.664756] INFO: derived_feature_extractor: 提取完成 std(open_0, 5), 0.995s

[2021-10-12 14:40:58.692898] INFO: derived_feature_extractor: 提取完成 std(high_0, 5), 1.026s

[2021-10-12 14:40:59.692323] INFO: derived_feature_extractor: 提取完成 std(turn_0, 5), 0.997s

[2021-10-12 14:41:00.697844] INFO: derived_feature_extractor: 提取完成 std(amount_0, 5), 1.003s

[2021-10-12 14:41:01.727010] INFO: derived_feature_extractor: 提取完成 std(return_0, 5), 1.027s

[2021-10-12 14:41:05.309611] INFO: derived_feature_extractor: 提取完成 ts_rank(close_0, 5), 3.580s

[2021-10-12 14:41:08.787597] INFO: derived_feature_extractor: 提取完成 ts_rank(low_0, 5), 3.476s

[2021-10-12 14:41:12.337357] INFO: derived_feature_extractor: 提取完成 ts_rank(open_0, 5), 3.548s

[2021-10-12 14:41:15.934908] INFO: derived_feature_extractor: 提取完成 ts_rank(high_0, 5), 3.595s

[2021-10-12 14:41:19.521613] INFO: derived_feature_extractor: 提取完成 ts_rank(turn_0, 5), 3.584s

[2021-10-12 14:41:23.119149] INFO: derived_feature_extractor: 提取完成 ts_rank(amount_0, 5), 3.596s

[2021-10-12 14:41:26.823435] INFO: derived_feature_extractor: 提取完成 ts_rank(return_0, 5), 3.703s

[2021-10-12 14:41:29.319866] INFO: derived_feature_extractor: 提取完成 decay_linear(close_0, 5), 2.495s

[2021-10-12 14:41:31.779866] INFO: derived_feature_extractor: 提取完成 decay_linear(low_0, 5), 2.458s

[2021-10-12 14:41:34.204644] INFO: derived_feature_extractor: 提取完成 decay_linear(open_0, 5), 2.422s

[2021-10-12 14:41:36.637214] INFO: derived_feature_extractor: 提取完成 decay_linear(high_0, 5), 2.430s

[2021-10-12 14:41:39.091091] INFO: derived_feature_extractor: 提取完成 decay_linear(turn_0, 5), 2.451s

[2021-10-12 14:41:41.788836] INFO: derived_feature_extractor: 提取完成 decay_linear(amount_0, 5), 2.695s

[2021-10-12 14:41:44.278394] INFO: derived_feature_extractor: 提取完成 decay_linear(return_0, 5), 2.487s

[2021-10-12 14:42:23.635952] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, return_0, 5), 39.355s

[2021-10-12 14:42:57.010430] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, high_0, 5), 33.373s

[2021-10-12 14:43:33.219952] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, low_0, 5), 36.208s

[2021-10-12 14:44:09.248375] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, close_0, 5), 36.026s

[2021-10-12 14:44:44.719369] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, open_0, 5), 35.469s

[2021-10-12 14:45:20.358758] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, turn_0, 5), 35.637s

[2021-10-12 14:45:57.490493] INFO: derived_feature_extractor: 提取完成 correlation(return_0, high_0, 5), 37.130s

[2021-10-12 14:46:32.668684] INFO: derived_feature_extractor: 提取完成 correlation(return_0, low_0, 5), 35.176s

[2021-10-12 14:47:09.475387] INFO: derived_feature_extractor: 提取完成 correlation(return_0, close_0, 5), 36.804s

[2021-10-12 14:47:40.162695] INFO: derived_feature_extractor: 提取完成 correlation(return_0, open_0, 5), 30.685s

[2021-10-12 14:48:18.009112] INFO: derived_feature_extractor: 提取完成 correlation(return_0, turn_0, 5), 37.844s

[2021-10-12 14:48:57.399124] INFO: derived_feature_extractor: 提取完成 correlation(high_0, low_0, 5), 39.388s

