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# 本代码由可视化策略环境自动生成 2020年3月11日 08:04
# 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='2010-01-01',
    end_date='2015-01-01',
    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/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
)

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
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
"""
)

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=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m5 = M.stock_ranker_train.v6(
    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,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m5.model,
    data=m14.data,
    m_lazy_run=False
)

m4 = M.factor_group__fast_backtest.v1(
    input_1=m8.predictions,
    input_2=m9.data,
    rabalance_period=21,
    long_port_is_big_factor=True,
    buy_commission_rate=0.0003,
    stamp_tax=0.001,
    sell_commission_rate=0.0003,
    rename_factor='position'
)

[2020-03-11 07:56:31.506690] INFO: bigquant: instruments.v2 开始运行..

[2020-03-11 07:56:31.531006] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.532447] INFO: bigquant: instruments.v2 运行完成[0.025745s].

[2020-03-11 07:56:31.534025] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2020-03-11 07:56:31.555568] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.556996] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.02296s].

[2020-03-11 07:56:31.558925] INFO: bigquant: input_features.v1 开始运行..

[2020-03-11 07:56:31.582179] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.583177] INFO: bigquant: input_features.v1 运行完成[0.02425s].

[2020-03-11 07:56:31.606291] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-03-11 07:56:31.631618] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.632913] INFO: bigquant: general_feature_extractor.v7 运行完成[0.026616s].

[2020-03-11 07:56:31.635034] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-03-11 07:56:31.657615] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.658860] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.023819s].

[2020-03-11 07:56:31.660845] INFO: bigquant: join.v3 开始运行..

[2020-03-11 07:56:31.682623] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.684015] INFO: bigquant: join.v3 运行完成[0.02316s].

[2020-03-11 07:56:31.685964] INFO: bigquant: dropnan.v1 开始运行..

[2020-03-11 07:56:31.710659] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.711683] INFO: bigquant: dropnan.v1 运行完成[0.025715s].

[2020-03-11 07:56:31.713382] INFO: bigquant: stock_ranker_train.v6 开始运行..

[2020-03-11 07:56:31.757566] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.891075] INFO: bigquant: stock_ranker_train.v6 运行完成[0.177669s].

[2020-03-11 07:56:31.893016] INFO: bigquant: instruments.v2 开始运行..

[2020-03-11 07:56:31.914528] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.915588] INFO: bigquant: instruments.v2 运行完成[0.022564s].

[2020-03-11 07:56:31.939190] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-03-11 07:56:31.959993] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.961453] INFO: bigquant: general_feature_extractor.v7 运行完成[0.02225s].

[2020-03-11 07:56:31.963719] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-03-11 07:56:31.984806] INFO: bigquant: 命中缓存

[2020-03-11 07:56:31.987821] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.024083s].

[2020-03-11 07:56:31.989754] INFO: bigquant: dropnan.v1 开始运行..

[2020-03-11 07:56:32.009396] INFO: bigquant: 命中缓存

[2020-03-11 07:56:32.010577] INFO: bigquant: dropnan.v1 运行完成[0.020819s].

[2020-03-11 07:56:32.012697] INFO: bigquant: stock_ranker_predict.v5 开始运行..

[2020-03-11 07:56:32.034245] INFO: bigquant: 命中缓存

[2020-03-11 07:56:32.035704] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.022993s].

[2020-03-11 07:56:32.039481] INFO: bigquant: factor_group__fast_backtest.v1 开始运行..

[2020-03-11 07:56:32.987833] INFO: bigquant: input_features.v1 开始运行..

[2020-03-11 07:56:33.008746] INFO: bigquant: 命中缓存

[2020-03-11 07:56:33.009945] INFO: bigquant: input_features.v1 运行完成[0.022119s].

[2020-03-11 07:56:33.011259] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-03-11 07:56:33.654239] INFO: derived_feature_extractor: 提取完成 rank_factor=rank(factor), 0.261s

[2020-03-11 07:56:33.659461] INFO: derived_feature_extractor: 提取完成 factor_group = where(rank_factor > 0.8,4,where(rank_factor > 0.6,3,where(rank_factor > 0.4,2,where(rank_factor > 0.2,1,0)))), 0.004s

[2020-03-11 07:56:33.987156] INFO: derived_feature_extractor: /data, 1202058

[2020-03-11 07:56:34.707135] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.695853s].

[2020-03-11 07:56:34.709150] INFO: bigquant: input_features.v1 开始运行..

[2020-03-11 07:56:34.729578] INFO: bigquant: 命中缓存

[2020-03-11 07:56:34.730942] INFO: bigquant: input_features.v1 运行完成[0.021787s].

[2020-03-11 07:56:34.753010] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-03-11 07:56:34.774059] INFO: bigquant: 命中缓存

[2020-03-11 07:56:34.775645] INFO: bigquant: general_feature_extractor.v7 运行完成[0.022639s].

[2020-03-11 07:56:34.778139] INFO: bigquant: join.v3 开始运行..

[2020-03-11 07:56:36.090430] INFO: join: /y_2015, 行数=565146/569698, 耗时=0.832776s

[2020-03-11 07:56:36.978941] INFO: join: /y_2016, 行数=636912/641546, 耗时=0.870611s

[2020-03-11 07:56:37.321589] INFO: join: 最终行数: 1202058

[2020-03-11 07:56:37.323829] INFO: bigquant: join.v3 运行完成[2.545685s].

[2020-03-11 07:56:43.465499] INFO: bigquant: factor_group__fast_backtest.v1 运行完成[11.426021s].

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