{
  "fft": {
    "daily_peaks": "   period_days  frequency     power  noise_level       snr\n0    39.615385   0.025243  0.007486     0.001178  6.355355\n1     3.111782   0.321359  0.007130     0.001352  5.273187\n2    14.372093   0.069579  0.006190     0.001187  5.216536\n3    13.261803   0.075405  0.006166     0.001188  5.188618",
    "multi_tf_peaks": "{'4h':     period_days  frequency     power  noise_level       snr\n0      2.182214   0.458250  0.001304     0.000203  6.414376\n1     39.587607   0.025260  0.001277     0.000201  6.340524\n2      2.210332   0.452421  0.001260     0.000203  6.199798\n3      3.075531   0.325147  0.001228     0.000202  6.069181\n4     13.309626   0.075134  0.001128     0.000201  5.598435\n5      3.382074   0.295677  0.001118     0.000202  5.527193\n6      2.482181   0.402871  0.001101     0.000203  5.428543\n7      4.893555   0.204350  0.001070     0.000202  5.303870\n8      4.184056   0.239003  0.001055     0.000202  5.222734\n9      2.789371   0.358504  0.001057     0.000203  5.219469\n10     2.177598   0.459222  0.001057     0.000203  5.197185\n11    14.362016   0.069628  0.001025     0.000201  5.089240, '1d':    period_days  frequency     power  noise_level       snr\n0    39.615385   0.025243  0.007486     0.001178  6.355355\n1     3.111782   0.321359  0.007130     0.001352  5.273187\n2    14.372093   0.069579  0.006190     0.001187  5.216536\n3    13.261803   0.075405  0.006166     0.001188  5.188618, '1w':    period_days  frequency     power  noise_level       snr\n0    39.454545   0.025346  0.057044     0.009711  5.873872}",
    "bandpass_variance_ratios": {
      "7d": 14.917052544334325,
      "30d": 3.770230414627656,
      "90d": 2.4054786415083576,
      "365d": 0.748511130559413,
      "1400d": 0.23278898254402042
    },
    "bandpass_components": "{'7d': array([-2.97061917e-03,  8.09307366e-04,  1.64398528e-03, ...,\n       -1.99758500e-02, -1.92389106e-02, -8.40156425e-05]), '30d': array([0.00811365, 0.01061458, 0.0131885 , ..., 0.00061353, 0.00056402,\n       0.00048904]), '90d': array([-3.81400112e-02, -3.57430742e-02, -3.31503607e-02, ...,\n       -3.86752623e-05, -3.43679665e-05, -3.02800847e-05]), '365d': array([-0.00037487, -0.00025267, -0.00013042, ..., -0.00074066,\n       -0.00061892, -0.00049698]), '1400d': array([-5.08113855e-05, -4.99293812e-05, -4.90661763e-05, ...,\n       -5.35684912e-05, -5.26311118e-05, -5.17120193e-05])}",
    "ar1_rho": -0.04993111698604458,
    "daily_spectrum": "{'freqs': array([3.23624595e-04, 6.47249191e-04, 9.70873786e-04, ...,\n       4.99029126e-01, 4.99352751e-01, 4.99676375e-01]), 'periods': array([3.09000000e+03, 1.54500000e+03, 1.03000000e+03, ...,\n       2.00389105e+00, 2.00259235e+00, 2.00129534e+00]), 'power': array([0.00062218, 0.00373931, 0.00464221, ..., 0.00234932, 0.00027632,\n       0.00022944]), 'noise_mean': array([0.0011765 , 0.0011765 , 0.0011765 , ..., 0.00143682, 0.00143682,\n       0.00143682]), 'noise_threshold': array([0.00352447, 0.00352448, 0.00352448, ..., 0.00430432, 0.00430433,\n       0.00430433])}",
    "multi_tf_results": "{'4h': {'freqs': array([3.23851676e-04, 6.47703352e-04, 9.71555028e-04, ...,\n       2.99919037e+00, 2.99951422e+00, 2.99983807e+00]), 'periods': array([3.08783333e+03, 1.54391667e+03, 1.02927778e+03, ...,\n       3.33423316e-01, 3.33387317e-01, 3.33351326e-01]), 'power': array([0.00010316, 0.00062397, 0.0007734 , ..., 0.00028183, 0.00066969,\n       0.00094334]), 'noise_mean': array([0.0002014 , 0.0002014 , 0.0002014 , ..., 0.00024194, 0.00024194,\n       0.00024194]), 'noise_threshold': array([0.00060335, 0.00060335, 0.00060335, ..., 0.00072478, 0.00072478,\n       0.00072478]), 'peaks':     period_days  frequency     power  noise_level       snr\n0      2.182214   0.458250  0.001304     0.000203  6.414376\n1     39.587607   0.025260  0.001277     0.000201  6.340524\n2      2.210332   0.452421  0.001260     0.000203  6.199798\n3      3.075531   0.325147  0.001228     0.000202  6.069181\n4     13.309626   0.075134  0.001128     0.000201  5.598435\n5      3.382074   0.295677  0.001118     0.000202  5.527193\n6      2.482181   0.402871  0.001101     0.000203  5.428543\n7      4.893555   0.204350  0.001070     0.000202  5.303870\n8      4.184056   0.239003  0.001055     0.000202  5.222734\n9      2.789371   0.358504  0.001057     0.000203  5.219469\n10     2.177598   0.459222  0.001057     0.000203  5.197185\n11    14.362016   0.069628  0.001025     0.000201  5.089240, 'log_ret': array([ 0.01761637, -0.01707629, -0.00624831, ..., -0.01471412,\n       -0.00540965, -0.00268024]), 'label': '4h'}, '1d': {'freqs': array([3.23624595e-04, 6.47249191e-04, 9.70873786e-04, ...,\n       4.99029126e-01, 4.99352751e-01, 4.99676375e-01]), 'periods': array([3.09000000e+03, 1.54500000e+03, 1.03000000e+03, ...,\n       2.00389105e+00, 2.00259235e+00, 2.00129534e+00]), 'power': array([0.00062218, 0.00373931, 0.00464221, ..., 0.00234932, 0.00027632,\n       0.00022944]), 'noise_mean': array([0.0011765 , 0.0011765 , 0.0011765 , ..., 0.00143682, 0.00143682,\n       0.00143682]), 'noise_threshold': array([0.00352447, 0.00352448, 0.00352448, ..., 0.00430432, 0.00430433,\n       0.00430433]), 'peaks':    period_days  frequency     power  noise_level       snr\n0    39.615385   0.025243  0.007486     0.001178  6.355355\n1     3.111782   0.321359  0.007130     0.001352  5.273187\n2    14.372093   0.069579  0.006190     0.001187  5.216536\n3    13.261803   0.075405  0.006166     0.001188  5.188618, 'log_ret': array([-0.04211287,  0.0076646 , -0.01305349, ..., -0.00461393,\n       -0.06774794, -0.02277265]), 'label': '1d'}, '1w': {'freqs': array([0.00032916, 0.00065833, 0.00098749, 0.00131666, 0.00164582,\n       0.00197498, 0.00230415, 0.00263331, 0.00296248, 0.00329164,\n       0.0036208 , 0.00394997, 0.00427913, 0.00460829, 0.00493746,\n       0.00526662, 0.00559579, 0.00592495, 0.00625411, 0.00658328,\n       0.00691244, 0.00724161, 0.00757077, 0.00789993, 0.0082291 ,\n       0.00855826, 0.00888743, 0.00921659, 0.00954575, 0.00987492,\n       0.01020408, 0.01053325, 0.01086241, 0.01119157, 0.01152074,\n       0.0118499 , 0.01217907, 0.01250823, 0.01283739, 0.01316656,\n       0.01349572, 0.01382488, 0.01415405, 0.01448321, 0.01481238,\n       0.01514154, 0.0154707 , 0.01579987, 0.01612903, 0.0164582 ,\n       0.01678736, 0.01711652, 0.01744569, 0.01777485, 0.01810402,\n       0.01843318, 0.01876234, 0.01909151, 0.01942067, 0.01974984,\n       0.020079  , 0.02040816, 0.02073733, 0.02106649, 0.02139566,\n       0.02172482, 0.02205398, 0.02238315, 0.02271231, 0.02304147,\n       0.02337064, 0.0236998 , 0.02402897, 0.02435813, 0.02468729,\n       0.02501646, 0.02534562, 0.02567479, 0.02600395, 0.02633311,\n       0.02666228, 0.02699144, 0.02732061, 0.02764977, 0.02797893,\n       0.0283081 , 0.02863726, 0.02896643, 0.02929559, 0.02962475,\n       0.02995392, 0.03028308, 0.03061224, 0.03094141, 0.03127057,\n       0.03159974, 0.0319289 , 0.03225806, 0.03258723, 0.03291639,\n       0.03324556, 0.03357472, 0.03390388, 0.03423305, 0.03456221,\n       0.03489138, 0.03522054, 0.0355497 , 0.03587887, 0.03620803,\n       0.0365372 , 0.03686636, 0.03719552, 0.03752469, 0.03785385,\n       0.03818302, 0.03851218, 0.03884134, 0.03917051, 0.03949967,\n       0.03982883, 0.040158  , 0.04048716, 0.04081633, 0.04114549,\n       0.04147465, 0.04180382, 0.04213298, 0.04246215, 0.04279131,\n       0.04312047, 0.04344964, 0.0437788 , 0.04410797, 0.04443713,\n       0.04476629, 0.04509546, 0.04542462, 0.04575379, 0.04608295,\n       0.04641211, 0.04674128, 0.04707044, 0.04739961, 0.04772877,\n       0.04805793, 0.0483871 , 0.04871626, 0.04904542, 0.04937459,\n       0.04970375, 0.05003292, 0.05036208, 0.05069124, 0.05102041,\n       0.05134957, 0.05167874, 0.0520079 , 0.05233706, 0.05266623,\n       0.05299539, 0.05332456, 0.05365372, 0.05398288, 0.05431205,\n       0.05464121, 0.05497038, 0.05529954, 0.0556287 , 0.05595787,\n       0.05628703, 0.05661619, 0.05694536, 0.05727452, 0.05760369,\n       0.05793285, 0.05826201, 0.05859118, 0.05892034, 0.05924951,\n       0.05957867, 0.05990783, 0.060237  , 0.06056616, 0.06089533,\n       0.06122449, 0.06155365, 0.06188282, 0.06221198, 0.06254115,\n       0.06287031, 0.06319947, 0.06352864, 0.0638578 , 0.06418697,\n       0.06451613, 0.06484529, 0.06517446, 0.06550362, 0.06583278,\n       0.06616195, 0.06649111, 0.06682028, 0.06714944, 0.0674786 ,\n       0.06780777, 0.06813693, 0.0684661 , 0.06879526, 0.06912442,\n       0.06945359, 0.06978275, 0.07011192, 0.07044108, 0.07077024,\n       0.07109941]), 'periods': array([3038.        , 1519.        , 1012.66666667,  759.5       ,\n        607.6       ,  506.33333333,  434.        ,  379.75      ,\n        337.55555556,  303.8       ,  276.18181818,  253.16666667,\n        233.69230769,  217.        ,  202.53333333,  189.875     ,\n        178.70588235,  168.77777778,  159.89473684,  151.9       ,\n        144.66666667,  138.09090909,  132.08695652,  126.58333333,\n        121.52      ,  116.84615385,  112.51851852,  108.5       ,\n        104.75862069,  101.26666667,   98.        ,   94.9375    ,\n         92.06060606,   89.35294118,   86.8       ,   84.38888889,\n         82.10810811,   79.94736842,   77.8974359 ,   75.95      ,\n         74.09756098,   72.33333333,   70.65116279,   69.04545455,\n         67.51111111,   66.04347826,   64.63829787,   63.29166667,\n         62.        ,   60.76      ,   59.56862745,   58.42307692,\n         57.32075472,   56.25925926,   55.23636364,   54.25      ,\n         53.29824561,   52.37931034,   51.49152542,   50.63333333,\n         49.80327869,   49.        ,   48.22222222,   47.46875   ,\n         46.73846154,   46.03030303,   45.34328358,   44.67647059,\n         44.02898551,   43.4       ,   42.78873239,   42.19444444,\n         41.61643836,   41.05405405,   40.50666667,   39.97368421,\n         39.45454545,   38.94871795,   38.4556962 ,   37.975     ,\n         37.50617284,   37.04878049,   36.60240964,   36.16666667,\n         35.74117647,   35.3255814 ,   34.91954023,   34.52272727,\n         34.13483146,   33.75555556,   33.38461538,   33.02173913,\n         32.66666667,   32.31914894,   31.97894737,   31.64583333,\n         31.31958763,   31.        ,   30.68686869,   30.38      ,\n         30.07920792,   29.78431373,   29.49514563,   29.21153846,\n         28.93333333,   28.66037736,   28.39252336,   28.12962963,\n         27.87155963,   27.61818182,   27.36936937,   27.125     ,\n         26.88495575,   26.64912281,   26.4173913 ,   26.18965517,\n         25.96581197,   25.74576271,   25.52941176,   25.31666667,\n         25.10743802,   24.90163934,   24.69918699,   24.5       ,\n         24.304     ,   24.11111111,   23.92125984,   23.734375  ,\n         23.5503876 ,   23.36923077,   23.19083969,   23.01515152,\n         22.84210526,   22.67164179,   22.5037037 ,   22.33823529,\n         22.17518248,   22.01449275,   21.85611511,   21.7       ,\n         21.54609929,   21.3943662 ,   21.24475524,   21.09722222,\n         20.95172414,   20.80821918,   20.66666667,   20.52702703,\n         20.38926174,   20.25333333,   20.1192053 ,   19.98684211,\n         19.85620915,   19.72727273,   19.6       ,   19.47435897,\n         19.35031847,   19.2278481 ,   19.10691824,   18.9875    ,\n         18.86956522,   18.75308642,   18.63803681,   18.52439024,\n         18.41212121,   18.30120482,   18.19161677,   18.08333333,\n         17.97633136,   17.87058824,   17.76608187,   17.6627907 ,\n         17.56069364,   17.45977011,   17.36      ,   17.26136364,\n         17.16384181,   17.06741573,   16.97206704,   16.87777778,\n         16.78453039,   16.69230769,   16.6010929 ,   16.51086957,\n         16.42162162,   16.33333333,   16.2459893 ,   16.15957447,\n         16.07407407,   15.98947368,   15.90575916,   15.82291667,\n         15.74093264,   15.65979381,   15.57948718,   15.5       ,\n         15.4213198 ,   15.34343434,   15.26633166,   15.19      ,\n         15.11442786,   15.03960396,   14.96551724,   14.89215686,\n         14.8195122 ,   14.74757282,   14.6763285 ,   14.60576923,\n         14.53588517,   14.46666667,   14.39810427,   14.33018868,\n         14.2629108 ,   14.19626168,   14.13023256,   14.06481481]), 'power': array([5.04731820e-03, 2.54202438e-02, 3.29256798e-02, 2.32601150e-02,\n       7.19168741e-03, 4.07702144e-03, 1.31016337e-02, 1.33647991e-02,\n       2.25619065e-02, 9.06932138e-03, 2.12605364e-02, 1.30420666e-02,\n       1.75090968e-02, 2.59934318e-02, 1.56038741e-02, 1.13190286e-02,\n       3.38425939e-03, 3.90450547e-03, 1.01345109e-02, 6.52712796e-03,\n       1.93218089e-03, 8.54169510e-03, 1.81711957e-02, 4.76819376e-03,\n       1.36862009e-03, 1.26675786e-02, 2.36832798e-02, 3.31122805e-03,\n       6.88655529e-03, 6.19429647e-03, 7.54168482e-04, 7.42115115e-03,\n       1.51507514e-02, 1.33937217e-02, 8.45247520e-03, 5.34075010e-03,\n       5.70067629e-03, 2.37223308e-02, 5.37421870e-03, 4.41235670e-04,\n       2.03187341e-04, 2.50551066e-03, 8.50939182e-03, 1.17597909e-02,\n       4.94199844e-03, 2.12761785e-03, 6.56113855e-03, 1.01714343e-02,\n       5.62639737e-03, 1.27106690e-02, 1.47849324e-02, 5.64655462e-03,\n       5.24277718e-03, 1.27782820e-02, 2.95455273e-02, 2.51233871e-02,\n       1.53276602e-03, 6.65242716e-03, 8.22040762e-03, 4.86617282e-03,\n       8.44698516e-03, 9.38048137e-04, 4.41104449e-04, 1.93021895e-04,\n       3.38715064e-03, 1.45972399e-02, 2.25114006e-02, 9.92147536e-03,\n       2.11296217e-03, 4.85833662e-03, 6.40711760e-03, 1.09797288e-02,\n       9.39549071e-03, 4.81773075e-03, 1.30206993e-02, 3.66594547e-02,\n       5.70439711e-02, 2.67748831e-02, 6.14358316e-03, 5.64149450e-03,\n       1.10069855e-03, 4.69164314e-03, 7.95593765e-03, 9.03137294e-03,\n       4.76333873e-03, 4.00992543e-03, 1.22710735e-02, 5.29245950e-03,\n       4.54922183e-04, 9.84103767e-04, 3.40059340e-04, 1.03051997e-03,\n       1.10483270e-02, 1.48678769e-02, 1.94958651e-02, 1.33427874e-02,\n       4.81017827e-03, 5.12395285e-03, 7.63732110e-03, 8.75867708e-03,\n       7.92267913e-03, 1.24800768e-02, 1.16172127e-02, 2.14708148e-02,\n       3.62692710e-02, 6.14068109e-03, 9.66533909e-03, 1.28510341e-02,\n       1.88876566e-02, 2.99603175e-02, 1.67991343e-02, 7.87212982e-03,\n       8.06670187e-03, 5.75849696e-03, 3.69292303e-03, 2.58726515e-03,\n       8.42621068e-04, 3.91378652e-03, 1.63645128e-02, 2.32006253e-02,\n       1.14690692e-03, 1.05130926e-03, 2.42622719e-03, 1.11230317e-03,\n       3.44273041e-03, 1.10922301e-02, 3.11889325e-03, 1.18718829e-02,\n       4.89384194e-03, 4.69456656e-03, 5.85533653e-03, 3.76823190e-03,\n       1.85373799e-03, 3.47645535e-03, 7.08100917e-04, 6.83875814e-03,\n       2.65207722e-03, 6.21014222e-03, 1.71526609e-02, 8.59171734e-03,\n       5.10323590e-03, 5.17942219e-03, 6.27372616e-03, 8.53180991e-03,\n       6.24270221e-03, 5.81497566e-04, 9.92337152e-03, 1.10193192e-02,\n       4.20970018e-03, 7.13428111e-03, 3.45337543e-03, 1.49498112e-03,\n       6.19954877e-03, 1.27991689e-02, 1.89395421e-02, 2.08645939e-02,\n       1.92147679e-02, 4.73156141e-04, 4.70553348e-03, 1.53947405e-04,\n       2.79408196e-04, 6.46436647e-03, 5.36361384e-03, 4.24775412e-03,\n       1.03779139e-02, 9.92528633e-03, 1.02800484e-03, 6.41102843e-04,\n       1.18737652e-03, 6.83700084e-03, 6.54711043e-04, 4.17566209e-03,\n       6.80877762e-03, 5.62059165e-03, 3.78821711e-03, 5.97856240e-03,\n       5.65389710e-03, 2.21457802e-03, 1.00680969e-02, 5.15797595e-03,\n       1.55669373e-03, 9.91450808e-04, 1.15631886e-02, 6.12263193e-03,\n       6.17465150e-03, 9.54098034e-04, 1.12148938e-02, 1.43629315e-02,\n       5.06139869e-03, 2.02965805e-04, 1.49012102e-03, 1.49465744e-03,\n       1.34334335e-04, 6.45759159e-04, 4.39451280e-03, 5.81617286e-03,\n       1.02399821e-02, 6.96526834e-03, 9.10156740e-04, 1.59545347e-02,\n       6.03389614e-03, 7.15972092e-05, 2.73526221e-04, 1.61236508e-03,\n       5.19602619e-03, 4.58085183e-03, 3.05963602e-03, 1.02597652e-02,\n       1.70438653e-03, 2.35821281e-02, 2.92636788e-02, 2.63374666e-02,\n       6.22409185e-03, 2.35373999e-04, 6.90831245e-04, 1.42413214e-02]), 'noise_mean': array([0.01059496, 0.01059442, 0.01059352, 0.01059225, 0.01059063,\n       0.01058865, 0.01058631, 0.01058361, 0.01058056, 0.01057715,\n       0.01057339, 0.01056927, 0.01056481, 0.01055999, 0.01055483,\n       0.01054933, 0.01054348, 0.01053729, 0.01053077, 0.01052391,\n       0.01051672, 0.0105092 , 0.01050135, 0.01049318, 0.01048469,\n       0.01047589, 0.01046677, 0.01045734, 0.01044761, 0.01043757,\n       0.01042724, 0.01041661, 0.01040569, 0.01039449, 0.010383  ,\n       0.01037124, 0.01035921, 0.0103469 , 0.01033434, 0.01032151,\n       0.01030843, 0.0102951 , 0.01028153, 0.01026771, 0.01025367,\n       0.01023939, 0.01022488, 0.01021016, 0.01019522, 0.01018008,\n       0.01016473, 0.01014918, 0.01013343, 0.0101175 , 0.01010139,\n       0.0100851 , 0.01006863, 0.010052  , 0.01003521, 0.01001826,\n       0.01000116, 0.00998392, 0.00996654, 0.00994902, 0.00993137,\n       0.0099136 , 0.00989571, 0.00987771, 0.0098596 , 0.00984139,\n       0.00982308, 0.00980468, 0.00978619, 0.00976762, 0.00974897,\n       0.00973026, 0.00971148, 0.00969263, 0.00967374, 0.00965479,\n       0.00963579, 0.00961676, 0.00959769, 0.00957859, 0.00955946,\n       0.00954031, 0.00952114, 0.00950196, 0.00948278, 0.00946359,\n       0.0094444 , 0.00942522, 0.00940605, 0.00938689, 0.00936776,\n       0.00934864, 0.00932956, 0.0093105 , 0.00929148, 0.0092725 ,\n       0.00925357, 0.00923468, 0.00921584, 0.00919706, 0.00917834,\n       0.00915967, 0.00914108, 0.00912255, 0.0091041 , 0.00908573,\n       0.00906743, 0.00904922, 0.00903109, 0.00901305, 0.00899511,\n       0.00897726, 0.00895951, 0.00894186, 0.00892432, 0.00890688,\n       0.00888955, 0.00887234, 0.00885524, 0.00883826, 0.0088214 ,\n       0.00880466, 0.00878805, 0.00877157, 0.00875522, 0.008739  ,\n       0.00872291, 0.00870697, 0.00869116, 0.00867549, 0.00865997,\n       0.00864459, 0.00862936, 0.00861428, 0.00859935, 0.00858457,\n       0.00856995, 0.00855548, 0.00854117, 0.00852703, 0.00851304,\n       0.00849922, 0.00848556, 0.00847206, 0.00845874, 0.00844558,\n       0.00843259, 0.00841978, 0.00840713, 0.00839467, 0.00838237,\n       0.00837026, 0.00835832, 0.00834656, 0.00833498, 0.00832358,\n       0.00831236, 0.00830132, 0.00829047, 0.0082798 , 0.00826932,\n       0.00825903, 0.00824892, 0.00823901, 0.00822928, 0.00821974,\n       0.00821039, 0.00820123, 0.00819227, 0.00818349, 0.00817491,\n       0.00816653, 0.00815834, 0.00815034, 0.00814254, 0.00813494,\n       0.00812753, 0.00812033, 0.00811332, 0.0081065 , 0.00809989,\n       0.00809347, 0.00808726, 0.00808125, 0.00807543, 0.00806982,\n       0.00806441, 0.0080592 , 0.00805419, 0.00804938, 0.00804478,\n       0.00804037, 0.00803618, 0.00803218, 0.00802839, 0.0080248 ,\n       0.00802142, 0.00801824, 0.00801526, 0.00801249, 0.00800993,\n       0.00800756, 0.00800541, 0.00800346, 0.00800171, 0.00800017,\n       0.00799883, 0.0079977 , 0.00799678, 0.00799606, 0.00799554,\n       0.00799523]), 'noise_threshold': array([0.03173966, 0.03173804, 0.03173534, 0.03173155, 0.03172669,\n       0.03172076, 0.03171375, 0.03170567, 0.03169652, 0.0316863 ,\n       0.03167503, 0.03166271, 0.03164933, 0.03163491, 0.03161945,\n       0.03160296, 0.03158544, 0.03156691, 0.03154736, 0.03152681,\n       0.03150527, 0.03148274, 0.03145924, 0.03143476, 0.03140933,\n       0.03138295, 0.03135563, 0.03132739, 0.03129823, 0.03126817,\n       0.03123721, 0.03120537, 0.03117267, 0.0311391 , 0.0311047 ,\n       0.03106946, 0.03103341, 0.03099655, 0.03095891, 0.03092048,\n       0.0308813 , 0.03084137, 0.03080071, 0.03075932, 0.03071724,\n       0.03067446, 0.03063102, 0.03058691, 0.03054216, 0.03049679,\n       0.0304508 , 0.03040422, 0.03035706, 0.03030933, 0.03026106,\n       0.03021225, 0.03016293, 0.03011311, 0.03006281, 0.03001204,\n       0.02996081, 0.02990915, 0.02985708, 0.0298046 , 0.02975173,\n       0.02969849, 0.0296449 , 0.02959097, 0.02953672, 0.02948216,\n       0.02942731, 0.02937218, 0.0293168 , 0.02926117, 0.02920532,\n       0.02914925, 0.02909299, 0.02903654, 0.02897992, 0.02892316,\n       0.02886626, 0.02880923, 0.0287521 , 0.02869488, 0.02863758,\n       0.02858021, 0.02852279, 0.02846534, 0.02840786, 0.02835038,\n       0.0282929 , 0.02823544, 0.028178  , 0.02812062, 0.02806329,\n       0.02800603, 0.02794885, 0.02789177, 0.02783479, 0.02777794,\n       0.02772121, 0.02766462, 0.02760819, 0.02755193, 0.02749584,\n       0.02743993, 0.02738423, 0.02732873, 0.02727345, 0.0272184 ,\n       0.02716359, 0.02710903, 0.02705473, 0.02700069, 0.02694694,\n       0.02689346, 0.02684029, 0.02678742, 0.02673486, 0.02668262,\n       0.02663072, 0.02657915, 0.02652793, 0.02647706, 0.02642655,\n       0.02637641, 0.02632665, 0.02627728, 0.02622829, 0.0261797 ,\n       0.02613151, 0.02608374, 0.02603638, 0.02598945, 0.02594295,\n       0.02589688, 0.02585125, 0.02580607, 0.02576134, 0.02571707,\n       0.02567327, 0.02562993, 0.02558707, 0.02554469, 0.02550279,\n       0.02546137, 0.02542045, 0.02538003, 0.02534011, 0.0253007 ,\n       0.02526179, 0.0252234 , 0.02518553, 0.02514817, 0.02511134,\n       0.02507505, 0.02503928, 0.02500405, 0.02496935, 0.0249352 ,\n       0.0249016 , 0.02486854, 0.02483603, 0.02480408, 0.02477268,\n       0.02474184, 0.02471157, 0.02468185, 0.02465271, 0.02462413,\n       0.02459613, 0.02456869, 0.02454183, 0.02451555, 0.02448985,\n       0.02446473, 0.0244402 , 0.02441625, 0.02439288, 0.02437011,\n       0.02434792, 0.02432632, 0.02430532, 0.02428491, 0.0242651 ,\n       0.02424588, 0.02422727, 0.02420925, 0.02419183, 0.02417501,\n       0.0241588 , 0.02414319, 0.02412819, 0.02411379, 0.0241    ,\n       0.02408681, 0.02407423, 0.02406227, 0.02405091, 0.02404016,\n       0.02403002, 0.0240205 , 0.02401158, 0.02400328, 0.02399559,\n       0.02398852, 0.02398206, 0.02397621, 0.02397098, 0.02396636,\n       0.02396236, 0.02395897, 0.0239562 , 0.02395404, 0.02395251,\n       0.02395158]), 'peaks':    period_days  frequency     power  noise_level       snr\n0    39.454545   0.025346  0.057044     0.009711  5.873872, 'log_ret': array([ 5.33027564e-02,  4.51529173e-02, -8.77261502e-02, -1.10036874e-01,\n       -1.08615051e-02,  1.79233020e-01,  5.80127340e-02,  2.07502906e-01,\n        4.11773085e-02,  3.63010155e-02,  1.74325575e-01, -2.34263335e-01,\n        3.24422460e-01,  1.27168508e-01,  2.01471803e-01,  2.88539264e-01,\n        2.35684467e-01, -3.34354652e-01,  1.58995955e-02,  1.63332626e-01,\n       -1.81086533e-01, -1.55883040e-01,  2.98997709e-02, -3.72572108e-01,\n       -1.48851671e-02,  2.52816436e-01, -7.94903774e-02,  1.82929644e-01,\n       -1.88831279e-01, -1.51906577e-01,  3.36355412e-02, -2.17714198e-01,\n        2.96442576e-02,  1.74381885e-01,  5.04154706e-02,  6.94532932e-02,\n        2.51622327e-02, -1.06903041e-01, -1.77528637e-02, -1.50034023e-01,\n        