[2021-10-12 14:49:38.554454] INFO: derived_feature_extractor: 提取完成 correlation(high_0, close_0, 5), 41.153s

[2021-10-12 14:50:19.561335] INFO: derived_feature_extractor: 提取完成 correlation(high_0, open_0, 5), 41.005s

[2021-10-12 14:51:01.065639] INFO: derived_feature_extractor: 提取完成 correlation(high_0, turn_0, 5), 41.502s

[2021-10-12 14:51:39.451301] INFO: derived_feature_extractor: 提取完成 correlation(low_0, close_0, 5), 38.383s

[2021-10-12 14:52:15.469768] INFO: derived_feature_extractor: 提取完成 correlation(low_0, open_0, 5), 36.016s

[2021-10-12 14:52:54.960337] INFO: derived_feature_extractor: 提取完成 correlation(low_0, turn_0, 5), 39.487s

[2021-10-12 14:53:33.439613] INFO: derived_feature_extractor: 提取完成 correlation(close_0, open_0, 5), 38.478s

[2021-10-12 14:54:12.780168] INFO: derived_feature_extractor: 提取完成 correlation(close_0, turn_0, 5), 39.338s

[2021-10-12 14:54:50.261145] INFO: derived_feature_extractor: 提取完成 correlation(open_0, turn_0, 5), 37.473s

[2021-10-12 14:54:50.955646] INFO: derived_feature_extractor: /y_2020, 32852

[2021-10-12 14:54:54.867296] INFO: derived_feature_extractor: /y_2021, 786470

[2021-10-12 14:54:59.938134] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[871.38465s].

[2021-10-12 14:54:59.944976] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-10-12 14:56:41.130929] INFO: moduleinvoker: standardlize.v8 运行完成[101.185913s].

[2021-10-12 14:56:41.144716] INFO: moduleinvoker: fillnan.v1 开始运行..

[2021-10-12 14:56:49.332525] INFO: moduleinvoker: fillnan.v1 运行完成[8.187817s].

[2021-10-12 14:56:49.346142] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-10-12 14:56:54.738922] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[5.392789s].

[2021-10-12 14:56:54.758589] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.006877s].

[2021-10-12 14:56:57.160307] INFO: moduleinvoker: dl_layer_batchnormalization.v1 运行完成[2.391353s].

[2021-10-12 14:56:57.205306] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.029064s].

[2021-10-12 14:56:57.237794] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.022208s].

[2021-10-12 14:56:57.256967] INFO: moduleinvoker: dl_layer_maxpooling1d.v1 运行完成[0.008468s].

[2021-10-12 14:56:57.286682] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.023005s].

[2021-10-12 14:56:57.330139] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.024296s].

[2021-10-12 14:56:57.354262] INFO: moduleinvoker: dl_layer_batchnormalization.v1 运行完成[0.017557s].

[2021-10-12 14:56:57.383099] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.022547s].

[2021-10-12 14:56:57.416108] INFO: moduleinvoker: dl_layer_batchnormalization.v1 运行完成[0.024817s].

[2021-10-12 14:56:57.445115] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.020226s].

[2021-10-12 14:56:57.475595] INFO: moduleinvoker: dl_layer_batchnormalization.v1 运行完成[0.023353s].

[2021-10-12 14:56:57.511785] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.027307s].

[2021-10-12 14:56:57.535666] INFO: moduleinvoker: dl_layer_add.v1 运行完成[0.00562s].

[2021-10-12 14:56:57.559132] INFO: moduleinvoker: dl_layer_globalmaxpooling1d.v1 运行完成[0.005916s].

[2021-10-12 14:56:57.575164] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.005784s].

[2021-10-12 14:56:57.598680] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.013896s].

[2021-10-12 14:56:57.688873] INFO: moduleinvoker: cached.v3 开始运行..

[2021-10-12 14:56:57.699115] INFO: moduleinvoker: 命中缓存

[2021-10-12 14:56:57.701897] INFO: moduleinvoker: cached.v3 运行完成[0.013038s].

[2021-10-12 14:56:57.709370] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.098021s].

