visualizer.init_visualizer()
WINDOW_SIZE = 60
BATCH_SIZE = 30
EPISODE = 8
LEARNING_RATE = 0.001
VALIDATION = 0 # train data에서 이 비율만큼 validation data로 사용
ENSEMBLE_NUM = 16
USE_TOP_N_AGENT = ENSEMBLE_NUM // 3
ROLLING_TRAIN_TEST = False
# 학습/ 테스트 data 설정
dm = Data_Manager('./gaps.db', 20151113, 20180615, split_ratio=(0.6, 0.2, 0.2))
df = dm.load_db()
train_df, val_df, test_df = dm.generate_feature_df(df, WINDOW_SIZE)
print('train: {} ~ {}'.format(train_df.iloc[0].name, train_df.iloc[-1].name))
print('val : {} ~ {}'.format(val_df.iloc[WINDOW_SIZE].name,
val_df.iloc[-1].name))
print('test: {} ~ {}'.format(test_df.iloc[WINDOW_SIZE].name,
test_df.iloc[-1].name))
print("데이터 수 train: {}, val: {}, test: {}".format(len(train_df), len(val_df),
len(test_df)))
visualizer.plot_dfs(
[train_df, val_df.iloc[WINDOW_SIZE:], test_df.iloc[WINDOW_SIZE:]],
['train', 'val', 'test'])
print("학습 데이터의 asset 개수 : ", len(train_df.columns.levels[0]))
# Random Agent Test
test_env = Environment(test_df, WINDOW_SIZE)
