from Code.lib.transformers import Transformers 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)
@author: kruegkj """ if __name__ == "__main__": from Code.lib.retrieve_data import DataRetrieve from Code.lib.ta_momentum_studies import TALibMomentumStudies from Code.lib.ta_volume_studies import TALibVolumeStudies, CustVolumeStudies from Code.lib.ta_volatility_studies import TALibVolatilityStudies from Code.lib.ta_overlap_studies import TALibOverlapStudies from Code.lib.transformers import Transformers from Code.lib.oscillator_studies import OscialltorStudies from Code.lib.candle_indicators import CandleIndicators taLibVolSt = TALibVolumeStudies() taLibMomSt = TALibMomentumStudies() transf = Transformers() oscSt = OscialltorStudies() vStud = TALibVolatilityStudies() feat_gen = FeatureGenerator() candle_ind = CandleIndicators() taLibVolSt = TALibVolumeStudies() custVolSt = CustVolumeStudies() taLibOS = TALibOverlapStudies() functionDict = { "RSI": taLibMomSt.RSI, "PPO": taLibMomSt.PPO, "CMO": taLibMomSt.CMO, "CCI": taLibMomSt.CCI, "ROC": taLibMomSt.rate_OfChg, "UltimateOscillator": taLibMomSt.UltOsc, "Normalized": transf.normalizer,
#from pandas.tseries.offsets import CustomBusinessDay #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)