example.toml

[location]
dir_resources = 'resources'
dir_logs = 'logs'
dir_output = 'output'

[s3]
use_s3 = true
# profile_name = 'default'
bucket_name = 'BUCKETNAME'
remote_dir_prices = 'finance/reuters/indices'
remote_nikkei_headline_filenames = [
    'finance/nikkei/fileservice_full_20160917_2011_dic6373_rule418_j.zip',
    'finance/nikkei/fileservice_full_20160917_2012_dic6373_rule418_j.zip',
    'finance/nikkei/fileservice_full_20160917_2013_dic6373_rule418_j.zip',
    'finance/nikkei/fileservice_full_20160917_2014_dic6373_rule418_j.zip',
    'finance/nikkei/fileservice_full_20160917_2015_dic6373_rule418_j.zip',
    'finance/nikkei/fileservice_full_20161001_2016_dic6419_rule428_j.zip'
]

[reuters]
username = 'USERNAME'
password = 'PASSSWORD'

[dataset]
# left-close right-open interval
train = ['2010-12-01 00:00:00+0900', '2015-10-01 00:00:00+0900']
valid = ['2015-10-01 00:00:00+0900', '2016-04-01 00:00:00+0900']
test = ['2016-04-01 00:00:00+0900', '2016-10-01 00:00:00+0900']

[postgres]
uri = 'postgresql://ubuntu@localhost:5432/DBNAME'

[postgres-test]
uri = 'postgresql://ubuntu@localhost:5432/TESTDBNAME'

[webapp]
n_items_per_page = 50
result = [
    ['Base',
     'output/base.csv'],
    ['Ours',
     'output/ours.csv']
]
demo_initial_date = '2016-04-01'

[train]
user_dict = 'user-dict.csv'
n_epochs = 60
learning_rate = 1e-4
use_init_token_tag = true
token_min_freq = 1
rics = [
    '.N225',
    '.TOPX',
    '.SPX',
    '.DJI',
    '.FTSE',
    # '.FCHI',
    # '.GDAXI',
    # '.STOXX50',
    '.HSI',
    # '.SSE50',
    # '.SSE180',
    '.SSEC',
    # '.CSI100',
    # '.CSI300',
    # '.JKSE',
    'JPY=',
    # 'EUR='
    'EURJPY=',
    # '.FOOD17.T',
    # '.ENGY17.T',
    # '.CNST17.T',
    # '.CHEM17.T',
    # '.PHAM17.T',
    # '.ATMO17.T',
    # '.STEL17.T',
    # '.MACH17.T',
    # '.ELEC17.T',
    # '.ITSV17.T',
    # '.PWGA17.T',
    # '.TRAN17.T',
    # '.TRAD17.T',
    # '.RETL17.T',
    # '.BANK17.T',
    # '.FNCL17.T',
    # '.RLTY17.T',
    'JNIc1'
]
base_ric = ''
use_standardization = true
patience = 20

[encoder]
enc_hidden_size = 256
base_ric_embed_size = 256
ric_embed_size = 32
enc_n_layers = 3
word_embed_size = 128
time_embed_size = 64

[decoder]
dec_hidden_size = 256

[attention]
attn_type = ''  # '', 'general', 'concat', and 'sum'