from models import LstmModel
from config import DIR_CONFIG, FILE_CONFIG
if __name__ == "__main__":
# Initialize Dates
# START_DATE = '2017-01-02'
# END_DATE = '2019-06-01'
START_DATE = '2015-07-06'
END_DATE = '2018-05-28'
# Initialize Model
lstm = LstmModel(name='Old2')
# Load List Of Companies
companies = []
with open(FILE_CONFIG["COMPANY_LIST"], 'r') as f:
for line in f:
companies.append(line.rstrip("\n"))
print(companies)
# Train Model With Different Companies Adj Close Values
for company in companies:
path = os.path.join(DIR_CONFIG["DATA_DIR"], '{}_data.csv'.format(company))
raw_data = pd.read_csv(path, index_col=0)
raw_data = raw_data.loc[:END_DATE]["Adj Close"].values
raw_data = np.reshape(raw_data, (raw_data.shape[0], 1))
print(raw_data.shape)
lstm.train_model(company=company, raw_data=raw_data)
import numpy
from models import LstmModel
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(CURRENT_DIR, 'old_data')
CHECKPOINT_DIR = os.path.join(CURRENT_DIR, 'checkpoint')
print("Data Stored In", DATA_DIR)
print("Checkpoint Stored In", CHECKPOINT_DIR)
if __name__ == "__main__":
# Initialize Model
lstm = LstmModel(name='Test2')
# Load List Of Companies
companies = []
with open(os.path.join(CURRENT_DIR, "company_list.txt"), 'r') as f:
for line in f:
companies.append(line.rstrip("\n"))
print(companies)
# Train Model With Different Companies Data
for company in companies:
path = os.path.join(DATA_DIR, '{}_data.csv'.format(company))
raw_data = pd.read_csv(path, index_col=0)
raw_data = raw_data.loc[:'2017-12-31'].values
#print(raw_data)
lstm.train_model(raw_data)