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
model_name = 'xgb_2cls'
config = mc.model_config(model_name).get
#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'
#corrDate_path = '/home/ubuntu/dataset/etf_prediction/0601/xcorr_date_data.pkl'
srcPath = '../../Data/0601/all_feature_data_Nm_1_MinMax_120.pkl'
metaPath = '../../Data/0601/all_meta_data_Nm_1_MinMax_120.pkl'
corrDate_path = '../../Data/0601/xcorr_date_data.pkl'
*_, meta = gu.read_metafile(metaPath)
corrDate = gu.read_datefile(corrDate_path)
corrDate_range = list(range(3, len(corrDate['0050']), 3))
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(corrDate_range)
progress = tqdm(total=total_progress)
progress.set_description("[SP][{}]".format(model_name))
for s in stock_list:
best_config[s] = {}
progress.set_description("[SP][{}][{}]".format(model_name, s))
#if s == '0050': _stock_list = ['0050', '2330']
#else: _stock_list = [s]
]
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
best_test_accuracy = 0
for consider_lagday in consider_lagdays:
for feature_list in feature_list_comb: