示例#1
0
    def generate_features(self, new_df, input_dict):
        funcDict = DataRetrieve.load_obj(self, 'func_dict')
        for key in input_dict.keys():
            print(key)
            path = [key, 'fname']
            print('fname: ', self.get_from_dict(input_dict, path))
            func_name = self.get_from_dict(input_dict, path)

            path = [key, 'params']
            print('params: ', self.get_from_dict(input_dict, path))
            params = self.get_from_dict(input_dict, path)
            new_df = funcDict[func_name](new_df, *params)
            #print(new_df.tail(10))
            print("Current feature: ", current_feature['Latest'])

            path = [key, 'transform']
            print('transform: ', self.get_from_dict(input_dict, path))
            do_transform = self.get_from_dict(input_dict, path)

            if do_transform:
                #print('!!!!', do_transform[0], )
                pass_params = (do_transform[1::])
                print("pass params", pass_params)
                #print("Current feature: ", current_feature['Latest'])
                new_df = funcDict[do_transform[0]](new_df,
                                                   current_feature['Latest'],
                                                   *pass_params)
                #print("Current feature: ", current_feature['Latest'])
        return new_df
示例#2
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        "DetrendPO": oscSt.detrend_PO,
        "ATR": vStud.ATR,
        "NATR": vStud.NATR,
        "ATRRatio": vStud.ATR_Ratio,
        "DeltaATRRatio": vStud.delta_ATR_Ratio,
        "BBWidth": vStud.BBWidth,
        "HigherClose": candle_ind.higher_close,
        "LowerClose": candle_ind.lower_close,
        "ChaikinAD": taLibVolSt.ChaikinAD,
        "ChaikinADOSC": taLibVolSt.ChaikinADOSC,
        "OBV": taLibVolSt.OBV,
        "MFI": taLibVolSt.MFI,
        "ease_OfMvmnt": custVolSt.ease_OfMvmnt,
        "exp_MA": taLibOS.exp_MA,
        "simple_MA": taLibOS.simple_MA,
        "weighted_MA": taLibOS.weighted_MA,
        "triple_EMA": taLibOS.triple_EMA,
        "triangMA": taLibOS.triangMA,
        "dblEMA": taLibOS.dblEMA,
        "kaufman_AMA": taLibOS.kaufman_AMA,
        "delta_MESA_AMA": taLibOS.delta_MESA_AMA,
        "inst_Trendline": taLibOS.inst_Trendline,
        "mid_point": taLibOS.mid_point,
        "mid_price": taLibOS.mid_price,
        "pSAR": taLibOS.pSAR
    }

    dSet = DataRetrieve()
    dSet.save_obj(functionDict, 'func_dict')
    funcDict = dSet.load_obj('func_dict')
    print(funcDict)
time_test_modelling.py
Goal: Test and verify in sample and out of sample time splits for dataset
"""
from Code.lib.plot_utils import PlotUtility
from Code.lib.time_utils import TimeUtility
from Code.lib.retrieve_data import DataRetrieve, ComputeTarget
from Code.utilities.stat_tests import stationarity_tests, mean_and_variance

import datetime
from dateutil.relativedelta import relativedelta
from pandas.tseries.offsets import BDay

if __name__ == "__main__":
    plotIt = PlotUtility()
    timeUtil = TimeUtility()
    dSet = DataRetrieve()
    ct = ComputeTarget()
    doPlot = False

    issue = "tlt"
    pivotDate = datetime.date(2018, 4, 2)
    is_oos_ratio = 2
    oos_months = 3
    segments = 3

    # get segmented dates
    isOosDates = timeUtil.is_oos_data_split(issue, pivotDate, is_oos_ratio,
                                            oos_months, segments)
    dataLoadStartDate = isOosDates[0]
    is_start_date = isOosDates[1]
    oos_start_date = isOosDates[2]
示例#4
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from Code.lib.retrieve_system_info import TradingSystemUtility
from Code.lib.candle_indicators import CandleIndicators
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 """"!!!')
    
