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Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='223.72616577148438,-47.70755577087402,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='37.47755432128906,199.96824645996094,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='681,-104,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-29' Position='381,188,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-35' Position='373,280.5859680175781,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='636,705,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='265,383,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='761.0623168945312,843.300048828125,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-70' Position='1078,236,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-76' Position='1081,327,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='413,581,200,200'/><node_position Node='-86' Position='1078,418,200,200'/><node_position Node='-427' Position='333,478,200,200'/><node_position Node='-106' Position='35.587493896484375,66.89046478271484,200,200'/><node_position Node='-122' Position='637.3322067260742,1062.3298950195312,200,200'/><node_position Node='-182' Position='626.947509765625,370.5768127441406,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [2]:
    # 本代码由可视化策略环境自动生成 2023年1月13日 09:58
# 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。


m1 = M.instruments.v2(
    start_date='2022-01-01',
    end_date='2022-11-1',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m16 = M.chinaa_stock_filter.v1(
    input_data=m1.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['全部'],
    delist_cond=['全部'],
    output_left_data=False
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m16.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 {{web_host_url}}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 <{{web_host_url}}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)

# 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))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)

# 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.HIX',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
# 
(close_0-open_0)/(high_0-low_0)
(close_0*volume_0)/(close_1*volume_1)
amount_0/volume_0
price_limit_status_0
"""
)

m4 = M.general_feature_extractor.v6(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m5 = M.derived_feature_extractor.v2(
    input_data=m4.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument'
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m5.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m12 = M.standardlize.v12(
    input_1=m7.data,
    standard_func='ZScoreNorm',
    columns_input='(close_0-close_1)/(volume_0-volume_1)'
)

m13 = M.dropnan.v1(
    input_data=m12.data
)

m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2022-11-1'),
    end_date=T.live_run_param('trading_date', '2022-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m10 = M.general_feature_extractor.v6(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m11 = M.derived_feature_extractor.v2(
    input_data=m10.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument'
)

m14 = M.dropnan.v1(
    input_data=m11.data
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m15 = M.strategy_top10_position_analysis.v1(

)

m17 = M.datahub_usertask2.v3(
    task_name='当前文件名称',
    description='当前文件名称',
    run_time='15:08',
    day_of_week='每天',
    day_of_month='每天',
    not_run_weekly=False,
    update_mode=False
)

    ---------------------------------------------------------------------------
HDF5ExtError                              Traceback (most recent call last)
<ipython-input-2-10a6208ac5aa> in <module>
     11 )
     12 
---> 13 m16 = M.chinaa_stock_filter.v1(
     14     input_data=m1.data,
     15     index_constituent_cond=['全部'],

HDF5ExtError: HDF5 error back trace

  File "H5F.c", line 509, in H5Fopen
    unable to open file
  File "H5Fint.c", line 1400, in H5F__open
    unable to open file
  File "H5Fint.c", line 1700, in H5F_open
    unable to read superblock
  File "H5Fsuper.c", line 411, in H5F__super_read
    file signature not found

End of HDF5 error back trace

Unable to open/create file '/tmp/5b77346da21d43868e70e83192a37ff5T.h5'