复制链接
克隆策略
In [42]:
#获取绩效数据
import requests
import json
def request_position_by_api_key(uname, key,notebook_id):
    position_url = 'https://bigquant.com/bigwebapi/algo_info/plot_return'
    r = requests.get(url=position_url, params={
        'owner': uname,
        'api_key': key,
        'notebook_id': notebook_id,
    })
    return json.loads(r.text)

uname = ''
key = ''
notebook_id = ''
data = request_position_by_api_key(uname,key,notebook_id )
data
Out[42]:
{'data': {'algo_info_plot_return': {'after_shared_cum_return_plot': [],
   'algo_name': '可视化AI策略',
   'annual_return': -0.0335536934630337,
   'before_shared_cum_return_plot': [],
   'benchmark_cum_return_plot': [[1659888000000.0, 0.0],
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   'description': '',
   'error_data': '',
   'first_date': '2022-08-08 00:00:00',
   'grade': -15,
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    [1667145600000.0, 0.09129023003901571],
    [1667232000000.0, 0.08127204426043],
    [1667318400000.0, 0.10035131290560884],
    [1667404800000.0, 0.12735065746115315],
    [1667491200000.0, 0.10485079427106392],
    [1667750400000.0, 0.10400246857464612],
    [1667836800000.0, 0.11313011061165779],
    [1667923200000.0, 0.1241600249842838],
    [1668009600000.0, 0.1341531975632948],
    [1668096000000.0, 0.10165469525899229],
    [1668355200000.0, 0.09702612761048757],
    [1668441600000.0, 0.08597046825658516],
    [1668528000000.0, 0.09895729826908428],
    [1668614400000.0, 0.10857678809276994],
    [1668700800000.0, 0.10339920294413818],
    [1668960000000.0, 0.1097526642769302],
    [1669046400000.0, 0.09678732758242536],
    [1669132800000.0, 0.07464685046963382],
    [1669219200000.0, 0.08402300427358811],
    [1669305600000.0, 0.07198079619711506],
    [1669564800000.0, 0.0782468205598017],
    [1669651200000.0, 0.05453973507203358],
    [1669737600000.0, 0.052754940132845984],
    [1669824000000.0, 0.04235487491583356],
    [1669910400000.0, 0.057643915842584326],
    [1670169600000.0, 0.04890067059017045],
    [1670256000000.0, 0.03421553044990899],
    [1670342400000.0, 0.03619535803655194],
    [1670428800000.0, 0.03290567952953016],
    [1670515200000.0, 0.020506638994076898],
    [1670774400000.0, 0.027508415925736163],
    [1670860800000.0, 0.045162582508075255],
    [1670947200000.0, 0.04869277303035435],
    [1671033600000.0, 0.048790559176279835],
    [1671120000000.0, 0.037806782481087264],
    [1671379200000.0, 0.027798500398328985],
    [1671465600000.0, 0.04201326317370113],
    [1671552000000.0, 0.03336485186176885],
    [1671638400000.0, 0.014299224917668774],
    [1671724800000.0, 0.007225154461465433],
    [1671984000000.0, 0.059920405872879945],
    [1672070400000.0, 0.061166249347058166],
    [1672156800000.0, 0.05730751125684175],
    [1672243200000.0, 0.06280377958394623],
    [1672329600000.0, 0.047719014756008704],
    [1672675200000.0, 0.0742835927925829],
    [1672761600000.0, 0.058370074858343335],
    [1672848000000.0, 0.05053589988557694],
    [1672934400000.0, 0.035853825249111626],
    [1673193600000.0, 0.041595799076769024],
    [1673280000000.0, 0.04358930973800934],
    [1673366400000.0, 0.015760196331340293]],
   'run_date': '2023-01-11 00:00:00',
   'run_status': 0,
   'shared': 0,
   'shared_date': '',
   'shelf_status': 0,
   'six_month_return': -1.0,
   'subscribe_limit': 50,
   'subscribe_price': 500,
   'subscribed': 0,
   'tags': '重仓;分散持股;中频轮仓;',
   'ten_days_return': -0.0246690698565792,
   'three_month_return': 0.0286323025540929,
   'today_return': -0.0284965213808768,
   'unique_id': 'yvan0617_f61dd07e-17be-11ed-a0cc-52c587c2b1a9',
   'update_time': '2023-01-11 17:34:33',
   'week_return': -0.0113073151636816,
   'win_ratio': 0.439153439,
   'year_return': -1.0,
   'viewtype': 'guest',
   'notebook_id': 'f61dd07e-17be-11ed-a0cc-52c587c2b1a9',
   'rank': -1,
   'notify_email': 0,
   'notify_wechat': 0,
   'market_type': 0,
   'status': 0,
   'festival_price_info': {},
   'new_come_price_info': {},
   'min_month_price': '',
   'original_month_price': '',
   'selection_price': {},
   'platform_guide_price': {'gp1': 0.0,
    'gp2': 500.0,
    'gp3': 800.0,
    'gp4': 1000.0,
    'gp5': 1500.0,
    'gp6': '',
    'gp7': '',
    'id': 1,
    'to_dict': '',
    'update_time': '2018-06-30',
    'zn1': 1.0,
    'zn12': 0.65,
    'zn18': '',
    'zn24': '',
    'zn3': 0.9,
    'zn6': 0.8},
   'subscriber_notify_wechat': '',
   'subscriber_notify_email': '',
   'subscriber_limit_time': '',
   'subscriber_left_days': 0,
   'subscriber_active_status': '',
   'subscribe_by_price': '',
   'follow_status': 0,
   'want_subscribe_status': 0,
   'benchmark_name': '沪深300',
   'max_drawdown_stamp': [[1663084800000.0, 0.028445371441239865],
    [1665331200000.0, -0.10825931096464414]],
   'alpha': 0.04239611346721195,
   'beta': 0.7130259055456739,
   'volatility': 0.278034037994995,
   'sharpe': -0.09120692845349214,
   'ir': 0.017137071038052655}},
 'message': 'SUCCESSED',
 'code': '200',
 'metadata': {'tabelname': 'algo_info_plot_return',
  'limit': 1,
  'page': 1,
  'total_count': None,
  'count': 68,
  'viewtype': None,
  'start_date': None,
  'end_date': None,
  'date_type': None,
  'key_words': None,
  'key_words_type': None}}
In [43]:
#转换成需要的数据
from datetime import datetime
r = data['data']['algo_info_plot_return']['cum_return_plot']
r
Out[43]:
[[1659888000000.0, 0.0],
 [1659974400000.0, 0.0012419673156961798],
 [1660060800000.0, 0.004439095028559678],
 [1660147200000.0, 0.012193600207440928],
 [1660233600000.0, 0.005255140720253065],
 [1660492800000.0, 0.0007182150088265539],
 [1660579200000.0, 0.00947545470900219],
 [1660665600000.0, 0.00850319941586908],
 [1660752000000.0, 0.016776168587192147],
 [1660838400000.0, 0.0009680229128729552],
 [1661097600000.0, 0.0012527628462847323],
 [1661184000000.0, 0.012009801290507242],
 [1661270400000.0, -0.02309957968611829],
 [1661356800000.0, -0.025015288994437085],
 [1661443200000.0, -0.03353664058749937],
 [1661702400000.0, -0.01568788316896204],
 [1661788800000.0, -0.016939449584399722],
 [1661875200000.0, -0.04799048755076975],
 [1661961600000.0, -0.049320147566132434],
 [1662048000000.0, -0.0261008391454272],
 [1662307200000.0, -0.03597192676360086],
 [1662393600000.0, -0.008209122085404582],
 [1662480000000.0, 0.019990111143084244],
 [1662566400000.0, 0.01343714209313821],
 [1662652800000.0, 0.004145066273180768],
 [1662998400000.0, 0.021239225142114983],
 [1663084800000.0, 0.028445371441239865],
 [1663171200000.0, -0.01838488376713302],
 [1663257600000.0, -0.03490193466679491],
 [1663516800000.0, -0.05015526403484084],
 [1663603200000.0, -0.01906731638667453],
 [1663689600000.0, -0.024502092825576662],
 [1663776000000.0, -0.011448755073511042],
 [1663862400000.0, -0.04157477648937702],
