复制链接
克隆策略
In [20]:
T=DataSource("basic_info_index_CN_STOCK_A").read()
T2=T[T['name']=='沪深300']
T2
Out[20]:
instrument name
429 000300.HIX 沪深300
11613 399300.ZIX 沪深300

    {"description":"实验创建于2022/10/9","graph":{"edges":[{"to_node_id":"-313:input_data","from_node_id":"-5:data"},{"to_node_id":"-5:instruments","from_node_id":"-12:data"},{"to_node_id":"-5:features","from_node_id":"-20:data"},{"to_node_id":"-313:features","from_node_id":"-20:data"}],"nodes":[{"node_id":"-5","module_id":"BigQuantSpace.use_datasource.use_datasource-v2","parameters":[{"name":"datasource_id","value":"bar1d_index_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"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":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-5"},{"name":"features","node_id":"-5"}],"output_ports":[{"name":"data","node_id":"-5"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-12","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2005-03-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2017-04-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-12"}],"output_ports":[{"name":"data","node_id":"-12"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-20","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 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\nclose\nbeta=ols('coefficient', low, high, 18) \n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-20"}],"output_ports":[{"name":"data","node_id":"-20"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-313","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-313"},{"name":"features","node_id":"-313"}],"output_ports":[{"name":"data","node_id":"-313"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-5' Position='150,173,200,200'/><node_position Node='-12' Position='8,68,200,200'/><node_position Node='-20' Position='326.179443359375,83,200,200'/><node_position Node='-313' Position='183.17941284179688,310.179443359375,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [106]:
    # 本代码由可视化策略环境自动生成 2022年10月9日 11:03
# 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。


m2 = M.instruments.v2(
    start_date='2005-03-01',
    end_date='2017-04-01',
    market='CN_STOCK_A',
    instrument_list='000300.HIX',
    max_count=0
)

m3 = 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
close
beta=ols('coefficient', low, high, 18) 
"""
)

m1 = M.use_datasource.v2(
    instruments=m2.data,
    features=m3.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.derived_feature_extractor.v3(
    input_data=m1.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)
    In [104]:
    df=m4.data.read_df()
df
    Out[104]:
    low date close instrument high T
    0 980.658020 2005-01-04 982.794006 000300.HIX 994.768982 NaN
    1 979.877014 2005-01-05 992.564026 000300.HIX 997.322998 NaN
    2 980.330017 2005-01-06 983.174011 000300.HIX 993.788025 NaN
    3 979.812012 2005-01-07 983.958008 000300.HIX 995.710999 NaN
    4 979.789001 2005-01-10 993.879028 000300.HIX 993.958984 NaN
    ... ... ... ... ... ... ...
    2969 3474.520508 2017-03-27 3478.038574 000300.HIX 3505.979004 [0.7448427]
    2970 3461.467773 2017-03-28 3469.809326 000300.HIX 3482.292236 [0.7691331]
    2971 3455.552979 2017-03-29 3465.193359 000300.HIX 3483.057129 [0.77960205]
    2972 3422.365967 2017-03-30 3436.758057 000300.HIX 3467.304932 [0.7822884]
    2973 3430.819580 2017-03-31 3456.045410 000300.HIX 3456.305176 [0.81047714]

    2974 rows × 6 columns

    In [88]:
    import statsmodels.api as sm
x = np.linspace(0, 10,1)
X = sm.add_constant(x)
    In [85]:
    D2=D.values.tolist()
D2
    Out[85]:
    [nan,
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 array([0.7760404], dtype=float32),
 array([0.7980748], dtype=float32),
 array([0.70969737], dtype=float32),
 array([0.7501754], dtype=float32),
 array([0.8402468], dtype=float32),
 array([0.96156377], dtype=float32),
 array([0.9626842], dtype=float32),
 array([0.97124964], dtype=float32),
 array([0.99355716], dtype=float32),
 array([0.99452305], dtype=float32),
 array([0.98726046], dtype=float32),
 array([0.9871926], dtype=float32),
 array([0.9930556], dtype=float32),
 array([0.9855314], dtype=float32),
 array([0.98648095], dtype=float32),
 array([1.007251], dtype=float32),
 array([0.9914975], dtype=float32),
 array([0.9858995], dtype=float32),
 array([0.96926415], dtype=float32),
 array([0.89435744], dtype=float32),
 array([0.88556933], dtype=float32),
 array([0.8846451], dtype=float32),
 array([0.87195724], dtype=float32),
 array([0.8645496], dtype=float32),
 array([0.86382216], dtype=float32),
 array([0.8693363], dtype=float32),
 array([0.84450287], dtype=float32),
 array([0.87416005], dtype=float32),
 array([0.8793357], dtype=float32),
 array([0.8871414], dtype=float32),
 array([0.85598916], dtype=float32),
 array([0.85897833], dtype=float32),
 array([0.7975307], dtype=float32),
 array([0.8032796], dtype=float32),
 array([0.896578], dtype=float32),
 array([0.8957781], dtype=float32),
 array([0.91702366], dtype=float32),
 array([0.95361626], dtype=float32),
 array([0.9702774], dtype=float32),
 array([0.954666], dtype=float32),
 array([0.9752756], dtype=float32),
 array([0.9376913], dtype=float32),
 array([0.89703685], dtype=float32),
 array([0.9315035], dtype=float32),
 array([0.94281536], dtype=float32),
 array([0.91521883], dtype=float32),
 array([0.90325236], dtype=float32),
 array([0.8995668], dtype=float32),
 array([0.8973469], dtype=float32),
 array([0.8969962], dtype=float32),
 array([0.8838688], dtype=float32),
 array([0.8800839], dtype=float32),
 ...]
    In [83]:
    D3=list()
for i in range(len(D)):
    print(D[int(i)][0])

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-83-389e689c5484> in <module>
      1 D3=list()
      2 for i in range(len(D)):
----> 3     print(D[int(i)][0])

TypeError: 'float' object is not subscriptable
    In [84]:
    int(9)
    Out[84]:
    9
    In [62]:
    D4=list()
D4.append(0.33)
D4.append(0.2)
D4[0]
    Out[62]:
    0.33
    In [71]:
    for dd in D2:
    t=list(dd)
    print(t[0])

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-71-5030624b7e7b> in <module>
      1 for dd in D2:
----> 2     t=list(dd)
      3     print(t[0])

TypeError: 'float' object is not iterable