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# 本代码由可视化策略环境自动生成 2022年5月7日 10:24
# 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。


def timex(df,s,x):
    return s * x
def sma(df,S,N,M=1):#1)高效的方法
    #return pd.Series(S).ewm(alpha=M/N,adjust=True).mean().values
    return pd.Series(S).ewm(com=N-M,adjust=True).mean().values
m2_user_functions_bigquant_run = {
    'timex' : timex,
    'sma' : sma
}

def timex(df,s,x):
    return s * x
def sma(df,S,N,M=1):#1)高效的方法
    #return pd.Series(S).ewm(alpha=M/N,adjust=True).mean().values
    return pd.Series(S).ewm(com=N-M,adjust=True).mean().values
m5_user_functions_bigquant_run = {
    'timex' : timex,
    'sma' : sma
}


m1 = 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
adjust_factor_0
timex = timex(close_0,open_0)
MA5 = mean(close_0, 5)
sma=sma(close_0/adjust_factor_0,20,1)
close_0/adjust_factor_0"""
)

m4 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-05-06',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

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

m2 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m2_user_functions_bigquant_run
)

m9 = M.filter.v3(
    input_data=m2.data,
    expr='instrument==\'000001.SZA\'',
    output_left_data=False
)

m8 = 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
adjust_factor_0
timex = timex(close_0,open_0)
MA5 = mean(close_0, 5)
sma=sma(close_0/adjust_factor_0,20,1)
close_0/adjust_factor_0"""
)

m10 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-05-06',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m10.data,
    features=m8.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m6 = M.filter.v3(
    input_data=m7.data,
    expr='instrument==\'000001.SZA\'',
    output_left_data=False
)

m5 = M.derived_feature_extractor.v3(
    input_data=m6.data,
    features=m8.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m5_user_functions_bigquant_run
)

[2022-05-07 10:15:26.278686] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-07 10:15:26.290882] INFO: moduleinvoker: 命中缓存

[2022-05-07 10:15:26.292489] INFO: moduleinvoker: input_features.v1 运行完成[0.013823s].

[2022-05-07 10:15:26.297080] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-07 10:15:26.304251] INFO: moduleinvoker: 命中缓存

[2022-05-07 10:15:26.305545] INFO: moduleinvoker: instruments.v2 运行完成[0.008466s].

[2022-05-07 10:15:26.320642] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-07 10:15:26.332933] INFO: moduleinvoker: 命中缓存

[2022-05-07 10:15:26.334306] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.013673s].

[2022-05-07 10:15:26.341908] INFO: moduleinvoker: filter.v3 开始运行..

[2022-05-07 10:15:26.350882] INFO: moduleinvoker: 命中缓存

[2022-05-07 10:15:26.352153] INFO: moduleinvoker: filter.v3 运行完成[0.010244s].

[2022-05-07 10:15:26.359991] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-07 10:15:26.368305] INFO: moduleinvoker: 命中缓存

[2022-05-07 10:15:26.369551] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009562s].