{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-322:features","from_node_id":"-331:data"},{"to_node_id":"-322:instruments","from_node_id":"-312:data"},{"to_node_id":"-1136:input_data","from_node_id":"-322:data"},{"to_node_id":"-322:user_functions","from_node_id":"-932:functions"},{"to_node_id":"-932:input_functions","from_node_id":"-937:functions"},{"to_node_id":"-937:input_functions","from_node_id":"-1111:functions"},{"to_node_id":"-1111:input_functions","from_node_id":"-1116:functions"},{"to_node_id":"-1116:input_functions","from_node_id":"-1121:functions"},{"to_node_id":"-1121:input_functions","from_node_id":"-1126:functions"},{"to_node_id":"-1136:features","from_node_id":"-1131:data"},{"to_node_id":"-1126:input_functions","from_node_id":"-584:functions"}],"nodes":[{"node_id":"-331","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"_c = close\n_o = open\n_ret = ret_sim(_c, _o)\n_rk1 = _ret.loc[93500:100000]\n_rk2 = _ret.loc[110000:113000]\n_rk3 = _ret.loc[143000:145700]\n\n# 1 收益方差\nRVar1 = RVar1(_ret)\nRVar2 = RVar2(_ret)\nRVar3 = (RVar2(_rk1) + RVar2(_rk2) + RVar2(_rk3)) / 3\n\n# 2 收益偏度\nRSkew1 = RSkew1(_ret, RVar1)\nRSkew2 = RSkew2(_ret, RVar2)\nRSkew3 = (RSkew2(_rk1, RVar2) + RSkew2(_rk2, RVar2) + RSkew2(_rk3, RVar2)) / 3\n\n\n# 3 收益峰度\nRKurt1 = RKurt1(_ret, RVar1)\nRKurt2 = RKurt2(_ret, RVar2)\nRKurt3 = (RKurt2(_rk1, RVar2) + RKurt2(_rk2, RVar2) + RKurt2(_rk3, RVar2)) / 3\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-331"}],"output_ports":[{"name":"data","node_id":"-331"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-312","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2014-03-03","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000001.SZA\n000002.SZA\n000005.SZA","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-312"}],"output_ports":[{"name":"data","node_id":"-312"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-322","module_id":"BigQuantSpace.feature_extractor_1m.feature_extractor_1m-v1","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"10","type":"Literal","bound_global_parameter":null},{"name":"workers","value":2,"type":"Literal","bound_global_parameter":null},{"name":"parallel_mode","value":"测试","type":"Literal","bound_global_parameter":null},{"name":"table_1m","value":"level2_bar1m_CN_STOCK_A","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-322"},{"name":"features","node_id":"-322"},{"name":"user_functions","node_id":"-322"}],"output_ports":[{"name":"data","node_id":"-322"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-932","module_id":"BigQuantSpace.feature_extractor_user_function.feature_extractor_user_function-v1","parameters":[{"name":"name","value":"RVar1","type":"Literal","bound_global_parameter":null},{"name":"func","value":"def bigquant_run(df, ret):\n return (ret**2).sum()\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_functions","node_id":"-932"}],"output_ports":[{"name":"functions","node_id":"-932"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-937","module_id":"BigQuantSpace.feature_extractor_user_function.feature_extractor_user_function-v1","parameters":[{"name":"name","value":"RVar2","type":"Literal","bound_global_parameter":null},{"name":"func","value":"def bigquant_run(df, ret):\n return np.power(ret - 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1\n return res\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_functions","node_id":"-584"}],"output_ports":[{"name":"functions","node_id":"-584"}],"cacheable":false,"seq_num":12,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-331' Position='79,-5,200,200'/><node_position Node='-312' Position='-256,-2,200,200'/><node_position Node='-322' Position='64,213,200,200'/><node_position Node='-932' Position='64,-114,200,200'/><node_position Node='-937' Position='65,-189,200,200'/><node_position Node='-1111' Position='66,-264,200,200'/><node_position Node='-1116' Position='69,-342,200,200'/><node_position Node='-1121' Position='56,-442,200,200'/><node_position Node='-1126' Position='50,-517,200,200'/><node_position Node='-1131' Position='394,202,200,200'/><node_position Node='-1136' Position='305,333,200,200'/><node_position Node='-584' Position='48.76287841796875,-603.71337890625,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2021年7月9日15:34
# 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 m12_func_bigquant_run(df, close, op):
    res = close.pct_change()
    res.iloc[0] = close.iloc[0] / op.iloc[0] - 1
    return res

