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#号开始的表示注释\n# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html\n# 添加benchmark_前缀，可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_\n\n# 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)\nshift(close, -5) / shift(open, -1)\n\n# 极值处理：用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类，这里使用20个分类\nall_wbins(label, 20)\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, 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# 本代码由可视化策略环境自动生成 2022年7月25日 17:09
# 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。


m1 = M.instruments.v2(
    start_date='2013-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='600004.SHA',
    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/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={}
)

m3 = 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
"""
)

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

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={}
)

m6 = M.join.v3(
    data1=m2.data,
    data2=m5.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m10 = M.dropnan.v2(
    input_data=m6.data
)

m11 = M.stock_ranker_train.v6(
    training_ds=m10.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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m13 = M.instruments.v2(
    start_date='2013-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list="""600004.SHA
""",
    max_count=0
)

m14 = M.general_feature_extractor.v7(
    instruments=m13.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m15 = M.derived_feature_extractor.v3(
    input_data=m14.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m16 = M.dropnan.v2(
    input_data=m15.data
)

m17 = M.stock_ranker_predict.v5(
    model=m11.model,
    data=m16.data,
    m_lazy_run=False
)

[2022-07-25 17:07:14.000144] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-25 17:07:14.048764] INFO: moduleinvoker: 命中缓存

[2022-07-25 17:07:14.056678] INFO: moduleinvoker: instruments.v2 运行完成[0.05657s].

[2022-07-25 17:07:15.443223] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-07-25 17:07:15.453939] INFO: moduleinvoker: 命中缓存

[2022-07-25 17:07:15.458240] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.015026s].

[2022-07-25 17:07:15.691338] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-25 17:07:15.704301] INFO: moduleinvoker: 命中缓存

[2022-07-25 17:07:15.708506] INFO: moduleinvoker: input_features.v1 运行完成[0.017198s].

[2022-07-25 17:07:16.406887] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-25 17:07:16.472540] INFO: moduleinvoker: 命中缓存

[2022-07-25 17:07:16.477293] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.070413s].

[2022-07-25 17:07:17.412704] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-25 17:07:17.427069] INFO: moduleinvoker: 命中缓存

[2022-07-25 17:07:17.430967] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.018261s].

[2022-07-25 17:07:17.845525] INFO: moduleinvoker: join.v3 开始运行..

[2022-07-25 17:07:29.066335] INFO: join: /y_2012, 行数=0/61, 耗时=1.014686s

[2022-07-25 17:07:30.186949] INFO: join: /y_2013, 行数=238/238, 耗时=1.117723s

[2022-07-25 17:07:30.944335] INFO: join: /y_2014, 行数=240/245, 耗时=0.754653s

[2022-07-25 17:07:32.474205] INFO: join: 最终行数: 478

[2022-07-25 17:07:32.826530] INFO: moduleinvoker: join.v3 运行完成[14.980996s].

[2022-07-25 17:07:33.400101] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-07-25 17:07:37.774052] INFO: dropnan: /y_2012, 0/0

[2022-07-25 17:07:38.250131] INFO: dropnan: /y_2013, 238/238

[2022-07-25 17:07:38.885412] INFO: dropnan: /y_2014, 240/240

[2022-07-25 17:07:39.797522] INFO: dropnan: 行数: 478/478

[2022-07-25 17:07:39.803436] INFO: moduleinvoker: dropnan.v2 运行完成[6.403346s].

[2022-07-25 17:07:40.332846] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-07-25 17:07:43.641826] INFO: StockRanker: 特征预处理 ..

[2022-07-25 17:07:44.214812] INFO: StockRanker: prepare data: training ..

[2022-07-25 17:07:45.378135] INFO: StockRanker训练: 3c586710 准备训练: 478 行数

[2022-07-25 17:07:45.380682] INFO: StockRanker训练: AI模型训练，将在478*13=0.62万数据上对模型训练进行20轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-07-25 17:07:46.019528] INFO: StockRanker训练: 正在训练 ..

[2022-07-25 17:07:46.107500] INFO: StockRanker训练: 任务状态: Pending

[2022-07-25 17:07:56.151542] INFO: StockRanker训练: 任务状态: Running

[2022-07-25 17:08:56.452666] INFO: StockRanker训练: 任务状态: Succeeded

[2022-07-25 17:08:56.479928] ERROR: moduleinvoker: module name: stock_ranker_train, module version: v6, trackeback: Exception: 模型训练失败：可能导致错误的原因是训练数据问题，请检查训练数据, err_code=1 (3c5867100bf911ed93a1ea1ef7738718)

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-92-2692f2205050> in <module>
     89 )
     90 
---> 91 m11 = M.stock_ranker_train.v6(
     92     training_ds=m10.data,
     93     features=m3.data,

Exception: 模型训练失败：可能导致错误的原因是训练数据问题，请检查训练数据, err_code=1 (3c5867100bf911ed93a1ea1ef7738718)