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Classify2赚钱

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liaozhaorun
2025-05-04 22:16:39 +08:00
parent aff5e32bb4
commit 721e72c599
8 changed files with 1042 additions and 778 deletions
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48
main/train/Classify2.ipynb
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@@ -1450,7 +1450,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 106, "execution_count": 133,
"id": "8f134d435f71e9e2", "id": "8f134d435f71e9e2",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@@ -1529,11 +1529,11 @@
" 'loss_function': 'CrossEntropy', # 适用于二分类\n", " 'loss_function': 'CrossEntropy', # 适用于二分类\n",
" 'eval_metric': 'Precision', # 评估指标\n", " 'eval_metric': 'Precision', # 评估指标\n",
" 'iterations': 500,\n", " 'iterations': 500,\n",
" 'learning_rate': 0.03,\n", " 'learning_rate': 0.05,\n",
" 'depth': 8, # 控制模型复杂度\n", " 'depth': 8, # 控制模型复杂度\n",
" 'l2_leaf_reg': 1, # L2 正则化\n", " 'l2_leaf_reg': 3, # L2 正则化\n",
" 'verbose': 500,\n", " 'verbose': 500,\n",
" 'early_stopping_rounds': 50,\n", " 'early_stopping_rounds': 100,\n",
" # 'one_hot_max_size': 50,\n", " # 'one_hot_max_size': 50,\n",
" # 'class_weights': [0.6, 1.2]\n", " # 'class_weights': [0.6, 1.2]\n",
" 'task_type': 'GPU'\n", " 'task_type': 'GPU'\n",
@@ -1553,7 +1553,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 107, "execution_count": 138,
"id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
"metadata": { "metadata": {
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@@ -1566,15 +1566,15 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"train data size: 363894\n", "train data size: 36400\n",
"原始样本数: 363894, 去除标签为空后样本数: 363894\n", "原始样本数: 36400, 去除标签为空后样本数: 36400\n",
"cat_features: [7]\n" "cat_features: [7]\n"
] ]
}, },
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "2c891d9511d34cd69044024e529641fc", "model_id": "4301941223d144c89c9302df7bb676ce",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
@@ -1589,10 +1589,10 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"0:\tlearn: 0.6250000\ttest: 0.0000000\tbest: 0.0000000 (0)\ttotal: 11.5ms\tremaining: 5.74s\n", "0:\tlearn: 0.6421053\ttest: 0.1200000\tbest: 0.1200000 (0)\ttotal: 98.9ms\tremaining: 49.3s\n",
"bestTest = 0.5761589404\n", "bestTest = 0.8\n",
"bestIteration = 1\n", "bestIteration = 17\n",
"Shrink model to first 2 iterations.\n" "Shrink model to first 18 iterations.\n"
] ]
} }
], ],
@@ -1604,12 +1604,12 @@
"# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n",
"# light_params['feature_contri'] = feature_contri\n", "# light_params['feature_contri'] = feature_contri\n",
"# print(f'feature_contri: {feature_contri}')\n", "# print(f'feature_contri: {feature_contri}')\n",
"model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv')), feature_columns)\n" "model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')), feature_columns)\n"
] ]
}, },
{ {
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"id": "5d1522a7538db91b", "id": "5d1522a7538db91b",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
@@ -1649,7 +1649,7 @@
