克隆策略

In [18]:

# 本代码由可视化策略环境自动生成 2021年8月12日 16:55
# 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='2016-01-01',
    end_date='2018-01-01',
    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, -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="""# #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
(close_0-low_0)/(high_0-close_0)
std(amount_0,10)
rank_pb_lf_0
rank_beta_industry1_10_0
rank_market_cap_0
(high_0-low_0)/close_1
std(mf_net_amount_l_0,20)
-1*rank(covariance(rank(high_0),rank(volume_0),5))
sum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,6)
-1*correlation(close_0-shift(close_0,1), amount_0/avg_amount_20,20)
-1*fs_total_liability_0/fs_total_operating_costs_0
sum(max(0,high_0-shift((high_0+low_0+close_0)/3,1)),26)/sum(max(0,delay((high_0+low_0+close_0)/3,1) - low_0), 26)*100
fs_net_profit_margin_0
close_0-shift(close_0, 5)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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
)

m5 = M.use_datasource.v1(
    datasource_id='dividend_send_CN_STOCK_A',
    start_date='2016-01-01',
    end_date=''
)

m4 = M.join.v3(
    data1=m5.data,
    data2=m7.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.chinaa_stock_filter.v1(
    input_data=m4.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部', '交通运输', '公用事业', '化工', '商业贸易', '建筑装饰', '房地产', '机械设备', '汽车/交运设备', '纺织服装'],
    st_cond=['正常'],
    delist_cond=['全部'],
    output_left_data=False
)

m19 = M.filter.v3(
    input_data=m11.data,
    expr='cash_dividend_ratio>10',
    output_left_data=False
)

[2021-08-12 16:47:53.441957] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-12 16:47:53.449161] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.453331] INFO: moduleinvoker: instruments.v2 运行完成[0.011349s].

[2021-08-12 16:47:53.460886] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-08-12 16:47:53.469022] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.471356] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.010489s].

[2021-08-12 16:47:53.474674] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-12 16:47:53.482095] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.488240] INFO: moduleinvoker: input_features.v1 运行完成[0.013574s].

[2021-08-12 16:47:53.511274] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-12 16:47:53.518074] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.520727] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009463s].

[2021-08-12 16:47:53.532274] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-12 16:47:53.539217] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.543731] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011462s].

[2021-08-12 16:47:53.551482] INFO: moduleinvoker: join.v3 开始运行..

[2021-08-12 16:47:53.559805] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.562364] INFO: moduleinvoker: join.v3 运行完成[0.010912s].

[2021-08-12 16:47:53.566999] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-08-12 16:47:53.573210] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.576126] INFO: moduleinvoker: use_datasource.v1 运行完成[0.009135s].

[2021-08-12 16:47:53.581154] INFO: moduleinvoker: join.v3 开始运行..

[2021-08-12 16:47:53.587140] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.589230] INFO: moduleinvoker: join.v3 运行完成[0.008086s].

[2021-08-12 16:47:53.594142] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-08-12 16:47:53.599848] INFO: moduleinvoker: 命中缓存

[2021-08-12 16:47:53.631492] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.036936s].

[2021-08-12 16:47:53.640379] INFO: moduleinvoker: filter.v3 开始运行..

[2021-08-12 16:47:53.663848] INFO: filter: 使用表达式 cash_dividend_ratio>10 过滤

[2021-08-12 16:47:54.011206] ERROR: moduleinvoker: module name: filter, module version: v3, trackeback: ValueError: unknown type object

During handling of the above exception, another exception occurred:

TypeError: '>' not supported between instances of 'str' and 'int'

During handling of the above exception, another exception occurred:

ValueError: unknown type object

During handling of the above exception, another exception occurred:

TypeError: '>' not supported between instances of 'str' and 'int'

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: unknown type object

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
TypeError: '>' not supported between instances of 'str' and 'int'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
ValueError: unknown type object

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-18-31b8d7421f2d> in <module>
    108 )
    109 
--> 110 m19 = M.filter.v3(
    111     input_data=m11.data,
    112     expr='cash_dividend_ratio>10',

TypeError: '>' not supported between instances of 'str' and 'int'