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# 本代码由可视化策略环境自动生成 2022年6月18日 15:09
# 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。


m5 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m12 = M.advanced_auto_labeler.v2(
    instruments=m5.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, -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.SHA',
    drop_na_label=True,
    cast_label_int=True,
    user_functions={}
)

m4 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
"""
)

m8 = M.general_feature_extractor.v7(
    instruments=m5.data,
    features=m4.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m9 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.join.v3(
    data1=m12.data,
    data2=m9.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m2 = M.dropnan.v2(
    input_data=m10.data
)

m1 = M.stockpool_select.v6(
    input_1=m2.data,
    self_instruments=[],
    input_concepts=[],
    input_industrys=[],
    input_indexs=[],
    input_st='过滤',
    input_suspend='过滤'
)

m11 = M.stock_ranker_train.v6(
    training_ds=m2.data,
    features=m4.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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m7 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

[2022-06-18 14:59:17.316706] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-06-18 14:59:17.324561] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.326528] INFO: moduleinvoker: instruments.v2 运行完成[0.009829s].

[2022-06-18 14:59:17.335187] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-06-18 14:59:17.341690] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.343454] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.008271s].

[2022-06-18 14:59:17.347559] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-18 14:59:17.353477] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.355161] INFO: moduleinvoker: input_features.v1 运行完成[0.007606s].

[2022-06-18 14:59:17.368166] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-06-18 14:59:17.374367] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.376924] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008741s].

[2022-06-18 14:59:17.385566] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-18 14:59:17.391414] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.393048] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.007487s].

[2022-06-18 14:59:17.400499] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-18 14:59:17.407049] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.409027] INFO: moduleinvoker: join.v3 运行完成[0.008526s].

[2022-06-18 14:59:17.416607] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-06-18 14:59:17.422375] INFO: moduleinvoker: 命中缓存

[2022-06-18 14:59:17.424229] INFO: moduleinvoker: dropnan.v2 运行完成[0.007626s].

[2022-06-18 14:59:17.430383] INFO: moduleinvoker: stockpool_select.v6 开始运行..

[2022-06-18 14:59:21.196642] ERROR: moduleinvoker: module name: stockpool_select, module version: v6, trackeback: KeyError: 'concept'

The above exception was the direct cause of the following exception:

KeyError: 'concept'

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
KeyError: 'concept'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-150-cca3002ffef6> in <module>
     89 )
     90 
---> 91 m1 = M.stockpool_select.v6(
     92     input_1=m2.data,
     93     self_instruments=[],

KeyError: 'concept'