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@@ -1657,19 +1657,19 @@
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"3 6.06\n", "3 6.06\n",
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" ... \n", " ... \n",
"179989 -0.82\n", "168084 5.03\n",
"179990 5.03\n", "168085 -0.98\n",
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"179993 2.34\n", "168088 -0.24\n",
"Name: pct_chg, Length: 179994, dtype: float64\n" "Name: pct_chg, Length: 168089, dtype: float64\n"
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} }
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@@ -1680,7 +1680,7 @@
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main/train/catboost_info/catboost_training.json
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@@ -1,56 +1,122 @@
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iter Precision CrossEntropy iter Precision CrossEntropy
0 0.625 0.6724952787 0 0.6421052632 0.6541943716
1 0.6610526316 0.6531548815 1 0.8571428571 0.6220139285
2 0.6608527132 0.6350465988 2 0.9473684211 0.5916510322
3 0.706744868 0.618008187 3 0.9142857143 0.5644849167
4 0.6714975845 0.6024762887 4 0.890625 0.5410338375
5 0.6420168067 0.5879065555 5 0.8 0.5196276588
6 0.6263736264 0.5743292798 6 0.8518518519 0.5010221829
7 0.6439688716 0.5617055549 7 0.8764044944 0.4849534885
8 0.6666666667 0.5498913927 8 0.8804347826 0.4703366289
9 0.6456211813 0.5390559395 9 0.8875 0.457428825
10 0.6502347418 0.5290345558 10 0.9358974359 0.4457376255
11 0.6625766871 0.5196715586 11 0.8846153846 0.4359375713
12 0.6554934823 0.5109634681 12 0.8510638298 0.4264152672
13 0.6492146597 0.5028802201 13 0.8764044944 0.4182715557
14 0.6483300589 0.4953289982 14 0.9024390244 0.4110272796
15 0.6659142212 0.4882966243 15 0.8735632184 0.4046110501
16 0.6694915254 0.4819027274 16 0.8953488372 0.3980741403
17 0.652173913 0.4758984087 17 0.8918918919 0.3925273723
18 0.6552380952 0.4702792469 18 0.8888888889 0.3880111628
19 0.6623616236 0.4649397326 19 0.8876404494 0.383472642
20 0.6696113074 0.4600861214 20 0.8850574713 0.3796266965
21 0.6750448833 0.4555856349 21 0.9 0.3761398694
22 0.6780821918 0.4513341042 22 0.8941176471 0.3727471701
23 0.6699834163 0.4475095463 23 0.8795180723 0.3698476705
24 0.6772575251 0.4437869897 24 0.8791208791 0.366884231
25 0.6731391586 0.4402876889 25 0.8736842105 0.3639221459
26 0.6736 0.4370960458 26 0.908045977 0.3615422916
27 0.6745283019 0.4341093749 27 0.8865979381 0.3594712663
28 0.6884422111 0.4312285273 28 0.9120879121 0.3576372534
29 0.690821256 0.4286504028 29 0.9318181818 0.3557858833
30 0.7 0.4262570842 30 0.9213483146 0.3540102147
31 0.6980854197 0.4239364156 31 0.9157894737 0.3527535855
32 0.6971935007 0.4217658394 32 0.9215686275 0.3512465785
33 0.6998514116 0.4197961278 33 0.9278350515 0.3498231139
34 0.6981981982 0.4178424181 34 0.9142857143 0.3482837805
35 0.7034161491 0.4159972699 35 0.9278350515 0.347266124
36 0.707165109 0.4143113417 36 0.9304347826 0.3461889827
37 0.7104 0.4126588434 37 0.9333333333 0.3452881929
38 0.7106518283 0.4112159095 38 0.9327731092 0.3436992116
39 0.7081339713 0.409833332 39 0.9268292683 0.3429076257
40 0.7125 0.4085317621 40 0.92 0.3419519488