[2021-10-12 14:56:57.737198] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2021-10-12 14:57:00.981454] INFO: dl_model_train: 准备训练，训练样本个数：2098830，迭代次数：10000

[2021-10-12 15:31:34.182620] INFO: dl_model_train: 训练结束，耗时：2073.20s

[2021-10-12 15:31:34.283036] INFO: moduleinvoker: dl_model_train.v1 运行完成[2076.545829s].

[2021-10-12 15:31:34.298488] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2021-10-12 15:31:57.407980] INFO: moduleinvoker: dl_model_predict.v1 运行完成[23.109503s].

[2021-10-12 15:31:57.430335] INFO: moduleinvoker: cached.v3 开始运行..

[2021-10-12 15:32:05.430150] INFO: moduleinvoker: cached.v3 运行完成[7.999841s].

[2021-10-12 15:32:07.860707] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-10-12 15:32:07.865629] INFO: backtest: biglearning backtest:V8.5.0

[2021-10-12 15:32:07.867418] INFO: backtest: product_type:stock by specified

[2021-10-12 15:32:08.497282] INFO: moduleinvoker: cached.v2 开始运行..

[2021-10-12 15:32:20.065556] INFO: backtest: 读取股票行情完成:1870264

[2021-10-12 15:32:26.595631] INFO: moduleinvoker: cached.v2 运行完成[18.098357s].

[2021-10-12 15:32:29.161896] INFO: algo: TradingAlgorithm V1.8.5

[2021-10-12 15:32:30.065310] INFO: algo: trading transform...

[2021-10-12 15:32:37.493913] INFO: Performance: Simulated 183 trading days out of 183.

[2021-10-12 15:32:37.496277] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2021-10-12 15:32:37.498223] INFO: Performance: last close: 2021-10-08 15:00:00+00:00

[2021-10-12 15:32:43.779362] INFO: moduleinvoker: backtest.v8 运行完成[35.918661s].

[2021-10-12 15:32:43.782602] INFO: moduleinvoker: trade.v4 运行完成[38.339204s].

Epoch 1/10000
4100/4100 - 219s - loss: 1.0947 - mse: 1.0947 - val_loss: 1.0138 - val_mse: 1.0138
Epoch 2/10000
4100/4100 - 204s - loss: 1.0193 - mse: 1.0193 - val_loss: 1.0135 - val_mse: 1.0135
Epoch 3/10000
4100/4100 - 205s - loss: 0.9910 - mse: 0.9910 - val_loss: 1.0145 - val_mse: 1.0145
Epoch 4/10000
4100/4100 - 207s - loss: 0.9904 - mse: 0.9904 - val_loss: 1.0141 - val_mse: 1.0141
Epoch 5/10000
4100/4100 - 206s - loss: 0.9901 - mse: 0.9901 - val_loss: 1.0127 - val_mse: 1.0127
Epoch 6/10000
4100/4100 - 206s - loss: 0.9894 - mse: 0.9894 - val_loss: 1.0127 - val_mse: 1.0127
Epoch 7/10000
4100/4100 - 206s - loss: 0.9886 - mse: 0.9886 - val_loss: 1.0132 - val_mse: 1.0132
Epoch 8/10000
4100/4100 - 206s - loss: 0.9880 - mse: 0.9880 - val_loss: 1.0133 - val_mse: 1.0133
Epoch 9/10000
4100/4100 - 206s - loss: 0.9874 - mse: 0.9874 - val_loss: 1.0136 - val_mse: 1.0136
Epoch 10/10000
4100/4100 - 206s - loss: 0.9870 - mse: 0.9870 - val_loss: 1.0133 - val_mse: 1.0133

1564/1564 - 21s
DataSource(3088e30c978347059eaa7d3cb05d4cd4T)

收益率46.72%
年化收益率69.53%
基准收益率-5.4%
阿尔法0.8
贝塔0.22
夏普比率1.49
胜率0.49
盈亏比1.26
收益波动率38.42%
信息比率0.1
最大回撤16.5%

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