 [1664121600000.0, -0.02294904504319001],
 [1664208000000.0, -0.0059058586849424985],
 [1664294400000.0, -0.051429795953878386],
 [1664380800000.0, -0.05585190886597037],
 [1664467200000.0, -0.07940447727738395],
 [1665331200000.0, -0.10825931096464414],
 [1665417600000.0, -0.09858687674412392],
 [1665504000000.0, -0.05815337539403178],
 [1665590400000.0, -0.04169208006227017],
 [1665676800000.0, -0.023770247793609277],
 [1665936000000.0, -0.019504501308536717],
 [1666022400000.0, -0.011898782386355847],
 [1666108800000.0, -0.030847688198620455],
 [1666195200000.0, -0.020787379724896325],
 [1666281600000.0, -0.02461798182298988],
 [1666540800000.0, -0.04052051636509523],
 [1666627200000.0, -0.031943453691309694],
 [1666713600000.0, -0.020719669062911533],
 [1666800000000.0, -0.04277192783244252],
 [1666886400000.0, -0.08570332823745459],
 [1667145600000.0, -0.07687015843133647],
 [1667232000000.0, -0.05233389758481476],
 [1667318400000.0, -0.02423436340022851],
 [1667404800000.0, -0.00822710887124557],
 [1667491200000.0, 0.004407632151017524],
 [1667750400000.0, 0.005802117064294591],
 [1667836800000.0, 0.007198474361628853],
 [1667923200000.0, 0.007761849052157253],
 [1668009600000.0, 0.008965334502480552],
 [1668096000000.0, 0.008182158987284452],
 [1668355200000.0, 0.0054308228481516245],
 [1668441600000.0, 0.014366782498825528],
 [1668528000000.0, 0.018210531452635674],
 [1668614400000.0, 0.02294729920750335],
 [1668700800000.0, 0.013590093105585873],
 [1668960000000.0, 0.01077668634106107],
 [1669046400000.0, -0.0009127589180862531],
 [1669132800000.0, -0.020032457038642093],
 [1669219200000.0, -0.015824706082827596],
 [1669305600000.0, -0.02178856375655085],
 [1669564800000.0, -0.027090159754609315],
 [1669651200000.0, -0.018464535630147903],
 [1669737600000.0, -0.018948893880572727],
 [1669824000000.0, -0.018014860665570946],
 [1669910400000.0, -0.00961728638999015],
 [1670169600000.0, 0.0016217672259975224],
 [1670256000000.0, -0.006990468198782019],
 [1670342400000.0, -0.0075311356147525835],
 [1670428800000.0, -0.010496767539447359],
 [1670515200000.0, -0.012611584619532712],
 [1670774400000.0, -0.016900816104529426],
 [1670860800000.0, -0.001939836223055236],
 [1670947200000.0, 0.003759516887316853],
 [1671033600000.0, 0.003117794006486051],
 [1671120000000.0, -0.006819457958265766],
 [1671379200000.0, -0.03172030198731702],
 [1671465600000.0, -0.034296693339477664],
 [1671552000000.0, -0.041928135524735975],
 [1671638400000.0, -0.058231522512094305],
 [1671724800000.0, -0.06671701041044444],
 [1671984000000.0, -0.014167513530936465],
 [1672070400000.0, -0.0016293103580594063],
 [1672156800000.0, -0.009519787374601699],
 [1672243200000.0, -0.00809709672238268],
 [1672329600000.0, -0.018334909515574576],
 [1672675200000.0, 0.010678992920298315],
 [1672761600000.0, -0.0029798972321068867],
 [1672848000000.0, 0.009009687204713747],
 [1672934400000.0, -0.001962008970057592],
 [1673193600000.0, 0.011650839368589409],
 [1673280000000.0, 0.01468461424398143],
 [1673366400000.0, -0.014253517758629658]]
In [44]:
#转成dataframe
df = pd.DataFrame(r,columns=['date','cum_returns'])
df
Out[44]:
date cum_returns
0 1.659888e+12 0.000000
1 1.659974e+12 0.001242
2 1.660061e+12 0.004439
3 1.660147e+12 0.012194
4 1.660234e+12 0.005255
... ... ...
101 1.672848e+12 0.009010
102 1.672934e+12 -0.001962
103 1.673194e+12 0.011651
104 1.673280e+12 0.014685
105 1.673366e+12 -0.014254

106 rows × 2 columns

In [45]:
#转换时间
df['date'] = df['date'].apply(lambda x:datetime.fromtimestamp(x/10**3))
df
Out[45]:
date cum_returns
0 2022-08-08 0.000000
1 2022-08-09 0.001242
2 2022-08-10 0.004439
3 2022-08-11 0.012194
4 2022-08-12 0.005255
... ... ...
101 2023-01-05 0.009010
102 2023-01-06 -0.001962
103 2023-01-09 0.011651
104 2023-01-10 0.014685
105 2023-01-11 -0.014254

106 rows × 2 columns

In [46]:
#转换成净值
df['returns'] = df['cum_returns'] + 1
#每日收益率
df['returns'] = (df['returns'] - df['returns'].shift(1)) / df['returns'].shift(1)
df['returns'] = df['returns'].fillna(0)
df
Out[46]:
date cum_returns returns
0 2022-08-08 0.000000 0.000000
1 2022-08-09 0.001242 0.001242
2 2022-08-10 0.004439 0.003193
3 2022-08-11 0.012194 0.007720
4 2022-08-12 0.005255 -0.006855
... ... ... ...
101 2023-01-05 0.009010 0.012025
102 2023-01-06 -0.001962 -0.010874
103 2023-01-09 0.011651 0.013640
104 2023-01-10 0.014685 0.002999
105 2023-01-11 -0.014254 -0.028519

106 rows × 3 columns

In [47]:
#保存绩效文件
df[['date','returns']].set_index('date').to_csv('test1.csv')
df[['date','returns']].set_index('date').to_csv('test2.csv')
df[['date','returns']].set_index('date').to_csv('test3.csv')
In [48]:
# #转换成需要的数据
# from datetime import datetime
# r = data['data']['algo_info_plot_return']['cum_return_plot']
# df = pd.DataFrame(r,columns=['date','cum_returns'])
# #转换时间
# df['date'] = df['date'].apply(lambda x:datetime.fromtimestamp(x/10**3))
# #转换成净值
# df['returns'] = df['cum_returns'] + 1
# #每日收益率
# df['returns'] = (df['returns'] - df['returns'].shift(1)) / df['returns'].shift(1)
# df['returns'] = df['returns'].fillna(0)
# df[['date','returns']].set_index('date').to_csv('test1.csv')
# df[['date','returns']].set_index('date').to_csv('test2.csv')
# df[['date','returns']].set_index('date').to_csv('test3.csv')

    {"description":"实验创建于2017/8/26","graph":{"edges":[],"nodes":[{"node_id":"-1748","module_id":"BigQuantSpace.strategy_asset_allocation.strategy_asset_allocation-v1","parameters":[{"name":"stra_dict","value":"{\n \"test1\":\"test1.csv\",\n \"test2\":\"test2.csv\",\n \"test3\":\"test3.csv\"\n}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2022-08-08","type":"Literal","bound_global_parameter":null},{"name":"upper_weight","value":1,"type":"Literal","bound_global_parameter":null},{"name":"lower_weight","value":0,"type":"Literal","bound_global_parameter":null},{"name":"target","value":"风险平价","type":"Literal","bound_global_parameter":null},{"name":"self_para","value":"","type":"Literal","bound_global_parameter":null},{"name":"balance_period","value":"1周","type":"Literal","bound_global_parameter":null},{"name":"cal_period","value":"全部历史","type":"Literal","bound_global_parameter":null},{"name":"cal_days","value":30,"type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"rebalance_data","node_id":"-1748"},{"name":"pnl_data","node_id":"-1748"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-1748' Position='7.121490478515625,-296.0467224121094,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [1]:
    # 本代码由可视化策略环境自动生成 2023年1月14日 14:42
# 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。


m4 = M.strategy_asset_allocation.v1(
    stra_dict={
    "test1":"test1.csv",
    "test2":"test2.csv",
    "test3":"test3.csv"
},
    start_date='2022-08-08',
    upper_weight=1,
    lower_weight=0,
    target='风险平价',
    balance_period='1周',
    cal_period='全部历史',
    cal_days=30
)

    再平衡日期和各策略对应权重:
2022-08-08 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-08-15 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-08-22 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-08-29 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-09-05 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-09-12 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-09-19 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-09-26 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-10-03 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-10-10 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-10-17 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-10-24 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-10-31 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-11-07 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-11-14 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-11-21 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-11-28 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-12-05 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-12-12 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-12-19 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2022-12-26 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2023-01-02 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}
2023-01-09 {'test1': 0.33, 'test2': 0.33, 'test3': 0.33}

    策略绩效指标

    策略名称 收益率 年化收益率 夏普比率 年化波动率 最大回撤 收益回撤比
    test1 -1.43% -3.36% -0.11 27.80% -13.29% 0.25
    test2 -1.43% -3.36% -0.11 27.80% -13.29% 0.25
    test3 -1.43% -3.36% -0.11 27.80% -13.29% 0.25
    组合收益 -1.43% -3.36% -0.11 27.80% -13.29% 0.25