def m9_func_bigquant_run(df, ret, RVar):
    if RVar == 0:
        result = 1
    else:
        result = (np.power(ret - (ret.mean()), 4).sum()) * (240) / np.power(RVar, 2)
    return result

def m8_func_bigquant_run(df, ret, RVar):
    if RVar == 0:
        result = 1
    else:
        result = (np.power(ret, 4).sum()) * (240) / np.power(RVar, 2)
    
    return result

def m7_func_bigquant_run(df, ret, RVar):
    if RVar == 0:
        result = 0
    else:
        result = (np.power(ret - (ret.mean()), 3).sum()) * np.sqrt(240) / np.power(RVar, 1.5)
    return result

def m6_func_bigquant_run(df, ret, RVar):
    if RVar == 0:
        result = 0
    else:
        result = (np.power(ret, 3).sum()) * np.sqrt(240) / np.power(RVar, 1.5)
    return result

def m5_func_bigquant_run(df, ret):
    return np.power(ret - (ret.mean()), 2).sum()

def m4_func_bigquant_run(df, ret):
    return (ret**2).sum()

def nanmean(df, x_name, N):
    return df.groupby('instrument')[x_name.name].apply(lambda x: x.rolling(N,1).mean())

m11_user_functions_bigquant_run = {
    'nanmean': nanmean
}

m1 = M.input_features.v1(
    features="""_c = close
_o = open
_ret = ret_sim(_c, _o)
_rk1 = _ret.loc[93500:100000]
_rk2 = _ret.loc[110000:113000]
_rk3 = _ret.loc[143000:145700]

# 1 收益方差
RVar1 = RVar1(_ret)
RVar2 = RVar2(_ret)
RVar3 = (RVar2(_rk1) + RVar2(_rk2) + RVar2(_rk3)) / 3

# 2 收益偏度
RSkew1 = RSkew1(_ret, RVar1)
RSkew2 = RSkew2(_ret, RVar2)
RSkew3 = (RSkew2(_rk1, RVar2) + RSkew2(_rk2, RVar2) + RSkew2(_rk3, RVar2)) / 3


# 3 收益峰度
RKurt1 = RKurt1(_ret, RVar1)
RKurt2 = RKurt2(_ret, RVar2)
RKurt3 = (RKurt2(_rk1, RVar2) + RKurt2(_rk2, RVar2) + RKurt2(_rk3, RVar2)) / 3

"""
)

m2 = M.instruments.v2(
    start_date='2014-03-03',
    end_date='2021-12-31',
    market='CN_STOCK_A',
    instrument_list="""000001.SZA
000002.SZA
000005.SZA""",
    max_count=0
)

m10 = M.input_features.v1(
    features="""RVar1_mean = nanmean(RVar1, 5)
RVar2_mean = nanmean(RVar2, 5)
RVar3_mean = nanmean(RVar3, 5)

RSkew1_mean = nanmean(RSkew1, 5)
RSkew2_mean = nanmean(RSkew2, 5)
RSkew3_mean = nanmean(RSkew3, 5)

RKurt1_mean = nanmean(RKurt1, 5)
RKurt2_mean = nanmean(RKurt2, 5)
RKurt3_mean = nanmean(RKurt3, 5)"""
)

m12 = M.feature_extractor_user_function.v1(
    name='ret_sim',
    func=m12_func_bigquant_run
)

m9 = M.feature_extractor_user_function.v1(
    input_functions=m12.functions,
    name='RKurt2',
    func=m9_func_bigquant_run
)

m8 = M.feature_extractor_user_function.v1(
    input_functions=m9.functions,
    name='RKurt1',
    func=m8_func_bigquant_run
)

m7 = M.feature_extractor_user_function.v1(
    input_functions=m8.functions,
    name='RSkew2',
    func=m7_func_bigquant_run
)

m6 = M.feature_extractor_user_function.v1(
    input_functions=m7.functions,
    name='RSkew1',
    func=m6_func_bigquant_run
)

m5 = M.feature_extractor_user_function.v1(
    input_functions=m6.functions,
    name='RVar2',
    func=m5_func_bigquant_run
)

m4 = M.feature_extractor_user_function.v1(
    input_functions=m5.functions,
    name='RVar1',
    func=m4_func_bigquant_run
)

m3 = M.feature_extractor_1m.v1(
    instruments=m2.data,
    features=m1.data,
    user_functions=m4.functions,
    start_date='',
    end_date='',
    before_start_days=10,
    workers=2,
    parallel_mode='测试',
    table_1m='level2_bar1m_CN_STOCK_A'
)

m11 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m10.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m11_user_functions_bigquant_run
)

