复制链接

克隆策略

In [4]:

# 本代码由可视化策略环境自动生成 2022年9月26日 15:16
# 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/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 <https://bigquant.com/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, -2) / shift(open, -1)-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))

# 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=False
)

m21 = M.input_features.v1(
    features="""cond1=sum(price_limit_status_0==3,10)>1  #70：统计80天内 涨停板的次数大于5
"""
)

m20 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m21.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m22 = M.derived_feature_extractor.v3(
    input_data=m20.data,
    features=m21.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m23 = M.filter.v3(
    input_data=m22.data,
    expr='cond1',
    output_left_data=False
)

m29 = M.chinaa_stock_filter.v1(
    input_data=m23.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m31 = M.select_columns.v3(
    input_ds=m29.data,
    columns='date,instrument',
    reverse_select=False
)

m35 = M.join.v3(
    data1=m31.data,
    data2=m2.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.standardlize.v8(
    input_1=m35.data,
    columns_input='label'
)

[2022-09-26 15:13:23.402409] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-26 15:13:23.440013] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.442377] INFO: moduleinvoker: instruments.v2 运行完成[0.039973s].

[2022-09-26 15:13:23.453156] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-09-26 15:13:23.465961] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.467663] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.014517s].

[2022-09-26 15:13:23.499042] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-26 15:13:23.530621] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.533093] INFO: moduleinvoker: input_features.v1 运行完成[0.034058s].

[2022-09-26 15:13:23.552854] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-26 15:13:23.561572] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.563832] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011028s].

[2022-09-26 15:13:23.589860] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-26 15:13:23.598360] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.600586] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010741s].

[2022-09-26 15:13:23.613264] INFO: moduleinvoker: filter.v3 开始运行..

[2022-09-26 15:13:23.621294] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.623567] INFO: moduleinvoker: filter.v3 运行完成[0.010308s].

[2022-09-26 15:13:23.633172] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-09-26 15:13:23.648174] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.650390] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.017221s].

[2022-09-26 15:13:23.667598] INFO: moduleinvoker: select_columns.v3 开始运行..

[2022-09-26 15:13:23.675193] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.676781] INFO: moduleinvoker: select_columns.v3 运行完成[0.009195s].

[2022-09-26 15:13:23.684876] INFO: moduleinvoker: join.v3 开始运行..

[2022-09-26 15:13:23.695457] INFO: moduleinvoker: 命中缓存

[2022-09-26 15:13:23.697590] INFO: moduleinvoker: join.v3 运行完成[0.012717s].

[2022-09-26 15:13:23.708135] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-09-26 15:13:26.520637] ERROR: moduleinvoker: module name: standardlize, module version: v8, trackeback: ValueError: cannot reindex from a duplicate axis

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-fe30abde0004> in <module>
     88 )
     89 
---> 90 m13 = M.standardlize.v8(
     91     input_1=m35.data,
     92     columns_input='label'

ValueError: cannot reindex from a duplicate axis