41 0.7115384615 0.4072980218 41 0.9206349206 0.3410037922
42 0.7117263844 0.4061158525 42 0.9236641221 0.3402700516
43 0.7138263666 0.4050283823 43 0.928057554 0.3396138301
44 0.7078651685 0.4039969044 44 0.9416058394 0.3387833599
45 0.7123966942 0.4029488541 45 0.9432624113 0.337743107
46 0.7119205298 0.4020060852 46 0.9371069182 0.3372271328
47 0.7152209493 0.4011418993 47 0.935483871 0.3365873061
48 0.7145187602 0.4003109723 48 0.9320987654 0.33607568
49 0.7149837134 0.3994980722 49 0.9272727273 0.3350486856
50 0.7171052632 0.3987265888 50 0.9226190476 0.3345484646
51 0.7159863946 0.3980214232 51 0.9341317365 0.3338950944
52 0.9281437126 0.3327000884
53 0.9180327869 0.33217777
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56 0.9214659686 0.3296911354
57 0.9162561576 0.3292216726
58 0.9099526066 0.3284372505
59 0.9107981221 0.3277626383
60 0.9128440367 0.3271785541
61 0.9132420091 0.3262657177
62 0.9144144144 0.3253470717
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64 0.9203539823 0.3235557567
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81 0.9219858156 0.3130961665
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83 0.9206896552 0.3123047231
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114 0.9270588235 0.2971854328
115 0.9270588235 0.2968307339
116 0.9285714286 0.2962460795
117 0.9292237443 0.2956911496
1 iter Precision CrossEntropy
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22 20 0.6696113074 0.8850574713 0.4600861214 0.3796266965
23 21 0.6750448833 0.9 0.4555856349 0.3761398694
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31 29 0.690821256 0.9318181818 0.4286504028 0.3557858833
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@@ -1,53 +1,119 @@
iter Precision CrossEntropy iter Precision CrossEntropy
0 0 0.6749635417 0 0.12 0.6594913737
1 0.5761589404 0.6578907986 1 0 0.6304362522
2 0.5324074074 0.6421141059 2 0 0.6035512695
3 0.5692307692 0.6273825521 3 0.3333333333 0.5795352105
4 0.5459770115 0.6136391493 4 0.5263157895 0.5591463216
5 0.512295082 0.6007397135 5 0.3333333333 0.540188151
6 0.5177865613 0.5887243056 6 0.2222222222 0.5237671441
7 0.5065502183 0.5778295139 7 0.3333333333 0.5084826931
8 0.5581395349 0.5673727431 8 0.3125 0.4955966797
9 0.5114155251 0.5576199219 9 0.25 0.4841406793
10 0.513368984 0.5486960937 10 0.1666666667 0.4736386176
11 0.51 0.540409158 11 0 0.4643907878
12 0.5502183406 0.5328111545 12 0.125 0.4563280165
13 0.5307017544 0.5257847222 13 0.5555555556 0.4490027941
14 0.5376884422 0.519156684 14 0.7142857143 0.4425872938
15 0.5444444444 0.5129134983 15 0.6 0.4368164334
16 0.5294117647 0.5070878038 16 0.75 0.4317668457
17 0.5124378109 0.5016838976 17 0.8 0.4277447374
18 0.5402843602 0.496865842 18 0.3846153846 0.4239844835
19 0.5286343612 0.4922393663 19 0.4615384615 0.4201444227
20 0.5281385281 0.4880423177 20 0.4166666667 0.416756429
21 0.5309734513 0.4839259549 21 0.3333333333 0.4137484809
22 0.5428571429 0.4801421441 22 0.4285714286 0.4109760471
23 0.5341365462 0.4764766927 23 0.375 0.4086826986
24 0.520661157 0.4732766059 24 0.5 0.4065253906
25 0.5182186235 0.4702747396 25 0.5 0.4043982476
26 0.5294117647 0.4675844618 26 0.375 0.4028764106
27 0.5254901961 0.4648925781 27 0.4285714286 0.4015186632
28 0.5234042553 0.4623679253 28 0.5 0.3997085232
29 0.5263157895 0.4600269097 29 0.5 0.3982244466
30 0.51953125 0.4579359809 30 0.4285714286 0.3970369737
31 0.531598513 0.4559443576 31 0.375 0.3956321886
32 0.5243445693 0.4540597222 32 0.375 0.3947504883
33 0.5296442688 0.4522280816 33 0.375 0.3937069499
34 0.528 0.4504705295 34 0.3333333333 0.3932520888
35 0.5267489712 0.4488604601 35 0.3333333333 0.3928204481
36 0.5316455696 0.4473857639 36 0.3684210526 0.3921121691
37 0.5283842795 0.446012283 37 0.3333333333 0.3916260851
38 0.5130434783 0.4447833333 38 0.3333333333 0.3911342231
39 0.5169491525 0.4436203125 39 0.380952381 0.3904462891
40 0.5147679325 0.4424763889 40 0.380952381 0.390167806
41 0.5244444444 0.4414494792 41 0.3846153846 0.3895910102
42 0.5330396476 0.4404545139 42 0.4137931034 0.3890837131
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47 0.5363636364 0.436516276 47 0.3513513514 0.3873875597
48 0.5272727273 0.4358205295 48 0.3488372093 0.3871630859
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51 0.5192307692 0.4341709635 51 0.3571428571 0.3872769097
52 0.3720930233 0.387190701
53 0.3617021277 0.3868662923
54 0.3863636364 0.3872360297
55 0.3863636364 0.3871567383
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62 0.3859649123 0.3864565701
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77 0.3818181818 0.386057373
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79 0.393442623 0.3860341254
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81 0.380952381 0.3866146918
82 0.390625 0.3866864692
83 0.3823529412 0.3868192817
84 0.3880597015 0.3866136882
85 0.4078947368 0.3865292697
86 0.4230769231 0.3863948568
87 0.4197530864 0.3865127767
88 0.4197530864 0.3865546061
89 0.4197530864 0.3864517144
90 0.4024390244 0.386520752
91 0.4 0.3865970052
92 0.4 0.3865461155
93 0.4 0.3866591254
94 0.4022988506 0.3867526584
95 0.4175824176 0.3869412435
96 0.4222222222 0.3870782606
97 0.4183673469 0.3872464464
98 0.4183673469 0.3871889377
99 0.4329896907 0.387300944
100 0.4270833333 0.3874012316
101 0.4226804124 0.387583686
102 0.4141414141 0.3879282498
103 0.4141414141 0.3878271484
104 0.387755102 0.387887424
105 0.4257425743 0.3880032281
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112 0.4077669903 0.3880919868
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114 0.4509803922 0.3881395128
115 0.4347826087 0.3886143934
116 0.4226804124 0.388562066
117 0.4387755102 0.3886107856
1 iter Precision CrossEntropy
2 0 0 0.12 0.6749635417 0.6594913737
3 1 0.5761589404 0 0.6578907986 0.6304362522
4 2 0.5324074074 0 0.6421141059 0.6035512695
5 3 0.5692307692 0.3333333333 0.6273825521 0.5795352105
6 4 0.5459770115 0.5263157895 0.6136391493 0.5591463216
7 5 0.512295082 0.3333333333 0.6007397135 0.540188151
8 6 0.5177865613 0.2222222222 0.5887243056 0.5237671441
9 7 0.5065502183 0.3333333333 0.5778295139 0.5084826931
10 8 0.5581395349 0.3125 0.5673727431 0.4955966797
11 9 0.5114155251 0.25 0.5576199219 0.4841406793
12 10 0.513368984 0.1666666667 0.5486960937 0.4736386176
13 11 0.51 0 0.540409158 0.4643907878
14 12 0.5502183406 0.125 0.5328111545 0.4563280165
15 13 0.5307017544 0.5555555556 0.5257847222 0.4490027941
16 14 0.5376884422 0.7142857143 0.519156684 0.4425872938
17 15 0.5444444444 0.6 0.5129134983 0.4368164334
18 16 0.5294117647 0.75 0.5070878038 0.4317668457
19 17 0.5124378109 0.8 0.5016838976 0.4277447374
20 18 0.5402843602 0.3846153846 0.496865842 0.4239844835
21 19 0.5286343612 0.4615384615 0.4922393663 0.4201444227
22 20 0.5281385281 0.4166666667 0.4880423177 0.416756429
23 21 0.5309734513 0.3333333333 0.4839259549 0.4137484809
24 22 0.5428571429 0.4285714286 0.4801421441 0.4109760471
25 23 0.5341365462 0.375 0.4764766927 0.4086826986
26 24 0.520661157 0.5 0.4732766059 0.4065253906
27 25 0.5182186235 0.5 0.4702747396 0.4043982476
28 26 0.5294117647 0.375 0.4675844618 0.4028764106
29 27 0.5254901961 0.4285714286 0.4648925781 0.4015186632
30 28 0.5234042553 0.5 0.4623679253 0.3997085232
31 29 0.5263157895 0.5 0.4600269097 0.3982244466
32 30 0.51953125 0.4285714286 0.4579359809 0.3970369737
33 31 0.531598513 0.375 0.4559443576 0.3956321886
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35 33 0.5296442688 0.375 0.4522280816 0.3937069499
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119 117 0.4387755102 0.3886107856

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@@ -1,53 +1,119 @@
iter Passed Remaining iter Passed Remaining
0 11 5738 0 98 49344
1 23 5826 1 126 31532
2 34 5760 2 223 37075
3 49 6147 3 318 39520
4 60 5967 4 418 41447
5 70 5801 5 507 41751
6 82 5812 6 596 42000
7 94 5815 7 689 42384
8 105 5763 8 779 42504
9 117 5779 9 868 42547
10 130 5800 10 960 42709
11 144 5864 11 1015 41302
12 156 5851 12 1109 41578
13 167 5811 13 1200 41659
14 178 5785 14 1292 41806
15 189 5746 15 1384 41867
16 201 5714 16 1476 41963
17 210 5638 17 1572 42116
18 220 5579 18 1668 42242
19 229 5519 19 1760 42262
20 239 5472 20 1854 42293
21 250 5440 21 1946 42282
22 261 5421 22 2038 42267
23 272 5398 23 2134 42338
24 282 5372 24 2228 42338
25 292 5338 25 2323 42355
26 302 5294 26 2415 42315
27 311 5251 27 2507 42272
28 321 5219 28 2598 42203
29 330 5179 29 2690 42158
30 339 5141 30 2781 42083
31 349 5110 31 2871 41998
32 360 5107 32 2963 41939
33 371 5090 33 3055 41879
34 381 5063 34 3145 41796
35 390 5035 35 3237 41726
36 404 5063 36 3328 41650
37 414 5044 37 3420 41586
38 424 5021 38 3515 41551
39 434 4993 39 3605 41461
40 443 4966 40 3698 41406
41 453 4942 41 3788 41317
42 462 4916 42 3879 41230
43 471 4889 43 3968 41132
44 480 4860 44 4062 41075
45 491 4848 45 4152 40987
46 501 4836 46 4245 40921
47 511 4817 47 4337 40843
48 521 4800 48 4426 40746
49 531 4786 49 4519 40676
50 543 4787 50 4610 40589
51 553 4769 51 4703 40519
52 4795 40441
53 4891 40402
54 4985 40333
55 5078 40267
56 5172 40203
57 5264 40117
58 5358 40049
59 5450 39968
60 5542 39888
61 5634 39804
62 5725 39715
63 5817 39629
64 5909 39548
65 6000 39460
66 6098 39411
67 6192 39339
68 6290 39292
69 6387 39237
70 6480 39155
71 6573 39073
72 6664 38982
73 6754 38885
74 6846 38799
75 6935 38695
76 7027 38604
77 7122 38533
78 7213 38440
79 7306 38358
80 7394 38250
81 7490 38183
82 7581 38088
83 7672 37995
84 7767 37922
85 7857 37826
86 7951 37747
87 8041 37647
88 8132 37554
89 8223 37463
90 8314 37369
91 8405 37274
92 8431 36899
93 8526 36829
94 8621 36754
95 8713 36667
96 8803 36574
97 8896 36493
98 8990 36417
99 9086 36347
100 9176 36252
101 9267 36161
102 9363 36088
103 9470 36060
104 9563 35975
105 9654 35885
106 9746 35797
107 9839 35713
108 9933 35632
109 10026 35547
110 10120 35467
111 10209 35370
112 10301 35278
113 10398 35208
114 10489 35118
115 10581 35029
116 10675 34945
117 10766 34853
1 iter Passed Remaining
2 0 11 98 5738 49344
3 1 23 126 5826 31532
4 2 34 223 5760 37075
5 3 49 318 6147 39520
6 4 60 418 5967 41447
7 5 70 507 5801 41751
8 6 82 596 5812 42000
9 7 94 689 5815 42384
10 8 105 779 5763 42504
11 9 117 868 5779 42547
12 10 130 960 5800 42709
13 11 144 1015 5864 41302
14 12 156 1109 5851 41578
15 13 167 1200 5811 41659
16 14 178 1292 5785 41806
17 15 189 1384 5746 41867
18 16 201 1476 5714 41963
19 17 210 1572 5638 42116
20 18 220 1668 5579 42242
21 19 229 1760 5519 42262
22 20 239 1854 5472 42293
23 21 250 1946 5440 42282
24 22 261 2038 5421 42267
25 23 272 2134 5398 42338
26 24 282 2228 5372 42338
27 25 292 2323 5338 42355
28 26 302 2415 5294 42315
29 27 311 2507 5251 42272
30 28 321 2598 5219 42203
31 29 330 2690 5179 42158
32 30 339 2781 5141 42083
33 31 349 2871 5110 41998
34 32 360 2963 5107 41939
35 33 371 3055 5090 41879
36 34 381 3145 5063 41796
37 35 390 3237 5035 41726
38 36 404 3328 5063 41650
39 37 414 3420 5044 41586
40 38 424 3515 5021 41551
41 39 434 3605 4993 41461
42 40 443 3698 4966 41406
43 41 453 3788 4942 41317
44 42 462 3879 4916 41230
45 43 471 3968 4889 41132
46 44 480 4062 4860 41075
47 45 491 4152 4848 40987
48 46 501 4245 4836 40921
49 47 511 4337 4817 40843
50 48 521 4426 4800 40746
51 49 531 4519 4786 40676
52 50 543 4610 4787 40589
53 51 553 4703 4769 40519
54 52 4795 40441
55 53 4891 40402
56 54 4985 40333
57 55 5078 40267
58 56 5172 40203
59 57 5264 40117
60 58 5358 40049
61 59 5450 39968
62 60 5542 39888
63 61 5634 39804
64 62 5725 39715
65 63 5817 39629
66 64 5909 39548
67 65 6000 39460
68 66 6098 39411
69 67 6192 39339
70 68 6290 39292
71 69 6387 39237
72 70 6480 39155
73 71 6573 39073
74 72 6664 38982
75 73 6754 38885
76 74 6846 38799
77 75 6935 38695
78 76 7027 38604
79 77 7122 38533
80 78 7213 38440
81 79 7306 38358
82 80 7394 38250
83 81 7490 38183
84 82 7581 38088
85 83 7672 37995
86 84 7767 37922
87 85 7857 37826
88 86 7951 37747
89 87 8041 37647
90 88 8132 37554
91 89 8223 37463
92 90 8314 37369
93 91 8405 37274
94 92 8431 36899
95 93 8526 36829
96 94 8621 36754
97 95 8713 36667
98 96 8803 36574
99 97 8896 36493
100 98 8990 36417
101 99 9086 36347
102 100 9176 36252
103 101 9267 36161
104 102 9363 36088
105 103 9470 36060
106 104 9563 35975
107 105 9654 35885
108 106 9746 35797
109 107 9839 35713
110 108 9933 35632
111 109 10026 35547
112 110 10120 35467
113 111 10209 35370
114 112 10301 35278
115 113 10398 35208
116 114 10489 35118
117 115 10581 35029
118 116 10675 34945
119 117 10766 34853

1092
main/train/predictions_test.tsv
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