['kdj'],
['rsi'],
['velocity'],
['velocity', 'cont'],
['ratio', 'cont'],
['rsi', 'cont'],
['kdj', 'cont'],
['macd', 'cont'],
#['ud', 'cont'],
]
predict_days = list(range(1, 6)) #The future # day wish model to predict
consider_lagdays = list(range(
1, 6)) #Contain # lagday information for a training input
config = mc.model_config('xgb').get
best_config = {}
srcPath = '/home/ubuntu/dataset/etf_prediction/0525/all_feature_data_Nm_1_MinMax_120.pkl'
metaPath = '/home/ubuntu/dataset/etf_prediction/0525/all_meta_data_Nm_1_MinMax_120.pkl'
#srcPath = '../../Data/0601/all_feature_data_Nm_1_MinMax_120.pkl'
#metaPath = '../../Data/0601/all_meta_data_Nm_1_MinMax_120.pkl'
*_, meta = gu.read_metafile(metaPath)
tv_gen = dp.train_validation_generaotr()
f = tv_gen._load_data(srcPath)
for s in stock_list:
best_config[s] = {}
for predict_day in predict_days:
stock_list = [
'0050', '0051', '0052', '0053', '0054', '0055', '0056', '0057', '0058',
'0059', '006201', '006203', '006204', '006208', '00690', '00692', '00701',
'00713'
]
stock_list = ['00690']
best_config = {}
predict_days = list(range(1, 6)) #The future # day wish model to predict
consider_lagdays = list(range(
1, 6)) #Contain # lagday information for a training input
feature_list_comb = [['velocity'], ['ma'], ['ratio'], ['rsi'], ['kdj'],
['macd'], ['ud']]
config = mc.model_config('stack').get
srcPath = '/home/ubuntu/dataset/etf_prediction/all_feature_data_Nm_1_MinMax_94.pkl'
metaPath = '/home/ubuntu/dataset/etf_prediction/all_meta_data_Nm_1_MinMax_94.pkl'
*_, meta = gu.read_metafile(metaPath)
corrDate = gu.read_datefile(
'/home/ubuntu/dataset/etf_prediction/corr_date/xcorr_date_data.pkl')
corrDate_range = list(range(3, len(corrDate['0050']) + 1))
tv_gen = dp.train_validation_generaotr()
f = tv_gen._load_data(srcPath)
for s in stock_list:
best_config[s] = {}
for predict_day in predict_days:
best_config[s][predict_day] = {}
best_accuracy = 0
['20130101', '20180414']
]
date_range_special = [['20130101', '20180414']]
feature_list_comb_noraml = [['velocity'], ['ma'], ['ratio'], ['rsi'], ['kdj'],
['macd'], ['ud']]
feature_list_comb_special = [['ratio'], ['rsi'], ['kdj'], ['macd'], ['ud']]
predict_days = list(range(5)) #The dow wish model to predict
consider_lagdays = list(range(
1, 6)) #Contain # lagday information for a training input
model_name = 'xgb'
config = mc.model_config(model_name).get
best_config = {}
srcPath = '/home/ubuntu/dataset/etf_prediction/0525/all_feature_data_Nm_1_MinMax_120.pkl'
metaPath = '/home/ubuntu/dataset/etf_prediction/0525/all_meta_data_Nm_1_MinMax_120.pkl'
#srcPath = '../../Data/0525/all_feature_data_Nm_1_MinMax_120.pkl'
#metaPath = '../../Data/0525/all_meta_data_Nm_1_MinMax_120.pkl'
*_, meta = gu.read_metafile(metaPath)
tv_gen = dp.train_validation_generaotr()
f = tv_gen._load_data(srcPath)
total_progress = len(stock_list) * len(predict_days) * len(
consider_lagdays) * len(feature_list_comb_noraml) * len(date_range_normal)
progress = tqdm(total=total_progress)
['kdj'],
['rsi'],
['velocity'],
['velocity', 'cont'],
['ratio', 'cont'],
['rsi', 'cont'],
['kdj', 'cont'],
['macd', 'cont'],
#['ud', 'cont'],
]
predict_days = list(range(1, 6)) #The future # day wish model to predict
consider_lagdays = list(range(
1, 6)) #Contain # lagday information for a training input
config = mc.model_config('xgb_2cls').get
best_config = {}
srcPath = '/home/ubuntu/dataset/etf_prediction/0601/all_feature_data_Nm_1_MinMax_120.pkl'
metaPath = '/home/ubuntu/dataset/etf_prediction/0601/all_meta_data_Nm_1_MinMax_120.pkl'
#srcPath = '../../Data/all_feature_data_Nm_1_MinMax_94.pkl'
#metaPath = '../../Data/all_meta_data_Nm_1_MinMax_94.pkl'
*_, meta = gu.read_metafile(metaPath)
tv_gen = dp.train_validation_generaotr()
f = tv_gen._load_data(srcPath)
for s in stock_list:
best_config[s] = {}
for predict_day in predict_days: