from Code.lib.ta_momentum_studies import TALibMomentumStudies
from Code.lib.model_utils import ModelUtility, TimeSeriesSplitImproved
from Code.lib.feature_generator import FeatureGenerator
from Code.lib.config import current_feature, feature_dict
from Code.models import models_utils
from Code.lib.model_algos import AlgoUtility
from Code.lib.tms_utils import TradeRisk
plotIt = PlotUtility()
timeUtil = TimeUtility()
ct = ComputeTarget()
candle_ind = CandleIndicators()
dSet = DataRetrieve()
taLibMomSt = TALibMomentumStudies()
transf = Transformers()
modelUtil = ModelUtility()
featureGen = FeatureGenerator()
dSet = DataRetrieve()
modelAlgo = AlgoUtility()
sysUtil = TradingSystemUtility()
tradeRisk = TradeRisk()
if __name__ == '__main__':
# set to existing system name OR set to blank if creating new
if len(sys.argv) < 2:
print('You must set a system_name or set to """"!!!')
system_name = sys.argv[1]
system_directory = sysUtil.get_system_dir(system_name)
#ext_input_dict = sys.argv[2]
#us_cal = CustomBusinessDay(calendar=USFederalHolidayCalendar())
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
if __name__ == "__main__":
plotIt = PlotUtility()
timeUtil = TimeUtility()
ct = ComputeTarget()
candle_ind = CandleIndicators()
dSet = DataRetrieve()
taLibMomSt = TALibMomentumStudies()
transf = Transformers()
modelUtil = ModelUtility()
featureGen = FeatureGenerator()
issue = "TLT"
# Set IS-OOS parameters
pivotDate = datetime.date(2019, 1, 3)
is_oos_ratio = 2
oos_months = 4
segments = 4
df = dSet.read_issue_data(issue)
dataLoadStartDate = df.Date[0]
lastRow = df.shape[0]
dataLoadEndDate = df.Date[lastRow - 1]
dataSet = dSet.set_date_range(df, dataLoadStartDate, dataLoadEndDate)
# Resolve any NA's for now