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# 本代码由可视化策略环境自动生成 2021年9月12日 09:59
# 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='2020-01-01',
    end_date='2021-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=100,
    m_cached=False
)

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/develop/datasource/deprecated/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/develop/bigexpr/usage.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)

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

m3 = M.input_features.v1(
    features="""mf_net_amount_0/amount_0+1
""",
    m_cached=False
)

m4 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90,
    m_cached=False
)

m5 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={},
    m_cached=False
)

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

m8 = M.fillnan.v1(
    input_data=m7.data,
    features=m3.data,
    fill_value='mean'
)

m6 = M.stock_ranker_train.v5(
    training_ds=m8.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
)

[2021-09-12 09:58:00.698398] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-09-12 09:58:00.901607] INFO: moduleinvoker: instruments.v2 运行完成[0.203214s].

[2021-09-12 09:58:00.912675] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-09-12 09:58:02.502944] INFO: 自动标注(股票): 加载历史数据: 23798 行

[2021-09-12 09:58:02.505312] INFO: 自动标注(股票): 开始标注 ..

[2021-09-12 09:58:02.647368] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[1.734688s].

[2021-09-12 09:58:02.653000] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-09-12 09:58:02.673038] INFO: moduleinvoker: input_features.v1 运行完成[0.020036s].

[2021-09-12 09:58:02.685403] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-09-12 09:58:03.280637] INFO: 基础特征抽取: 年份 2019, 特征行数=5961

[2021-09-12 09:58:03.957257] INFO: 基础特征抽取: 年份 2020, 特征行数=23798

[2021-09-12 09:58:04.358430] INFO: 基础特征抽取: 年份 2021, 特征行数=0

[2021-09-12 09:58:04.398907] INFO: 基础特征抽取: 总行数: 29759

[2021-09-12 09:58:04.400455] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[1.715057s].

[2021-09-12 09:58:04.408116] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-09-12 09:58:04.564239] INFO: derived_feature_extractor: 提取完成 mf_net_amount_0/amount_0+1, 0.002s

[2021-09-12 09:58:04.644335] INFO: derived_feature_extractor: /y_2019, 5961

[2021-09-12 09:58:04.757068] INFO: derived_feature_extractor: /y_2020, 23798

[2021-09-12 09:58:04.808305] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.400174s].

[2021-09-12 09:58:04.817506] INFO: moduleinvoker: join.v3 开始运行..

[2021-09-12 09:58:05.023530] INFO: join: /y_2019, 行数=0/5961, 耗时=0.064093s

[2021-09-12 09:58:05.139071] INFO: join: /y_2020, 行数=23490/23798, 耗时=0.113886s

[2021-09-12 09:58:05.175037] INFO: join: 最终行数: 23490

[2021-09-12 09:58:05.182536] INFO: moduleinvoker: join.v3 运行完成[0.365024s].

[2021-09-12 09:58:05.191605] INFO: moduleinvoker: fillnan.v1 开始运行..

[2021-09-12 09:58:05.371134] INFO: moduleinvoker: fillnan.v1 运行完成[0.179511s].

[2021-09-12 09:58:05.380936] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-09-12 09:58:05.491037] INFO: StockRanker: 特征预处理 ..

[2021-09-12 09:58:05.521082] INFO: StockRanker: prepare data: training ..

[2021-09-12 09:58:05.530731] INFO: StockRanker: sort ..

[2021-09-12 09:58:05.693098] ERROR: moduleinvoker: module name: cached, module version: v2, trackeback: struct.error: required argument is not an integer

[2021-09-12 09:58:05.697130] ERROR: moduleinvoker: module name: stock_ranker_train, module version: v5, trackeback: struct.error: required argument is not an integer

---------------------------------------------------------------------------
error                                     Traceback (most recent call last)
<ipython-input-2-30b77d970e77> in <module>
     79 )
     80 
---> 81 m6 = M.stock_ranker_train.v5(
     82     training_ds=m8.data,
     83     features=m3.data,

error: required argument is not an integer