π SMILE - Stock Movement predIction with Latent Embedding optimization
- Yejin Hwang:
- π«: SNU Graduate School of Data Science
- π§: evergreen97@snu.ac.kr
- Jisoo Jang:
- π«: SNU Graduate School of Data Science
- π§: simonjisu@snu.ac.kr
- Minseok Chae:
- π«: SNU Department of Mechanical Engineering
- π§: mschae1811@snu.ac.kr
- Taehun Kim:
- π«: SNU Department of Mechanical Engineering
- π§: crown3633@snu.ac.kr
For pipenv user
# this command will automatically generate lock file by your system
$ pipenv lock
# install the env
$ pipenv install
For other users
# window users
pip install -r requirements_win.txt
# other users
pip install -r requirements.txt
- First write a experiment yaml file(see
settings.yml
in theexperiments
folder- There are also some pre-defined settings see the comments in the
settings.yml
- There are also some pre-defined settings see the comments in the
- Run the experiments
$ sh ./run_train.sh [your_exp_filename] # e.g. $ sh ./run_train.sh acl18.0
You can also run test on all the experiments by
$ sh ./run_test.sh [number of meta test for each iteration]
# e.g.
$ sh ./run_test.sh 100
Experiment File | Experiment | Test Type | Test Accuracy | Test Loss | Train Accuracy | Train Loss |
---|---|---|---|---|---|---|
acl18.0 | acl18-baseline | test1 | 0.5118 | 7.4329 | 0.8633 | 0.3078 |
test2 | 0.6877 | 3.8232 | ||||
test3 | 0.5135 | 6.6364 | ||||
acl18.1 | acl18-nohis | test1 | 0.5113 | 6.1526 | 0.8933 | 0.2813 |
test2 | 0.6567 | 4.0848 | ||||
test3 | 0.5148 | 6.1174 | ||||
acl18.2 | acl18-n_finetune5 | test1 | 0.5168 | 6.2921 | 0.8350 | 0.3608 |
test2 | 0.6402 | 4.0439 | ||||
test3 | 0.4868 | 6.3577 | ||||
acl18.3 | acl18-n_layer1 | test1 | 0.5040 | 6.0520 | 0.8850 | 0.2911 |
test2 | 0.5545 | 4.9809 | ||||
test3 | 0.5008 | 5.7756 | ||||
acl18.4 | acl18-n_stock1.n_sample5 | test1 | 0.5095 | 4.7025 | 0.8100 | 0.4778 |
test2 | 0.5390 | 4.1183 | ||||
test3 | 0.5170 | 4.3116 | ||||
acl18.5 | acl18-n_stock1.n_sample10 | test1 | 0.4810 | 5.6018 | 0.8475 | 0.3668 |
test2 | 0.5423 | 4.4572 | ||||
test3 | 0.5100 | 5.0539 | ||||
acl18.6 | acl18-n_inner10 | test1 | 0.5122 | 8.8750 | 0.8858 | 0.2751 |
test2 | 0.7078 | 4.4255 | ||||
test3 | 0.5123 | 8.6040 | ||||
kdd17.0 | kdd17-baseline | test1 | 0.5127 | 3.1997 | 0.6550 | 0.6209 |
test2 | 0.5958 | 2.6747 | ||||
test3 | 0.5188 | 3.2512 | ||||
kdd17.1 | kdd17-nohis | test1 | 0.5042 | 3.2292 | 0.6667 | 0.6026 |
test2 | 0.5888 | 2.8033 | ||||
test3 | 0.5183 | 3.1743 | ||||
kdd17.2 | kdd17-n_finetune5 | test1 | 0.5145 | 3.1745 | 0.6767 | 0.6149 |
test2 | 0.5448 | 3.0430 | ||||
test3 | 0.5108 | 3.2819 | ||||
kdd17.3 | kdd17-n_layer1 | test1 | 0.5063 | 3.4224 | 0.6650 | 0.6271 |
test2 | 0.5532 | 3.3240 | ||||
test3 | 0.5110 | 3.3782 | ||||
kdd17.4 | kdd17-n_stock1.n_sample5 | test1 | 0.4865 | 3.1318 | 0.6700 | 0.6482 |
test2 | 0.5360 | 3.0204 | ||||
test3 | 0.4980 | 3.0609 | ||||
kdd17.5 | kdd17-n_stock1.n_sample10 | test1 | 0.5110 | 3.1398 | 0.6500 | 0.6657 |
test2 | 0.5475 | 3.0745 | ||||
test3 | 0.5145 | 3.1941 | ||||
kdd17.6 | kdd17-n_inner10 | test1 | 0.5195 | 3.4049 | 0.6658 | 0.6209 |
test2 | 0.6364 | 2.6134 | ||||
test3 | 0.5288 | 3.4379 |