diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9ec2924 --- /dev/null +++ b/.gitignore @@ -0,0 +1,31 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.egg-info/ +*.egg +dist/ +build/ + +# Virtual environments +.venv/ +venv/ +env/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo + +# OS +.DS_Store +Thumbs.db + +# Testing +.pytest_cache/ +.coverage +htmlcov/ + +# Jupyter +.ipynb_checkpoints/ diff --git a/output/acf/acf_grid.png b/output/acf/acf_grid.png index f5c4b9f..e756917 100644 Binary files a/output/acf/acf_grid.png and b/output/acf/acf_grid.png differ diff --git a/output/acf/pacf_grid.png b/output/acf/pacf_grid.png index ce77ae5..85e2928 100644 Binary files a/output/acf/pacf_grid.png and b/output/acf/pacf_grid.png differ diff --git a/output/acf/significant_lags_heatmap.png b/output/acf/significant_lags_heatmap.png index 23d2ecf..82462bb 100644 Binary files a/output/acf/significant_lags_heatmap.png and b/output/acf/significant_lags_heatmap.png differ diff --git a/output/all_results.json b/output/all_results.json index cf32c07..2de7768 100644 --- a/output/all_results.json +++ b/output/all_results.json @@ -1,4 +1,840 @@ { + "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, 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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. 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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 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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 [ 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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]", @@ -40,5 +876,33 @@ ], "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 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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., 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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 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differ diff --git a/output/综合结论报告.txt b/output/综合结论报告.txt index fa84500..9a2e1dc 100644 --- a/output/综合结论报告.txt +++ b/output/综合结论报告.txt @@ -15,8 +15,23 @@ BTC/USDT 价格规律性分析 — 综合结论报告 ---------------------------------------------------------------------- 模块 得分 强度 发现数 ---------------------------------------------------------------------- +fft 0.00 none 0 +fractal 0.00 none 0 +power_law 0.00 none 0 +wavelet 0.00 none 0 +acf 0.00 none 0 +returns 0.00 none 0 +volatility 0.00 none 0 +hurst 0.00 none 0 +volume_price 0.00 none 0 +time_series 0.00 none 0 +causality 0.00 none 0 +calendar 0.00 none 0 +halving 0.00 none 0 indicators 0.00 none 0 patterns 0.00 none 0 +clustering 0.00 none 0 +anomaly 0.00 none 0 ---------------------------------------------------------------------- ## 强证据规律（可重复、有经济意义）: @@ -26,8 +41,23 @@ patterns 0.00 none 0 （无） ## 弱证据/不显著: - * indicators + * fft + * time_series + * clustering * patterns + * indicators + * halving + * calendar + * causality + * volume_price + * fractal + * hurst + * volatility + * returns + * acf + * wavelet + * power_law + * anomaly ====================================================================== 注: 得分基于各模块自报告的统计检验结果。 diff --git a/src/acf_analysis.py b/src/acf_analysis.py index af06b6a..28b47d8 100644 --- a/src/acf_analysis.py +++ b/src/acf_analysis.py @@ -10,6 +10,9 @@ import pandas as pd import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt + +from src.font_config import configure_chinese_font +configure_chinese_font() from statsmodels.tsa.stattools import acf, pacf from statsmodels.stats.diagnostic import acorr_ljungbox from pathlib import Path diff --git a/src/anomaly.py b/src/anomaly.py index c4cb03c..924d590 100644 --- a/src/anomaly.py +++ b/src/anomaly.py @@ -705,9 +705,8 @@ def run_anomaly_analysis( print(f"数据范围: {df.index.min()} ~ {df.index.max()}") print(f"样本数量: {len(df)}") - # 设置中文字体 - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() # --- 集成异常检测 --- print("\n>>> [1/5] 