4.98693332e-02, -1.31309781e-01, -4.77411196e-02, -4.96885289e-02,\n        3.51955442e-02,  5.43851885e-02, -5.49831514e-02,  1.51846982e-01,\n        1.04699021e-01, -1.56114813e-01, -1.07488924e-01,  2.64682826e-02,\n        3.37682615e-02,  8.60421263e-02, -1.55438597e-01,  3.98600566e-02,\n        3.07297320e-02, -1.22135190e-02, -1.70822352e-03, -4.26044393e-02,\n        3.87953782e-02, -1.53182955e-02, -6.28863950e-04, -5.57220787e-03,\n       -1.30273486e-01, -3.26264534e-01,  1.82451807e-02, -1.60115306e-01,\n       -9.35722392e-02,  1.96495344e-01, -3.30620598e-02,  4.76859741e-02,\n       -1.37074339e-01,  1.78081099e-02,  3.26087859e-03, -2.64619982e-02,\n        6.22068593e-02, -3.39701237e-03,  2.05049863e-02,  1.70014332e-02,\n        2.82416366e-02,  1.62881104e-02,  2.76923717e-03,  2.76124769e-02,\n        2.30974987e-01, -7.56587314e-03,  2.40378787e-02,  9.72776579e-03,\n        8.45209271e-02,  1.87583601e-01,  1.56602196e-01,  5.56072973e-02,\n        1.28660786e-02, -1.34462053e-01,  1.60182800e-01,  1.97293985e-01,\n       -4.77662618e-03,  4.96176853e-02, -1.14307436e-01,  4.00050841e-02,\n       -1.04203324e-01,  1.35785953e-01,  5.52419882e-02, -1.13880908e-01,\n       -1.60486821e-02, -4.20554160e-02,  6.52967185e-02, -7.65552960e-03,\n       -2.68710882e-02, -2.20563839e-01, -2.38493082e-02,  5.21853432e-02,\n       -6.26246110e-03,  1.47459704e-01, -3.58090537e-02, -1.70277743e-02,\n       -6.12520678e-02, -2.08351816e-01,  6.82514998e-02,  1.60019521e-02,\n       -5.35404407e-02,  5.23853300e-02, -1.52054552e-02, -3.99946556e-03,\n        1.06410686e-01,  6.12176415e-02, -1.00135389e-02,  7.98919753e-02,\n        8.42492591e-02, -2.33684226e-02,  1.92710022e-03, -1.52395046e-01,\n       -6.02130886e-02, -4.04390164e-01,  8.14389252e-02,  1.11528212e-02,\n        1.41113408e-01,  1.91588984e-02,  3.09411164e-02,  7.73121706e-02,\n        1.45071058e-01, -1.94580336e-02,  1.04129185e-01, -1.04407806e-01,\n        8.01734280e-02,  3.11268634e-02, -4.24274530e-02, -5.08779702e-03,\n       -1.93737647e-02, -5.16229228e-03,  2.54028430e-02, -1.01298745e-02,\n        7.55353708e-02,  1.08645139e-01,  5.36611122e-02,  1.94405402e-02,\n       -2.23166908e-02,  5.39658123e-03, -1.32659833e-01,  7.44380419e-03,\n        5.52954061e-02, -1.34625830e-02, -1.00388514e-02,  6.37719357e-02,\n        1.17279293e-02,  1.24544553e-01,  5.47102439e-02,  1.17357442e-01,\n        3.06653238e-02,  1.43236992e-01, -1.25380679e-02,  6.25815611e-02,\n       -9.57126389e-03,  2.01499036e-01,  1.13765496e-01,  2.27637719e-01,\n        1.45017205e-01, -6.27795457e-02, -1.04921763e-01,  2.54962024e-02,\n        1.58987983e-01,  2.24857273e-01,  1.67027165e-01, -2.40520974e-01,\n        1.21598936e-01,  1.45728623e-01, -2.78001364e-02, -2.78271517e-02,\n        4.25469973e-02,  3.04651712e-02, -6.63582021e-02, -1.34844836e-01,\n        1.42441905e-01,  2.89628981e-02, -2.26608719e-01, -2.92528879e-01,\n        2.80550068e-02,  4.34057938e-03,  8.62441275e-02, -9.17388108e-02,\n       -2.56006471e-02,  1.67512527e-02, -2.95517259e-02, -7.51571635e-02,\n        1.07383670e-01,  1.18832451e-01,  9.44973007e-02,  7.00851549e-02,\n        4.71000352e-02, -9.61958625e-03,  5.94864960e-02, -1.17367429e-01,\n        2.60880939e-02, -9.03430427e-02,  1.10424236e-01,  1.25754657e-01,\n        1.18383834e-01, -1.10494172e-02,  7.32827837e-03,  3.16914422e-02,\n        3.48736807e-02, -1.11231309e-01, -2.32482607e-02, -1.47986003e-01,\n        1.32242949e-02, -6.97582681e-02,  8.40716012e-02, -7.11956826e-02,\n       -1.21776996e-01,  2.84241614e-02, -1.72576215e-01,  4.41807046e-02,\n        1.12227351e-01, -7.75065931e-03, -9.12304268e-02, -1.80805693e-02,\n        1.89638142e-02, -1.68897879e-02,  8.82351935e-02,  1.26531544e-01,\n       -9.01833761e-03, -9.60132250e-02, -6.06447246e-02, -5.76255967e-03,\n       -2.52015173e-02, -1.22346526e-01, -8.29486240e-02, -3.35929825e-02,\n       -2.76393702e-02, -1.03355084e-01, -2.55925096e-01,  2.23055109e-02,\n       -8.38180262e-03, -3.08732974e-03,  8.21858655e-02,  3.11162374e-02,\n       -5.11883455e-03,  4.76441753e-02, -1.21913247e-01, -9.55165164e-02,\n        1.09879578e-01, -1.17035041e-01, -3.18573931e-02,  3.30330402e-02,\n       -9.09726702e-03,  1.58331014e-02,  5.26059468e-02, -2.33629446e-01,\n       -3.04323315e-03,  9.08318664e-03,  3.91691372e-02, -2.05096823e-02,\n        5.59424187e-03, -1.28771489e-02,  3.02934032e-02,  1.97679966e-01,\n        8.43274088e-02,  4.45462666e-02, -3.46852706e-02, -5.14183581e-02,\n        1.08158896e-01, -2.99817906e-02, -4.89216956e-02, -1.94989673e-02,\n        2.40324031e-01, -1.72324660e-04,  7.26117513e-03,  5.37706601e-03,\n        6.76001377e-02, -9.38260311e-02,  5.78302568e-02, -2.78569487e-02,\n       -5.46673523e-02, -6.32961152e-03,  4.80717692e-02, -3.44284233e-02,\n       -4.48658561e-02,  1.58585884e-02,  1.45414114e-01,  5.05471880e-03,\n       -1.50163061e-02,  2.36055130e-03, -4.91547641e-03, -2.70432437e-02,\n       -6.60175834e-03,  7.37840862e-03, -1.12341289e-01, -3.37414857e-03,\n       -5.01451131e-03, -5.00263334e-03,  2.61963243e-02, -1.05780349e-02,\n        6.43348450e-02, -2.70150453e-03, -2.77077241e-02,  9.94161275e-02,\n        1.40763461e-01,  1.39779551e-02,  5.69622231e-02,  7.94721048e-03,\n        2.34121675e-03,  6.52476308e-02,  9.12085774e-02, -5.67259165e-02,\n        3.83340473e-02, -1.66035907e-02,  3.81763185e-02, -5.12985421e-02,\n       -3.64938851e-03,  1.07816499e-02,  1.30434258e-02,  1.25979059e-01,\n        7.64563665e-02, -7.87206717e-03,  1.98926487e-01,  8.85247401e-02,\n       -8.18938134e-03, -1.74556013e-02,  5.87940263e-02, -2.72999682e-02,\n       -5.47980205e-02, -1.10364597e-02, -2.84656620e-02,  1.40547526e-02,\n       -4.02937501e-02,  7.50209986e-02,  3.31478955e-02, -1.08905755e-02,\n        2.74008607e-02, -4.35978787e-02, -5.33954415e-02, -6.95340120e-03,\n       -1.16699919e-01,  8.47460588e-02,  1.14380811e-01,  1.23945130e-03,\n       -1.59960649e-01,  9.43915722e-03, -4.87008212e-03,  9.45305865e-02,\n       -1.13981605e-01, -4.33672441e-02,  7.48063932e-02,  7.25072193e-02,\n        3.13268834e-02, -4.33342743e-02,  7.97359067e-04,  9.35005998e-02,\n       -1.47432484e-02,  1.10279296e-02,  1.55786395e-01,  1.11567181e-01,\n        8.57386804e-02, -7.32872741e-03,  3.95867474e-02,  3.26374406e-02,\n       -9.30067100e-02, -1.53299184e-02,  4.81651283e-02, -3.95943723e-02,\n        6.93214650e-02,  1.26348342e-02, -6.18759221e-02, -3.57907167e-03,\n        1.45423480e-03, -1.75867704e-01,  2.25368965e-02,  4.16048593e-02,\n       -4.38421898e-02, -4.92574417e-02,  6.57490593e-02,  1.68021756e-02,\n        9.58664560e-02,  5.61963564e-03,  9.92810375e-02,  2.21905380e-02,\n        2.36709125e-02, -3.13214991e-02,  8.61683492e-04, -1.32486006e-03,\n       -4.48389161e-02,  7.06679553e-02,  7.78554220e-03,  8.66350679e-02,\n       -1.54139412e-02,  1.81720727e-02, -4.45810749e-02,  4.35627662e-02,\n       -1.59615372e-02, -3.38832215e-02, -4.73367115e-02,  2.63570857e-02,\n        3.64932049e-02, -3.09934523e-04, -2.69883639e-02,  9.61362667e-02,\n       -7.15241036e-02, -5.65085507e-02,  5.30282132e-02, -3.57098678e-02,\n       -5.40652110e-02, -1.05246189e-01, -8.21200142e-02,  3.98495075e-02,\n        3.90693922e-04, -2.49010052e-02,  5.50459078e-03, -7.99668745e-03,\n        3.98645539e-02, -5.65434227e-03,  2.88019933e-02, -7.76995318e-02,\n       -1.18719477e-01,  2.08465140e-02]), 'label': '1w'}}",
    "status": "success"
  },
  "wavelet": {
    "coeffs": "[[ 0.02328069+0.26052616j -0.20646269+0.21707895j -0.34924539-0.00701636j\n  ... -0.25097443+0.39542646j -0.4658674 +0.05792538j\n  -0.33212563-0.31214098j]\n [-0.05380191+0.28136408j -0.27530143+0.17218711j -0.35580267-0.10443455j\n  ... -0.29294848+0.33406657j -0.45377476-0.01811872j\n  -0.27502538-0.35805475j]\n [-0.14159683+0.27926617j -0.32962257+0.11214316j -0.33855591-0.21560006j\n  ... -0.35441039+0.28241528j -0.45698499-0.09949252j\n  -0.20063119-0.3801326j ]\n ...\n [ 0.77259572+0.56913313j  0.09758785-0.78071374j -1.63931615+2.88811983j\n  ... -1.01527336-0.81246447j -0.80740554+0.29699673j\n  -0.88811535+0.19742563j]\n [-3.71582342-1.56739578j  0.84204529+2.33937542j -0.73357428+0.85279372j\n  ...  0.23236656+1.79168259j -0.95454735-0.74439472j\n  -1.29585406+0.00785753j]\n [ 1.84842209-1.76757202j -0.20839682+3.25082443j -1.55862037+0.87078631j\n  ...  0.50350641+1.02651633j -0.4299536 -0.54308497j\n  -0.98618845+0.31493207j]]",
    "power": "[[ 0.06841587  0.08975011  0.12202157 ...  0.21935025  0.22038779\n   0.20773942]\n [ 0.08206039  0.10543928  0.13750212 ...  0.19741928  0.20623982\n   0.20384217]\n [ 0.09803925  0.12122713  0.16110349 ...  0.20536512  0.21873404\n   0.18475367]\n ...\n [ 0.92081667  0.61903733 11.02859358 ...  1.6908785   0.74011076\n   0.82772576]\n [16.2640732   6.18171765  1.26538835 ...  3.26412073  1.46528414\n   1.67929949]\n [ 6.54097509 10.61128869  3.18756627 ...  1.30725448  0.47980138\n   1.07174987]]",
    "periods": "[   7.            7.14889947    7.30096623    7.45626766    7.61487256\n    7.7768512     7.94227535    8.11121829    8.28375487    8.45996154\n    8.63991637    8.82369908    9.0113911     9.20307557    9.39883744\n    9.59876343    9.80294211   10.01146395   10.22442133   10.44190861\n   10.66402213   10.8908603    11.12252364   11.35911476   11.6007385\n   11.8475019    12.09951429   12.35688733   12.61973504   12.88817387\n   13.16232277   13.44230318   13.72823915   14.02025737   14.31848721\n   14.6230608    14.93411309   15.25178187   15.57620791   15.90753492\n   16.24590971   16.59148218   16.94440545   17.30483588   17.67293315\n   18.04886034   18.43278402   18.82487427   19.22530481   19.63425305\n   20.05190018   20.47843122   20.91403515   21.35890497   21.81323778\n   22.27723485   22.75110177   23.23504849   23.7292894    24.23404349\n   24.74953437   25.27599045   25.81364496   26.36273611   26.92350717\n   27.49620659   28.0810881    28.67841083   29.28843942   29.91144415\n   30.54770103   31.19749196   31.86110481   32.53883361   33.23097862\n   33.93784649   34.6597504    35.39701017   36.14995246   36.91891086\n   37.70422603   38.50624593   39.32532587   40.16182875   41.01612517\n   41.88859364   42.77962069   43.6896011    44.61893801   45.56804319\n   46.53733711   47.52724922   48.53821811   49.57069167   50.62512734\n   51.70199228   52.8017636    53.92492854   55.07198472   56.24344033\n   57.43981439   58.66163695   59.90944932   61.18380435   62.48526664\n   63.81441279   65.17183167   66.5581247    67.97390605   69.41980298\n   70.8964561    72.40451963   73.94466171   75.51756469   77.12392545\n   78.76445568   80.4398822    82.15094731   83.89840909   85.68304175\n   87.50563596   89.36699921   91.26795618   93.20934907   95.19203802\n   97.21690144   99.28483644  101.39675923  103.55360546  105.75633073\n  108.00591095  110.30334279  112.6496441   115.04585442  117.49303538\n  119.99227118  122.54466911  125.15136     127.81349873  130.53226475\n  133.3088626   136.14452245  139.04050061  141.99808015  145.01857141\n  148.1033126   151.25367041  154.47104059  157.75684859  161.11255017\n  164.53963206  168.03961262  171.6140425   175.26450534  178.99261846\n  182.80003359  186.68843759  190.6595532   194.71513982  198.85699425\n  203.08695153  207.40688572  211.81871075  216.32438127  220.9258935\n  225.62528612  230.42464117  235.326085    240.33178917  245.44397145\n  250.66489675  255.9968782   261.4422781   267.00350903  272.68303486\n  278.48337189  284.40708994  290.45681348  296.63522282  302.94505529\n  309.38910644  315.97023126  322.69134552  329.55542697  336.56551671\n  343.72472055  351.03621034  358.50322541  366.12907398  373.91713468\n  381.87085796  389.99376769  398.28946271  406.76161837  415.41398825\n  424.25040574  433.27478579  442.49112661  451.90351146  461.51611047\n  471.33318247  481.35907687  491.5982356   502.0551951   512.73458826\n  523.64114657  534.77970213  546.15518983  557.77264954  569.63722834\n  581.75418277  594.12888122  606.76680625  619.67355704  632.85485188\n  646.31653068  660.06455761  674.10502367  688.44414944  703.08828781\n  718.04392681  733.31769248  748.91635182  764.84681573  781.11614217\n  797.73153919  814.70036818  832.03014712  849.72855389  867.80342973\n  886.26278262  905.11479092  924.36780694  944.03036065  964.11116349\n  984.61911216 1005.56329264 1026.95298418 1048.79766339 1071.10700847\n 1093.89090351 1117.15944282 1140.92293543 1165.19190968 1189.97711784\n 1215.28954091 1241.14039345 1267.54112861 1294.50344311 1322.03928252\n 1350.16084648 1378.88059415 1408.2112497  1438.16580797 1468.75754022\n 1500.        ]",
    "scales": "[   7.            7.14889947    7.30096623    7.45626766    7.61487256\n    7.7768512     7.94227535    8.11121829    8.28375487    8.45996154\n    8.63991637    8.82369908    9.0113911     9.20307557    9.39883744\n    9.59876343    9.80294211   10.01146395   10.22442133   10.44190861\n   10.66402213   10.8908603    11.12252364   11.35911476   11.6007385\n   11.8475019    12.09951429   12.35688733   12.61973504   12.88817387\n   13.16232277   13.44230318   13.72823915   14.02025737   14.31848721\n   14.6230608    14.93411309   15.25178187   15.57620791   15.90753492\n   16.24590971   16.59148218   16.94440545   17.30483588   17.67293315\n   18.04886034   18.43278402   18.82487427   19.22530481   19.63425305\n   20.05190018   20.47843122   20.91403515   21.35890497   21.81323778\n   22.27723485   22.75110177   23.23504849   23.7292894    24.23404349\n   24.74953437   25.27599045   25.81364496   26.36273611   26.92350717\n   27.49620659   28.0810881    28.67841083   29.28843942   29.91144415\n   30.54770103   31.19749196   31.86110481   32.53883361   33.23097862\n   33.93784649   34.6597504    35.39701017   36.14995246   36.91891086\n   37.70422603   38.50624593   39.32532587   40.16182875   41.01612517\n   41.88859364   42.77962069   43.6896011    44.61893801   45.56804319\n   46.53733711   47.52724922   48.53821811   49.57069167   50.62512734\n   51.70199228   52.8017636    53.92492854   55.07198472   56.24344033\n   57.43981439   58.66163695   59.90944932   61.18380435   62.48526664\n   63.81441279   65.17183167   66.5581247    67.97390605   69.41980298\n   70.8964561    72.40451963   73.94466171   75.51756469   77.12392545\n   78.76445568   80.4398822    82.15094731   83.89840909   85.68304175\n   87.50563596   89.36699921   91.26795618   93.20934907   95.19203802\n   97.21690144   99.28483644  101.39675923  103.55360546  105.75633073\n  108.00591095  110.30334279  112.6496441   115.04585442  117.49303538\n  119.99227118  122.54466911  125.15136     127.81349873  130.53226475\n  133.3088626   136.14452245  139.04050061  141.99808015  145.01857141\n  148.1033126   151.25367041  154.47104059  157.75684859  161.11255017\n  164.53963206  168.03961262  171.6140425   175.26450534  178.99261846\n  182.80003359  186.68843759  190.6595532   194.71513982  198.85699425\n  203.08695153  207.40688572  211.81871075  216.32438127  220.9258935\n  225.62528612  230.42464117  235.326085    240.33178917  245.44397145\n  250.66489675  255.9968782   261.4422781   267.00350903  272.68303486\n  278.48337189  284.40708994  290.45681348  296.63522282  302.94505529\n  309.38910644  315.97023126  322.69134552  329.55542697  336.56551671\n  343.72472055  351.03621034  358.50322541  366.12907398  373.91713468\n  381.87085796  389.99376769  398.28946271  406.76161837  415.41398825\n  424.25040574  433.27478579  442.49112661  451.90351146  461.51611047\n  471.33318247  481.35907687  491.5982356   502.0551951   512.73458826\n  523.64114657  534.77970213  546.15518983  557.77264954  569.63722834\n  581.75418277  594.12888122  606.76680625  619.67355704  632.85485188\n  646.31653068  660.06455761  674.10502367  688.44414944  703.08828781\n  718.04392681  733.31769248  748.91635182  764.84681573  781.11614217\n  797.73153919  814.70036818  832.03014712  849.72855389  867.80342973\n  886.26278262  905.11479092  924.36780694  944.03036065  964.11116349\n  984.61911216 1005.56329264 1026.95298418 1048.79766339 1071.10700847\n 1093.89090351 1117.15944282 1140.92293543 1165.19190968 1189.97711784\n 1215.28954091 1241.14039345 1267.54112861 1294.50344311 1322.03928252\n 1350.16084648 1378.88059415 1408.2112497  1438.16580797 1468.75754022\n 1500.        ]",
    "global_spectrum": "[0.25077391 0.24918094 0.24941687 0.2504881  0.25240582 0.25525391\n 0.25898862 0.26376429 0.26935332 0.27455489 0.27947742 0.28353609\n 0.28676357 0.2891057  0.29210646 0.29362188 0.29412211 0.29445745\n 0.2939006  0.29286818 0.29179009 0.29204764 0.29293345 0.29365153\n 0.29497844 0.29571596 0.29647632 0.29805719 0.30002801 0.30242879\n 0.30747759 0.31185429 0.31553841 0.31908541 0.32174943 0.32216001\n 0.32193909 0.32039994 0.31822881 0.31398254 0.31506872 0.3158115\n 0.31656951 0.31765911 0.31945808 0.32277086 0.32437837 0.32459836\n 0.32487274 0.32467153 0.32209146 0.31883766 0.31417485 0.30792675\n 0.30683324 0.30430268 0.30232617 0.30232136 0.29807977 0.30136326\n 0.30111445 0.30160486 0.30229138 0.30617061 0.30775735 0.30909131\n 0.31023575 0.31078703 0.31346378 0.31258885 0.31289966 0.31232306\n 0.31093536 0.31629751 0.32231713 0.32760407 0.33524067 0.34372877\n 0.34958807 0.36029236 0.36393259 0.36960937 0.380321   0.37777465\n 0.38660412 0.3817282  0.38142149 0.37392835 0.3771821  0.36536764\n 0.35949935 0.35521001 0.35054834 0.3391412  0.33515301 0.33574682\n 0.33469351 0.33629778 0.32900688 0.33618414 0.33406263 0.33411697\n 0.33366681 0.3332418  0.33072821 0.33175012 0.33514366 0.34296791\n 0.35151023 0.35826434 0.36626216 0.37209976 0.3786771  0.39493223\n 0.41150026 0.41399706 0.4195743  0.44312145 0.45001722 0.43049709\n 0.43983845 0.48198567 0.46729729 0.47646645 0.44549643 0.45087622\n 0.44784329 0.46497273 0.49352648 0.49749856 0.53352332 0.51200799\n 0.53818475 0.54044329 0.55697902 0.69658938 0.62025399 0.5851625\n 0.73589538 0.60995301 0.63926042 0.86816171 0.70925519 0.71070872\n 0.67506702 0.70610452 0.76686938 0.70570503 0.85371279 1.03531524\n 0.93146292 0.78058382 0.78541885 0.9627667  0.86513844 0.94032968\n 0.87357484 0.97332314 0.96129918 1.09247097 1.04543466 0.96682145\n 1.53955322 1.42474782 1.19435845 1.15774524 1.14200367 1.30176847\n 1.38728933 1.22529162 1.76475319 1.58469429 1.53138518 1.7918166\n 1.6967658  1.93441562 1.75576069 1.58405978 1.98106888 1.7876563\n 1.82735277 2.28674511 1.80980915 1.89057293 1.98579934 2.02493933\n 1.92600972 1.50439865 1.67566079 1.35280685 1.87119419 2.0870223\n 1.65084514 1.95687734 2.11703605 1.82268409 2.13709538 1.78782023\n 1.93012932 2.22704236 2.16854292 1.96853606 1.95824631 2.23369913\n 2.47086236 2.41820332 2.67291022 2.67348578 3.03726617 2.83673764\n 3.00076609 3.20914749 3.50504162 3.02953861 3.72850302 3.70003235\n 2.7976817  2.72329216 3.83344964 3.2395254  2.80703328 2.93938568\n 3.33617831 3.11893059 3.2023908  3.57161901 3.99088314 4.31406106\n 3.26689451 3.57459583 3.45445192 4.07236594 3.52213288 4.06689934\n 3.91357891 4.1029707  4.72966818 3.5443605  4.87534288 5.63786981\n 4.27792425 4.83971537 5.11896333 4.99969839 4.98329126 5.04698897\n 6.30182074 5.70438178 5.23567811 5.3025015  5.92358113 5.82026112\n 5.34002515 5.36422926 4.77090983 6.78411209]",
    "significance_threshold": "[0.32059109 0.32034576 0.32137139 0.32171169 0.3220394  0.3227787\n 0.32306636 0.32249046 0.32356305 0.32383931 0.32605108 0.32601491\n 0.32628265 0.32602384 0.32773452 0.3286617  0.32924628 0.33012448\n 0.33098881 0.33207269 0.33070675 0.33293743 0.33429595 0.33480052\n 0.33646451 0.33624654 0.33512066 0.33478793 0.33532374 0.33647977\n 0.3400494  0.34252272 0.34458403 0.34862861 0.34935883 0.35120582\n 0.35081204 0.34946446 0.34929904 0.34946795 0.35308668 0.35610222\n 0.3579281  0.36080829 0.36106964 0.35990912 0.35948469 0.36032976\n 0.35942677 0.35875676 0.35674629 0.35553062 0.35524292 0.35246702\n 0.35605134 0.35819701 0.36111867 0.36493847 0.36667499 0.3682861\n 0.37045654 0.37230951 0.37206124 0.37122094 0.3719268  0.37178344\n 0.37241458 0.37394713 0.3753374  0.37411819 0.37298609 0.37195877\n 0.37223742 0.3745387  0.37716889 0.37940813 0.38298291 0.3862863\n 0.39371023 0.39464904 0.39854882 0.40354847 0.40812076 0.41172165\n 0.41732554 0.42056548 0.4233097  0.42751974 0.42677551 0.42958672\n 0.43227243 0.43071772 0.42819891 0.43353045 0.43623476 0.43194615\n 0.42764474 0.42722706 0.42466364 0.42352605 0.42591561 0.42125877\n 0.41894647 0.41495988 0.41193899 0.41269935 0.41338079 0.42186647\n 0.42928977 0.43681649 0.44421849 0.45074203 0.46380284 0.4718612\n 0.48261419 0.49096699 0.50047583 0.50858729 0.51469839 0.52897375\n 0.54216927 0.55237461 0.5678475  0.58039907 0.59365508 0.61248644\n 0.62331872 0.63103024 0.65519679 0.66627606 0.68233508 0.71176567\n 0.72366062 0.73370075 0.75579744 0.78790309 0.78706514 0.80643025\n 0.88957624 0.8349449  0.86191608 0.88844233 0.90740232 0.92514995\n 0.95153783 0.95524355 1.00592679 0.98984328 1.01116049 1.03465135\n 1.0656006  1.07812401 1.10734688 1.09903608 1.14475211 1.15672434\n 1.20196582 1.21460134 1.2232702  1.28082091 1.30846799 1.32987165\n 1.35210738 1.3752512  1.39705735 1.43532667 1.45979443 1.48527367\n 1.50267643 1.55414828 1.57960198 1.60310296 1.64576765 1.6554778\n 1.70885242 1.73032155 1.79524331 1.82102073 1.82422713 1.86346766\n 1.92354131 1.9586259  2.01676233 2.07146309 2.08322202 2.11523072\n 2.20025718 2.18479054 2.27146889 2.2990042  2.37795339 2.4261683\n 2.45638761 2.46214359 2.57676944 2.56274466 2.69367552 2.68364644\n 2.73173025 2.8275726  2.86944395 2.87803218 3.0358048  3.02160946\n 3.11173506 3.13930219 3.24274685 3.24195306 3.34297808 3.43492652\n 3.56552477 3.47321863 3.61978421 3.62160822 3.72593593 3.7702992\n 3.88460804 3.98525763 4.06289566 4.26717868 4.16014696 4.27487866\n 4.34735961 4.40921197 4.45520602 4.57869407 4.70453082 4.76210103\n 4.81253173 4.85567132 5.01397897 5.12493253 5.12322935 5.26596318\n 5.2912091  5.4959322  5.51322923 5.66887711 5.72748205 5.84904821\n 5.92549917 6.03547683 6.14720246 6.19921027 6.29156422 6.27575138\n 6.53942774 6.50943472 6.61774326 6.6675835  6.8285217  6.79276376\n 7.14321363 7.17137905 7.37121113 7.26783884]",
    "significant_periods": [
      {
        "period": 632.854851875512,
        "power": 3.7285030183071055,
        "threshold": 3.7259359318502976,
        "ratio": 1.0006889776162986
      },
      {
        "period": 315.9702312638088,
        "power": 2.2867451103683614,
        "threshold": 1.9586258959940284,
        "ratio": 1.1675252099165208
      },
      {
        "period": 296.63522282351886,
        "power": 1.9810688755923727,
        "threshold": 1.8242271344913545,
        "ratio": 1.085977090317073
      },
      {
        "period": 278.48337189110833,
        "power": 1.9344156199117137,
        "threshold": 1.7303215470604576,
        "ratio": 1.1179515294125417
      },
      {
        "period": 267.0035090282511,
        "power": 1.791816604081105,
        "threshold": 1.655477804875396,
        "ratio": 1.0823561625557228
      },
      {
        "period": 250.66489674822904,
        "power": 1.7647531853264709,
        "threshold": 1.579601980196533,
        "ratio": 1.11721383452995
      },
      {
        "period": 211.818710750956,
        "power": 1.5395532153660547,
        "threshold": 1.3521073845934857,
        "ratio": 1.1386323548768467
      },
      {
        "period": 161.11255016597912,
        "power": 1.0353152362298805,
        "threshold": 1.0346513496469452,
        "ratio": 1.0006416524592192
      }
    ],
    "key_period_power": "{30: {'power': array([0.22918997, 0.28504621, 0.25108058, ..., 0.06011753, 0.09682399,\n       0.08969407]), 'actual_period': 29.911444149464828}, 90: {'power': array([0.01491735, 0.01706459, 0.09584873, ..., 0.23423086, 0.09229694,\n       0.31491783]), 'actual_period': 89.3669992111722}, 365: {'power': array([0.13578548, 2.4333446 , 1.01885187, ..., 1.05534137, 0.46123795,\n       0.19825597]), 'actual_period': 366.1290739845146}, 1400: {'power': array([3.1543086 , 2.39629301, 3.47997683, ..., 6.31334402, 6.21208785,\n       4.25926692]), 'actual_period': 1408.2112496976965}}",
    "coi_periods": "[1.         1.41421356 2.82842712 ... 2.82842712 1.41421356 1.        ]",
    "ar1_alpha": -0.04993111698604457,
    "dates": "DatetimeIndex(['2017-08-18', '2017-08-19', '2017-08-20', '2017-08-21',\n               '2017-08-22', '2017-08-23', '2017-08-24', '2017-08-25',\n               '2017-08-26', '2017-08-27',\n               ...\n               '2026-01-23', '2026-01-24', '2026-01-25', '2026-01-26',\n               '2026-01-27', '2026-01-28', '2026-01-29', '2026-01-30',\n               '2026-01-31', '2026-02-01'],\n              dtype='datetime64[ns]', name='datetime', length=3090, freq=None)",
    "wavelet": "cmor1.5-1.0",
    "signal_length": 3090,
    "status": "success"
  },
  "acf": {
    "acf": "{'log_return': {'values': array([ 1.00000000e+00, -4.99311170e-02,  4.57749631e-02,  4.83708574e-03,\n        9.46251081e-03,  2.22693693e-02,  9.49020710e-03, -8.86443864e-03,\n       -2.40845498e-02,  1.02819105e-02,  4.51758386e-02, -1.24242901e-02,\n        2.36526371e-03,  1.86696403e-02, -9.53146602e-03,  1.38683198e-02,\n       -2.56086002e-02,  4.51782774e-02, -3.99563475e-03,  1.36648595e-02,\n        2.77474974e-02, -2.53052682e-02, -9.74322012e-03, -2.73115464e-02,\n        2.31136072e-02,  1.82907358e-02,  4.81710118e-03,  3.39146483e-03,\n        1.15691911e-02, -3.52202094e-02,  1.81525109e-02,  2.71625506e-02,\n       -2.64690669e-02,  4.56467919e-02, -9.91307736e-03,  9.77528668e-05,\n       -3.35170412e-02,  3.07773202e-02,  2.45121097e-03,  1.44379805e-02,\n        2.52359747e-02, -9.38786512e-03,  3.80632484e-03,  3.13717354e-02,\n        3.81689260e-02, -1.15791437e-02, -3.44874692e-02,  6.24004757e-03,\n        4.77409795e-03, -2.97030031e-02,  3.47922553e-03, -1.50718504e-02,\n        2.88881955e-03,  8.95437558e-03,  9.79159362e-03,  2.13849797e-02,\n        7.12672096e-03,  2.26820122e-03,  2.56756942e-02,  3.61415657e-02,\n       -3.63628183e-02, -2.36357127e-02, -1.28902589e-02, -1.33605795e-02,\n       -2.60550324e-03,  4.62823643e-02, -2.61581171e-02,  2.95217336e-02,\n       -6.61831079e-03, -2.04441594e-02,  2.55784878e-02, -2.93033879e-03,\n       -1.74829568e-02, -6.62495078e-03,  3.58046103e-03,  3.57117130e-02,\n       -7.59544339e-03,  4.63077308e-03,  4.58066027e-03, -2.57933008e-03,\n        2.60163524e-02, -1.07131082e-02,  2.77795645e-02, -2.05483151e-02,\n        1.79392452e-02,  5.21385651e-03, -9.56107310e-04,  1.60853228e-02,\n       -3.01915143e-02, -6.75758697e-03, -4.26058723e-03,  8.92553823e-03,\n        2.90220417e-02, -2.72513636e-02, -1.61091292e-02, -2.47893124e-03,\n       -2.19665856e-02,  1.91883085e-02, -2.94662228e-02,  2.16478164e-02,\n        1.98360572e-03]), 'confint': array([[ 1.00000000e+00,  1.00000000e+00],\n       [-8.51900245e-02, -1.46722095e-02],\n       [ 1.04282604e-02,  8.11216658e-02],\n       [-3.05832365e-02,  4.02574080e-02],\n       [-2.59586326e-02,  4.48836542e-02],\n       [-1.31549166e-02,  5.76936551e-02],\n       [-2.59514786e-02,  4.49318928e-02],\n       [-4.43092834e-02,  2.65804061e-02],\n       [-5.95321505e-02,  1.13630510e-02],\n       [-2.51860280e-02,  4.57498490e-02],\n       [ 9.70419483e-03,  8.06474825e-02],\n       [-4.79673888e-02,  2.31188086e-02],\n       [-3.31832337e-02,  3.79137611e-02],\n       [-1.68790528e-02,  5.42183333e-02],\n       [-4.50923465e-02,  2.60294144e-02],\n       [-2.16957365e-02,  4.94323762e-02],\n       [-6.11793791e-02,  9.96217864e-03],\n       [ 9.58458584e-03,  8.07719690e-02],\n       [-3.96605443e-02,  3.16692748e-02],\n       [-2.20006066e-02,  4.93303255e-02],\n       [-7.92447683e-03,  6.34194717e-02],\n       [-6.10040647e-02,  1.03935283e-02],\n       [-4.54643097e-02,  2.59778695e-02],\n       [-6.30359396e-02,  8.41284692e-03],\n       [-1.26367343e-02,  5.88639487e-02],\n       [-1.74781786e-02,  5.40596503e-02],\n       [-3.09634391e-02,  4.05976414e-02],\n       [-3.23898816e-02,  3.91728113e-02],\n       [-2.42125550e-02,  4.73509372e-02],\n       [-7.10066055e-02,  5.66186707e-04],\n       [-1.76769520e-02,  5.39819738e-02],\n       [-8.67834373e-03,  6.30034450e-02],\n       [-6.23355438e-02,  9.39741009e-03],\n       [ 9.75603881e-03,  8.15375450e-02],\n       [-4.58759312e-02,  2.60497765e-02],\n       [-3.58684978e-02,  3.60640036e-02],\n       [-6.94832922e-02,  2.44920984e-03],\n       [-5.22774046e-03,  6.67823809e-02],\n       [-3.35865415e-02,  3.84889634e-02],\n       [-2.15999792e-02,  5.04759403e-02],\n       [-1.08091753e-02,  6.12811247e-02],\n       [-4.54549734e-02,  2.66792432e-02],\n       [-3.22638212e-02,  3.98764708e-02],\n       [-4.69890998e-03,  6.74423807e-02],\n       [ 2.06437618e-03,  7.42734758e-02],\n       [-4.77338231e-02,  2.45755357e-02],\n       [-7.06467586e-02,  1.67182015e-03],\n       [-2.99601109e-02,  4.24402060e-02],\n       [-3.14273977e-02,  4.09755936e-02],\n       [-6.59052815e-02,  6.49927522e-03],\n       [-3.27533374e-02,  3.97117884e-02],\n       [-5.13048286e-02,  2.11611278e-02],\n       [-3.33519520e-02,  3.91295911e-02],\n       [-2.72866822e-02,  4.51954334e-02],\n       [-2.64522145e-02,  4.60354018e-02],\n       [-1.48621169e-02,  5.76320763e-02],\n       [-2.91360572e-02,  4.33894991e-02],\n       [-3.39963181e-02,  3.85327205e-02],\n       [-1.05890015e-02,  6.19403899e-02],\n       [-1.45722381e-04,  7.24288538e-02],\n       [-7.26948293e-02, -3.08073324e-05],\n       [-6.00129398e-02,  1.27415144e-02],\n       [-4.92865727e-02,  2.35060549e-02],\n       [-4.97625684e-02,  2.30414094e-02],\n       [-3.90135878e-02,  3.38025814e-02],\n       [ 9.87404787e-03,  8.26906807e-02],\n       [-6.26395024e-02,  1.03232682e-02],\n       [-6.98296159e-03,  6.60264288e-02],\n       [-4.31526746e-02,  2.99160530e-02],\n       [-5.69800136e-02,  1.60916949e-02],\n       [-1.09715856e-02,  6.21285611e-02],\n       [-3.95026589e-02,  3.36419813e-02],\n       [-5.40555688e-02,  1.90896552e-02],\n       [-4.32079512e-02,  2.99580496e-02],\n       [-3.30040309e-02,  4.01649529e-02],\n       [-8.73214556e-04,  7.22966405e-02],\n       [-4.42236821e-02,  2.90327953e-02],\n       [-3.19994236e-02,  4.12609698e-02],\n       [-3.20502642e-02,  4.12115848e-02],\n       [-3.92109667e-02,  3.40523065e-02],\n       [-1.06155100e-02,  6.26482148e-02],\n       [-4.73679340e-02,  2.59417175e-02],\n       [-8.87915358e-03,  6.44382826e-02],\n       [-5.72331943e-02,  1.61365641e-02],\n       [-1.87599401e-02,  5.46384304e-02],\n       [-3.14962287e-02,  4.19239417e-02],\n       [-3.76671131e-02,  3.57548985e-02],\n       [-2.06257139e-02,  5.27963596e-02],\n       [-6.69113119e-02,  6.52828336e-03],\n       [-4.35082325e-02,  2.99930585e-02],\n       [-4.10127774e-02,  3.24916030e-02],\n       [-2.78272660e-02,  4.56783425e-02],\n       [-7.73345715e-03,  6.57775406e-02],\n       [-6.40353401e-02,  9.53261280e-03],\n       [-5.29181960e-02,  2.06999377e-02],\n       [-3.92967615e-02,  3.43388991e-02],\n       [-5.87846234e-02,  1.48514521e-02],\n       [-1.76460187e-02,  5.60226358e-02],\n       [-6.63129747e-02,  7.38052908e-03],\n       [-1.52282185e-02,  5.85238513e-02],\n       [-3.49082245e-02,  3.88754359e-02]]), 'significant_lags': [1, 2, 10, 17, 33, 44, 59, 60, 65, 75], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'squared_return': {'values': array([ 1.00000000e+00,  1.02653192e-01,  3.20545399e-02,  2.29973442e-02,\n        7.78292151e-02,  4.34819920e-02,  3.30282872e-02,  6.67284036e-02,\n        2.35056717e-02,  1.98387597e-02,  2.98393393e-02,  5.66689263e-02,\n        2.47920754e-02,  1.04139545e-02,  2.87428229e-02,  4.24149402e-02,\n        3.45524415e-02,  3.06341072e-02,  2.67352816e-02,  2.09007391e-02,\n        2.57768613e-02,  4.33097679e-02,  1.37203718e-02,  2.03443463e-02,\n        3.05603266e-02,  4.28286931e-02,  1.40305762e-02,  1.54338134e-02,\n        1.92783134e-02,  3.06205711e-02,  1.18403307e-02,  1.70852974e-02,\n        1.84187526e-02,  1.48458009e-02,  7.62125866e-03,  3.41023023e-02,\n        2.62772331e-02,  2.09576181e-02,  1.37041975e-02,  1.72937635e-02,\n        2.70472006e-02,  3.70961355e-02,  9.38186857e-03,  4.29858164e-03,\n        2.32871014e-02,  2.39570419e-02,  1.60131530e-02,  9.24196843e-03,\n        5.48078835e-02,  5.81256896e-03,  4.63221537e-03,  1.46050136e-03,\n        7.96728343e-03,  7.11812679e-03,  9.99116446e-03,  2.54949418e-03,\n        4.19762237e-02,  1.40952448e-02,  3.13433925e-02,  2.98914714e-02,\n        1.94419913e-02,  1.20048936e-02,  3.18753266e-02,  1.12525941e-02,\n        1.22923275e-02,  1.12236197e-02,  1.85317299e-02,  1.09289717e-02,\n        2.21909607e-03,  8.50253557e-03,  2.19780239e-02,  2.27763020e-03,\n        1.44769808e-02,  5.75677585e-03,  2.85303209e-03,  2.86174334e-04,\n       -2.20485889e-03,  1.17541113e-02,  7.21400106e-03,  2.70018625e-03,\n        3.14322385e-03,  3.10666008e-02,  2.18093183e-02,  2.08393812e-02,\n        2.29260992e-02,  2.81165093e-02,  2.45944498e-02, -5.17530450e-04,\n        5.83012550e-03,  6.37845587e-03,  9.85075304e-04,  1.67279255e-02,\n        7.53665602e-03,  7.01917059e-03,  8.25443902e-03,  1.06326000e-02,\n        6.99491171e-03,  4.28281196e-03,  1.03125233e-02,  9.49174507e-03,\n        7.27489475e-03]), 'confint': array([[ 1.00000000e+00,  1.00000000e+00],\n       [ 6.73942847e-02,  1.37912100e-01],\n       [-3.57397731e-03,  6.76830571e-02],\n       [-1.26670075e-02,  5.86616959e-02],\n       [ 4.21464325e-02,  1.13511998e-01],\n       [ 7.58879010e-03,  7.93751939e-02],\n       [-2.93034045e-03,  6.89869149e-02],\n       [ 3.07320814e-02,  1.02724726e-01],\n       [-1.26441039e-02,  5.96554473e-02],\n       [-1.63300120e-02,  5.60075314e-02],\n       [-6.34295785e-03,  6.60216364e-02],\n       [ 2.04560493e-02,  9.28818033e-02],\n       [-1.15308807e-02,  6.11150316e-02],\n       [-2.59300325e-02,  4.67579416e-02],\n       [-7.60487364e-03,  6.50905194e-02],\n       [ 6.03899808e-03,  7.87908823e-02],\n       [-1.88493258e-03,  7.09898157e-02],\n       [-5.84397733e-03,  6.71121917e-02],\n       [-9.77477168e-03,  6.32453349e-02],\n       [-1.56336446e-02,  5.74351229e-02],\n       [-1.07723842e-02,  6.23261068e-02],\n       [ 6.73792879e-03,  7.98816070e-02],\n       [-2.29151740e-02,  5.03559175e-02],\n       [-1.62975869e-02,  5.69862795e-02],\n       [-6.09564647e-03,  6.72162997e-02],\n       [ 6.14105921e-03,  7.95163269e-02],\n       [-2.27191618e-02,  5.07803141e-02],\n       [-2.13225833e-02,  5.21902102e-02],\n       [-1.74861391e-02,  5.60427658e-02],\n       [-6.15644669e-03,  6.73975888e-02],\n       [-2.49683682e-02,  4.86490296e-02],\n       [-1.97281361e-02,  5.38987309e-02],\n       [-1.84045374e-02,  5.52420425e-02],\n       [-2.19889407e-02,  5.16805425e-02],\n       [-2.92209207e-02,  4.44634380e-02],\n       [-2.74183697e-03,  7.09464416e-02],\n       [-1.06061260e-02,  6.31605922e-02],\n       [-1.59490074e-02,  5.78642436e-02],\n       [-2.32172201e-02,  5.06256151e-02],\n       [-1.96339772e-02,  5.42215042e-02],\n       [-9.89060720e-03,  6.39850084e-02],\n       [ 1.33714553e-04,  7.40585564e-02],\n       [-2.76268078e-02,  4.63905450e-02],\n       [-3.27130514e-02,  4.13102146e-02],\n       [-1.37251523e-02,  6.02993550e-02],\n       [-1.30734220e-02,  6.09875058e-02],\n       [-2.10365743e-02,  5.30628803e-02],\n       [-2.78163620e-02,  4.63002988e-02],\n       [ 1.77466879e-02,  9.18690792e-02],\n       [-3.13492538e-02,  4.29743918e-02],\n       [-3.25307377e-02,  4.17951684e-02],\n       [-3.57031695e-02,  3.86241722e-02],\n       [-2.91964588e-02,  4.51310256e-02],\n       [-3.00477388e-02,  4.42839924e-02],\n       [-2.71763959e-02,  4.71587248e-02],\n       [-3.46214049e-02,  3.97203933e-02],\n       [ 4.80510717e-03,  7.91473402e-02],\n       [-2.31347554e-02,  5.13252450e-02],\n       [-5.89324136e-03,  6.85800263e-02],\n       [-7.37794687e-03,  6.71608897e-02],\n       [-1.78572194e-02,  5.67412021e-02],\n       [-2.53069135e-02,  4.93167007e-02],\n       [-5.44128210e-03,  6.91919352e-02],\n       [-2.60978481e-02,  4.86030364e-02],\n       [-2.50623290e-02,  4.96469840e-02],\n       [-2.61360653e-02,  4.85833046e-02],\n       [-1.88321466e-02,  5.58956064e-02],\n       [-2.64463296e-02,  4.83042731e-02],\n       [-3.51601780e-02,  3.95983702e-02],\n       [-2.88769023e-02,  4.58819735e-02],\n       [-1.54038183e-02,  5.93598660e-02],\n       [-3.51202726e-02,  3.96755330e-02],\n       [-2.29210944e-02,  5.18750560e-02],\n       [-3.16482657e-02,  4.31618174e-02],\n       [-3.45531109e-02,  4.02591751e-02],\n       [-3.71202392e-02,  3.76925878e-02],\n       [-3.96112751e-02,  3.52015573e-02],\n       [-2.56524665e-02,  4.91606891e-02],\n       [-3.01971681e-02,  4.46251702e-02],\n       [-3.47127122e-02,  4.01130847e-02],\n       [-3.42699169e-02,  4.05563646e-02],\n       [-6.34686821e-03,  6.84800699e-02],\n       [-1.56362069e-02,  5.92548435e-02],\n       [-1.66219321e-02,  5.83006945e-02],\n       [-1.45496233e-02,  6.04018218e-02],\n       [-9.37664526e-03,  6.56096638e-02],\n       [-1.29249081e-02,  6.21138077e-02],\n       [-3.80569257e-02,  3.70218648e-02],\n       [-3.17092786e-02,  4.33695296e-02],\n       [-3.11620739e-02,  4.39189856e-02],\n       [-3.65568017e-02,  3.85269524e-02],\n       [-2.08139836e-02,  5.42698347e-02],\n       [-3.00145183e-02,  4.50878303e-02],\n       [-3.05338842e-02,  4.45722254e-02],\n       [-2.93002467e-02,  4.58091248e-02],\n       [-2.69243412e-02,  4.81895412e-02],\n       [-3.05657715e-02,  4.45555949e-02],\n       [-3.32794907e-02,  4.18451146e-02],\n       [-2.72503864e-02,  4.78754330e-02],\n       [-2.80746842e-02,  4.70581744e-02],\n       [-3.02945159e-02,  4.48443054e-02]]), 'significant_lags': [1, 4, 5, 7, 11, 15, 21, 25, 41, 48, 56], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'abs_return': {'values': array([1.        , 0.16941215, 0.11681494, 0.11477814, 0.18185448,\n       0.14259819, 0.14312719, 0.13894497, 0.12687252, 0.10913528,\n       0.10631101, 0.12944533, 0.11684757, 0.09762454, 0.15017366,\n       0.12411898, 0.12326963, 0.11562807, 0.0844657 , 0.07607039,\n       0.09505224, 0.16106874, 0.07148222, 0.09370097, 0.113692  ,\n       0.11214878, 0.07064554, 0.09680625, 0.10452395, 0.09941853,\n       0.06644419, 0.07644115, 0.10051518, 0.07849206, 0.07073167,\n       0.1368152 , 0.10616077, 0.07564097, 0.07883957, 0.07680061,\n       0.07310946, 0.11918992, 0.07922182, 0.06456951, 0.08403174,\n       0.07692813, 0.08377337, 0.04391864, 0.10519398, 0.07786352,\n       0.05243764, 0.02742507, 0.04606311, 0.05652268, 0.04697355,\n       0.04920116, 0.12268232, 0.0759553 , 0.09233128, 0.05483661,\n       0.06063155, 0.05068447, 0.10495384, 0.07299428, 0.05869396,\n       0.06155175, 0.06108528, 0.05599143, 0.04059884, 0.04826835,\n       0.09459542, 0.04167287, 0.06421634, 0.03641472, 0.0238428 ,\n       0.02949051, 0.028975  , 0.07587434, 0.04076047, 0.02183433,\n       0.04071859, 0.07915313, 0.04777162, 0.06885369, 0.07274957,\n       0.04513606, 0.07586587, 0.0313361 , 0.03066215, 0.03402811,\n       0.02781086, 0.07376816, 0.04940179, 0.03161297, 0.05145948,\n       0.06577359, 0.02750272, 0.03582021, 0.06441121, 0.03604022,\n       0.03358751]), 'confint': array([[ 1.00000000e+00,  1.00000000e+00],\n       [ 1.34153246e-01,  2.04671061e-01],\n       [ 8.05582047e-02,  1.53071676e-01],\n       [ 7.80564948e-02,  1.51499789e-01],\n       [ 1.44689510e-01,  2.19019451e-01],\n       [ 1.04342965e-01,  1.80853416e-01],\n       [ 1.04216766e-01,  1.82037610e-01],\n       [ 9.93854553e-02,  1.78504490e-01],\n       [ 8.67108828e-02,  1.67034149e-01],\n       [ 6.84784368e-02,  1.49792129e-01],\n       [ 6.52915878e-02,  1.47330437e-01],\n       [ 8.80847870e-02,  1.70805869e-01],\n       [ 7.49864184e-02,  1.58708731e-01],\n       [ 5.53598547e-02,  1.39889230e-01],\n       [ 1.07629564e-01,  1.92717764e-01],\n       [ 8.09209100e-02,  1.67317058e-01],\n       [ 7.96304592e-02,  1.66908809e-01],\n       [ 7.15581367e-02,  1.59698007e-01],\n       [ 4.00202076e-02,  1.28911190e-01],\n       [ 3.14257836e-02,  1.20714992e-01],\n       [ 5.02467872e-02,  1.39857694e-01],\n       [ 1.16013302e-01,  2.06124187e-01],\n       [ 2.57165398e-02,  1.17247899e-01],\n       [ 4.77967033e-02,  1.39605246e-01],\n       [ 6.75505582e-02,  1.59833433e-01],\n       [ 6.56603885e-02,  1.58637179e-01],\n       [ 2.38220132e-02,  1.17469073e-01],\n       [ 4.98503969e-02,  1.43762100e-01],\n       [ 5.73206374e-02,  1.51727268e-01],\n       [ 5.19283470e-02,  1.46908710e-01],\n       [ 1.86959667e-02,  1.14192413e-01],\n       [ 2.85781179e-02,  1.24304181e-01],\n       [ 5.25006120e-02,  1.48529739e-01],\n       [ 3.02166086e-02,  1.26767506e-01],\n       [ 2.22978210e-02,  1.19165516e-01],\n       [ 8.82531058e-02,  1.85377291e-01],\n       [ 5.71218244e-02,  1.55199710e-01],\n       [ 2.63171452e-02,  1.24964795e-01],\n       [ 2.93717487e-02,  1.28307398e-01],\n       [ 2.71768192e-02,  1.26424393e-01],\n       [ 2.33381275e-02,  1.22880796e-01],\n       [ 6.92852573e-02,  1.69094584e-01],\n       [ 2.89645069e-02,  1.29479134e-01],\n       [ 1.41571878e-02,  1.14981834e-01],\n       [ 3.35167098e-02,  1.34546776e-01],\n       [ 2.62396159e-02,  1.27616650e-01],\n       [ 3.29399180e-02,  1.34606825e-01],\n       [-7.08615530e-03,  9.49234398e-02],\n       [ 5.41421874e-02,  1.56245767e-01],\n       [ 2.65429673e-02,  1.29184069e-01],\n       [ 9.70431440e-04,  1.03904842e-01],\n       [-2.41085090e-02,  7.89586539e-02],\n       [-5.48861036e-03,  9.76148350e-02],\n       [ 4.91981281e-03,  1.08125544e-01],\n       [-4.70622811e-03,  9.86533242e-02],\n       [-2.53167160e-03,  1.00933984e-01],\n       [ 7.08913550e-02,  1.74473292e-01],\n       [ 2.38042974e-02,  1.28106298e-01],\n       [ 4.00429278e-02,  1.44619622e-01],\n       [ 2.34596598e-03,  1.07327256e-01],\n       [ 8.06973624e-03,  1.13193368e-01],\n       [-1.96422361e-03,  1.03333163e-01],\n       [ 5.22445180e-02,  1.57663154e-01],\n       [ 2.00257931e-02,  1.25962764e-01],\n       [ 5.60057060e-03,  1.11787355e-01],\n       [ 8.37775031e-03,  1.14725742e-01],\n       [ 7.82277750e-03,  1.14347775e-01],\n       [ 2.64190697e-03,  1.09340951e-01],\n       [-1.28236898e-02,  9.40213645e-02],\n       [-5.19251627e-03,  1.01729224e-01],\n       [ 4.10803944e-02,  1.48110436e-01],\n       [-1.20496255e-02,  9.53953621e-02],\n       [ 1.04536716e-02,  1.17979004e-01],\n       [-1.74432220e-02,  9.02726533e-02],\n       [-3.00457398e-02,  7.77313351e-02],\n       [-2.44111395e-02,  8.33921614e-02],\n       [-2.49467077e-02,  8.28967029e-02],\n       [ 2.19332808e-02,  1.29815397e-01],\n       [-1.33131085e-02,  9.48340436e-02],\n       [-3.22774314e-02,  7.59460881e-02],\n       [-1.34041188e-02,  9.48413041e-02],\n       [ 2.49923475e-02,  1.33313912e-01],\n       [-6.53277812e-03,  1.02076025e-01],\n       [ 1.44970700e-02,  1.23210313e-01],\n       [ 1.82846279e-02,  1.27214510e-01],\n       [-9.44954947e-03,  9.97216733e-02],\n       [ 2.12338768e-02,  1.30497858e-01],\n       [-2.34267105e-02,  8.60989043e-02],\n       [-2.41229492e-02,  8.54472398e-02],\n       [-2.07783124e-02,  8.88345370e-02],\n       [-2.70218246e-02,  8.26435428e-02],\n       [ 1.89179440e-02,  1.28618377e-01],\n       [-5.57162947e-03,  1.04375204e-01],\n       [-2.34156124e-02,  8.66415478e-02],\n       [-3.59167249e-03,  1.06510634e-01],\n       [ 1.06626737e-02,  1.20884515e-01],\n       [-2.77057056e-02,  8.27111426e-02],\n       [-1.94052402e-02,  9.10456682e-02],\n       [ 9.15688357e-03,  1.19665545e-01],\n       [-1.93073728e-02,  9.13878221e-02],\n       [-2.17892566e-02,  8.89642734e-02]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 77, 78, 80, 81, 82, 83, 84, 85, 86, 91, 92, 94, 95, 97, 98, 99], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'volume': {'values': array([1.        , 0.89231835, 0.81972046, 0.79411199, 0.78488161,\n       0.76746046, 0.78558329, 0.80074225, 0.7502537 , 0.69843297,\n       0.68093924, 0.67189199, 0.67136443, 0.69291745, 0.71696258,\n       0.68159883, 0.64649686, 0.64016457, 0.63638299, 0.63722759,\n       0.6702784 , 0.6956126 , 0.66121152, 0.62442741, 0.62038527,\n       0.62485345, 0.63041244, 0.65331817, 0.67503497, 0.64368851,\n       0.60795348, 0.60475173, 0.60388998, 0.60859536, 0.63361714,\n       0.65690603, 0.62538197, 0.5897418 , 0.58138651, 0.58239673,\n       0.58570772, 0.61105092, 0.63535735, 0.60272863, 0.56910026,\n       0.55028958, 0.54658496, 0.55471161, 0.5798828 , 0.59119837,\n       0.55674306, 0.52942871, 0.5216846 , 0.51598195, 0.52168578,\n       0.55186523, 0.57864299, 0.54795346, 0.52391621, 0.52105344,\n       0.51781606, 0.51474923, 0.53668783, 0.55946177, 0.53080459,\n       0.50084144, 0.48232368, 0.47273645, 0.47226484, 0.49453748,\n       0.51165976, 0.48139221, 0.45445747, 0.44718943, 0.43952767,\n       0.44420806, 0.46662036, 0.48865304, 0.46132897, 0.4369049 ,\n       0.43427533, 0.42918134, 0.42685324, 0.44946594, 0.47015197,\n       0.44810237, 0.42646501, 0.4187618 , 0.41185523, 0.41157762,\n       0.43753807, 0.46203514, 0.43947459, 0.41311037, 0.40156217,\n       0.40221022, 0.4031838 , 0.43138235, 0.45709229, 0.43832808,\n       0.41973737]), 'confint': array([[1.        , 1.        ],\n       [0.85706515, 0.92757155],\n       [0.76295881, 0.8764821 ],\n       [0.72416882, 0.86405516],\n       [0.70451068, 0.86525253],\n       [0.67806976, 0.85685117],\n       [0.68834805, 0.88281853],\n       [0.69591552, 0.90556898],\n       [0.63808256, 0.86242485],\n       [0.58018979, 0.81667615],\n       [0.55767556, 0.80420292],\n       [0.54403879, 0.7997452 ],\n       [0.53919587, 0.80353299],\n       [0.55657651, 0.82925839],\n       [0.57631314, 0.85761202],\n       [0.5364784 , 0.82671926],\n       [0.49745096, 0.79554276],\n       [0.48767344, 0.79265571],\n       [0.48058773, 0.79217825],\n       [0.47823457, 0.79622061],\n       [0.50814243, 0.83241436],\n       [0.53006873, 0.86115647],\n       [0.49207404, 0.830349  ],\n       [0.45210741, 0.79674741],\n       [0.44527578, 0.79549475],\n       [0.44703338, 0.80267351],\n       [0.44988418, 0.81094069],\n       [0.47007444, 0.83656191],\n       [0.48891895, 0.86115099],\n       [0.45455422, 0.8328228 ],\n       [0.41611594, 0.79979102],\n       [0.41053451, 0.79896896],\n       [0.40734643, 0.80043352],\n       [0.40975921, 0.80743151],\n       [0.43247927, 0.83475501],\n       [0.45330267, 0.86050939],\n       [0.41916141, 0.83160253],\n       [0.38117758, 0.79830602],\n       [0.37076005, 0.79201298],\n       [0.36978521, 0.79500824],\n       [0.37112271, 0.80029274],\n       [0.39448819, 0.82761365],\n       [0.41666238, 0.85405231],\n       [0.38175157, 0.8237057 ],\n       [0.34608944, 0.79211109],\n       [0.32548111, 0.77509804],\n       [0.32010865, 0.77306128],\n       [0.32660176, 0.78282146],\n       [0.35010263, 0.80966297],\n       [0.35960662, 0.82279011],\n       [0.32328325, 0.79020287],\n       [0.29432465, 0.76453277],\n       [0.2851035 , 0.75826569],\n       [0.27797548, 0.75398841],\n       [0.28229315, 0.7610784 ],\n       [0.31106387, 0.79266659],\n       [0.3362749 , 0.82101108],\n       [0.30387452, 0.7920324 ],\n       [0.27831321, 0.76951921],\n       [0.27406539, 0.76804148],\n       [0.26946566, 0.76616645],\n       [0.26506065, 0.76443781],\n       [0.28568388, 0.78769177],\n       [0.30703572, 0.81188782],\n       [0.27684222, 0.78476697],\n       [0.245504  , 0.75617889],\n       [0.22576823, 0.73887913],\n       [0.21505655, 0.73041636],\n       [0.21350934, 0.73102034],\n       [0.23471297, 0.75436199],\n       [0.25066806, 0.77265146],\n       [0.21915686, 0.74362757],\n       [0.19112615, 0.71778879],\n       [0.18288518, 0.71149367],\n       [0.17428477, 0.70477057],\n       [0.17806153, 0.71035458],\n       [0.19955403, 0.7336867 ],\n       [0.22057539, 0.75673068],\n       [0.19214662, 0.73051131],\n       [0.16674175, 0.70706805],\n       [0.1632355 , 0.70531515],\n       [0.15727814, 0.70108455],\n       [0.15410943, 0.69959706],\n       [0.17589315, 0.72303873],\n       [0.19566298, 0.74464096],\n       [0.1726144 , 0.72359035],\n       [0.15007269, 0.70285734],\n       [0.1415529 , 0.6959707 ],\n       [0.13386125, 0.6898492 ],\n       [0.13282636, 0.69032888],\n       [0.15803259, 0.71704355],\n       [0.18167974, 0.74239054],\n       [0.15817446, 0.72077472],\n       [0.13095825, 0.6952625 ],\n       [0.11865934, 0.684465  ],\n       [0.1185999 , 0.68582055],\n       [0.11886547, 0.68750214],\n       [0.14635435, 0.71641036],\n       [0.17125404, 0.74293054],\n       [0.15158285, 0.7250733 ],\n       [0.13216062, 0.70731411]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], 'n_obs': 3091, 'threshold': np.float64(0.03525385133986227)}}",
    "pacf": "{'log_return': {'values': array([ 1.00000000e+00, -4.99311170e-02,  4.33900230e-02,  9.23036809e-03,\n        8.18649253e-03,  2.25859092e-02,  1.08753469e-02, -1.00537506e-02,\n       -2.64333041e-02,  8.16874978e-03,  4.80345725e-02, -8.72829103e-03,\n       -2.23183314e-03,  2.03449106e-02, -8.43022566e-03,  8.60247795e-03,\n       -2.51125774e-02,  4.36334443e-02,  3.77581992e-03,  8.26840455e-03,\n        2.71619216e-02, -2.20098621e-02, -1.70741813e-02, -2.99492331e-02,\n        2.19931901e-02,  2.42602576e-02,  7.32939669e-03,  4.41252105e-04,\n        1.34148338e-02, -3.79342601e-02,  6.55922416e-03,  3.46333098e-02,\n       -2.36390122e-02,  4.56576583e-02, -6.47888130e-03, -5.76066449e-03,\n       -3.46499714e-02,  2.10708312e-02,  9.73902402e-03,  1.62381715e-02,\n        2.76939727e-02]), 'confint': array([[ 1.00000000e+00,  1.00000000e+00],\n       [-8.51900245e-02, -1.46722095e-02],\n       [ 8.13111555e-03,  7.86489305e-02],\n       [-2.60285394e-02,  4.44892756e-02],\n       [-2.70724150e-02,  4.34454000e-02],\n       [-1.26729983e-02,  5.78448166e-02],\n       [-2.43835606e-02,  4.61342544e-02],\n       [-4.53126581e-02,  2.52051569e-02],\n       [-6.16922115e-02,  8.82560342e-03],\n       [-2.70901577e-02,  4.34276573e-02],\n       [ 1.27756650e-02,  8.32934800e-02],\n       [-4.39871985e-02,  2.65306165e-02],\n       [-3.74907406e-02,  3.30270743e-02],\n       [-1.49139968e-02,  5.56038181e-02],\n       [-4.36891331e-02,  2.68286818e-02],\n       [-2.66564295e-02,  4.38613854e-02],\n       [-6.03714849e-02,  1.01463301e-02],\n       [ 8.37453685e-03,  7.88923518e-02],\n       [-3.14830876e-02,  3.90347274e-02],\n       [-2.69905029e-02,  4.35273120e-02],\n       [-8.09698588e-03,  6.24208291e-02],\n       [-5.72687696e-02,  1.32490454e-02],\n       [-5.23330888e-02,  1.81847262e-02],\n       [-6.52081405e-02,  5.30967443e-03],\n       [-1.32657174e-02,  5.72520976e-02],\n       [-1.09986498e-02,  5.95191651e-02],\n       [-2.79295108e-02,  4.25883042e-02],\n       [-3.48176554e-02,  3.57001596e-02],\n       [-2.18440737e-02,  4.86737413e-02],\n       [-7.31931676e-02, -2.67535262e-03],\n       [-2.86996833e-02,  4.18181316e-02],\n       [-6.25597633e-04,  6.98922173e-02],\n       [-5.88979196e-02,  1.16198953e-02],\n       [ 1.03987508e-02,  8.09165658e-02],\n       [-4.17377888e-02,  2.87800262e-02],\n       [-4.10195720e-02,  2.94982430e-02],\n       [-6.99088789e-02,  6.08936041e-04],\n       [-1.41880763e-02,  5.63297386e-02],\n       [-2.55198835e-02,  4.49979315e-02],\n       [-1.90207359e-02,  5.14970790e-02],\n       [-7.56493478e-03,  6.29528802e-02]]), 'significant_lags': [1, 2, 10, 17, 29, 33], 'n_obs': 3090}, 'squared_return': {'values': array([ 1.00000000e+00,  1.02653192e-01,  2.17460146e-02,  1.77413548e-02,\n        7.39038779e-02,  2.79217712e-02,  2.24279580e-02,  5.83814243e-02,\n        3.96823112e-03,  9.01011557e-03,  2.07117331e-02,  4.13952079e-02,\n        8.07840032e-03, -5.34218188e-04,  1.84199368e-02,  2.80457870e-02,\n        1.98428487e-02,  1.80050750e-02,  1.09748160e-02,  7.03584386e-03,\n        1.40689820e-02,  2.89451441e-02, -5.60858789e-03,  9.39476228e-03,\n        1.91062551e-02,  2.62675594e-02, -2.86436891e-03,  3.41980066e-03,\n        5.21343161e-03,  1.74205386e-02, -2.36957364e-03,  4.82802442e-03,\n        3.86515697e-03,  3.46754946e-03, -2.44553615e-03,  2.35198824e-02,\n        9.35939171e-03,  9.89788821e-03,  3.32702025e-03,  3.93295112e-03,\n        1.35836465e-02]), 'confint': array([[ 1.        ,  1.        ],\n       [ 0.06739428,  0.1379121 ],\n       [-0.01351289,  0.05700492],\n       [-0.01751755,  0.05300026],\n       [ 0.03864497,  0.10916279],\n       [-0.00733714,  0.06318068],\n       [-0.01283095,  0.05768687],\n       [ 0.02312252,  0.09364033],\n       [-0.03129068,  0.03922714],\n       [-0.02624879,  0.04426902],\n       [-0.01454717,  0.05597064],\n       [ 0.0061363 ,  0.07665412],\n       [-0.02718051,  0.04333731],\n       [-0.03579313,  0.03472469],\n       [-0.01683897,  0.05367884],\n       [-0.00721312,  0.06330469],\n       [-0.01541606,  0.05510176],\n       [-0.01725383,  0.05326398],\n       [-0.02428409,  0.04623372],\n       [-0.02822306,  0.04229475],\n       [-0.02118993,  0.04932789],\n       [-0.00631376,  0.06420405],\n       [-0.0408675 ,  0.02965032],\n       [-0.02586415,  0.04465367],\n       [-0.01615265,  0.05436516],\n       [-0.00899135,  0.06152647],\n       [-0.03812328,  0.03239454],\n       [-0.03183911,  0.03867871],\n       [-0.03004548,  0.04047234],\n       [-0.01783837,  0.05267945],\n       [-0.03762848,  0.03288933],\n       [-0.03043088,  0.04008693],\n       [-0.03139375,  0.03912406],\n       [-0.03179136,  0.03872646],\n       [-0.03770444,  0.03281337],\n       [-0.01173903,  0.05877879],\n       [-0.02589952,  0.0446183 ],\n       [-0.02536102,  0.0451568 ],\n       [-0.03193189,  0.03858593],\n       [-0.03132596,  0.03919186],\n       [-0.02167526,  0.04884255]]), 'significant_lags': [1, 4, 7, 11], 'n_obs': 3090}, 'abs_return': {'values': array([ 1.00000000e+00,  1.69412153e-01,  9.07181160e-02,  8.45160567e-02,\n        1.48113259e-01,  8.36243602e-02,  8.39548850e-02,  7.37673497e-02,\n        5.10596543e-02,  3.28447583e-02,  2.96940137e-02,  5.42549634e-02,\n        3.63184226e-02,  1.85683191e-02,  8.05149603e-02,  3.77250817e-02,\n        4.12392165e-02,  3.41284438e-02, -9.72034046e-03, -9.84520886e-03,\n        1.27001117e-02,  8.39925053e-02, -2.28472785e-02,  2.13091973e-02,\n        4.15301452e-02,  2.18423321e-02, -1.18171075e-02,  2.07553150e-02,\n        1.81763460e-02,  1.51906758e-02, -1.32874581e-02, -9.00555399e-04,\n        2.38174973e-02,  2.25403203e-03,  3.63227988e-03,  6.64400444e-02,\n        2.52184620e-02, -3.92201324e-03,  3.82418903e-03, -6.13910297e-03,\n       -4.26325672e-03]), 'confint': array([[ 1.        ,  1.        ],\n       [ 0.13415325,  0.20467106],\n       [ 0.05545921,  0.12597702],\n       [ 0.04925715,  0.11977496],\n       [ 0.11285435,  0.18337217],\n       [ 0.04836545,  0.11888327],\n       [ 0.04869598,  0.11921379],\n       [ 0.03850844,  0.10902626],\n       [ 0.01580075,  0.08631856],\n       [-0.00241415,  0.06810367],\n       [-0.00556489,  0.06495292],\n       [ 0.01899606,  0.08951387],\n       [ 0.00105952,  0.07157733],\n       [-0.01669059,  0.05382723],\n       [ 0.04525605,  0.11577387],\n       [ 0.00246617,  0.07298399],\n       [ 0.00598031,  0.07649812],\n       [-0.00113046,  0.06938735],\n       [-0.04497925,  0.02553857],\n       [-0.04510412,  0.0254137 ],\n       [-0.0225588 ,  0.04795902],\n       [ 0.0487336 ,  0.11925141],\n       [-0.05810619,  0.01241163],\n       [-0.01394971,  0.0565681 ],\n       [ 0.00627124,  0.07678905],\n       [-0.01341658,  0.05710124],\n       [-0.04707601,  0.0234418 ],\n       [-0.01450359,  0.05601422],\n       [-0.01708256,  0.05343525],\n       [-0.02006823,  0.05044958],\n       [-0.04854637,  0.02197145],\n       [-0.03615946,  0.03435835],\n       [-0.01144141,  0.0590764 ],\n       [-0.03300488,  0.03751294],\n       [-0.03162663,  0.03889119],\n       [ 0.03118114,  0.10169895],\n       [-0.01004045,  0.06047737],\n       [-0.03918092,  0.03133689],\n       [-0.03143472,  0.0390831 ],\n       [-0.04139801,  0.0291198 ],\n       [-0.03952216,  0.03099565]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 14, 15, 16, 21, 24, 35], 'n_obs': 3090}, 'volume': {'values': array([ 1.        ,  0.89231835,  0.11527043,  0.21942261,  0.1554976 ,\n        0.06485283,  0.25620594,  0.13648006, -0.19802398, -0.08229763,\n        0.00989658,  0.00234771,  0.09028939,  0.12117182,  0.13904393,\n       -0.08325209, -0.00542368,  0.03812697, -0.01864103,  0.03616058,\n        0.13651273,  0.07230708, -0.08350945, -0.0322421 ,  0.02475962,\n        0.04246932,  0.0395711 ,  0.03403851,  0.06132399, -0.0559987 ,\n       -0.02640301,  0.0326686 , -0.02576092,  0.03724004,  0.05686875,\n        0.06370599, -0.04903855, -0.03191649, -0.01561977,  0.00630843,\n        0.00846462]), 'confint': array([[ 1.00000000e+00,  1.00000000e+00],\n       [ 8.57065146e-01,  9.27571553e-01],\n       [ 8.00172271e-02,  1.50523634e-01],\n       [ 1.84169404e-01,  2.54675811e-01],\n       [ 1.20244394e-01,  1.90750801e-01],\n       [ 2.95996237e-02,  1.00106031e-01],\n       [ 2.20952737e-01,  2.91459144e-01],\n       [ 1.01226860e-01,  1.71733267e-01],\n       [-2.33277182e-01, -1.62770775e-01],\n       [-1.17550835e-01, -4.70444278e-02],\n       [-2.53566232e-02,  4.51497839e-02],\n       [-3.29054919e-02,  3.76009152e-02],\n       [ 5.50361839e-02,  1.25542591e-01],\n       [ 8.59186213e-02,  1.56425028e-01],\n       [ 1.03790727e-01,  1.74297134e-01],\n       [-1.18505292e-01, -4.79988851e-02],\n       [-4.06768818e-02,  2.98295253e-02],\n       [ 2.87376303e-03,  7.33801701e-02],\n       [-5.38942318e-02,  1.66121753e-02],\n       [ 9.07379314e-04,  7.14137864e-02],\n       [ 1.01259525e-01,  1.71765933e-01],\n       [ 3.70538724e-02,  1.07560279e-01],\n       [-1.18762654e-01, -4.82562473e-02],\n       [-6.74953046e-02,  3.01110252e-03],\n       [-1.04935824e-02,  6.00128247e-02],\n       [ 7.21611805e-03,  7.77225251e-02],\n       [ 4.31790064e-03,  7.48243077e-02],\n       [-1.21469134e-03,  6.92917157e-02],\n       [ 2.60707872e-02,  9.65771943e-02],\n       [-9.12519074e-02, -2.07455003e-02],\n       [-6.16562097e-02,  8.85019736e-03],\n       [-2.58460841e-03,  6.79217987e-02],\n       [-6.10141262e-02,  9.49228091e-03],\n       [ 1.98683953e-03,  7.24932466e-02],\n       [ 2.16155489e-02,  9.21219560e-02],\n       [ 2.84527896e-02,  9.89591967e-02],\n       [-8.42917577e-02, -1.37853506e-02],\n       [-6.71696908e-02,  3.33671624e-03],\n       [-5.08729734e-02,  1.96334337e-02],\n       [-2.89447760e-02,  4.15616311e-02],\n       [-2.67885833e-02,  4.37178238e-02]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 17, 19, 20, 21, 22, 25, 26, 28, 29, 33, 34, 35, 36], 'n_obs': 3091}}",
    "ljungbox": "{'log_return':    lag     lb_stat  lb_pvalue\n0   10   25.059139   0.005235\n1   20   38.928469   0.006805\n2   50   87.268713   0.000866\n3  100  148.676050   0.001151, 'squared_return':    lag     lb_stat     lb_pvalue\n0   10   84.891973  5.482156e-14\n1   20  117.535801  8.139704e-16\n2   50  172.418996  2.287873e-15\n3  100  211.175390  6.046088e-10, 'abs_return':    lag      lb_stat      lb_pvalue\n0   10   582.314852  1.081991e-118\n1   20   981.305481  3.779643e-195\n2   50  1767.938519   0.000000e+00\n3  100  2294.606367   0.000000e+00, 'volume':    lag        lb_stat  lb_pvalue\n0   10   18825.923581        0.0\n1   20   32655.505952        0.0\n2   50   67729.164244        0.0\n3  100  103242.290924        0.0}",
    "periodic_patterns": {
      "log_return": [
        {
          "period": 2,
          "hits": [
            1,
            10,
            17,
            17,
            33,
            33,
            44,
            59,
            59,
            65,
            65,
            75,
            75
          ],
          "count": 13,
          "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 3,
          "hits": [
            2,
            10,
            17,
            33,
            44,
            59,
            65,
            75
          ],
          "count": 8,
          "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 5,
          "hits": [
            10,
            44,
            59,
            65,
            75
          ],
          "count": 5,
          "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 11,
          "hits": [
            10,
            33,
            44,
            65
          ],
          "count": 4,
          "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过"
        }
      ],
      "squared_return": [
        {
          "period": 2,
          "hits": [
            1,
            4,
            5,
            7,
            11,
            11,
            15,
            15,
            21,
            21,
            25,
            25,
            41,
            41,
            48,
            56
          ],
          "count": 16,
          "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 3,
          "hits": [
            4,
            5,
            11,
            15,
            21,
            25,
            41,
            48,
            56
          ],
          "count": 9,
          "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 5,
          "hits": [
            4,
            11,
            15,
            21,
            25,
            41,
            56
          ],
          "count": 7,
          "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 7,
          "hits": [
            7,
            15,
            21,
            41,
            48,
            56
          ],
          "count": 6,
          "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 11,
          "hits": [
            11,
            21,
            56
          ],
          "count": 3,
          "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过"
        }
      ],
      "abs_return": [
        {
          "period": 2,
          "hits": [
            1,
            3,
            5,
            7,
            9,
            11,
            13,
            15,
            17,
            19,
            21,
            23,
            25,
            27,
            29,
            31,
            33,
            35,
            37,
            39,
            41,
            43,
            45,
            47,
            49,
            52,
            53,
            55,
            57,
            59,
            61,
            63,
            65,
            67,
            69,
            71,
            73,
            77,
            77,
            80,
            81,
            83,
            85,
            91,
            91,
            94,
            95,
            97,
            99
          ],
          "count": 49,
          "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 3,
          "hits": [
            2,
            5,
            8,
            11,
            14,
            17,
            20,
            23,
            26,
            29,
            32,
            35,
            38,
            41,
            44,
            47,
            50,
            53,
            56,
            59,
            62,
            65,
            68,
            71,
            77,
            80,
            83,
            86,
            91,
            92,
            95,
            98
          ],
          "count": 32,
          "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 5,
          "hits": [
            4,
            9,
            14,
            19,
            24,
            29,
            34,
            39,
            44,
            49,
            54,
            59,
            64,
            69,
            80,
            84,
            91,
            94,
            99
          ],
          "count": 19,
          "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 7,
          "hits": [
            6,
            13,
            20,
            27,
            34,
            41,
            48,
            55,
            62,
            69,
            77,
            83,
            91,
            97
          ],
          "count": 14,
          "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 11,
          "hits": [
            10,
            21,
            32,
            43,
            54,
            65,
            77,
            98
          ],
          "count": 8,
          "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 13,
          "hits": [
            12,
            25,
            38,
            52,
            64,
            77,
            91
          ],
          "count": 7,
          "fft_note": "若FFT频谱在 f=0.0769 (1/13天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 17,
          "hits": [
            16,
            33,
            50,
            67,
            84
          ],
          "count": 5,
          "fft_note": "若FFT频谱在 f=0.0588 (1/17天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 19,
          "hits": [
            18,
            37,
            56,
            77,
            94
          ],
          "count": 5,
          "fft_note": "若FFT频谱在 f=0.0526 (1/19天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 23,
          "hits": [
            22,
            45,
            68,
            91
          ],
          "count": 4,
          "fft_note": "若FFT频谱在 f=0.0435 (1/23天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 29,
          "hits": [
            28,
            57,
            86
          ],
          "count": 3,
          "fft_note": "若FFT频谱在 f=0.0345 (1/29天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 31,
          "hits": [
            30,
            61,
            92
          ],
          "count": 3,
          "fft_note": "若FFT频谱在 f=0.0323 (1/31天) 处存在峰值，则交叉验证通过"
        }
      ],
      "volume": [
        {
          "period": 2,
          "hits": [
            1,
            3,
            5,
            7,
            9,
            11,
            13,
            15,
            17,
            19,
            21,
            23,
            25,
            27,
            29,
            31,
            33,
            35,
            37,
            39,
            41,
            43,
            45,
            47,
            49,
            51,
            53,
            55,
            57,
            59,
            61,
            63,
            65,
            67,
            69,
            71,
            73,
            75,
            77,
            79,
            81,
            83,
            85,
            87,
            89,
            91,
            93,
            95,
            97,
            99
          ],
          "count": 50,
          "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 3,
          "hits": [
            2,
            5,
            8,
            11,
            14,
            17,
            20,
            23,
            26,
            29,
            32,
            35,
            38,
            41,
            44,
            47,
            50,
            53,
            56,
            59,
            62,
            65,
            68,
            71,
            74,
            77,
            80,
            83,
            86,
            89,
            92,
            95,
            98
          ],
          "count": 33,
          "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 5,
          "hits": [
            4,
            9,
            14,
            19,
            24,
            29,
            34,
            39,
            44,
            49,
            54,
            59,
            64,
            69,
            74,
            79,
            84,
            89,
            94,
            99
          ],
          "count": 20,
          "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 7,
          "hits": [
            6,
            13,
            20,
            27,
            34,
            41,
            48,
            55,
            62,
            69,
            76,
            83,
            90,
            97
          ],
          "count": 14,
          "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 11,
          "hits": [
            10,
            21,
            32,
            43,
            54,
            65,
            76,
            87,
            98
          ],
          "count": 9,
          "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 13,
          "hits": [
            12,
            25,
            38,
            51,
            64,
            77,
            90
          ],
          "count": 7,
          "fft_note": "若FFT频谱在 f=0.0769 (1/13天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 17,
          "hits": [
            16,
            33,
            50,
            67,
            84
          ],
          "count": 5,
          "fft_note": "若FFT频谱在 f=0.0588 (1/17天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 19,
          "hits": [
            18,
            37,
            56,
            75,
            94
          ],
          "count": 5,
          "fft_note": "若FFT频谱在 f=0.0526 (1/19天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 23,
          "hits": [
            22,
            45,
            68,
            91
          ],
          "count": 4,
          "fft_note": "若FFT频谱在 f=0.0435 (1/23天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 29,
          "hits": [
            28,
            57,
            86
          ],
          "count": 3,
          "fft_note": "若FFT频谱在 f=0.0345 (1/29天) 处存在峰值，则交叉验证通过"
        },
        {
          "period": 31,
          "hits": [
            30,
            61,
            92
          ],
          "count": 3,
          "fft_note": "若FFT频谱在 f=0.0323 (1/31天) 处存在峰值，则交叉验证通过"
        }
      ]
    },
    "summary": "{'log_return': {'label': '对数收益率', 'acf_significant_count': 10, 'pacf_significant_count': 6, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 4, 'periodic_periods': [2, 3, 5, 11]}, 'squared_return': {'label': '平方收益率', 'acf_significant_count': 11, 'pacf_significant_count': 4, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 5, 'periodic_periods': [2, 3, 5, 7, 11]}, 'abs_return': {'label': '绝对收益率', 'acf_significant_count': 88, 'pacf_significant_count': 16, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 11, 'periodic_periods': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]}, 'volume': {'label': '成交量', 'acf_significant_count': 100, 'pacf_significant_count': 26, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 11, 'periodic_periods': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]}}",
    "status": "success"
  },
  "returns": {
    "normality": {
      "ks_statistic": 0.09736697106568132,
      "ks_pvalue": 5.971577286037212e-26,
      "jb_statistic": 31996.30395577554,
      "jb_pvalue": 0.0,
      "ad_statistic": 64.17928613929007,
      "ad_critical_values": {
        "15.0%": 0.575,
        "10.0%": 0.655,
        "5.0%": 0.786,
        "2.5%": 0.917,
        "1.0%": 1.091
      }
    },
    "fat_tail": {
      "excess_kurtosis": 15.645612143331558,
      "skewness": -0.9656348742170849,
      "exceed_3sigma_actual": 0.015533980582524271,
      "exceed_3sigma_normal": 0.002699796063260207,
      "exceed_3sigma_ratio": 5.753760735455633,
      "exceed_4sigma_actual": 0.005501618122977346,
      "exceed_4sigma_normal": 6.334248366623996e-05,
      "exceed_4sigma_ratio": 86.85510583964641
    },
    "multi_timeframe": "{'1h': datetime\n2017-08-17 05:00:00    0.001505\n2017-08-17 06:00:00    0.002090\n2017-08-17 07:00:00    0.005912\n2017-08-17 08:00:00    0.002457\n2017-08-17 09:00:00    0.018925\n                         ...   \n2026-02-01 19:00:00   -0.011011\n2026-02-01 20:00:00   -0.000930\n2026-02-01 21:00:00   -0.007512\n2026-02-01 22:00:00    0.009407\n2026-02-01 23:00:00   -0.003646\nName: close, Length: 74052, dtype: float64, '4h': datetime\n2017-08-17 08:00:00    0.017616\n2017-08-17 12:00:00   -0.017076\n2017-08-17 16:00:00   -0.006248\n2017-08-17 20:00:00   -0.009326\n2017-08-18 00:00:00    0.001704\n                         ...   \n2026-02-01 04:00:00   -0.006682\n2026-02-01 08:00:00    0.005100\n2026-02-01 12:00:00   -0.014714\n2026-02-01 16:00:00   -0.005410\n2026-02-01 20:00:00   -0.002680\nName: close, Length: 18527, dtype: float64, '1d': datetime\n2017-08-18   -0.042113\n2017-08-19    0.007665\n2017-08-20   -0.013053\n2017-08-21   -0.017351\n2017-08-22    0.005958\n                ...   \n2026-01-28    0.000560\n2026-01-29   -0.053474\n2026-01-30   -0.004614\n2026-01-31   -0.067748\n2026-02-01   -0.022773\nName: close, Length: 3090, dtype: float64, '1w': datetime\n2017-08-21    0.053303\n2017-08-28    0.045153\n2017-09-04   -0.087726\n2017-09-11   -0.110037\n2017-09-18   -0.010862\n                ...   \n2026-01-05   -0.005654\n2026-01-12    0.028802\n2026-01-19   -0.077700\n2026-01-26   -0.118719\n2026-02-02    0.020847\nName: close, Length: 434, dtype: float64}",
    "garch": "{'model_summary': '                     Constant Mean - GARCH Model Results                      \\n==============================================================================\\nDep. Variable:                  close   R-squared:                       0.000\\nMean Model:             Constant Mean   Adj. R-squared:                  0.000\\nVol Model:                      GARCH   Log-Likelihood:               -8091.64\\nDistribution:                  Normal   AIC:                           16191.3\\nMethod:            Maximum Likelihood   BIC:                           16215.4\\n                                        No. Observations:                 3090\\nDate:                Tue, Feb 03 2026   Df Residuals:                     3089\\nTime:                        11:15:47   Df Model:                            1\\n                                Mean Model                                \\n==========================================================================\\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\\n--------------------------------------------------------------------------\\nmu             0.1295  5.806e-02      2.231  2.568e-02 [1.573e-02,  0.243]\\n                             Volatility Model                             \\n==========================================================================\\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\\n--------------------------------------------------------------------------\\nomega          0.4388      0.207      2.121  3.393e-02 [3.329e-02,  0.844]\\nalpha[1]       0.0962  4.760e-02      2.022  4.319e-02 [2.944e-03,  0.190]\\nbeta[1]        0.8768  4.647e-02     18.866  2.158e-79   [  0.786,  0.968]\\n==========================================================================\\n\\nCovariance estimator: robust', 'omega': np.float64(0.43881933719318955), 'alpha': np.float64(0.09623144766619043), 'beta': np.float64(0.876807221573444), 'persistence': np.float64(0.9730386692396344), 'log_likelihood': -8091.636710514733, 'aic': 16191.273421029466, 'bic': np.float64(16215.417126509034), 'conditional_volatility': datetime\n2017-08-18    0.045564\n2017-08-19    0.045227\n2017-08-20    0.042910\n2017-08-21    0.040965\n2017-08-22    0.039354\n                ...   \n2026-01-28    0.024847\n2026-01-29    0.024192\n2026-01-30    0.029081\n2026-01-31    0.028085\n2026-02-01    0.034557\nName: cond_vol, Length: 3090, dtype: float64, 'result_obj':                      Constant Mean - GARCH Model Results                      \n==============================================================================\nDep. Variable:                  close   R-squared:                       0.000\nMean Model:             Constant Mean   Adj. R-squared:                  0.000\nVol Model:                      GARCH   Log-Likelihood:               -8091.64\nDistribution:                  Normal   AIC:                           16191.3\nMethod:            Maximum Likelihood   BIC:                           16215.4\n                                        No. Observations:                 3090\nDate:                Tue, Feb 03 2026   Df Residuals:                     3089\nTime:                        11:15:47   Df Model:                            1\n                                Mean Model                                \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu             0.1295  5.806e-02      2.231  2.568e-02 [1.573e-02,  0.243]\n                             Volatility Model                             \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega          0.4388      0.207      2.121  3.393e-02 [3.329e-02,  0.844]\nalpha[1]       0.0962  4.760e-02      2.022  4.319e-02 [2.944e-03,  0.190]\nbeta[1]        0.8768  4.647e-02     18.866  2.158e-79   [  0.786,  0.968]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x1256660a0}",
    "status": "success"
  },
  "volatility": {
    "realized_vol": "               rv_7d    rv_30d    rv_90d\ndatetime                                \n2017-08-24  0.508880       NaN       NaN\n2017-08-25  0.412318       NaN       NaN\n2017-08-26  0.419513       NaN       NaN\n2017-08-27  0.411345       NaN       NaN\n2017-08-28  0.411973       NaN       NaN\n...              ...       ...       ...\n2026-01-28  0.263750  0.322936  0.386842\n2026-01-29  0.467542  0.369624  0.400849\n2026-01-30  0.468716  0.368485  0.400856\n2026-01-31  0.676833  0.435210  0.423360\n2026-02-01  0.664176  0.440095  0.419459\n\n[3084 rows x 3 columns]",
    "acf_power_law": "{'d': np.float64(0.6351287691927425), 'd_nonlinear': np.float64(0.3448903462999068), 'r_squared': np.float64(0.42313817191006875), 'slope': np.float64(-0.6351287691927425), 'intercept': np.float64(-0.4744814497920893), 'p_value': np.float64(5.8241517539033605e-25), 'std_err': np.float64(0.053242166415478624), 'lags': array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n       196, 197, 198, 199, 200]), 'acf_values': array([ 0.16941215,  0.11681494,  0.11477814,  0.18185448,  0.14259819,\n        0.14312719,  0.13894497,  0.12687252,  0.10913528,  0.10631101,\n        0.12944533,  0.11684757,  0.09762454,  0.15017366,  0.12411898,\n        0.12326963,  0.11562807,  0.0844657 ,  0.07607039,  0.09505224,\n        0.16106874,  0.07148222,  0.09370097,  0.113692  ,  0.11214878,\n        0.07064554,  0.09680625,  0.10452395,  0.09941853,  0.06644419,\n        0.07644115,  0.10051518,  0.07849206,  0.07073167,  0.1368152 ,\n        0.10616077,  0.07564097,  0.07883957,  0.07680061,  0.07310946,\n        0.11918992,  0.07922182,  0.06456951,  0.08403174,  0.07692813,\n        0.08377337,  0.04391864,  0.10519398,  0.07786352,  0.05243764,\n        0.02742507,  0.04606311,  0.05652268,  0.04697355,  0.04920116,\n        0.12268232,  0.0759553 ,  0.09233128,  0.05483661,  0.06063155,\n        0.05068447,  0.10495384,  0.07299428,  0.05869396,  0.06155175,\n        0.06108528,  0.05599143,  0.04059884,  0.04826835,  0.09459542,\n        0.04167287,  0.06421634,  0.03641472,  0.0238428 ,  0.02949051,\n        0.028975  ,  0.07587434,  0.04076047,  0.02183433,  0.04071859,\n        0.07915313,  0.04777162,  0.06885369,  0.07274957,  0.04513606,\n        0.07586587,  0.0313361 ,  0.03066215,  0.03402811,  0.02781086,\n        0.07376816,  0.04940179,  0.03161297,  0.05145948,  0.06577359,\n        0.02750272,  0.03582021,  0.06441121,  0.03604022,  0.03358751,\n        0.01675996,  0.0422471 ,  0.06591494,  0.06885818,  0.04818789,\n        0.04963618,  0.03163956,  0.01832079,  0.01964259,  0.01123289,\n        0.04754563,  0.07196065,  0.05327285,  0.01016024,  0.02099561,\n        0.03435864,  0.02275624,  0.0509572 ,  0.07064521,  0.01823375,\n        0.03585885,  0.04181538,  0.03457026,  0.03414933,  0.0637637 ,\n        0.07646433,  0.03986097,  0.02894514,  0.03613122,  0.04108859,\n        0.00557398,  0.02362922,  0.05843189,  0.07093583,  0.01379967,\n        0.03433454,  0.04788753,  0.03138599,  0.08687262,  0.06597871,\n        0.04592148,  0.02997983,  0.03153215,  0.03551219,  0.02675903,\n        0.03524658,  0.05154921,  0.03958578,  0.03302279,  0.03481268,\n        0.02006116,  0.00173977,  0.05858255,  0.03622785,  0.01146138,\n        0.02265825,  0.04005955,  0.01007684,  0.01566085,  0.02396771,\n        0.03186424,  0.04805276,  0.01635648,  0.01976088,  0.02343394,\n        0.00273027,  0.02632813,  0.04456983,  0.01092182,  0.05303205,\n        0.01297113,  0.010092  ,  0.02187145,  0.05982095,  0.07711737,\n       -0.00194965,  0.00163255,  0.01963515,  0.01550529,  0.00465648,\n        0.0280234 ,  0.04069632,  0.00792728,  0.00806326,  0.01348062,\n        0.00751679,  0.01850354,  0.00878234,  0.02196925,  0.00988049,\n        0.00236659, -0.00252685,  0.00163724, -0.00829965,  0.02765656,\n        0.04718163,  0.03509459,  0.01161449, -0.01577184,  0.00061623]), 'lags_positive': array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n       170, 171, 172, 173, 174, 175, 177, 178, 179, 180, 181, 182, 183,\n       184, 185, 186, 187, 188, 189, 190, 191, 193, 195, 196, 197, 198,\n       200]), 'acf_positive': array([0.16941215, 0.11681494, 0.11477814, 0.18185448, 0.14259819,\n       0.14312719, 0.13894497, 0.12687252, 0.10913528, 0.10631101,\n       0.12944533, 0.11684757, 0.09762454, 0.15017366, 0.12411898,\n       0.12326963, 0.11562807, 0.0844657 , 0.07607039, 0.09505224,\n       0.16106874, 0.07148222, 0.09370097, 0.113692  , 0.11214878,\n       0.07064554, 0.09680625, 0.10452395, 0.09941853, 0.06644419,\n       0.07644115, 0.10051518, 0.07849206, 0.07073167, 0.1368152 ,\n       0.10616077, 0.07564097, 0.07883957, 0.07680061, 0.07310946,\n       0.11918992, 0.07922182, 0.06456951, 0.08403174, 0.07692813,\n       0.08377337, 0.04391864, 0.10519398, 0.07786352, 0.05243764,\n       0.02742507, 0.04606311, 0.05652268, 0.04697355, 0.04920116,\n       0.12268232, 0.0759553 , 0.09233128, 0.05483661, 0.06063155,\n       0.05068447, 0.10495384, 0.07299428, 0.05869396, 0.06155175,\n       0.06108528, 0.05599143, 0.04059884, 0.04826835, 0.09459542,\n       0.04167287, 0.06421634, 0.03641472, 0.0238428 , 0.02949051,\n       0.028975  , 0.07587434, 0.04076047, 0.02183433, 0.04071859,\n       0.07915313, 0.04777162, 0.06885369, 0.07274957, 0.04513606,\n       0.07586587, 0.0313361 , 0.03066215, 0.03402811, 0.02781086,\n       0.07376816, 0.04940179, 0.03161297, 0.05145948, 0.06577359,\n       0.02750272, 0.03582021, 0.06441121, 0.03604022, 0.03358751,\n       0.01675996, 0.0422471 , 0.06591494, 0.06885818, 0.04818789,\n       0.04963618, 0.03163956, 0.01832079, 0.01964259, 0.01123289,\n       0.04754563, 0.07196065, 0.05327285, 0.01016024, 0.02099561,\n       0.03435864, 0.02275624, 0.0509572 , 0.07064521, 0.01823375,\n       0.03585885, 0.04181538, 0.03457026, 0.03414933, 0.0637637 ,\n       0.07646433, 0.03986097, 0.02894514, 0.03613122, 0.04108859,\n       0.00557398, 0.02362922, 0.05843189, 0.07093583, 0.01379967,\n       0.03433454, 0.04788753, 0.03138599, 0.08687262, 0.06597871,\n       0.04592148, 0.02997983, 0.03153215, 0.03551219, 0.02675903,\n       0.03524658, 0.05154921, 0.03958578, 0.03302279, 0.03481268,\n       0.02006116, 0.00173977, 0.05858255, 0.03622785, 0.01146138,\n       0.02265825, 0.04005955, 0.01007684, 0.01566085, 0.02396771,\n       0.03186424, 0.04805276, 0.01635648, 0.01976088, 0.02343394,\n       0.00273027, 0.02632813, 0.04456983, 0.01092182, 0.05303205,\n       0.01297113, 0.010092  , 0.02187145, 0.05982095, 0.07711737,\n       0.00163255, 0.01963515, 0.01550529, 0.00465648, 0.0280234 ,\n       0.04069632, 0.00792728, 0.00806326, 0.01348062, 0.00751679,\n       0.01850354, 0.00878234, 0.02196925, 0.00988049, 0.00236659,\n       0.00163724, 0.02765656, 0.04718163, 0.03509459, 0.01161449,\n       0.00061623]), 'is_long_memory': np.True_}",
    "model_comparison": "{'GARCH': {'params': {'mu': np.float64(0.12952422372058514), 'omega': np.float64(0.43881933719318955), 'alpha[1]': np.float64(0.09623144766619043), 'beta[1]': np.float64(0.876807221573444)}, 'aic': 16191.273421029466, 'bic': np.float64(16215.417126509034), 'log_likelihood': -8091.636710514733, 'conditional_volatility': datetime\n2017-08-18    0.045564\n2017-08-19    0.045227\n2017-08-20    0.042910\n2017-08-21    0.040965\n2017-08-22    0.039354\n                ...   \n2026-01-28    0.024847\n2026-01-29    0.024192\n2026-01-30    0.029081\n2026-01-31    0.028085\n2026-02-01    0.034557\nName: cond_vol, Length: 3090, dtype: float64, 'result_obj':                      Constant Mean - GARCH Model Results                      \n==============================================================================\nDep. Variable:                  close   R-squared:                       0.000\nMean Model:             Constant Mean   Adj. R-squared:                  0.000\nVol Model:                      GARCH   Log-Likelihood:               -8091.64\nDistribution:                  Normal   AIC:                           16191.3\nMethod:            Maximum Likelihood   BIC:                           16215.4\n                                        No. Observations:                 3090\nDate:                Tue, Feb 03 2026   Df Residuals:                     3089\nTime:                        11:15:48   Df Model:                            1\n                                Mean Model                                \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu             0.1295  5.806e-02      2.231  2.568e-02 [1.573e-02,  0.243]\n                             Volatility Model                             \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega          0.4388      0.207      2.121  3.393e-02 [3.329e-02,  0.844]\nalpha[1]       0.0962  4.760e-02      2.022  4.319e-02 [2.944e-03,  0.190]\nbeta[1]        0.8768  4.647e-02     18.866  2.158e-79   [  0.786,  0.968]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x12606b700}, 'EGARCH': {'params': {'mu': np.float64(0.11417527305176238), 'omega': np.float64(0.11719203779011349), 'alpha[1]': np.float64(0.17325396230660126), 'beta[1]': np.float64(0.9600892426630561)}, 'aic': 16209.480929812882, 'bic': np.float64(16233.62463529245), 'log_likelihood': -8100.740464906441, 'conditional_volatility': datetime\n2017-08-18    0.045606\n2017-08-19    0.046114\n2017-08-20    0.043460\n2017-08-21    0.041720\n2017-08-22    0.040522\n                ...   \n2026-01-28    0.024748\n2026-01-29    0.023667\n2026-01-30    0.027635\n2026-01-31    0.026737\n2026-02-01    0.031800\nName: cond_vol, Length: 3090, dtype: float64, 'leverage_param': nan, 'result_obj':                      Constant Mean - EGARCH Model Results                     \n==============================================================================\nDep. Variable:                  close   R-squared:                       0.000\nMean Model:             Constant Mean   Adj. R-squared:                  0.000\nVol Model:                     EGARCH   Log-Likelihood:               -8100.74\nDistribution:                  Normal   AIC:                           16209.5\nMethod:            Maximum Likelihood   BIC:                           16233.6\n                                        No. Observations:                 3090\nDate:                Tue, Feb 03 2026   Df Residuals:                     3089\nTime:                        11:15:48   Df Model:                            1\n                                Mean Model                                \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu             0.1142  5.823e-02      1.961  4.991e-02 [4.720e-05,  0.228]\n                             Volatility Model                             \n==========================================================================\n                 coef    std err          t      P>|t|    95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega          0.1172  4.701e-02      2.493  1.266e-02 [2.506e-02,  0.209]\nalpha[1]       0.1733  5.445e-02      3.182  1.463e-03 [6.653e-02,  0.280]\nbeta[1]        0.9601  1.594e-02     60.213      0.000   [  0.929,  0.991]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x124f5a760}, 'GJR-GARCH': {'params': {'mu': np.float64(0.08097224546042321), 'omega': np.float64(0.48386363868276294), 'alpha[1]': np.float64(0.06779768885138529), 'gamma[1]': np.float64(0.06548062265277206), 'beta[1]': np.float64(0.8693004769146425)}, 'aic': 16170.677755626308, 'bic': np.float64(16200.857387475768), 'log_likelihood': -8080.338877813154, 'conditional_volatility': datetime\n2017-08-18    0.045540\n2017-08-19    0.045790\n2017-08-20    0.043293\n2017-08-21    0.041271\n2017-08-22    0.039661\n                ...   \n2026-01-28    0.025648\n2026-01-29    0.024905\n2026-01-30    0.031310\n2026-01-31    0.030075\n2026-02-01    0.038224\nName: cond_vol, Length: 3090, dtype: float64, 'leverage_param': np.float64(0.06548062265277206), 'result_obj':                    Constant Mean - GJR-GARCH Model Results                    \n==============================================================================\nDep. Variable:                  close   R-squared:                       0.000\nMean Model:             Constant Mean   Adj. R-squared:                  0.000\nVol Model:                  GJR-GARCH   Log-Likelihood:               -8080.34\nDistribution:                  Normal   AIC:                           16170.7\nMethod:            Maximum Likelihood   BIC:                           16200.9\n                                        No. Observations:                 3090\nDate:                Tue, Feb 03 2026   Df Residuals:                     3089\nTime:                        11:15:48   Df Model:                            1\n                                 Mean Model                                \n===========================================================================\n                 coef    std err          t      P>|t|     95.0% Conf. Int.\n---------------------------------------------------------------------------\nmu             0.0810  5.352e-02      1.513      0.130 [-2.392e-02,  0.186]\n                              Volatility Model                             \n===========================================================================\n                 coef    std err          t      P>|t|     95.0% Conf. Int.\n---------------------------------------------------------------------------\nomega          0.4839      0.237      2.044  4.094e-02  [1.993e-02,  0.948]\nalpha[1]       0.0678  2.275e-02      2.979  2.887e-03  [2.320e-02,  0.112]\ngamma[1]       0.0655  6.205e-02      1.055      0.291 [-5.613e-02,  0.187]\nbeta[1]        0.8693  4.827e-02     18.009  1.663e-72    [  0.775,  0.964]\n===========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x124ec2d60}}",
    "leverage_effect": "{'5d': {'pearson_correlation': np.float64(-0.061984247861208444), 'pearson_pvalue': np.float64(0.0005717052330372451), 'spearman_correlation': np.float64(-0.013703868249034022), 'spearman_pvalue': np.float64(0.44672985128105513), 'n_samples': 3085, 'return_series': datetime\n2017-08-18   -0.042113\n2017-08-19    0.007665\n2017-08-20   -0.013053\n2017-08-21   -0.017351\n2017-08-22    0.005958\n                ...   \n2026-01-23    0.000453\n2026-01-24   -0.004193\n2026-01-25   -0.029053\n2026-01-26    0.019161\n2026-01-27    0.010168\nName: return, Length: 3085, dtype: float64, 'future_vol_series': datetime\n2017-08-18    0.012436\n2017-08-19    0.020490\n2017-08-20    0.019523\n2017-08-21    0.018687\n2017-08-22    0.018765\n                ...   \n2026-01-23    0.012627\n2026-01-24    0.022483\n2026-01-25    0.017596\n2026-01-26    0.027313\n2026-01-27    0.029834\nName: future_vol, Length: 3085, dtype: float64}, '10d': {'pearson_correlation': np.float64(-0.033668665650756026), 'pearson_pvalue': np.float64(0.06171938986587776), 'spearman_correlation': np.float64(0.0022075568332161587), 'spearman_pvalue': np.float64(0.9025308817972548), 'n_samples': 3080, 'return_series': datetime\n2017-08-18   -0.042113\n2017-08-19    0.007665\n2017-08-20   -0.013053\n2017-08-21   -0.017351\n2017-08-22    0.005958\n                ...   \n2026-01-18   -0.015620\n2026-01-19   -0.011188\n2026-01-20   -0.046439\n2026-01-21    0.011548\n2026-01-22    0.001172\nName: return, Length: 3080, dtype: float64, 'future_vol_series': datetime\n2017-08-18    0.015548\n2017-08-19    0.019258\n2017-08-20    0.018660\n2017-08-21    0.020583\n2017-08-22    0.022289\n                ...   \n2026-01-18    0.013394\n2026-01-19    0.017622\n2026-01-20    0.013440\n2026-01-21    0.019060\n2026-01-22    0.021220\nName: future_vol, Length: 3080, dtype: float64}, '20d': {'pearson_correlation': np.float64(-0.017638367572489176), 'pearson_pvalue': np.float64(0.32858040373635033), 'spearman_correlation': np.float64(0.006272837436601455), 'spearman_pvalue': np.float64(0.7282723382926714), 'n_samples': 3070, 'return_series': datetime\n2017-08-18   -0.042113\n2017-08-19    0.007665\n2017-08-20   -0.013053\n2017-08-21   -0.017351\n2017-08-22    0.005958\n                ...   \n2026-01-08   -0.002896\n2026-01-09   -0.005048\n2026-01-10   -0.001508\n2026-01-11    0.005608\n2026-01-12    0.003100\nName: return, Length: 3070, dtype: float64, 'future_vol_series': datetime\n2017-08-18    0.029149\n2017-08-19    0.033324\n2017-08-20    0.032953\n2017-08-21    0.033616\n2017-08-22    0.034255\n                ...   \n2026-01-08    0.012185\n2026-01-09    0.014607\n2026-01-10    0.014762\n2026-01-11    0.017869\n2026-01-12    0.018853\nName: future_vol, Length: 3070, dtype: float64}}",
    "status": "success"
  },
  "hurst": {
    "R/S Hurst": 0.599066670965807,
    "DFA Hurst": 0.5868487366138886,
    "交叉验证": {
      "R/S Hurst": 0.599066670965807,
      "DFA Hurst": 0.5868487366138886,
      "两种方法差异": 0.012217934351918425,
      "平均值": 0.5929577037898478
    },
    "综合Hurst": 0.5929577037898478,
    "综合解读": "趋势性 (H=0.5930 > 0.55)：序列具有长程正相关，价格趋势倾向于持续",
    "滚动Hurst": {
      "窗口数": 87,
      "趋势占比": 0.9885057471264368,
      "随机游走占比": 0.011494252873563218,
      "均值回归占比": 0.0,
      "Hurst范围": [
        0.548657473865875,
        0.6540287499669682
      ],
      "Hurst均值": 0.5913167465022056
    },
    "多时间框架": {
      "1h": {
        "R/S Hurst": 0.5551829664830917,
        "DFA Hurst": 0.5559270762382792,
        "平均Hurst": 0.5555550213606855,
        "数据量": 74052,
        "解读": "趋势性 (H=0.5556 > 0.55)：序列具有长程正相关，价格趋势倾向于持续"
      },
      "4h": {
        "R/S Hurst": 0.5749044947852355,
        "DFA Hurst": 0.577134099743992,
        "平均Hurst": 0.5760192972646138,
        "数据量": 18527,
        "解读": "趋势性 (H=0.5760 > 0.55)：序列具有长程正相关，价格趋势倾向于持续"
      },
      "1d": {
        "R/S Hurst": 0.599066670965807,
        "DFA Hurst": 0.5868487366138886,
        "平均Hurst": 0.5929577037898478,
        "数据量": 3090,
        "解读": "趋势性 (H=0.5930 > 0.55)：序列具有长程正相关，价格趋势倾向于持续"
      },
      "1w": {
        "R/S Hurst": 0.6863567334278854,
        "DFA Hurst": 0.6551931131151767,
        "平均Hurst": 0.670774923271531,
        "数据量": 434,
        "解读": "趋势性 (H=0.6708 > 0.55)：序列具有长程正相关，价格趋势倾向于持续"
      }
    },
    "status": "success"
  },
  "fractal": {
    "盒计数分形维数": 1.33981806810231,
    "维数解读": "序列较为光滑，具有一定趋势持续性",
    "Hurst(从D推算)": 0.66018193189769,
    "蒙特卡洛检验": "{'BTC分形维数': np.float64(1.33981806810231), '随机游走均值': np.float64(1.380548625079417), '随机游走标准差': np.float64(0.029469822925662244), '随机游走范围': (np.float64(1.2773941560871607), np.float64(1.4351289056018914)), 'Z统计量': np.float64(-1.3821106791123259), 'p值': np.float64(0.16693771997826756), '显著性(α=0.05)': np.False_}",
    "多尺度自相似性": {
      "缩放指数(H估计)": 0.5274203951268943
    },
    "status": "success"
  },
  "power_law": {
    "r_squared": 0.5678120109582484,
    "power_exponent": 0.7699636561390698,
    "intercept": 4.629820314237845,
    "corridor_prices": {
      "0.05": 16879.14611194412,
      "0.5": 51706.664285887106,
      "0.95": 119339.81281975961
    },
    "model_comparison": {
      "power_law_aic": 68300.50392355697,
      "power_law_bic": 68312.57642344123,
      "exponential_aic": 67807.4540823288,
      "exponential_bic": 67819.52658221306,
      "preferred": "exponential"
    },
    "current_price": 76968.21,
    "current_percentile": 67.87447428016823,
    "status": "success"
  },
  "volume_price": {
    "spearman": {
      "correlation": 0.3214920649731082,
      "p_value": 3.1129979914822277e-75,
      "n_samples": 3090
    },
    "lead_lag": {
      "significant_lags": []
    },
    "granger": {
      "volume_to_returns_sig_lags": [],
      "returns_to_volume_sig_lags": []
    },
    "obv_divergences": {
      "total": 82,
      "bearish": 49,
      "bullish": 33
    },
    "status": "success"
  },
  "calendar": {
    "status": "success",
    "findings": []
  },
  "halving": {
    "status": "success",
    "findings": []
  },
  "indicators": {
    "train_results": "              n_buy  n_sell  ...  ic_rejected  any_fdr_pass\nindicator                    ...                           \nSMA_5_20       47.0    48.0  ...        False         False\nEMA_5_20       53.0    54.0  ...        False         False\nSMA_10_50      21.0    22.0  ...        False         False\nEMA_10_50      19.0    20.0  ...        False         False\nSMA_20_100      7.0     8.0  ...        False         False\nEMA_20_100      9.0    10.0  ...        False         False\nSMA_50_200      4.0     5.0  ...        False         False\nEMA_50_200      6.0     7.0  ...        False         False\nRSI_7_30_70    66.0    78.0  ...        False         False\nRSI_7_25_75    48.0    62.0  ...        False         False\nRSI_7_20_80    21.0    41.0  ...        False         False\nRSI_14_30_70   24.0    47.0  ...        False         False\nRSI_14_25_75   15.0    27.0  ...        False         False\nRSI_14_20_80    4.0    17.0  ...        False         False\nRSI_21_30_70   14.0    29.0  ...        False         False\nRSI_21_25_75    4.0    16.0  ...        False         False\nRSI_21_20_80    2.0    11.0  ...        False         False\nMACD_12_26_9   65.0    65.0  ...        False         False\nMACD_8_17_9    92.0    92.0  ...        False         False\nMACD_5_35_5   123.0   123.0  ...        False         False\nBB_20_2        39.0    59.0  ...        False         False\n\n[21 rows x 23 columns]",
    "val_results": "              n_buy  n_sell  ...  ic_rejected  any_fdr_pass\nindicator                    ...                           \nSMA_5_20       21.0    21.0  ...        False         False\nEMA_5_20       17.0    17.0  ...        False         False\nSMA_10_50       7.0     7.0  ...        False         False\nEMA_10_50       8.0     8.0  ...        False         False\nSMA_20_100      4.0     4.0  ...        False         False\nEMA_20_100      3.0     3.0  ...        False         False\nSMA_50_200      2.0     1.0  ...        False         False\nEMA_50_200      2.0     1.0  ...        False         False\nRSI_7_30_70    16.0    27.0  ...        False         False\nRSI_7_25_75     9.0    16.0  ...        False         False\nRSI_7_20_80     4.0    17.0  ...        False         False\nRSI_14_30_70    4.0    17.0  ...        False         False\nRSI_14_25_75    3.0     6.0  ...        False         False\nRSI_14_20_80    1.0     7.0  ...        False         False\nRSI_21_30_70    1.0     7.0  ...        False         False\nRSI_21_25_75    0.0     9.0  ...        False         False\nRSI_21_20_80    0.0     7.0  ...        False         False\nMACD_12_26_9   22.0    23.0  ...        False         False\nMACD_8_17_9    28.0    29.0  ...        False         False\nMACD_5_35_5    42.0    43.0  ...        False         False\nBB_20_2        12.0    26.0  ...        False         False\n\n[21 rows x 23 columns]",
    "fdr_passed_train": [],
    "fdr_passed_val": [],
    "permutation_results": {
      "RSI_14_30_70": {
        "observed_diff": -0.004977440100087348,
        "perm_pval": 0.5664335664335665
      },
      "RSI_14_25_75": {
        "observed_diff": -0.03017610738336842,
        "perm_pval": 0.014985014985014986
      },
      "RSI_21_30_70": {
        "observed_diff": -0.012247499113796413,
        "perm_pval": 0.2677322677322677
      },
      "RSI_7_25_75": {
        "observed_diff": -0.014302431427126703,
        "perm_pval": 0.02097902097902098
      },
      "RSI_21_20_80": {
        "observed_diff": -0.0252918754365221,
        "perm_pval": 0.3026973026973027
      }
    },
    "all_signals": "{'SMA_5_20': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'EMA_5_20': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'SMA_10_50': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'EMA_10_50': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'SMA_20_100': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'EMA_20_100': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'SMA_50_200': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'EMA_50_200': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_7_30_70': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_7_25_75': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_7_20_80': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_14_30_70': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_14_25_75': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_14_20_80': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_21_30_70': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_21_25_75': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'RSI_21_20_80': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'MACD_12_26_9': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'MACD_8_17_9': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'MACD_5_35_5': datetime\n2017-08-17    0\n2017-08-18   -1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'BB_20_2': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64}",
    "status": "success"
  },
  "patterns": {
    "train_results": "                      n_occurrences  ...  any_fdr_pass\npattern                              ...              \nDoji                          219.0  ...         False\nHammer                         49.0  ...         False\nInverted_Hammer                17.0  ...         False\nShooting_Star                   6.0  ...         False\nPin_Bar_Bull                  116.0  ...         False\nPin_Bar_Bear                   57.0  ...         False\nBullish_Engulfing             159.0  ...         False\nBearish_Engulfing             149.0  ...         False\nMorning_Star                   23.0  ...         False\nEvening_Star                   20.0  ...         False\nThree_White_Soldiers           11.0  ...         False\nThree_Black_Crows               4.0  ...         False\n\n[12 rows x 41 columns]",
    "val_results": "                      n_occurrences  ...  any_fdr_pass\npattern                              ...              \nDoji                           81.0  ...          True\nHammer                         12.0  ...         False\nInverted_Hammer                 6.0  ...         False\nShooting_Star                   3.0  ...         False\nPin_Bar_Bull                   28.0  ...          True\nPin_Bar_Bear                   20.0  ...         False\nBullish_Engulfing              69.0  ...          True\nBearish_Engulfing              47.0  ...         False\nMorning_Star                    5.0  ...         False\nEvening_Star                    6.0  ...         False\nThree_White_Soldiers            4.0  ...         False\nThree_Black_Crows               0.0  ...         False\n\n[12 rows x 41 columns]",
    "fdr_passed_train": [],
    "fdr_passed_val": [
      "Doji",
      "Pin_Bar_Bull",
      "Bullish_Engulfing"
    ],
    "all_patterns": "{'Doji': datetime\n2017-08-17    1\n2017-08-18    0\n2017-08-19    1\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    1\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Hammer': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    1\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Inverted_Hammer': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Shooting_Star': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Pin_Bar_Bull': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    1\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    1\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Pin_Bar_Bear': datetime\n2017-08-17    1\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    1\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Bullish_Engulfing': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Bearish_Engulfing': datetime\n2017-08-17    0\n2017-08-18    1\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    1\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Morning_Star': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Evening_Star': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    1\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Three_White_Soldiers': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64, 'Three_Black_Crows': datetime\n2017-08-17    0\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3091, dtype: int64}",
    "status": "success"
  },
  "clustering": {
    "kmeans": "{'best_k': 3, 'labels': array([1, 1, 2, ..., 0, 1, 0], dtype=int32), 'cluster_desc':                  log_return  abs_return    vol_7d  ...  state_cn  count   pct\ncluster_K-Means                                    ...                       \n0                 -0.000096    0.012406  0.465074  ...      横盘整理   2253  73.6\n1                 -0.056358    0.056570  0.951770  ...      急剧下跌    361  11.8\n2                  0.052789    0.052932  0.876194  ...      强势上涨    447  14.6\n\n[3 rows x 14 columns], 'all_results': {3: {'silhouette': np.float64(0.33788983182490917), 'inertia': 21341.973506600978, 'labels': array([1, 1, 2, ..., 0, 1, 0], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=3, n_init=20, random_state=42)}, 4: {'silhouette': np.float64(0.2383690756014495), 'inertia': 19697.42665658707, 'labels': array([3, 3, 0, ..., 1, 2, 3], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=4, n_init=20, random_state=42)}, 5: {'silhouette': np.float64(0.1748258615401955), 'inertia': 18264.213417509138, 'labels': array([4, 4, 0, ..., 2, 3, 4], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=5, n_init=20, random_state=42)}, 6: {'silhouette': np.float64(0.17344301823232902), 'inertia': 17141.26396179242, 'labels': array([2, 2, 0, ..., 3, 3, 3], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=6, n_init=20, random_state=42)}, 7: {'silhouette': np.float64(0.17323332216663148), 'inertia': 16243.10046274255, 'labels': array([3, 4, 1, ..., 5, 5, 5], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=7, n_init=20, random_state=42)}}}",
    "gmm": "{'best_k': 7, 'labels': array([3, 1, 3, ..., 6, 5, 6]), 'cluster_desc':              log_return  abs_return    vol_7d  ...  state_cn  count   pct\ncluster_GMM                                    ...                       \n0              0.010128    0.010128  0.357685  ...      温和上涨    698  22.8\n1             -0.072434    0.072434  1.041344  ...      急剧下跌    122   4.0\n2             -0.012261    0.012261  0.381069  ...      温和下跌    783  25.6\n3              0.059049    0.059049  1.051605  ...      强势上涨    241   7.9\n4              0.025606    0.025606  0.656672  ...      强势上涨    634  20.7\n5             -0.043943    0.043943  0.803047  ...      急剧下跌    290   9.5\n6             -0.011326    0.011326  0.700769  ...      温和下跌    293   9.6\n\n[7 rows x 14 columns], 'all_results': {3: {'bic': np.float64(7219.829483951697), 'aic': np.float64(6032.609586979451), 'silhouette': np.float64(0.06549802389899598), 'labels': array([0, 2, 0, ..., 2, 2, 1]), 'model': GaussianMixture(max_iter=500, n_components=3, n_init=5, random_state=42)}, 4: {'bic': np.float64(-101.52417196679198), 'aic': np.float64(-1686.4928669094352), 'silhouette': np.float64(0.05590661985706378), 'labels': array([3, 2, 3, ..., 2, 2, 0]), 'model': GaussianMixture(max_iter=500, n_components=4, n_init=5, random_state=42)}, 5: {'bic': np.float64(-2057.235977022373), 'aic': np.float64(-4039.9534699354135), 'silhouette': np.float64(0.03830870117896256), 'labels': array([2, 4, 2, ..., 0, 0, 0]), 'model': GaussianMixture(max_iter=500, n_components=5, n_init=5, random_state=42)}, 6: {'bic': np.float64(-1810.3804844055708), 'aic': np.float64(-4190.846775289008), 'silhouette': np.float64(0.04026910587877369), 'labels': array([2, 5, 0, ..., 3, 3, 3]), 'model': GaussianMixture(max_iter=500, n_components=6, n_init=5, random_state=42)}, 7: {'bic': np.float64(-3434.449804148429), 'aic': np.float64(-6212.664893002264), 'silhouette': np.float64(0.01891584304771941), 'labels': array([3, 1, 3, ..., 6, 5, 6]), 'model': GaussianMixture(max_iter=500, n_components=7, n_init=5, random_state=42)}}}",
    "hdbscan": "{'labels': array([-1, -1, -1, ..., -1, -1, -1]), 'info': {'n_clusters': 0, 'n_noise': np.int64(3061), 'noise_pct': np.float64(100.0), 'labels': array([-1, -1, -1, ..., -1, -1, -1]), 'model': HDBSCAN(min_cluster_size=30, min_samples=10)}}",
    "markov": "{'transition_matrix': array([[0.81971581, 0.07726465, 0.10301954],\n       [0.45152355, 0.2299169 , 0.31855956],\n       [0.5458613 , 0.23042506, 0.22371365]]), 'stationary_distribution': array([0.73645018, 0.11757031, 0.14597951]), 'holding_time': array([5.54679803, 1.29856115, 1.28818444])}",
    "features": "{'df_clean':                 open      high  ...  log_return_lag1  log_return_lag2\ndatetime                        ...                                  \n2017-09-16   3674.01   3950.00  ...         0.148619        -0.212657\n2017-09-17   3685.23   3748.21  ...         0.004032         0.148619\n2017-09-18   3690.00   4123.20  ...        -0.004035         0.004032\n2017-09-19   4060.00   4089.97  ...         0.086679        -0.004035\n2017-09-20   3910.04   4046.08  ...        -0.031461         0.086679\n...              ...       ...  ...              ...              ...\n2026-01-28  89249.99  90600.00  ...         0.010168         0.019161\n2026-01-29  89300.00  89348.00  ...         0.000560         0.010168\n2026-01-30  84650.16  84735.75  ...        -0.053474         0.000560\n2026-01-31  84260.50  84270.02  ...        -0.004614        -0.053474\n2026-02-01  78741.10  79424.00  ...        -0.067748        -0.004614\n\n[3061 rows x 24 columns], 'X_scaled': array([[ 0.08513551, -0.72523644,  4.04658793, ...,  0.26911597,\n         4.12039156, -5.93472476],\n       [-0.14073076, -0.72513592,  4.04721932, ...,  0.06727386,\n         0.08334177,  4.10039685],\n       [ 2.39898724,  2.3484655 ,  4.39080301, ...,  2.5979057 ,\n        -0.14191333,  0.08422571],\n       ...,\n       [-0.15693614, -0.70360882, -0.3029424 , ..., -0.17615044,\n        -1.52232198, -0.012228  ],\n       [-1.92450248,  1.64441202,  0.13494686, ..., -1.89826004,\n        -0.15807487, -1.51313393],\n       [-0.66532683, -0.02826674,  0.12068419, ..., -0.68265688,\n        -1.92085845, -0.15594238]]), 'scaler': StandardScaler()}",
    "status": "success"
  },
  "time_series": {
    "metrics": "{'Random Walk': {'name': 'Random Walk', 'rmse': np.float64(0.02531781370478331), 'rmse_ratio_vs_rw': np.float64(1.0), 'direction_accuracy': np.float64(0.0), 'dm_stat_vs_rw': nan, 'dm_pval_vs_rw': nan, 'predictions': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), 'errors': array([-5.76555882e-03, -1.32482993e-02,  2.95881469e-02,  3.54700668e-02,\n       -8.86807695e-03, -9.85029250e-03, -2.18074843e-02, -5.75794217e-03,\n        1.08397524e-03, -1.59268553e-02, -3.76363267e-03,  4.99954856e-03,\n        1.13988381e-02, -1.02822908e-02, -5.62356239e-03,  1.01006875e-02,\n        1.47830061e-02, -1.14422775e-02, -1.05833258e-02, -4.29965744e-03,\n        6.40996125e-03,  2.08399000e-03,  1.88814048e-02, -1.23744128e-02,\n        3.80839730e-02,  3.38584068e-02, -2.32062173e-02,  1.45149105e-02,\n        1.05229017e-02, -8.79361510e-03, -6.65109064e-03, -3.47534413e-04,\n       -1.63299430e-02,  2.77405884e-03,  4.55002685e-02,  7.10756813e-03,\n       -1.86613858e-02, -1.51557084e-02, -1.04539949e-01, -1.52567779e-01,\n        1.00211568e-01, -3.06235437e-02, -1.52430215e-02, -2.91029542e-02,\n        1.75795765e-02,  1.67730512e-02, -1.41710599e-02,  1.78682189e-03,\n        4.72553995e-04,  1.37719892e-05, -2.54979489e-02, -3.11264271e-02,\n        2.78477629e-02,  2.29172032e-02, -2.50586862e-04, -4.63814070e-03,\n       -3.85498546e-03, -1.81163932e-03, -1.32268364e-02,  1.40634323e-02,\n        4.29219220e-02, -1.09119120e-02,  6.77253117e-03, -1.22163134e-02,\n        1.29742427e-02, -8.17977063e-03,  7.20067031e-03, -1.48751770e-02,\n        2.27521019e-02, -5.56232014e-03, -6.24707169e-05, -2.48096293e-03,\n        7.27692009e-03,  3.22953657e-02,  1.59931002e-03, -2.54175504e-02,\n       -4.26220920e-02,  8.64452424e-03, -2.28615995e-03, -1.80448690e-02,\n        2.74016047e-02, -4.20460385e-03, -1.92592901e-04, -2.55536386e-03,\n        3.42827356e-03, -2.38206762e-04,  5.17192981e-03, -1.26708163e-02,\n       -9.56593440e-03,  5.19337964e-03, -1.56373421e-03, -3.92641443e-03,\n        4.48444092e-03,  3.37162474e-03,  1.38538835e-04,  1.04506352e-02,\n       -1.09909664e-03,  7.03325626e-03, -4.17770352e-04,  1.08162151e-02,\n        2.94000490e-03,  1.51596309e-02,  2.84102952e-02,  4.91190305e-02,\n        5.58930485e-02,  5.01468277e-02, -3.98887166e-03,  1.49394737e-02,\n       -2.40262154e-03, -2.18767424e-02,  1.88809860e-02,  7.29936991e-02,\n        5.11939752e-03, -3.32678352e-03,  9.14299248e-03, -1.24508352e-02,\n        1.87361192e-02, -2.22658408e-03,  2.79968354e-03, -2.23703451e-03,\n        3.07819252e-02, -3.93514588e-02,  1.30130964e-02,  2.59322536e-02,\n       -1.03224865e-02, -2.43133022e-03, -4.49371291e-03, -1.70316321e-02,\n       -7.45767311e-03,  2.07794016e-02, -1.20105003e-02, -5.21417004e-02,\n       -7.88368568e-03,  1.09162885e-02, -3.62048863e-03, -4.39419065e-04,\n        1.93698620e-02,  9.13804912e-02, -3.37113933e-02,  4.37707860e-02,\n        2.51941521e-03, -1.47308461e-02,  2.32302854e-02, -1.58252644e-02,\n       -1.11013167e-02, -1.00581845e-02, -3.20410285e-02, -1.21789238e-03,\n        1.70316106e-02, -2.66797531e-03, -1.50332014e-02,  2.08429342e-02,\n       -6.94991510e-03, -4.85031008e-02, -3.47643933e-04,  3.73720671e-03,\n       -9.02760617e-04, -9.50689505e-03, -2.24374724e-02, -6.38816973e-02,\n       -1.04426921e-02,  1.50245079e-02,  7.26480423e-02,  9.18599391e-02,\n        2.28335296e-02, -1.57184971e-02,  2.89409710e-02,  9.15382367e-02,\n       -1.79605739e-02,  3.88304254e-02, -9.18880774e-03,  1.39179152e-02,\n       -3.08751025e-02,  3.76104839e-02, -3.01706045e-02,  3.08827329e-04,\n        1.82249637e-02, -3.06102933e-02,  5.00718327e-03,  3.91179691e-02,\n       -1.13547262e-02,  1.54649894e-02, -4.43795644e-04, -9.92015143e-03,\n       -1.32879417e-02,  1.30607389e-02,  1.61177290e-04, -4.84629843e-03,\n       -4.55809363e-03,  1.14782595e-03,  1.36996576e-02,  4.53340244e-02,\n        1.88207788e-02, -1.03964274e-02,  1.61223075e-02,  3.06012145e-03,\n       -5.65617990e-03,  3.15512907e-04, -2.92774296e-02,  3.17610898e-02,\n       -5.35101387e-02, -1.94060341e-02, -3.53440508e-02,  2.01173494e-02,\n       -8.16681719e-03, -2.89175475e-03,  2.83063392e-02,  4.03766112e-03,\n        3.65394057e-02, -5.48003250e-03, -2.77577958e-03,  9.44176263e-05,\n       -4.06659912e-02,  2.12069962e-02,  1.23510956e-02, -6.49798223e-03,\n        2.28809047e-02, -2.25328154e-02, -1.45991563e-02, -2.71433799e-02,\n       -1.46553930e-03, -1.06904203e-03, -2.30965140e-02, -6.45829173e-03,\n       -7.36602355e-04,  5.30201697e-03,  9.04300116e-03, -4.73467736e-03,\n        1.36583224e-02, -2.15521347e-02,  2.19658593e-03,  8.23120354e-03,\n       -1.31719125e-02,  3.78715326e-03,  1.36990369e-02, -3.32662963e-02,\n        5.48381384e-03,  8.73007673e-03,  5.53957629e-03,  4.40984084e-02,\n       -1.17776184e-02, -1.51540328e-03, -1.76328111e-02, -1.45267220e-02,\n        1.57108639e-02, -6.38426850e-03,  1.69753603e-03, -5.25071585e-02,\n        5.67345780e-02, -3.32586056e-02,  6.02863973e-03, -7.85255065e-04,\n       -2.43365193e-02,  3.25846449e-03, -7.85634203e-04,  1.12115420e-03,\n       -3.15578492e-02,  1.85267257e-02,  2.87451383e-02,  6.50715597e-03,\n       -6.69810227e-03,  1.89678131e-02,  5.30887265e-02,  5.78495973e-02,\n       -3.63968891e-03,  2.65339862e-02, -5.26236787e-03, -2.12395241e-03,\n       -6.41095285e-03,  1.39256517e-02, -2.02419785e-02,  1.22232571e-02,\n        8.10580466e-04,  3.73088927e-03,  1.01727165e-03,  1.74568722e-02,\n       -1.25865016e-02, -8.54238464e-03, -2.01787502e-02,  1.49162470e-02,\n       -1.98154984e-03, -4.10023911e-03,  8.28304413e-03,  6.89884239e-03,\n       -7.93747334e-03,  3.47489761e-02, -3.69894697e-02, -7.42225519e-04,\n       -1.90114283e-03, -3.11380128e-03, -9.29617828e-03,  1.67580400e-03,\n       -3.65806621e-03,  3.40761031e-03, -3.60897287e-03,  9.67812791e-03,\n       -3.06215681e-02,  1.79469724e-03,  4.20103631e-03, -4.41078217e-03,\n        3.12145136e-03,  1.32135542e-03, -2.44943371e-03, -1.66936331e-03,\n        1.60761571e-02, -1.76592241e-02,  2.61392456e-04, -2.73206267e-03,\n       -1.43883136e-03,  5.60173534e-04,  4.20724785e-03,  1.89678784e-02,\n       -6.34955289e-03, -4.27659299e-03, -1.00948044e-03,  1.40681866e-04,\n       -4.30177319e-03,  4.32759670e-03, -7.87745288e-03, -1.62090850e-02,\n       -7.61687204e-02, -2.16195967e-02,  1.76439019e-03,  3.44157922e-03,\n       -2.41107629e-03, -2.71813243e-03,  1.43545687e-02, -9.60496634e-03,\n       -4.59571491e-03, -1.63756355e-03,  3.23873624e-03,  6.98176287e-04,\n        5.93208266e-02, -1.51357951e-02, -5.10700881e-02, -5.24603862e-03,\n        2.49484595e-03,  3.92356165e-03, -5.60610597e-03, -1.31426735e-03,\n       -1.24728325e-03,  1.90354868e-02, -1.32081551e-02, -3.43163021e-04,\n       -2.31914546e-03, -2.66303977e-02,  2.65719631e-02,  1.46712042e-02,\n        1.14033481e-02,  2.90911459e-03, -1.51731596e-03, -1.21159210e-03,\n        8.81971984e-03,  1.65920805e-02, -3.13829620e-03, -2.07453166e-02,\n        4.53825178e-04, -1.57272637e-04, -1.24027749e-02,  2.14753935e-03,\n       -3.16564405e-03,  5.75421222e-03,  2.42883942e-02, -4.24378538e-03,\n        2.06424782e-03,  3.74898808e-02, -1.79527694e-02, -2.47810754e-03,\n        1.27566193e-02, -1.33427215e-02,  1.88182667e-02,  9.15406228e-04,\n       -1.41819827e-03, -1.17798757e-02, -7.27537320e-03, -1.89665293e-02,\n       -4.32142654e-03,  3.81823993e-03, -3.54466807e-04,  1.11717075e-02,\n        4.84015661e-02, -3.68633461e-03, -2.67685189e-03,  1.38064402e-02,\n        3.27293688e-02,  8.08210803e-03,  2.75983085e-03,  9.76802079e-02,\n        2.54590666e-02,  1.67595336e-02, -1.00336308e-02, -7.63160547e-03,\n        5.56045581e-03,  1.29694331e-02, -1.48288550e-03,  4.77585206e-03,\n        2.23145603e-02, -1.36391940e-02, -6.45466670e-03,  9.89677584e-03,\n       -1.43248693e-03,  9.76619868e-04,  1.00229087e-02,  6.35281784e-03,\n        2.97666760e-02,  1.62305705e-02, -4.61175724e-03, -1.77561263e-03,\n       -1.63535275e-02, -2.53224984e-02,  6.28739714e-02, -4.57970017e-02,\n        1.23778966e-02, -1.25222031e-03,  2.14205904e-02,  2.37726663e-03,\n       -4.66574572e-02,  4.55769988e-02, -3.05371053e-03,  1.11800130e-02,\n        1.77761953e-03, -8.85951339e-03, -5.48213031e-03,  1.53522319e-02,\n        9.45377125e-04, -3.45812519e-03,  2.50923821e-02,  1.96551008e-02,\n        1.31427943e-02,  4.92719775e-02,  4.83969196e-02, -7.07298282e-03,\n       -1.12495087e-02,  2.05361196e-02, -1.04089723e-02,  1.73502448e-03,\n       -5.96607574e-02,  5.77650330e-03,  3.26396169e-02,  3.56800236e-03,\n       -2.54704776e-02,  8.02038726e-03, -2.15991913e-02,  3.05417667e-02,\n       -8.99082855e-03,  3.24175526e-02,  4.40691657e-03,  2.44196807e-03,\n       -6.08822109e-03, -1.63951071e-02,  1.35071016e-02, -2.47953519e-02,\n        2.14097953e-02, -2.01207798e-02, -1.17407902e-02,  1.74165612e-03,\n        3.39477815e-03,  4.38631813e-02,  1.72200032e-02, -4.78876952e-02,\n        3.00235215e-02, -1.35679663e-04, -4.01278755e-03, -8.94225073e-04,\n        6.65302071e-02, -1.80755104e-02,  1.17286080e-02, -6.77106223e-03,\n       -7.98528755e-02,  1.52421964e-03, -2.63821287e-02,  1.84886109e-02,\n        1.46379018e-02, -8.42358473e-03, -3.44513975e-02,  7.99001389e-03,\n        8.88008430e-04, -2.77894136e-03, -4.96060027e-02,  8.29495547e-03,\n        4.68303414e-03, -3.09297517e-03,  4.55523909e-02,  7.07902281e-03,\n       -2.12877564e-03,  2.98061199e-02, -8.38557946e-03, -8.44474980e-03,\n        1.17424356e-02,  2.71340017e-03, -4.38250583e-03, -1.00056949e-02,\n        2.95035199e-03,  9.09598831e-03,  2.86050547e-02,  2.09527597e-02,\n        3.99120636e-02,  1.30333811e-02,  1.14294597e-02,  3.29356814e-02,\n       -4.37035149e-03,  4.13002181e-02,  1.63645777e-03,  4.69424618e-03,\n       -9.27991780e-03,  9.54003229e-03, -6.98552773e-03,  9.30648803e-03,\n       -7.86551053e-03, -1.08781742e-02, -1.06686554e-02,  1.61092509e-02,\n        3.11006168e-03,  5.17532124e-02,  4.59372758e-02,  9.03734251e-02,\n       -2.10607909e-02,  2.03525710e-02, -6.44214361e-03,  1.80129369e-02,\n        7.81724353e-02, -6.85531603e-02,  3.62145168e-02,  1.12739452e-02,\n        1.92824863e-02,  2.77167460e-03,  9.36284219e-03,  4.42833736e-02,\n       -8.72421781e-03,  2.24248318e-02, -2.33079119e-02, -2.68183611e-02,\n       -6.23229103e-02,  4.62758144e-02, -1.15217453e-02, -8.76315556e-02,\n        9.10353116e-02, -3.50899774e-02, -2.63690074e-02,  3.02645304e-03,\n        4.90949198e-02,  3.89577312e-02,  1.54417041e-03, -7.42893802e-03,\n        1.86900911e-02, -1.32271353e-02, -3.84931645e-03,  2.41074234e-02,\n       -2.31360947e-02, -6.19796083e-02,  7.59800309e-03,  3.75572576e-02,\n       -9.78920270e-03,  1.57318427e-02,  6.71783411e-03,  3.20584093e-02,\n       -3.51541534e-02,  2.12500631e-02, -8.88603617e-03, -4.21540247e-02,\n       -4.87273575e-02,  2.68150789e-02, -3.47388234e-02,  5.87046854e-03,\n       -4.02389732e-02,  3.51580070e-02,  5.46682516e-03,  1.74374130e-02,\n        8.62322910e-06,  2.85107851e-02, -6.08438265e-03, -3.25101569e-02,\n        3.24176626e-03, -1.13564699e-02, -4.84203072e-03, -5.42517324e-03,\n        1.17713252e-02, -5.13048318e-02, -3.87664255e-02,  1.18483236e-02,\n        6.26959085e-02,  1.59346707e-02,  1.87578180e-03, -1.33172075e-02,\n       -1.35980508e-02, -1.81220133e-02,  3.02755047e-02, -3.67191743e-02,\n        4.27540241e-04,  1.07596510e-02,  2.34063470e-02, -2.18867810e-02,\n        7.24822638e-02, -1.47792916e-02,  2.70514104e-02, -1.62461809e-03,\n       -9.62833181e-03,  7.51527831e-02, -1.83384548e-02, -1.40937664e-02,\n       -1.74570943e-02,  8.50197811e-03,  1.07455618e-02, -1.13631121e-02,\n        1.34659479e-02, -1.50623714e-02, -1.09661600e-02,  1.02901847e-02,\n       -1.19531480e-02,  3.35297431e-03, -1.80030647e-05,  1.52924957e-02,\n        2.48016735e-02,  8.05054465e-03, -4.35412429e-03, -2.05988305e-02,\n       -6.51060564e-04,  4.86016216e-03, -1.55386834e-03, -3.25303224e-02,\n        1.40105906e-02, -2.20834378e-02, -1.09779218e-02,  2.78607392e-03,\n        6.75100709e-03, -2.59105784e-03, -2.01861887e-02, -3.08798522e-03,\n       -1.60777137e-03, -1.12614168e-02,  1.84493607e-03, -1.65059577e-02,\n       -4.72416882e-02,  2.47796225e-02, -1.53424764e-02,  1.37306551e-02,\n       -2.09388618e-02,  9.20555377e-03,  2.88537938e-02])}, 'Historical Mean': {'name': 'Historical Mean', 'rmse': np.float64(0.02527207039143165), 'rmse_ratio_vs_rw': np.float64(0.9981932360398474), 'direction_accuracy': np.float64(0.49921752738654146), 'dm_stat_vs_rw': np.float64(-1.4321267915623999), 'dm_pval_vs_rw': np.float64(0.15210753827918308), 'predictions': array([0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818]), 'errors': array([-6.57373991e-03, -1.40564804e-02,  2.87799658e-02,  3.46618857e-02,\n       -9.67625803e-03, -1.06584736e-02, -2.26156654e-02, -6.56612326e-03,\n        2.75794152e-04, -1.67350364e-02, -4.57181376e-03,  4.19136747e-03,\n        1.05906570e-02, -1.10904719e-02, -6.43174348e-03,  9.29250645e-03,\n        1.39748250e-02, -1.22504585e-02, -1.13915069e-02, -5.10783852e-03,\n        5.60178017e-03,  1.27580892e-03,  1.80732237e-02, -1.31825939e-02,\n        3.72757919e-02,  3.30502257e-02, -2.40143984e-02,  1.37067294e-02,\n        9.71472062e-03, -9.60179619e-03, -7.45927173e-03, -1.15571550e-03,\n       -1.71381241e-02,  1.96587776e-03,  4.46920874e-02,  6.29938704e-03,\n       -1.94695669e-02, -1.59638895e-02, -1.05348130e-01, -1.53375960e-01,\n        9.94033874e-02, -3.14317247e-02, -1.60512026e-02, -2.99111353e-02,\n        1.67713954e-02,  1.59648702e-02, -1.49792410e-02,  9.78640803e-04,\n       -3.35627090e-04, -7.94409096e-04, -2.63061300e-02, -3.19346082e-02,\n        2.70395818e-02,  2.21090221e-02, -1.05876795e-03, -5.44632178e-03,\n       -4.66316654e-03, -2.61982040e-03, -1.40350175e-02,  1.32552512e-02,\n        4.21137410e-02, -1.17200931e-02,  5.96435008e-03, -1.30244944e-02,\n        1.21660616e-02, -8.98795172e-03,  6.39248922e-03, -1.56833581e-02,\n        2.19439208e-02, -6.37050123e-03, -8.70651802e-04, -3.28914402e-03,\n        6.46873900e-03,  3.14871846e-02,  7.91128938e-04, -2.62257315e-02,\n       -4.34302731e-02,  7.83634316e-03, -3.09434103e-03, -1.88530501e-02,\n        2.65934236e-02, -5.01278494e-03, -1.00077399e-03, -3.36354494e-03,\n        2.62009247e-03, -1.04638785e-03,  4.36374873e-03, -1.34789974e-02,\n       -1.03741155e-02,  4.38519856e-03, -2.37191529e-03, -4.73459552e-03,\n        3.67625984e-03,  2.56344365e-03, -6.69642250e-04,  9.64245412e-03,\n       -1.90727772e-03,  6.22507517e-03, -1.22595144e-03,  1.00080341e-02,\n        2.13182381e-03,  1.43514498e-02,  2.76021141e-02,  4.83108494e-02,\n        5.50848674e-02,  4.93386466e-02, -4.79705274e-03,  1.41312926e-02,\n       -3.21080263e-03, -2.26849235e-02,  1.80728049e-02,  7.21855180e-02,\n        4.31121644e-03, -4.13496460e-03,  8.33481139e-03, -1.32590163e-02,\n        1.79279381e-02, -3.03476517e-03,  1.99150245e-03, -3.04521560e-03,\n        2.99737441e-02, -4.01596399e-02,  1.22049154e-02,  2.51240725e-02,\n       -1.11306676e-02, -3.23951130e-03, -5.30189400e-03, -1.78398132e-02,\n       -8.26585419e-03,  1.99712205e-02, -1.28186814e-02, -5.29498814e-02,\n       -8.69186676e-03,  1.01081074e-02, -4.42866972e-03, -1.24760015e-03,\n        1.85616809e-02,  9.05723101e-02, -3.45195744e-02,  4.29626049e-02,\n        1.71123412e-03, -1.55390272e-02,  2.24221043e-02, -1.66334455e-02,\n       -1.19094978e-02, -1.08663656e-02, -3.28492095e-02, -2.02607346e-03,\n        1.62234295e-02, -3.47615640e-03, -1.58413825e-02,  2.00347532e-02,\n       -7.75809619e-03, -4.93112819e-02, -1.15582502e-03,  2.92902562e-03,\n       -1.71094170e-03, -1.03150761e-02, -2.32456535e-02, -6.46898784e-02,\n       -1.12508732e-02,  1.42163268e-02,  7.18398612e-02,  9.10517580e-02,\n        2.20253485e-02, -1.65266782e-02,  2.81327899e-02,  9.07300556e-02,\n       -1.87687549e-02,  3.80222443e-02, -9.99698883e-03,  1.31097341e-02,\n       -3.16832836e-02,  3.68023028e-02, -3.09787855e-02, -4.99353756e-04,\n        1.74167826e-02, -3.14184744e-02,  4.19900218e-03,  3.83097880e-02,\n       -1.21629073e-02,  1.46568084e-02, -1.25197673e-03, -1.07283325e-02,\n       -1.40961228e-02,  1.22525578e-02, -6.47003795e-04, -5.65447952e-03,\n       -5.36627472e-03,  3.39644860e-04,  1.28914765e-02,  4.45258434e-02,\n        1.80125977e-02, -1.12046085e-02,  1.53141264e-02,  2.25194036e-03,\n       -6.46436099e-03, -4.92668178e-04, -3.00856107e-02,  3.09529087e-02,\n       -5.43183198e-02, -2.02142151e-02, -3.61522319e-02,  1.93091683e-02,\n       -8.97499827e-03, -3.69993583e-03,  2.74981581e-02,  3.22948003e-03,\n        3.57312246e-02, -6.28821359e-03, -3.58396066e-03, -7.13763459e-04,\n       -4.14741723e-02,  2.03988151e-02,  1.15429145e-02, -7.30616331e-03,\n        2.20727236e-02, -2.33409965e-02, -1.54073373e-02, -2.79515610e-02,\n       -2.27372038e-03, -1.87722312e-03, -2.39046950e-02, -7.26647281e-03,\n       -1.54478344e-03,  4.49383588e-03,  8.23482008e-03, -5.54285844e-03,\n        1.28501413e-02, -2.23603158e-02,  1.38840485e-03,  7.42302245e-03,\n       -1.39800936e-02,  2.97897217e-03,  1.28908558e-02, -3.40744774e-02,\n        4.67563276e-03,  7.92189564e-03,  4.73139521e-03,  4.32902273e-02,\n       -1.25857995e-02, -2.32358436e-03, -1.84409921e-02, -1.53349030e-02,\n        1.49026828e-02, -7.19244958e-03,  8.89354943e-04, -5.33153395e-02,\n        5.59263969e-02, -3.40667866e-02,  5.22045865e-03, -1.59343615e-03,\n       -2.51447004e-02,  2.45028341e-03, -1.59381529e-03,  3.12973119e-04,\n       -3.23660303e-02,  1.77185446e-02,  2.79369572e-02,  5.69897488e-03,\n       -7.50628335e-03,  1.81596321e-02,  5.22805454e-02,  5.70414162e-02,\n       -4.44786999e-03,  2.57258052e-02, -6.07054896e-03, -2.93213350e-03,\n       -7.21913393e-03,  1.31174707e-02, -2.10501596e-02,  1.14150760e-02,\n        2.39938085e-06,  2.92270819e-03,  2.09090561e-04,  1.66486912e-02,\n       -1.33946827e-02, -9.35056572e-03, -2.09869313e-02,  1.41080659e-02,\n       -2.78973093e-03, -4.90842020e-03,  7.47486305e-03,  6.09066131e-03,\n       -8.74565442e-03,  3.39407951e-02, -3.77976508e-02, -1.55040660e-03,\n       -2.70932391e-03, -3.92198236e-03, -1.01043594e-02,  8.67622918e-04,\n       -4.46624730e-03,  2.59942923e-03, -4.41715395e-03,  8.86994682e-03,\n       -3.14297492e-02,  9.86516155e-04,  3.39285522e-03, -5.21896326e-03,\n        2.31327027e-03,  5.13174335e-04, -3.25761480e-03, -2.47754439e-03,\n        1.52679760e-02, -1.84674052e-02, -5.46788630e-04, -3.54024375e-03,\n       -2.24701245e-03, -2.48007551e-04,  3.39906676e-03,  1.81596973e-02,\n       -7.15773397e-03, -5.08477407e-03, -1.81766152e-03, -6.67499220e-04,\n       -5.10995428e-03,  3.51941561e-03, -8.68563397e-03, -1.70172660e-02,\n       -7.69769015e-02, -2.24277777e-02,  9.56209110e-04,  2.63339814e-03,\n       -3.21925737e-03, -3.52631352e-03,  1.35463876e-02, -1.04131474e-02,\n       -5.40389599e-03, -2.44574463e-03,  2.43055516e-03, -1.10004798e-04,\n        5.85126455e-02, -1.59439762e-02, -5.18782691e-02, -6.05421970e-03,\n        1.68666487e-03,  3.11538057e-03, -6.41428706e-03, -2.12244844e-03,\n       -2.05546433e-03,  1.82273058e-02, -1.40163362e-02, -1.15134411e-03,\n       -3.12732655e-03, -2.74385788e-02,  2.57637820e-02,  1.38630231e-02,\n        1.05951670e-02,  2.10093351e-03, -2.32549704e-03, -2.01977318e-03,\n        8.01153875e-03,  1.57838994e-02, -3.94647728e-03, -2.15534977e-02,\n       -3.54355907e-04, -9.65453722e-04, -1.32109560e-02,  1.33935826e-03,\n       -3.97382514e-03,  4.94603113e-03,  2.34802131e-02, -5.05196647e-03,\n        1.25606674e-03,  3.66816997e-02, -1.87609505e-02, -3.28628862e-03,\n        1.19484382e-02, -1.41509026e-02,  1.80100856e-02,  1.07225143e-04,\n       -2.22637936e-03, -1.25880567e-02, -8.08355428e-03, -1.97747104e-02,\n       -5.12960763e-03,  3.01005884e-03, -1.16264789e-03,  1.03635264e-02,\n        4.75933850e-02, -4.49451569e-03, -3.48503297e-03,  1.29982591e-02,\n        3.19211877e-02,  7.27392694e-03,  1.95164976e-03,  9.68720268e-02,\n        2.46508855e-02,  1.59513525e-02, -1.08418119e-02, -8.43978655e-03,\n        4.75227472e-03,  1.21612520e-02, -2.29106658e-03,  3.96767098e-03,\n        2.15063792e-02, -1.44473750e-02, -7.26284778e-03,  9.08859475e-03,\n       -2.24066801e-03,  1.68438783e-04,  9.21472765e-03,  5.54463675e-03,\n        2.89584949e-02,  1.54223894e-02, -5.41993832e-03, -2.58379372e-03,\n       -1.71617086e-02, -2.61306795e-02,  6.20657903e-02, -4.66051828e-02,\n        1.15697155e-02, -2.06040140e-03,  2.06124093e-02,  1.56908555e-03,\n       -4.74656383e-02,  4.47688177e-02, -3.86189161e-03,  1.03718319e-02,\n        9.69438443e-04, -9.66769448e-03, -6.29031139e-03,  1.45440509e-02,\n        1.37196040e-04, -4.26630628e-03,  2.42842010e-02,  1.88469197e-02,\n        1.23346132e-02,  4.84637964e-02,  4.75887386e-02, -7.88116391e-03,\n       -1.20576898e-02,  1.97279385e-02, -1.12171533e-02,  9.26843396e-04,\n       -6.04689384e-02,  4.96832222e-03,  3.18314358e-02,  2.75982127e-03,\n       -2.62786587e-02,  7.21220617e-03, -2.24073724e-02,  2.97335856e-02,\n       -9.79900963e-03,  3.16093716e-02,  3.59873548e-03,  1.63378698e-03,\n       -6.89640217e-03, -1.72032881e-02,  1.26989206e-02, -2.56035330e-02,\n        2.06016142e-02, -2.09289609e-02, -1.25489713e-02,  9.33475030e-04,\n        2.58659706e-03,  4.30550002e-02,  1.64118221e-02, -4.86958763e-02,\n        2.92153404e-02, -9.43860748e-04, -4.82096863e-03, -1.70240616e-03,\n        6.57220260e-02, -1.88836915e-02,  1.09204269e-02, -7.57924332e-03,\n       -8.06610566e-02,  7.16038555e-04, -2.71903098e-02,  1.76804298e-02,\n        1.38297207e-02, -9.23176581e-03, -3.52595785e-02,  7.18183281e-03,\n        7.98273448e-05, -3.58712245e-03, -5.04141838e-02,  7.48677439e-03,\n        3.87485306e-03, -3.90115625e-03,  4.47442098e-02,  6.27084172e-03,\n       -2.93695672e-03,  2.89979389e-02, -9.19376055e-03, -9.25293088e-03,\n        1.09342545e-02,  1.90521908e-03, -5.19068692e-03, -1.08138759e-02,\n        2.14217090e-03,  8.28780723e-03,  2.77968736e-02,  2.01445786e-02,\n        3.91038825e-02,  1.22252000e-02,  1.06212786e-02,  3.21275004e-02,\n       -5.17853258e-03,  4.04920370e-02,  8.28276688e-04,  3.88606510e-03,\n       -1.00880989e-02,  8.73185121e-03, -7.79370882e-03,  8.49830695e-03,\n       -8.67369161e-03, -1.16863553e-02, -1.14768364e-02,  1.53010698e-02,\n        2.30188059e-03,  5.09450313e-02,  4.51290947e-02,  8.95652440e-02,\n       -2.18689719e-02,  1.95443899e-02, -7.25032470e-03,  1.72047559e-02,\n        7.73642542e-02, -6.93613414e-02,  3.54063357e-02,  1.04657642e-02,\n        1.84743052e-02,  1.96349351e-03,  8.55466110e-03,  4.34751925e-02,\n       -9.53239890e-03,  2.16166507e-02, -2.41160930e-02, -2.76265422e-02,\n       -6.31310914e-02,  4.54676333e-02, -1.23299263e-02, -8.84397367e-02,\n        9.02271305e-02, -3.58981585e-02, -2.71771885e-02,  2.21827195e-03,\n        4.82867387e-02,  3.81495501e-02,  7.35989329e-04, -8.23711911e-03,\n        1.78819100e-02, -1.40353164e-02, -4.65749754e-03,  2.32992423e-02,\n       -2.39442758e-02, -6.27877893e-02,  6.78982201e-03,  3.67490766e-02,\n       -1.05973838e-02,  1.49236616e-02,  5.90965303e-03,  3.12502282e-02,\n       -3.59623345e-02,  2.04418820e-02, -9.69421725e-03, -4.29622058e-02,\n       -4.95355385e-02,  2.60068978e-02, -3.55470045e-02,  5.06228745e-03,\n       -4.10471543e-02,  3.43498259e-02,  4.65864408e-03,  1.66292319e-02,\n       -7.99557856e-04,  2.77026040e-02, -6.89256374e-03, -3.33183379e-02,\n        2.43358517e-03, -1.21646510e-02, -5.65021180e-03, -6.23335433e-03,\n        1.09631441e-02, -5.21130129e-02, -3.95746066e-02,  1.10401426e-02,\n        6.18877274e-02,  1.51264896e-02,  1.06760071e-03, -1.41253886e-02,\n       -1.44062319e-02, -1.89301944e-02,  2.94673236e-02, -3.75273554e-02,\n       -3.80640844e-04,  9.95146989e-03,  2.25981659e-02, -2.26949621e-02,\n        7.16740827e-02, -1.55874727e-02,  2.62432293e-02, -2.43279918e-03,\n       -1.04365129e-02,  7.43446021e-02, -1.91466358e-02, -1.49019475e-02,\n       -1.82652754e-02,  7.69379703e-03,  9.93738069e-03, -1.21712932e-02,\n        1.26577669e-02, -1.58705525e-02, -1.17743411e-02,  9.48200360e-03,\n       -1.27613291e-02,  2.54479322e-03, -8.26184150e-04,  1.44843146e-02,\n        2.39934924e-02,  7.24236356e-03, -5.16230537e-03, -2.14070115e-02,\n       -1.45924165e-03,  4.05198107e-03, -2.36204942e-03, -3.33385035e-02,\n        1.32024095e-02, -2.28916189e-02, -1.17861029e-02,  1.97789284e-03,\n        5.94282601e-03, -3.39923893e-03, -2.09943697e-02, -3.89616631e-03,\n       -2.41595246e-03, -1.20695978e-02,  1.03675498e-03, -1.73141388e-02,\n       -4.80498693e-02,  2.39714414e-02, -1.61506574e-02,  1.29224740e-02,\n       -2.17470428e-02,  8.39737269e-03,  2.80456127e-02])}}",
    "model_predictions": "{'Random Walk': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), 'Historical Mean': array([0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818, 0.00080818,\n       0.00080818, 0.00080818, 0.00080818, 0.00080818])}",
    "val_dates": "DatetimeIndex(['2022-10-01', '2022-10-02', '2022-10-03', '2022-10-04',\n               '2022-10-05', '2022-10-06', '2022-10-07', '2022-10-08',\n               '2022-10-09', '2022-10-10',\n               ...\n               '2024-06-21', '2024-06-22', '2024-06-23', '2024-06-24',\n               '2024-06-25', '2024-06-26', '2024-06-27', '2024-06-28',\n               '2024-06-29', '2024-06-30'],\n              dtype='datetime64[ns]', name='datetime', length=639, freq=None)",