[2021-07-09 10:57:16.569089] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-07-09 10:57:16.774595] INFO: moduleinvoker: input_features.v1 运行完成[0.205508s].

[2021-07-09 10:57:16.776877] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-09 10:57:16.800720] INFO: moduleinvoker: instruments.v2 运行完成[0.023844s].

[2021-07-09 10:57:16.802511] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-07-09 10:57:16.808254] INFO: moduleinvoker: 命中缓存

[2021-07-09 10:57:16.809338] INFO: moduleinvoker: input_features.v1 运行完成[0.006828s].

[2021-07-09 10:57:16.811599] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[0.000106s].

[2021-07-09 10:57:16.813758] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[6.7e-05s].

[2021-07-09 10:57:16.815927] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[6.9e-05s].

[2021-07-09 10:57:16.818142] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[7.1e-05s].

[2021-07-09 10:57:16.820311] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[6.5e-05s].

[2021-07-09 10:57:16.822471] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[6.5e-05s].

[2021-07-09 10:57:16.824640] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[6.4e-05s].

[2021-07-09 10:57:16.831492] INFO: moduleinvoker: feature_extractor_1m.v1 开始运行..

[2021-07-09 10:57:16.851123] INFO: 高频特征抽取-分钟到日频: 测试模式运行, ['000002.SZA', '000001.SZA', '000005.SZA']

[2021-07-09 10:57:16.852085] INFO: fe1m_utils: extract chunk 3 instruments, 15 features ..

[2021-07-09 10:57:16.853118] INFO: fe1m_utils: extract chunk 3 instruments, n_jobs=18=(12+24)/2, 并行=False ..

[2021-07-09 10:57:24.933125] INFO: fe1m_utils: extracted chunk 3/3 instruments, (729, 11).

[2021-07-09 10:57:25.031520] INFO: 高频特征抽取-分钟到日频: extracted 3/3 instruments, (729, 11)

[2021-07-09 10:57:25.037196] INFO: moduleinvoker: feature_extractor_1m.v1 运行完成[8.205701s].

[2021-07-09 10:57:25.039800] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-09 10:57:25.081859] INFO: derived_feature_extractor: 提取完成 RVar1_mean = nanmean(RVar1, 5), 0.004s

[2021-07-09 10:57:25.086389] INFO: derived_feature_extractor: 提取完成 RVar2_mean = nanmean(RVar2, 5), 0.003s

[2021-07-09 10:57:25.090086] INFO: derived_feature_extractor: 提取完成 RVar3_mean = nanmean(RVar3, 5), 0.003s

[2021-07-09 10:57:25.093782] INFO: derived_feature_extractor: 提取完成 RSkew1_mean = nanmean(RSkew1, 5), 0.003s

[2021-07-09 10:57:25.097374] INFO: derived_feature_extractor: 提取完成 RSkew2_mean = nanmean(RSkew2, 5), 0.003s

[2021-07-09 10:57:25.101134] INFO: derived_feature_extractor: 提取完成 RSkew3_mean = nanmean(RSkew3, 5), 0.003s

[2021-07-09 10:57:25.104807] INFO: derived_feature_extractor: 提取完成 RKurt1_mean = nanmean(RKurt1, 5), 0.003s

[2021-07-09 10:57:25.108544] INFO: derived_feature_extractor: 提取完成 RKurt2_mean = nanmean(RKurt2, 5), 0.003s

[2021-07-09 10:57:25.112755] INFO: derived_feature_extractor: 提取完成 RKurt3_mean = nanmean(RKurt3, 5), 0.004s

[2021-07-09 10:57:25.137553] INFO: derived_feature_extractor: /data, 729

[2021-07-09 10:57:25.198219] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.158413s].