执行集成异常检测...") diff --git a/src/calendar_analysis.py b/src/calendar_analysis.py index 667a0cc..6111eb9 100644 --- a/src/calendar_analysis.py +++ b/src/calendar_analysis.py @@ -12,9 +12,8 @@ from pathlib import Path from itertools import combinations from scipy import stats -# 中文显示配置 -plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] -plt.rcParams['axes.unicode_minus'] = False +from src.font_config import configure_chinese_font +configure_chinese_font() # 星期名称映射（中英文） WEEKDAY_NAMES_CN = {0: '周一', 1: '周二', 2: '周三', 3: '周四', diff --git a/src/causality.py b/src/causality.py index 56b7a95..2c6ed47 100644 --- a/src/causality.py +++ b/src/causality.py @@ -543,9 +543,8 @@ def run_causality_analysis( print(f"因果变量对数: {len(CAUSALITY_PAIRS)}") print(f"总检验次数（含所有滞后）: {len(CAUSALITY_PAIRS) * len(TEST_LAGS)}") - # 设置中文字体 - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() # --- 日线级 Granger 因果检验 --- print("\n>>> [1/4] 执行日线级 Granger 因果检验...") diff --git a/src/clustering.py b/src/clustering.py index 9b39e9f..8a093fd 100644 --- a/src/clustering.py +++ b/src/clustering.py @@ -632,9 +632,8 @@ def run_clustering_analysis(df: pd.DataFrame, output_dir: "str | Path" = "output output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - # 设置中文字体（macOS） - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() print("=" * 60) print(" BTC 市场状态聚类与马尔可夫链分析") diff --git a/src/fft_analysis.py b/src/fft_analysis.py index 63e371e..f2df843 100644 --- a/src/fft_analysis.py +++ b/src/fft_analysis.py @@ -3,6 +3,9 @@ import matplotlib matplotlib.use("Agg") +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/src/font_config.py b/src/font_config.py new file mode 100644 index 0000000..f4e32a1 --- /dev/null +++ b/src/font_config.py @@ -0,0 +1,60 @@ +""" +统一 matplotlib 中文字体配置。 + +所有绘图模块在创建图表前应调用 configure_chinese_font()。 +""" + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.font_manager as fm + +_configured = False + +# 按优先级排列的中文字体候选列表 +_CHINESE_FONT_CANDIDATES = [ + 'Noto Sans SC', # Google 思源黑体（最佳渲染质量） + 'Hiragino Sans GB', # macOS 系统自带 + 'STHeiti', # macOS 系统自带 + 'Arial Unicode MS', # macOS/Windows 通用 + 'SimHei', # Windows 黑体 + 'WenQuanYi Micro Hei', # Linux 文泉驿 + 'DejaVu Sans', # 最终回退（不支持中文，但不会崩溃） +] + + +def _find_available_chinese_fonts(): + """检测系统中实际可用的中文字体。""" + available = [] + for font_name in _CHINESE_FONT_CANDIDATES: + try: + path = fm.findfont( + fm.FontProperties(family=font_name), + fallback_to_default=False + ) + if path and 'LastResort' not in path: + available.append(font_name) + except Exception: + continue + return available if available else ['DejaVu Sans'] + + +def configure_chinese_font(): + """ + 配置 matplotlib 使用中文字体。 + + - 自动检测系统可用的中文字体 + - 设置 sans-serif 字体族 + - 修复负号显示问题 + - 仅在首次调用时执行，后续调用为空操作 + """ + global _configured + if _configured: + return + + available = _find_available_chinese_fonts() + + plt.rcParams['font.sans-serif'] = available + plt.rcParams['axes.unicode_minus'] = False + plt.rcParams['font.family'] = 'sans-serif' + + _configured = True diff --git a/src/fractal_analysis.py b/src/fractal_analysis.py index 5fc4dc4..13d33ed 100644 --- a/src/fractal_analysis.py +++ b/src/fractal_analysis.py @@ -13,6 +13,9 @@ import matplotlib matplotlib.use('Agg') +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/src/halving_analysis.py b/src/halving_analysis.py index c6be485..0541ac5 100644 --- a/src/halving_analysis.py +++ b/src/halving_analysis.py @@ -10,9 +10,8 @@ import matplotlib.ticker as mticker from pathlib import Path from scipy import stats -# 中文显示配置 -plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] -plt.rcParams['axes.unicode_minus'] = False +from src.font_config import configure_chinese_font +configure_chinese_font() # BTC 减半日期（数据范围 2017-2026 内的两次减半） HALVING_DATES = [ diff --git a/src/hurst_analysis.py b/src/hurst_analysis.py index 87e111d..3c00bc2 100644 --- a/src/hurst_analysis.py +++ b/src/hurst_analysis.py @@ -15,6 +15,9 @@ Hurst指数分析模块 import matplotlib matplotlib.use('Agg') +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/src/indicators.py b/src/indicators.py index cd2ef4b..0762820 100644 --- a/src/indicators.py +++ b/src/indicators.py @@ -9,6 +9,9 @@ import matplotlib matplotlib.use('Agg') +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/src/patterns.py b/src/patterns.py index b706226..f63ee71 100644 --- a/src/patterns.py +++ b/src/patterns.py @@ -8,6 +8,9 @@ K线形态识别与统计验证模块 import matplotlib matplotlib.use('Agg') +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/src/power_law_analysis.py b/src/power_law_analysis.py index e83c67f..32b3c0c 100644 --- a/src/power_law_analysis.py +++ b/src/power_law_analysis.py @@ -15,9 +15,8 @@ from scipy.optimize import curve_fit from pathlib import Path from typing import Tuple, Dict -# 中文显示支持 -plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] -plt.rcParams['axes.unicode_minus'] = False +from src.font_config import configure_chinese_font +configure_chinese_font() def _compute_days_since_start(df: pd.DataFrame) -> np.ndarray: diff --git a/src/returns_analysis.py b/src/returns_analysis.py index f965756..31244e8 100644 --- a/src/returns_analysis.py +++ b/src/returns_analysis.py @@ -446,9 +446,8 @@ def run_returns_analysis(df: pd.DataFrame, output_dir: str = "output/returns"): # --- 生成可视化 --- print("\n>>> 生成可视化图表...") - # 设置中文字体（兼容多系统） - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() plot_histogram_vs_normal(daily_returns, output_dir) plot_qq(daily_returns, output_dir) diff --git a/src/time_series.py b/src/time_series.py index 3f20e8d..05e99b9 100644 --- a/src/time_series.py +++ b/src/time_series.py @@ -643,9 +643,8 @@ def run_time_series_analysis(df: pd.DataFrame, output_dir: "str | Path" = "outpu output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - # 设置中文字体（macOS） - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() print("=" * 60) print(" BTC 时间序列预测分析") diff --git a/src/visualization.py b/src/visualization.py index 9dd9f43..8d16e0a 100644 --- a/src/visualization.py +++ b/src/visualization.py @@ -55,11 +55,8 @@ EVIDENCE_COLORS = { def apply_style(): """应用全局matplotlib样式""" plt.rcParams.update(STYLE_CONFIG) - try: - plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "SimHei", "DejaVu Sans"] - plt.rcParams["axes.unicode_minus"] = False - except Exception: - pass + from src.font_config import configure_chinese_font + configure_chinese_font() def ensure_dir(path): diff --git a/src/volatility_analysis.py b/src/volatility_analysis.py index 6cfdca3..b87f81f 100644 --- a/src/volatility_analysis.py +++ b/src/volatility_analysis.py @@ -584,9 +584,8 @@ def run_volatility_analysis(df: pd.DataFrame, output_dir: str = "output/volatili daily_returns = log_returns(df['close']) print(f"日对数收益率样本数: {len(daily_returns)}") - # 设置中文字体（兼容多系统） - plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] - plt.rcParams['axes.unicode_minus'] = False + from src.font_config import configure_chinese_font + configure_chinese_font() # 固定随机种子以保证杠杆效应散点图采样可复现 np.random.seed(42) diff --git a/src/volume_price_analysis.py b/src/volume_price_analysis.py index 2afe8cf..d875ea4 100644 --- a/src/volume_price_analysis.py +++ b/src/volume_price_analysis.py @@ -15,9 +15,8 @@ from statsmodels.tsa.stattools import grangercausalitytests from pathlib import Path from typing import Dict, List, Tuple -# 中文显示支持 -plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] -plt.rcParams['axes.unicode_minus'] = False +from src.font_config import configure_chinese_font +configure_chinese_font() # ============================================================================= diff --git a/src/wavelet_analysis.py b/src/wavelet_analysis.py index 0795a6d..c038e8e 100644 --- a/src/wavelet_analysis.py +++ b/src/wavelet_analysis.py @@ -3,6 +3,9 @@ import matplotlib matplotlib.use('Agg') +from src.font_config import configure_chinese_font +configure_chinese_font() + import numpy as np import pandas as pd import pywt