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    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label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"close_0/mean(close_0,5)\nclose_0/mean(close_0,10)\nclose_0/mean(close_0,20)\nclose_0/open_0\nopen_0/mean(close_0,5)\nopen_0/mean(close_0,10)\nopen_0/mean(close_0,20)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"name":"data2","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-106","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-106"},{"name":"features","node_id":"-106"}],"output_ports":[{"name":"data","node_id":"-106"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-113","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"True","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-113"},{"name":"features","node_id":"-113"}],"output_ports":[{"name":"data","node_id":"-113"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-160","module_id":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","parameters":[{"name":"shape","value":"7","type":"Literal","bound_global_parameter":null},{"name":"batch_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"dtype","value":"float32","type":"Literal","bound_global_parameter":null},{"name":"sparse","value":"False","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-160"}],"output_ports":[{"name":"data","node_id":"-160"}],"cacheable":false,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-168","module_id":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","parameters":[{"name":"units","value":"256","type":"Literal","bound_global_parameter":null},{"name":"activation","value":"relu","type":"Literal","bound_global_parameter":null},{"name":"user_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"use_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_initializer","value":"glorot_uniform","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_initializer","value":"Zeros","type":"Literal","bound_global_parameter":null},{"name":"user_bias_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年12月14日 18:11
# 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。


# 用户的自定义层需要写到字典中，比如
# {
#   "MyLayer": MyLayer
# }
m5_custom_objects_bigquant_run = {
    
}


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-12-31',
    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, -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)
""",
    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',
    m_cached=False
)

m3 = M.input_features.v1(
    features="""close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)""",
    m_cached=False
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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='[]'
)

m7 = M.join.v3(
    data1=m13.data,
    data2=m14.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=5,
    flatten=True,
    window_along_col='instrument'
)

m6 = M.dl_layer_input.v1(
    shape='7',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m8 = M.dl_layer_dense.v1(
    inputs=m6.data,
    units=256,
    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=''
)

m21 = M.dl_layer_dropout.v1(
    inputs=m8.data,
    rate=0.1,
    noise_shape='',
    name=''
)

m20 = M.dl_layer_dense.v1(
    inputs=m21.data,
    units=128,
    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=''
)

m22 = M.dl_layer_dropout.v1(
    inputs=m20.data,
    rate=0.1,
    noise_shape='',
    name=''
)

m23 = M.dl_layer_dense.v1(
    inputs=m22.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=''
)

m4 = M.dl_model_init.v1(
    inputs=m6.data,
    outputs=m23.data
)

m5 = M.dl_model_train.v1(
    input_model=m4.data,
    training_data=m26.data,
    optimizer='Adam',
    loss='mean_squared_error',
    metrics='mse',
    batch_size=1024,
    epochs=5,
    custom_objects=m5_custom_objects_bigquant_run,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

    Epoch 1/5
3061/3061 - 14s - loss: 0.9903 - mse: 0.9903
Epoch 2/5
3061/3061 - 13s - loss: 0.9891 - mse: 0.9891
Epoch 3/5
3061/3061 - 16s - loss: 0.9888 - mse: 0.9888
Epoch 4/5
3061/3061 - 13s - loss: 0.9886 - mse: 0.9886
Epoch 5/5
3061/3061 - 10s - loss: 0.9885 - mse: 0.9885
    In [6]:
    m5.data.read()
    Out[6]:
    {'model_graph': 'backend: tensorflow\nclass_name: Functional\nconfig:\n  input_layers:\n  - - L0\n    - 0\n    - 0\n  layers:\n  - class_name: InputLayer\n    config:\n      batch_input_shape: !!python/tuple\n      - null\n      - 7\n      dtype: float32\n      name: L0\n      ragged: false\n      sparse: false\n    inbound_nodes: []\n    name: L0\n  - class_name: Dense\n    config:\n      activation: relu\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      dtype: float32\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: dense\n      trainable: true\n      units: 256\n      use_bias: true\n    inbound_nodes:\n    - - - L0\n        - 0\n        - 0\n        - {}\n    name: dense\n  - class_name: Dropout\n    config:\n      dtype: float32\n      name: dropout\n      noise_shape: null\n      rate: 0.1\n      seed: null\n      trainable: true\n    inbound_nodes:\n    - - - dense\n        - 0\n        - 0\n        - {}\n    name: dropout\n  - class_name: Dense\n    config:\n      activation: relu\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      dtype: float32\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: dense_1\n      trainable: true\n      units: 128\n      use_bias: true\n    inbound_nodes:\n    - - - dropout\n        - 0\n        - 0\n        - {}\n    name: dense_1\n  - class_name: Dropout\n    config:\n      dtype: float32\n      name: dropout_1\n      noise_shape: null\n      rate: 0.1\n      seed: null\n      trainable: true\n    inbound_nodes:\n    - - - dense_1\n        - 0\n        - 0\n        - {}\n    name: dropout_1\n  - class_name: Dense\n    config:\n      activation: linear\n      activity_regularizer: null\n      bias_constraint: null\n      bias_initializer:\n        class_name: Zeros\n        config: {}\n      bias_regularizer: null\n      dtype: float32\n      kernel_constraint: null\n      kernel_initializer:\n        class_name: GlorotUniform\n        config:\n          seed: null\n      kernel_regularizer: null\n      name: dense_2\n      trainable: true\n      units: 1\n      use_bias: true\n    inbound_nodes:\n    - - - dropout_1\n        - 0\n        - 0\n        - {}\n    name: dense_2\n  name: BigQuantDL\n  output_layers:\n  - - dense_2\n    - 0\n    - 0\nkeras_version: 2.4.0\n',
 'model_weights': [array([[-0.154027  ,  0.00604708,  0.08911502, ...,  0.12364112,
           0.02252254,  0.00457029],
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         [-1.95365369e-01],
         [ 8.46237764e-02],
         [-8.88548568e-02],
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         [ 5.85992029e-03],
         [ 6.69707544e-04],
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         [ 3.59975919e-02],
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         [ 5.75502496e-03],
         [ 1.07736088e-01],
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         [ 8.55320245e-02],
         [ 9.97826830e-02],
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         [ 1.90752950e-02],
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         [ 3.79879139e-02],
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         [ 6.41700104e-02],
         [ 5.08924527e-03],
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         [-4.90439013e-02],
         [ 7.02767540e-03]], dtype=float32),
  array([-0.0331731], dtype=float32)],
 'history': {'loss': [0.9903290867805481,
   0.9891356825828552,
   0.9888054132461548,
   0.9886475205421448,
   0.9885059595108032],
  'mse': [0.9903290867805481,
   0.9891356825828552,
   0.9888054132461548,
   0.9886475205421448,
   0.9885059595108032]},
 'custom_objects': {}}
    In [5]:
    m5.plot_result()
    In [ ]: