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The prediction accuracy is not good enough to tell the trend happens in the real market #28

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Zhang-Jack opened this issue Dec 18, 2018 · 2 comments
Open

The prediction accuracy is not good enough to tell the trend happens in the real market #28

Zhang-Jack opened this issue Dec 18, 2018 · 2 comments

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@Zhang-Jack
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Hi,
Thank you for your sharing, it is very good tutorial for us to learn how to predict stock price with LSTM method. I tested the SP500 data with lstm = 128 and epoch =500, but the result is not so good.
As the log shows below, the days that the network predict correctly is always up and down around the base value 103, and the profit is also going up and down around 0.
So I wonder how to optimize this network to make it more accuracy and profitable? Could you please give me some hint or tutorial? Thank you very much!
log_20181218.txt

(base) D:\Codes\lstm_stock_prediction\stock-rnn>python main.py --stock_symbol=SP500 --train --input_size=1 --lstm_size=128 --max_epoch=500
Start training for stocks: ['SP500']
Step:1 [Epoch:0] [Learning rate: 0.001000] train_loss:0.010059 test_loss:0.005225
profit = -0.025619479475635365, correct days = 103
Step:101 [Epoch:0] [Learning rate: 0.001000] train_loss:0.001643 test_loss:0.001153
profit = -0.025619479475635365, correct days = 103
Step:201 [Epoch:0] [Learning rate: 0.001000] train_loss:0.000441 test_loss:0.000495
profit = -0.025619479475635365, correct days = 103
Step:301 [Epoch:1] [Learning rate: 0.001000] train_loss:0.000119 test_loss:0.000147
profit = 0.03595531261737828, correct days = 109
Step:401 [Epoch:1] [Learning rate: 0.001000] train_loss:0.000701 test_loss:0.000571
profit = -0.025619479475635365, correct days = 103
Step:501 [Epoch:1] [Learning rate: 0.001000] train_loss:0.000854 test_loss:0.000868
profit = -0.025619479475635365, correct days = 103
Step:601 [Epoch:2] [Learning rate: 0.001000] train_loss:0.000214 test_loss:0.000535
profit = -0.0031643654923697584, correct days = 96
Step:701 [Epoch:2] [Learning rate: 0.001000] train_loss:0.000310 test_loss:0.000181
profit = -0.07838784660630804, correct days = 99
Step:801 [Epoch:3] [Learning rate: 0.001000] train_loss:0.000716 test_loss:0.000284
profit = 0.013234707421988623, correct days = 98
Step:901 [Epoch:3] [Learning rate: 0.001000] train_loss:0.000177 test_loss:0.000135
profit = -0.03711239240076847, correct days = 105
Step:1001 [Epoch:3] [Learning rate: 0.001000] train_loss:0.000171 test_loss:0.000161
profit = -0.15784789364253704, correct days = 94
Step:1101 [Epoch:4] [Learning rate: 0.001000] train_loss:0.000148 test_loss:0.000140
profit = -0.09045673542451504, correct days = 103
Step:1201 [Epoch:4] [Learning rate: 0.001000] train_loss:0.000112 test_loss:0.000126
profit = 0.011780450020617894, correct days = 96
Step:1301 [Epoch:5] [Learning rate: 0.000990] train_loss:0.000123 test_loss:0.000091
profit = 0.2644821257042925, correct days = 108
Step:1401 [Epoch:5] [Learning rate: 0.000990] train_loss:0.000087 test_loss:0.000110
profit = 0.03148794396024146, correct days = 109
Step:1501 [Epoch:5] [Learning rate: 0.000990] train_loss:0.000095 test_loss:0.000151
profit = -0.018273540195676286, correct days = 91
Step:1601 [Epoch:6] [Learning rate: 0.000980] train_loss:0.000209 test_loss:0.000134
profit = -0.10868626366788636, correct days = 95
Step:1701 [Epoch:6] [Learning rate: 0.000980] train_loss:0.000080 test_loss:0.000109
profit = 0.020400411456001355, correct days = 99
Step:1801 [Epoch:6] [Learning rate: 0.000980] train_loss:0.000095 test_loss:0.000112
profit = -0.014259134507715987, correct days = 102
Step:1901 [Epoch:7] [Learning rate: 0.000970] train_loss:0.000123 test_loss:0.000107
profit = -0.00934783026174768, correct days = 104
Step:2001 [Epoch:7] [Learning rate: 0.000970] train_loss:0.000070 test_loss:0.000113
profit = 0.1037896495909153, correct days = 102
Step:2101 [Epoch:8] [Learning rate: 0.000961] train_loss:0.000118 test_loss:0.000111
profit = -0.08947914245565769, correct days = 97
Step:2201 [Epoch:8] [Learning rate: 0.000961] train_loss:0.000136 test_loss:0.000178
profit = -0.02450670956447809, correct days = 102
Step:2301 [Epoch:8] [Learning rate: 0.000961] train_loss:0.000100 test_loss:0.000102
profit = -0.21570318289249812, correct days = 98
Step:2401 [Epoch:9] [Learning rate: 0.000951] train_loss:0.000088 test_loss:0.000083
profit = 0.09128718752173082, correct days = 107
Step:2501 [Epoch:9] [Learning rate: 0.000951] train_loss:0.000074 test_loss:0.000093
profit = -0.08180089271755475, correct days = 88
Step:2601 [Epoch:10] [Learning rate: 0.000941] train_loss:0.000094 test_loss:0.000108
profit = 0.011144804352940407, correct days = 100
Step:2701 [Epoch:10] [Learning rate: 0.000941] train_loss:0.000228 test_loss:0.000209
profit = 0.021127617819259314, correct days = 98
Step:2801 [Epoch:10] [Learning rate: 0.000941] train_loss:0.000115 test_loss:0.000087
profit = -0.032739640272618664, correct days = 96
Step:2901 [Epoch:11] [Learning rate: 0.000932] train_loss:0.000293 test_loss:0.000187
profit = 0.007579035723463634, correct days = 98
Step:3001 [Epoch:11] [Learning rate: 0.000932] train_loss:0.000024 test_loss:0.000093
profit = -0.0618005583512683, correct days = 100
Step:3101 [Epoch:12] [Learning rate: 0.000923] train_loss:0.000091 test_loss:0.000100
profit = -0.05968355101690015, correct days = 102
Step:3201 [Epoch:12] [Learning rate: 0.000923] train_loss:0.000051 test_loss:0.000099
profit = -0.09661448827067709, correct days = 104
Step:3301 [Epoch:12] [Learning rate: 0.000923] train_loss:0.000093 test_loss:0.000084
profit = 0.0021639726211727384, correct days = 94
Step:3401 [Epoch:13] [Learning rate: 0.000914] train_loss:0.000090 test_loss:0.000090
profit = 0.06872824583359372, correct days = 101
Step:3501 [Epoch:13] [Learning rate: 0.000914] train_loss:0.000041 test_loss:0.000098
profit = 0.023954294962177713, correct days = 103
Step:3601 [Epoch:13] [Learning rate: 0.000914] train_loss:0.000076 test_loss:0.000096
profit = -0.04558137123357253, correct days = 97
Step:3701 [Epoch:14] [Learning rate: 0.000904] train_loss:0.000054 test_loss:0.000085
profit = 0.15187505724517514, correct days = 108
Step:3801 [Epoch:14] [Learning rate: 0.000904] train_loss:0.000096 test_loss:0.000129
profit = 0.07888562137218325, correct days = 101
Step:3901 [Epoch:15] [Learning rate: 0.000895] train_loss:0.000121 test_loss:0.000094
profit = 0.0340183348742743, correct days = 96
Step:4001 [Epoch:15] [Learning rate: 0.000895] train_loss:0.000532 test_loss:0.000122
profit = 0.031501323257416725, correct days = 101
Step:4101 [Epoch:15] [Learning rate: 0.000895] train_loss:0.000069 test_loss:0.000086
profit = -0.21811953823266494, correct days = 90
Step:4201 [Epoch:16] [Learning rate: 0.000886] train_loss:0.000150 test_loss:0.000087
profit = 0.19773472237464762, correct days = 111
Step:4301 [Epoch:16] [Learning rate: 0.000886] train_loss:0.000075 test_loss:0.000092
profit = -0.045499799223870485, correct days = 100
Step:4401 [Epoch:17] [Learning rate: 0.000878] train_loss:0.000101 test_loss:0.000096
profit = 0.0660783915371651, correct days = 91
Step:4501 [Epoch:17] [Learning rate: 0.000878] train_loss:0.000078 test_loss:0.000110
profit = -0.028332832025963373, correct days = 98
Step:4601 [Epoch:17] [Learning rate: 0.000878] train_loss:0.000160 test_loss:0.000092
profit = -0.031151056620531414, correct days = 98
Step:4701 [Epoch:18] [Learning rate: 0.000869] train_loss:0.000140 test_loss:0.000083
profit = 0.17060743776145848, correct days = 113
Step:4801 [Epoch:18] [Learning rate: 0.000869] train_loss:0.000104 test_loss:0.000081
profit = 0.02184867743697627, correct days = 99
Step:4901 [Epoch:18] [Learning rate: 0.000869] train_loss:0.000080 test_loss:0.000093
profit = -0.02471952081072526, correct days = 95
Step:5001 [Epoch:19] [Learning rate: 0.000860] train_loss:0.000087 test_loss:0.000096
profit = 0.2184404621144641, correct days = 107
Step:5101 [Epoch:19] [Learning rate: 0.000860] train_loss:0.000172 test_loss:0.000083
profit = -0.011192151034966291, correct days = 98
Step:5201 [Epoch:20] [Learning rate: 0.000851] train_loss:0.000226 test_loss:0.000089
profit = 0.14785778266773175, correct days = 107
Step:5301 [Epoch:20] [Learning rate: 0.000851] train_loss:0.000060 test_loss:0.000084
profit = 0.1429639411365925, correct days = 103
Step:5401 [Epoch:20] [Learning rate: 0.000851] train_loss:0.000066 test_loss:0.000092
profit = 0.08617641740816884, correct days = 103
Step:5501 [Epoch:21] [Learning rate: 0.000843] train_loss:0.000162 test_loss:0.000096
profit = 0.15679533090121722, correct days = 103
Step:5601 [Epoch:21] [Learning rate: 0.000843] train_loss:0.000127 test_loss:0.000091
profit = -0.06225312490171231, correct days = 93
Step:5701 [Epoch:22] [Learning rate: 0.000835] train_loss:0.000086 test_loss:0.000094
profit = 0.006754305152290363, correct days = 103
Step:5801 [Epoch:22] [Learning rate: 0.000835] train_loss:0.000057 test_loss:0.000080
profit = 0.07339482376505135, correct days = 98
Step:5901 [Epoch:22] [Learning rate: 0.000835] train_loss:0.000087 test_loss:0.000091
profit = -0.0453350830909246, correct days = 98
Step:6001 [Epoch:23] [Learning rate: 0.000826] train_loss:0.000055 test_loss:0.000094
profit = -0.06847208303629626, correct days = 105
Step:6101 [Epoch:23] [Learning rate: 0.000826] train_loss:0.000188 test_loss:0.000078
profit = 0.11029498773202973, correct days = 108
Step:6201 [Epoch:24] [Learning rate: 0.000818] train_loss:0.000147 test_loss:0.000083
profit = -0.028336466555966555, correct days = 91
Step:6301 [Epoch:24] [Learning rate: 0.000818] train_loss:0.000090 test_loss:0.000084
profit = -0.1484526580651776, correct days = 86
Step:6401 [Epoch:24] [Learning rate: 0.000818] train_loss:0.000113 test_loss:0.000086
profit = 0.012674613668498425, correct days = 99
Step:6501 [Epoch:25] [Learning rate: 0.000810] train_loss:0.000153 test_loss:0.000088
profit = 0.04810375951538559, correct days = 109
Step:6601 [Epoch:25] [Learning rate: 0.000810] train_loss:0.000066 test_loss:0.000083
profit = -0.1404029246915609, correct days = 93
Step:6701 [Epoch:25] [Learning rate: 0.000810] train_loss:0.000041 test_loss:0.000088
profit = -0.05891110625639906, correct days = 91
Step:6801 [Epoch:26] [Learning rate: 0.000802] train_loss:0.000057 test_loss:0.000088
profit = 0.1322223423826442, correct days = 104
Step:6901 [Epoch:26] [Learning rate: 0.000802] train_loss:0.000063 test_loss:0.000085
profit = -0.06354392570483236, correct days = 88
Step:7001 [Epoch:27] [Learning rate: 0.000794] train_loss:0.000021 test_loss:0.000084
profit = -0.05990871436288636, correct days = 92
Step:7101 [Epoch:27] [Learning rate: 0.000794] train_loss:0.000073 test_loss:0.000083
profit = -0.06882257311584372, correct days = 95
Step:7201 [Epoch:27] [Learning rate: 0.000794] train_loss:0.000085 test_loss:0.000084
profit = 0.009471227578168873, correct days = 101
Step:7301 [Epoch:28] [Learning rate: 0.000786] train_loss:0.000332 test_loss:0.000088
profit = 0.013246611564542321, correct days = 98
Step:7401 [Epoch:28] [Learning rate: 0.000786] train_loss:0.000026 test_loss:0.000080
profit = -0.004331457631420066, correct days = 90
Step:7501 [Epoch:29] [Learning rate: 0.000778] train_loss:0.000102 test_loss:0.000082
profit = 0.08324904433963864, correct days = 105
Step:7601 [Epoch:29] [Learning rate: 0.000778] train_loss:0.000149 test_loss:0.000089
profit = -0.06631912575570487, correct days = 96
Step:7701 [Epoch:29] [Learning rate: 0.000778] train_loss:0.000032 test_loss:0.000078
profit = 0.11948236955133507, correct days = 108
Step:7801 [Epoch:30] [Learning rate: 0.000770] train_loss:0.000074 test_loss:0.000078
profit = 0.139726090498706, correct days = 106
Step:7901 [Epoch:30] [Learning rate: 0.000770] train_loss:0.000034 test_loss:0.000076
profit = -0.016972300396177586, correct days = 99
Step:8001 [Epoch:31] [Learning rate: 0.000762] train_loss:0.001605 test_loss:0.000131
profit = -0.09545949421338473, correct days = 90
Step:8101 [Epoch:31] [Learning rate: 0.000762] train_loss:0.000035 test_loss:0.000081
profit = 0.08556577392037978, correct days = 103
Step:8201 [Epoch:31] [Learning rate: 0.000762] train_loss:0.000091 test_loss:0.000091
profit = -0.19325060724099796, correct days = 94
Step:8301 [Epoch:32] [Learning rate: 0.000755] train_loss:0.000044 test_loss:0.000087
profit = -0.10109258556147382, correct days = 100
Step:8401 [Epoch:32] [Learning rate: 0.000755] train_loss:0.000031 test_loss:0.000079
profit = -0.11426217750847367, correct days = 103
Step:8501 [Epoch:32] [Learning rate: 0.000755] train_loss:0.000041 test_loss:0.000083
profit = 0.15642649504928452, correct days = 109
Step:8601 [Epoch:33] [Learning rate: 0.000747] train_loss:0.000079 test_loss:0.000079
profit = -0.13959935335863138, correct days = 97
Step:8701 [Epoch:33] [Learning rate: 0.000747] train_loss:0.000135 test_loss:0.000082
profit = -0.13748259876316216, correct days = 95
Step:8801 [Epoch:34] [Learning rate: 0.000740] train_loss:0.000061 test_loss:0.000081
profit = -0.1009435581331456, correct days = 100
Step:8901 [Epoch:34] [Learning rate: 0.000740] train_loss:0.000121 test_loss:0.000080
profit = -0.15250229851266173, correct days = 100
Step:9001 [Epoch:34] [Learning rate: 0.000740] train_loss:0.000032 test_loss:0.000084
profit = -0.0038252172792784256, correct days = 106
Step:9101 [Epoch:35] [Learning rate: 0.000732] train_loss:0.000088 test_loss:0.000082
profit = -0.026277332670350595, correct days = 94
Step:9201 [Epoch:35] [Learning rate: 0.000732] train_loss:0.000059 test_loss:0.000085
profit = -0.23764513492667605, correct days = 91
Step:9301 [Epoch:36] [Learning rate: 0.000725] train_loss:0.000049 test_loss:0.000085
profit = -0.13331856363652206, correct days = 101
Step:9401 [Epoch:36] [Learning rate: 0.000725] train_loss:0.000121 test_loss:0.000084
profit = -0.08096660712024006, correct days = 104
Step:9501 [Epoch:36] [Learning rate: 0.000725] train_loss:0.000050 test_loss:0.000079
profit = -0.035681628188596215, correct days = 97
Step:9601 [Epoch:37] [Learning rate: 0.000718] train_loss:0.000047 test_loss:0.000074
profit = 0.25255923647534206, correct days = 111
Step:9701 [Epoch:37] [Learning rate: 0.000718] train_loss:0.000052 test_loss:0.000082
profit = 0.1405303968308479, correct days = 105
Step:9801 [Epoch:37] [Learning rate: 0.000718] train_loss:0.000047 test_loss:0.000076
profit = -0.10761144730282124, correct days = 105
Step:9901 [Epoch:38] [Learning rate: 0.000711] train_loss:0.000035 test_loss:0.000078
profit = -0.23780492199793624, correct days = 93
Step:10001 [Epoch:38] [Learning rate: 0.000711] train_loss:0.000218 test_loss:0.000087
profit = 0.0986011845157021, correct days = 100
Step:10101 [Epoch:39] [Learning rate: 0.000703] train_loss:0.000072 test_loss:0.000075
profit = 0.3832697111375293, correct days = 113
Step:10201 [Epoch:39] [Learning rate: 0.000703] train_loss:0.000168 test_loss:0.000080
profit = 0.02633109726977001, correct days = 103
Step:10301 [Epoch:39] [Learning rate: 0.000703] train_loss:0.000037 test_loss:0.000082
profit = -0.07761773262888172, correct days = 102
Step:10401 [Epoch:40] [Learning rate: 0.000696] train_loss:0.000065 test_loss:0.000075
profit = 0.06679532257823129, correct days = 105
Step:10501 [Epoch:40] [Learning rate: 0.000696] train_loss:0.000049 test_loss:0.000082
profit = 0.017065803047925487, correct days = 110
Step:10601 [Epoch:41] [Learning rate: 0.000689] train_loss:0.000040 test_loss:0.000074
profit = 0.004761161756203225, correct days = 97
Step:10701 [Epoch:41] [Learning rate: 0.000689] train_loss:0.000043 test_loss:0.000078
profit = 0.14491704099142855, correct days = 100
Step:10801 [Epoch:41] [Learning rate: 0.000689] train_loss:0.000886 test_loss:0.000095
profit = -0.02807158063995374, correct days = 96
Step:10901 [Epoch:42] [Learning rate: 0.000683] train_loss:0.000043 test_loss:0.000078
profit = 0.2315954200925946, correct days = 111
Step:11001 [Epoch:42] [Learning rate: 0.000683] train_loss:0.000119 test_loss:0.000083
profit = 0.03790362437540595, correct days = 97
Step:11101 [Epoch:43] [Learning rate: 0.000676] train_loss:0.000064 test_loss:0.000089
profit = -0.005365731314907918, correct days = 103
Step:11201 [Epoch:43] [Learning rate: 0.000676] train_loss:0.000112 test_loss:0.000088
profit = 0.1674707399976857, correct days = 112
Step:11301 [Epoch:43] [Learning rate: 0.000676] train_loss:0.000103 test_loss:0.000081
profit = -0.11721664955763622, correct days = 93
Step:11401 [Epoch:44] [Learning rate: 0.000669] train_loss:0.000014 test_loss:0.000097
profit = 0.057232079693976146, correct days = 107
Step:11501 [Epoch:44] [Learning rate: 0.000669] train_loss:0.000081 test_loss:0.000079
profit = -0.052250048124326764, correct days = 93
Step:11601 [Epoch:44] [Learning rate: 0.000669] train_loss:0.000064 test_loss:0.000077
profit = 0.1571850086461729, correct days = 110
Step:11701 [Epoch:45] [Learning rate: 0.000662] train_loss:0.000121 test_loss:0.000078
profit = -0.18832862493339786, correct days = 92
Step:11801 [Epoch:45] [Learning rate: 0.000662] train_loss:0.000043 test_loss:0.000076
profit = 0.042838998582060195, correct days = 102
Step:11901 [Epoch:46] [Learning rate: 0.000656] train_loss:0.000250 test_loss:0.000078
profit = 0.043138550283145505, correct days = 101
Step:12001 [Epoch:46] [Learning rate: 0.000656] train_loss:0.000115 test_loss:0.000083
profit = -0.09371476046460014, correct days = 98
Step:12101 [Epoch:46] [Learning rate: 0.000656] train_loss:0.000033 test_loss:0.000080
profit = -0.05255161267961761, correct days = 94
Step:12201 [Epoch:47] [Learning rate: 0.000649] train_loss:0.000227 test_loss:0.000078
profit = -0.1225627615973589, correct days = 96
Step:12301 [Epoch:47] [Learning rate: 0.000649] train_loss:0.000050 test_loss:0.000080
profit = 0.15474970939366506, correct days = 117
Step:12401 [Epoch:48] [Learning rate: 0.000643] train_loss:0.000034 test_loss:0.000078
profit = 0.24680065663228767, correct days = 109
Step:12501 [Epoch:48] [Learning rate: 0.000643] train_loss:0.000111 test_loss:0.000080
profit = 0.09462146415524841, correct days = 105
Step:12601 [Epoch:48] [Learning rate: 0.000643] train_loss:0.000213 test_loss:0.000080
profit = 0.018383620867890582, correct days = 94
Step:12701 [Epoch:49] [Learning rate: 0.000636] train_loss:0.000107 test_loss:0.000079
profit = 0.06755930317461545, correct days = 102
Step:12801 [Epoch:49] [Learning rate: 0.000636] train_loss:0.000192 test_loss:0.000083
profit = -0.10714939290982162, correct days = 98
Step:12901 [Epoch:50] [Learning rate: 0.000630] train_loss:0.000079 test_loss:0.000081
profit = 0.1120229320455085, correct days = 104
Step:13001 [Epoch:50] [Learning rate: 0.000630] train_loss:0.000037 test_loss:0.000077
profit = 0.22241459898910543, correct days = 106
Step:13101 [Epoch:50] [Learning rate: 0.000630] train_loss:0.000149 test_loss:0.000078
profit = -0.03342298517580489, correct days = 102
...
Step:127901 [Epoch:495] [Learning rate: 0.000007] train_loss:0.000223 test_loss:0.000072
profit = -0.006937739971851875, correct days = 99
Step:128001 [Epoch:496] [Learning rate: 0.000007] train_loss:0.000021 test_loss:0.000072
profit = 0.07255974747543392, correct days = 105
Step:128101 [Epoch:496] [Learning rate: 0.000007] train_loss:0.000048 test_loss:0.000072
profit = -0.03654345662007563, correct days = 100
Step:128201 [Epoch:496] [Learning rate: 0.000007] train_loss:0.000022 test_loss:0.000072
profit = 0.0370857372949388, correct days = 106
Step:128301 [Epoch:497] [Learning rate: 0.000007] train_loss:0.000044 test_loss:0.000071
profit = 0.08336513334204343, correct days = 105
Step:128401 [Epoch:497] [Learning rate: 0.000007] train_loss:0.000052 test_loss:0.000072
profit = -0.001579383542026247, correct days = 101
Step:128501 [Epoch:498] [Learning rate: 0.000007] train_loss:0.000073 test_loss:0.000071
profit = 0.010168036018454285, correct days = 101
Step:128601 [Epoch:498] [Learning rate: 0.000007] train_loss:0.000269 test_loss:0.000072
profit = -0.032829104619034655, correct days = 103
Step:128701 [Epoch:498] [Learning rate: 0.000007] train_loss:0.000168 test_loss:0.000072
profit = 0.00882121564952476, correct days = 104
Step:128801 [Epoch:499] [Learning rate: 0.000007] train_loss:0.000034 test_loss:0.000071
profit = -0.00046510322141923854, correct days = 99
Step:128901 [Epoch:499] [Learning rate: 0.000007] train_loss:0.000060 test_loss:0.000071
profit = 0.03810759671788244, correct days = 98
@dmallon1
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How did you get profit to show up in the output? Did you add that yourself?

@Zhang-Jack
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How did you get profit to show up in the output? Did you add that yourself?
Hi dmalion,
Yes, I added the codes to statics the profit days as below, you can check:

@@ -254,12 +256,24 @@ class LstmRNN(object):
preds = _flatten(preds)[-200:]
days = range(len(truths))[-200:]

  •    profit = 0

  •    correct_days = 0

  •    for i in range(200):

  •        if(truths[i] > 0 and preds[i] > 0) or (truths[i] < 0 and preds[i] < 0):

  •            profit = profit + abs(truths[i])

  •            correct_days = correct_days + 1

  •        else:

  •            profit = profit - abs(truths[i])

  •    print("profit = {}, correct days = {}".format(profit, correct_days))

  •    plt.figure(figsize=(12, 6))
   plt.plot(days, truths, label='truth')
   plt.plot(days, preds, label='pred')
   plt.legend(loc='upper left', frameon=False)
   plt.xlabel("day")
   plt.ylabel("normalized price")

  •    #plt.text(3, 8, 'profit = {}, correct days = {}'.format(profit, correct_days), style='italic',

  •    #        bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})
   plt.ylim((min(truths), max(truths)))
   plt.grid(ls='--')

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@dmallon1 @Zhang-Jack