    "y_true": "[-5.76555882e-03 -1.32482993e-02  2.95881469e-02  3.54700668e-02\n -8.86807695e-03 -9.85029250e-03 -2.18074843e-02 -5.75794217e-03\n  1.08397524e-03 -1.59268553e-02 -3.76363267e-03  4.99954856e-03\n  1.13988381e-02 -1.02822908e-02 -5.62356239e-03  1.01006875e-02\n  1.47830061e-02 -1.14422775e-02 -1.05833258e-02 -4.29965744e-03\n  6.40996125e-03  2.08399000e-03  1.88814048e-02 -1.23744128e-02\n  3.80839730e-02  3.38584068e-02 -2.32062173e-02  1.45149105e-02\n  1.05229017e-02 -8.79361510e-03 -6.65109064e-03 -3.47534413e-04\n -1.63299430e-02  2.77405884e-03  4.55002685e-02  7.10756813e-03\n -1.86613858e-02 -1.51557084e-02 -1.04539949e-01 -1.52567779e-01\n  1.00211568e-01 -3.06235437e-02 -1.52430215e-02 -2.91029542e-02\n  1.75795765e-02  1.67730512e-02 -1.41710599e-02  1.78682189e-03\n  4.72553995e-04  1.37719892e-05 -2.54979489e-02 -3.11264271e-02\n  2.78477629e-02  2.29172032e-02 -2.50586862e-04 -4.63814070e-03\n -3.85498546e-03 -1.81163932e-03 -1.32268364e-02  1.40634323e-02\n  4.29219220e-02 -1.09119120e-02  6.77253117e-03 -1.22163134e-02\n  1.29742427e-02 -8.17977063e-03  7.20067031e-03 -1.48751770e-02\n  2.27521019e-02 -5.56232014e-03 -6.24707169e-05 -2.48096293e-03\n  7.27692009e-03  3.22953657e-02  1.59931002e-03 -2.54175504e-02\n -4.26220920e-02  8.64452424e-03 -2.28615995e-03 -1.80448690e-02\n  2.74016047e-02 -4.20460385e-03 -1.92592901e-04 -2.55536386e-03\n  3.42827356e-03 -2.38206762e-04  5.17192981e-03 -1.26708163e-02\n -9.56593440e-03  5.19337964e-03 -1.56373421e-03 -3.92641443e-03\n  4.48444092e-03  3.37162474e-03  1.38538835e-04  1.04506352e-02\n -1.09909664e-03  7.03325626e-03 -4.17770352e-04  1.08162151e-02\n  2.94000490e-03  1.51596309e-02  2.84102952e-02  4.91190305e-02\n  5.58930485e-02  5.01468277e-02 -3.98887166e-03  1.49394737e-02\n -2.40262154e-03 -2.18767424e-02  1.88809860e-02  7.29936991e-02\n  5.11939752e-03 -3.32678352e-03  9.14299248e-03 -1.24508352e-02\n  1.87361192e-02 -2.22658408e-03  2.79968354e-03 -2.23703451e-03\n  3.07819252e-02 -3.93514588e-02  1.30130964e-02  2.59322536e-02\n -1.03224865e-02 -2.43133022e-03 -4.49371291e-03 -1.70316321e-02\n -7.45767311e-03  2.07794016e-02 -1.20105003e-02 -5.21417004e-02\n -7.88368568e-03  1.09162885e-02 -3.62048863e-03 -4.39419065e-04\n  1.93698620e-02  9.13804912e-02 -3.37113933e-02  4.37707860e-02\n  2.51941521e-03 -1.47308461e-02  2.32302854e-02 -1.58252644e-02\n -1.11013167e-02 -1.00581845e-02 -3.20410285e-02 -1.21789238e-03\n  1.70316106e-02 -2.66797531e-03 -1.50332014e-02  2.08429342e-02\n -6.94991510e-03 -4.85031008e-02 -3.47643933e-04  3.73720671e-03\n -9.02760617e-04 -9.50689505e-03 -2.24374724e-02 -6.38816973e-02\n -1.04426921e-02  1.50245079e-02  7.26480423e-02  9.18599391e-02\n  2.28335296e-02 -1.57184971e-02  2.89409710e-02  9.15382367e-02\n -1.79605739e-02  3.88304254e-02 -9.18880774e-03  1.39179152e-02\n -3.08751025e-02  3.76104839e-02 -3.01706045e-02  3.08827329e-04\n  1.82249637e-02 -3.06102933e-02  5.00718327e-03  3.91179691e-02\n -1.13547262e-02  1.54649894e-02 -4.43795644e-04 -9.92015143e-03\n -1.32879417e-02  1.30607389e-02  1.61177290e-04 -4.84629843e-03\n -4.55809363e-03  1.14782595e-03  1.36996576e-02  4.53340244e-02\n  1.88207788e-02 -1.03964274e-02  1.61223075e-02  3.06012145e-03\n -5.65617990e-03  3.15512907e-04 -2.92774296e-02  3.17610898e-02\n -5.35101387e-02 -1.94060341e-02 -3.53440508e-02  2.01173494e-02\n -8.16681719e-03 -2.89175475e-03  2.83063392e-02  4.03766112e-03\n  3.65394057e-02 -5.48003250e-03 -2.77577958e-03  9.44176263e-05\n -4.06659912e-02  2.12069962e-02  1.23510956e-02 -6.49798223e-03\n  2.28809047e-02 -2.25328154e-02 -1.45991563e-02 -2.71433799e-02\n -1.46553930e-03 -1.06904203e-03 -2.30965140e-02 -6.45829173e-03\n -7.36602355e-04  5.30201697e-03  9.04300116e-03 -4.73467736e-03\n  1.36583224e-02 -2.15521347e-02  2.19658593e-03  8.23120354e-03\n -1.31719125e-02  3.78715326e-03  1.36990369e-02 -3.32662963e-02\n  5.48381384e-03  8.73007673e-03  5.53957629e-03  4.40984084e-02\n -1.17776184e-02 -1.51540328e-03 -1.76328111e-02 -1.45267220e-02\n  1.57108639e-02 -6.38426850e-03  1.69753603e-03 -5.25071585e-02\n  5.67345780e-02 -3.32586056e-02  6.02863973e-03 -7.85255065e-04\n -2.43365193e-02  3.25846449e-03 -7.85634203e-04  1.12115420e-03\n -3.15578492e-02  1.85267257e-02  2.87451383e-02  6.50715597e-03\n -6.69810227e-03  1.89678131e-02  5.30887265e-02  5.78495973e-02\n -3.63968891e-03  2.65339862e-02 -5.26236787e-03 -2.12395241e-03\n -6.41095285e-03  1.39256517e-02 -2.02419785e-02  1.22232571e-02\n  8.10580466e-04  3.73088927e-03  1.01727165e-03  1.74568722e-02\n -1.25865016e-02 -8.54238464e-03 -2.01787502e-02  1.49162470e-02\n -1.98154984e-03 -4.10023911e-03  8.28304413e-03  6.89884239e-03\n -7.93747334e-03  3.47489761e-02 -3.69894697e-02 -7.42225519e-04\n -1.90114283e-03 -3.11380128e-03 -9.29617828e-03  1.67580400e-03\n -3.65806621e-03  3.40761031e-03 -3.60897287e-03  9.67812791e-03\n -3.06215681e-02  1.79469724e-03  4.20103631e-03 -4.41078217e-03\n  3.12145136e-03  1.32135542e-03 -2.44943371e-03 -1.66936331e-03\n  1.60761571e-02 -1.76592241e-02  2.61392456e-04 -2.73206267e-03\n -1.43883136e-03  5.60173534e-04  4.20724785e-03  1.89678784e-02\n -6.34955289e-03 -4.27659299e-03 -1.00948044e-03  1.40681866e-04\n -4.30177319e-03  4.32759670e-03 -7.87745288e-03 -1.62090850e-02\n -7.61687204e-02 -2.16195967e-02  1.76439019e-03  3.44157922e-03\n -2.41107629e-03 -2.71813243e-03  1.43545687e-02 -9.60496634e-03\n -4.59571491e-03 -1.63756355e-03  3.23873624e-03  6.98176287e-04\n  5.93208266e-02 -1.51357951e-02 -5.10700881e-02 -5.24603862e-03\n  2.49484595e-03  3.92356165e-03 -5.60610597e-03 -1.31426735e-03\n -1.24728325e-03  1.90354868e-02 -1.32081551e-02 -3.43163021e-04\n -2.31914546e-03 -2.66303977e-02  2.65719631e-02  1.46712042e-02\n  1.14033481e-02  2.90911459e-03 -1.51731596e-03 -1.21159210e-03\n  8.81971984e-03  1.65920805e-02 -3.13829620e-03 -2.07453166e-02\n  4.53825178e-04 -1.57272637e-04 -1.24027749e-02  2.14753935e-03\n -3.16564405e-03  5.75421222e-03  2.42883942e-02 -4.24378538e-03\n  2.06424782e-03  3.74898808e-02 -1.79527694e-02 -2.47810754e-03\n  1.27566193e-02 -1.33427215e-02  1.88182667e-02  9.15406228e-04\n -1.41819827e-03 -1.17798757e-02 -7.27537320e-03 -1.89665293e-02\n -4.32142654e-03  3.81823993e-03 -3.54466807e-04  1.11717075e-02\n  4.84015661e-02 -3.68633461e-03 -2.67685189e-03  1.38064402e-02\n  3.27293688e-02  8.08210803e-03  2.75983085e-03  9.76802079e-02\n  2.54590666e-02  1.67595336e-02 -1.00336308e-02 -7.63160547e-03\n  5.56045581e-03  1.29694331e-02 -1.48288550e-03  4.77585206e-03\n  2.23145603e-02 -1.36391940e-02 -6.45466670e-03  9.89677584e-03\n -1.43248693e-03  9.76619868e-04  1.00229087e-02  6.35281784e-03\n  2.97666760e-02  1.62305705e-02 -4.61175724e-03 -1.77561263e-03\n -1.63535275e-02 -2.53224984e-02  6.28739714e-02 -4.57970017e-02\n  1.23778966e-02 -1.25222031e-03  2.14205904e-02  2.37726663e-03\n -4.66574572e-02  4.55769988e-02 -3.05371053e-03  1.11800130e-02\n  1.77761953e-03 -8.85951339e-03 -5.48213031e-03  1.53522319e-02\n  9.45377125e-04 -3.45812519e-03  2.50923821e-02  1.96551008e-02\n  1.31427943e-02  4.92719775e-02  4.83969196e-02 -7.07298282e-03\n -1.12495087e-02  2.05361196e-02 -1.04089723e-02  1.73502448e-03\n -5.96607574e-02  5.77650330e-03  3.26396169e-02  3.56800236e-03\n -2.54704776e-02  8.02038726e-03 -2.15991913e-02  3.05417667e-02\n -8.99082855e-03  3.24175526e-02  4.40691657e-03  2.44196807e-03\n -6.08822109e-03 -1.63951071e-02  1.35071016e-02 -2.47953519e-02\n  2.14097953e-02 -2.01207798e-02 -1.17407902e-02  1.74165612e-03\n  3.39477815e-03  4.38631813e-02  1.72200032e-02 -4.78876952e-02\n  3.00235215e-02 -1.35679663e-04 -4.01278755e-03 -8.94225073e-04\n  6.65302071e-02 -1.80755104e-02  1.17286080e-02 -6.77106223e-03\n -7.98528755e-02  1.52421964e-03 -2.63821287e-02  1.84886109e-02\n  1.46379018e-02 -8.42358473e-03 -3.44513975e-02  7.99001389e-03\n  8.88008430e-04 -2.77894136e-03 -4.96060027e-02  8.29495547e-03\n  4.68303414e-03 -3.09297517e-03  4.55523909e-02  7.07902281e-03\n -2.12877564e-03  2.98061199e-02 -8.38557946e-03 -8.44474980e-03\n  1.17424356e-02  2.71340017e-03 -4.38250583e-03 -1.00056949e-02\n  2.95035199e-03  9.09598831e-03  2.86050547e-02  2.09527597e-02\n  3.99120636e-02  1.30333811e-02  1.14294597e-02  3.29356814e-02\n -4.37035149e-03  4.13002181e-02  1.63645777e-03  4.69424618e-03\n -9.27991780e-03  9.54003229e-03 -6.98552773e-03  9.30648803e-03\n -7.86551053e-03 -1.08781742e-02 -1.06686554e-02  1.61092509e-02\n  3.11006168e-03  5.17532124e-02  4.59372758e-02  9.03734251e-02\n -2.10607909e-02  2.03525710e-02 -6.44214361e-03  1.80129369e-02\n  7.81724353e-02 -6.85531603e-02  3.62145168e-02  1.12739452e-02\n  1.92824863e-02  2.77167460e-03  9.36284219e-03  4.42833736e-02\n -8.72421781e-03  2.24248318e-02 -2.33079119e-02 -2.68183611e-02\n -6.23229103e-02  4.62758144e-02 -1.15217453e-02 -8.76315556e-02\n  9.10353116e-02 -3.50899774e-02 -2.63690074e-02  3.02645304e-03\n  4.90949198e-02  3.89577312e-02  1.54417041e-03 -7.42893802e-03\n  1.86900911e-02 -1.32271353e-02 -3.84931645e-03  2.41074234e-02\n -2.31360947e-02 -6.19796083e-02  7.59800309e-03  3.75572576e-02\n -9.78920270e-03  1.57318427e-02  6.71783411e-03  3.20584093e-02\n -3.51541534e-02  2.12500631e-02 -8.88603617e-03 -4.21540247e-02\n -4.87273575e-02  2.68150789e-02 -3.47388234e-02  5.87046854e-03\n -4.02389732e-02  3.51580070e-02  5.46682516e-03  1.74374130e-02\n  8.62322910e-06  2.85107851e-02 -6.08438265e-03 -3.25101569e-02\n  3.24176626e-03 -1.13564699e-02 -4.84203072e-03 -5.42517324e-03\n  1.17713252e-02 -5.13048318e-02 -3.87664255e-02  1.18483236e-02\n  6.26959085e-02  1.59346707e-02  1.87578180e-03 -1.33172075e-02\n -1.35980508e-02 -1.81220133e-02  3.02755047e-02 -3.67191743e-02\n  4.27540241e-04  1.07596510e-02  2.34063470e-02 -2.18867810e-02\n  7.24822638e-02 -1.47792916e-02  2.70514104e-02 -1.62461809e-03\n -9.62833181e-03  7.51527831e-02 -1.83384548e-02 -1.40937664e-02\n -1.74570943e-02  8.50197811e-03  1.07455618e-02 -1.13631121e-02\n  1.34659479e-02 -1.50623714e-02 -1.09661600e-02  1.02901847e-02\n -1.19531480e-02  3.35297431e-03 -1.80030647e-05  1.52924957e-02\n  2.48016735e-02  8.05054465e-03 -4.35412429e-03 -2.05988305e-02\n -6.51060564e-04  4.86016216e-03 -1.55386834e-03 -3.25303224e-02\n  1.40105906e-02 -2.20834378e-02 -1.09779218e-02  2.78607392e-03\n  6.75100709e-03 -2.59105784e-03 -2.01861887e-02 -3.08798522e-03\n -1.60777137e-03 -1.12614168e-02  1.84493607e-03 -1.65059577e-02\n -4.72416882e-02  2.47796225e-02 -1.53424764e-02  1.37306551e-02\n -2.09388618e-02  9.20555377e-03  2.88537938e-02]",
    "status": "success"
  },
  "causality": {
    "daily_results": "              cause           effect  ...  significant_raw  significant_corrected\n0            volume       log_return  ...            False                  False\n1            volume       log_return  ...            False                  False\n2            volume       log_return  ...            False                  False\n3            volume       log_return  ...            False                  False\n4            volume       log_return  ...            False                  False\n5        log_return           volume  ...            False                  False\n6        log_return           volume  ...            False                  False\n7        log_return           volume  ...            False                  False\n8        log_return           volume  ...            False                  False\n9        log_return           volume  ...            False                  False\n10       abs_return           volume  ...             True                   True\n11       abs_return           volume  ...             True                   True\n12       abs_return           volume  ...             True                   True\n13       abs_return           volume  ...             True                   True\n14       abs_return           volume  ...             True                   True\n15           volume       abs_return  ...            False                  False\n16           volume       abs_return  ...            False                  False\n17           volume       abs_return  ...            False                  False\n18           volume       abs_return  ...            False                  False\n19           volume       abs_return  ...             True                   True\n20  taker_buy_ratio       log_return  ...             True                  False\n21  taker_buy_ratio       log_return  ...            False                  False\n22  taker_buy_ratio       log_return  ...            False                  False\n23  taker_buy_ratio       log_return  ...             True                  False\n24  taker_buy_ratio       log_return  ...             True                  False\n25       log_return  taker_buy_ratio  ...             True                   True\n26       log_return  taker_buy_ratio  ...             True                   True\n27       log_return  taker_buy_ratio  ...             True                   True\n28       log_return  taker_buy_ratio  ...             True                   True\n29       log_return  taker_buy_ratio  ...             True                   True\n30   squared_return           volume  ...            False                  False\n31   squared_return           volume  ...             True                   True\n32   squared_return           volume  ...             True                   True\n33   squared_return           volume  ...             True                   True\n34   squared_return           volume  ...             True                   True\n35           volume   squared_return  ...            False                  False\n36           volume   squared_return  ...            False                  False\n37           volume   squared_return  ...            False                  False\n38           volume   squared_return  ...            False                  False\n39           volume   squared_return  ...            False                  False\n40        range_pct       log_return  ...            False                  False\n41        range_pct       log_return  ...            False                  False\n42        range_pct       log_return  ...            False                  False\n43        range_pct       log_return  ...            False                  False\n44        range_pct       log_return  ...             True                  False\n45       log_return        range_pct  ...            False                  False\n46       log_return        range_pct  ...             True                  False\n47       log_return        range_pct  ...             True                  False\n48       log_return        range_pct  ...             True                  False\n49       log_return        range_pct  ...             True                   True\n\n[50 rows x 8 columns]",
    "cross_timeframe_results": "                    cause      effect  ...  significant_raw  significant_corrected\n0     hourly_intraday_vol  log_return  ...            False                  False\n1     hourly_intraday_vol  log_return  ...            False                  False\n2     hourly_intraday_vol  log_return  ...            False                  False\n3     hourly_intraday_vol  log_return  ...            False                  False\n4     hourly_intraday_vol  log_return  ...             True                   True\n5       hourly_volume_sum  log_return  ...            False                  False\n6       hourly_volume_sum  log_return  ...            False                  False\n7       hourly_volume_sum  log_return  ...            False                  False\n8       hourly_volume_sum  log_return  ...            False                  False\n9       hourly_volume_sum  log_return  ...            False                  False\n10  hourly_max_abs_return  log_return  ...            False                  False\n11  hourly_max_abs_return  log_return  ...            False                  False\n12  hourly_max_abs_return  log_return  ...            False                  False\n13  hourly_max_abs_return  log_return  ...            False                  False\n14  hourly_max_abs_return  log_return  ...             True                  False\n\n[15 rows x 8 columns]",
    "all_results": "                    cause      effect  ...  significant_raw  significant_corrected\n0                  volume  log_return  ...            False                  False\n1                  volume  log_return  ...            False                  False\n2                  volume  log_return  ...            False                  False\n3                  volume  log_return  ...            False                  False\n4                  volume  log_return  ...            False                  False\n..                    ...         ...  ...              ...                    ...\n60  hourly_max_abs_return  log_return  ...            False                  False\n61  hourly_max_abs_return  log_return  ...            False                  False\n62  hourly_max_abs_return  log_return  ...            False                  False\n63  hourly_max_abs_return  log_return  ...            False                  False\n64  hourly_max_abs_return  log_return  ...             True                  False\n\n[65 rows x 8 columns]",
    "status": "success"
  },
  "anomaly": {
    "anomaly_result": "            log_return  abs_return  ...  anomaly_votes  anomaly_ensemble\ndatetime                            ...                                 \n2017-09-05    0.062941    0.062941  ...              3                 1\n2017-09-06    0.056390    0.056390  ...              0                 0\n2017-09-07    0.015431    0.015431  ...              0                 0\n2017-09-08   -0.091169    0.091169  ...              3                 1\n2017-09-09   -0.005617    0.005617  ...              1                 0\n...                ...         ...  ...            ...               ...\n2026-01-28    0.000560    0.000560  ...              0                 0\n2026-01-29   -0.053474    0.053474  ...              1                 0\n2026-01-30   -0.004614    0.004614  ...              0                 0\n2026-01-31   -0.067748    0.067748  ...              1                 0\n2026-02-01   -0.022773    0.022773  ...              0                 0\n\n[3072 rows x 11 columns]",
    "garch_anomaly": "datetime\n2017-08-18    0\n2017-08-19    0\n2017-08-20    0\n2017-08-21    0\n2017-08-22    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nLength: 3090, dtype: int64",
    "event_alignment": "   anomaly_date event_date   event_name  diff_days\n0    2020-05-10 2020-05-11        第三次减半          1\n1    2017-12-16 2017-12-17    2017年牛市顶点          1\n2    2017-12-19 2017-12-17    2017年牛市顶点          2\n3    2017-12-20 2017-12-17    2017年牛市顶点          3\n4    2017-12-21 2017-12-17    2017年牛市顶点          4\n5    2017-12-22 2017-12-17    2017年牛市顶点          5\n6    2018-12-20 2018-12-15    2018年熊市底部          5\n7    2020-03-08 2020-03-12      新冠黑色星期四          4\n8    2020-03-12 2020-03-12      新冠黑色星期四          0\n9    2020-03-13 2020-03-12      新冠黑色星期四          1\n10   2020-03-14 2020-03-12      新冠黑色星期四          2\n11   2020-03-15 2020-03-12      新冠黑色星期四          3\n12   2020-03-16 2020-03-12      新冠黑色星期四          4\n13   2020-03-17 2020-03-12      新冠黑色星期四          5\n14   2022-06-13 2022-06-18  Luna/3AC 暴跌          5\n15   2022-06-16 2022-06-18  Luna/3AC 暴跌          2\n16   2022-06-18 2022-06-18  Luna/3AC 暴跌          0\n17   2022-06-19 2022-06-18  Luna/3AC 暴跌          1\n18   2022-11-08 2022-11-09       FTX 崩盘          1\n19   2022-11-09 2022-11-09       FTX 崩盘          0\n20   2022-11-10 2022-11-09       FTX 崩盘          1",
    "precursor_results": "{'auc': np.float64(0.993544609941045), 'feature_importances': range_pct_max_5d           0.085575\nrange_pct_std_5d           0.083581\nabs_return_std_5d          0.060453\nabs_return_max_5d          0.058301\nrange_pct_deviation_20d    0.056184\n                             ...   \nvolume_ratio_min_20d       0.000683\nvol_7d_min_5d              0.000681\nrange_pct_min_20d          0.000581\nvolume_ratio_min_5d        0.000497\ntaker_buy_ratio_min_5d     0.000308\nLength: 90, dtype: float64, 'y_true': datetime\n2017-09-24    0\n2017-09-25    1\n2017-09-26    0\n2017-09-27    0\n2017-09-28    0\n             ..\n2026-01-28    0\n2026-01-29    0\n2026-01-30    0\n2026-01-31    0\n2026-02-01    0\nName: anomaly_ensemble, Length: 3053, dtype: int64, 'y_prob': array([0.17759581, 0.02449301, 0.14630271, ..., 0.        , 0.24951762,\n       0.        ]), 'fpr': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.85166153e-04,\n       6.85166153e-04, 1.02774923e-03, 1.02774923e-03, 1.71291538e-03,\n       1.71291538e-03, 2.05549846e-03, 2.05549846e-03, 2.39808153e-03,\n       2.39808153e-03, 2.74066461e-03, 2.74066461e-03, 3.42583076e-03,\n       3.42583076e-03, 3.76841384e-03, 3.76841384e-03, 4.11099692e-03,\n       4.11099692e-03, 4.45357999e-03, 4.45357999e-03, 6.85166153e-03,\n       6.85166153e-03, 8.56457691e-03, 8.56457691e-03, 8.90715999e-03,\n       8.90715999e-03, 1.09626584e-02, 1.09626584e-02, 1.16478246e-02,\n       1.16478246e-02, 1.23329908e-02, 1.23329908e-02, 1.30181569e-02,\n       1.30181569e-02, 1.50736554e-02, 1.50736554e-02, 1.78143200e-02,\n       1.78143200e-02, 1.91846523e-02, 1.91846523e-02, 2.02124015e-02,\n       2.02124015e-02, 2.46659815e-02, 2.46659815e-02, 2.77492292e-02,\n       2.77492292e-02, 3.08324769e-02, 3.08324769e-02, 3.11750600e-02,\n       3.11750600e-02, 3.49434738e-02, 3.49434738e-02, 3.97396369e-02,\n       3.97396369e-02, 6.85166153e-02, 6.85166153e-02, 7.39979445e-02,\n       7.39979445e-02, 8.70161014e-02, 8.70161014e-02, 9.76361768e-02,\n       9.76361768e-02, 1.09284001e-01, 1.10311751e-01, 1.14765331e-01,\n       1.15450497e-01, 1.17505995e-01, 1.18191161e-01, 1.26070572e-01,\n       1.29838986e-01, 1.32237067e-01, 1.32922234e-01, 1.35320315e-01,\n       1.36348064e-01, 1.38746146e-01, 1.40116478e-01, 1.41144227e-01,\n       1.42514560e-01, 1.43542309e-01, 1.44227475e-01, 1.47995889e-01,\n       1.48681055e-01, 1.50051387e-01, 1.68550874e-01, 1.75402535e-01,\n       1.76087701e-01, 1.77800617e-01, 1.78485783e-01, 1.79513532e-01,\n       1.80198698e-01, 1.81226447e-01, 1.82254197e-01, 1.86707777e-01,\n       1.88763275e-01, 1.89448441e-01, 1.90476190e-01, 1.91846523e-01,\n       1.94587187e-01, 1.95957520e-01, 1.96985269e-01, 1.99040767e-01,\n       2.00068517e-01, 2.00411100e-01, 2.01781432e-01, 2.02124015e-01,\n       2.03151764e-01, 2.04864680e-01, 1.00000000e+00]), 'tpr': array([0.        , 0.00746269, 0.3880597 , 0.3880597 , 0.44029851,\n       0.44029851, 0.49253731, 0.49253731, 0.51492537, 0.51492537,\n       0.55970149, 0.55970149, 0.56716418, 0.56716418, 0.64179104,\n       0.64179104, 0.74626866, 0.74626866, 0.7761194 , 0.7761194 ,\n       0.78358209, 0.78358209, 0.79104478, 0.79104478, 0.79850746,\n       0.79850746, 0.80597015, 0.80597015, 0.8358209 , 0.8358209 ,\n       0.84328358, 0.84328358, 0.88059701, 0.88059701, 0.8880597 ,\n       0.8880597 , 0.89552239, 0.89552239, 0.90298507, 0.90298507,\n       0.91044776, 0.91044776, 0.91791045, 0.91791045, 0.92537313,\n       0.92537313, 0.93283582, 0.93283582, 0.94029851, 0.94029851,\n       0.94776119, 0.94776119, 0.95522388, 0.95522388, 0.96268657,\n       0.96268657, 0.97014925, 0.97014925, 0.97761194, 0.97761194,\n       0.98507463, 0.98507463, 0.99253731, 0.99253731, 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        , 1.        , 1.        , 1.        , 1.        ,\n       1.        ])}",
    "status": "success"
  }
}