        """
        col_name = 'ROC_' + str(period)
        current_feature['Latest'] = col_name
        feature_dict[col_name] = 'Keep'

        df[col_name] = ta.ROC(df.Close, period)
        return df


if __name__ == "__main__":
    from Code.lib.plot_utils import PlotUtility
    from Code.lib.retrieve_data import DataRetrieve

    plotIt = PlotUtility()
    taLibMomSt = TALibMomentumStudies()
    dSet = DataRetrieve()

    issue = "TLT"
    df = dSet.read_issue_data(issue)

    lastRow = df.shape[0]
    dataLoadEndDate = df.Date[lastRow - 100]
    pprint(dataLoadEndDate)

    dataLoadStartDate = df.Date[lastRow - 3000]
    pprint(dataLoadStartDate)

    dataSet = dSet.set_date_range(df, dataLoadStartDate, dataLoadEndDate)
    pprint(dataSet.tail(10))

    # Resolve any NA's for now
示例#6
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import pickle
#from pandas.tseries.holiday import USFederalHolidayCalendar
#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]
示例#7
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#from Code.lib.model_utils import ModelUtility, TimeSeriesSplitImproved
#from Code.lib.feature_generator import FeatureGenerator
#from Code.utilities.stat_tests import stationarity_tests
#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()
#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]

    print("Existing system")
示例#8
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            for test_start in test_starts:
                yield (indices[:test_start],
                       indices[test_start:test_start + test_size])


if __name__ == "__main__":
    from Code.lib.plot_utils import PlotUtility
    from Code.lib.retrieve_data import DataRetrieve, ComputeTarget
    from Code.lib.time_utils import TimeUtility
    from Code.lib.feature_generator import FeatureGenerator
    from Code.utilities.stat_tests import stationarity_tests
    #    from Code.lib.config import current_feature, feature_dict

    plotIt = PlotUtility()
    ct = ComputeTarget()
    dSet = DataRetrieve()
    featureGen = FeatureGenerator()
    timeUtil = TimeUtility()
    modelUtil = ModelUtility()
    #timeSeriesImp = TimeSeriesSplitImproved()

    dataLoadStartDate = "2014-09-26"
    dataLoadEndDate = "2018-04-05"
    issue = "TLT"
    # Set IS-OOS parameters
    pivotDate = datetime.date(2018, 4, 2)
    is_oos_ratio = 2
    oos_months = 4
    segments = 4

    dataSet = dSet.read_issue_data(issue)
# -*- coding: utf-8 -*-
"""
Created on Wed Mar  6 14:30:17 2019

@author: KRUEGKJ
"""
import os
import datetime
import random
from Code.lib.retrieve_data import DataRetrieve

dSet = DataRetrieve()

current_directory = os.getcwd()

# create  
list_of_input_dicts = []

def gen_dict_name():
    # generate random input_dict name
    a = random.sample(range(10),3)
    sys_date = datetime.datetime.now().strftime("%Y%m%d")
    name = "input_dict_" + str(sys_date) + "_" + str(a[0]) + str(a[1]) + str(a[2])
    return name

test_input_dict = {} # initialize
test_input_dict = {'f1': 
                  {'fname' : 'PPO', 
                   'params' : [2,5],
                   'transform' : ['Normalized', 20]
                   },
# -*- coding: utf-8 -*-
"""
Created on Mon Mar  4 11:06:54 2019

@author: KRUEGKJ
"""

import random
import os
import os.path
import sys
from Code.lib.retrieve_data import DataRetrieve
dSet = DataRetrieve()


class TradingSystemUtility:
    # Function to generate system number
    def get_sys_number(self):
        # Generate four digit random number
        a = random.sample(range(10), 3)
        number = int(
            str(random.randint(1, 9)) + str(a[0]) + str(a[1]) + str(a[2]))
        return number

    # function to create system directory based on system name if one doesn't already exist
    def get_system_dir(self, system_name):
        # Create system directory in current path
        current_directory = os.getcwd()
        system_directory = os.path.join(current_directory, system_name)
        if not os.path.exists(system_directory):
            os.makedirs(system_directory)
示例#11
0
from Code.lib.retrieve_system_info import TradingSystemUtility
#from Code.lib.candle_indicators import CandleIndicators
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.utilities.stat_tests import stationarity_tests
from Code.lib.config import current_feature, feature_dict
from Code.models import models_utils
from Code.lib.model_algos import AlgoUtility

plotIt = PlotUtility()
timeUtil = TimeUtility()
ct = ComputeTarget()
#candle_ind = CandleIndicators()
dSet = DataRetrieve()
#taLibMomSt = TALibMomentumStudies()
transf = Transformers()
modelUtil = ModelUtility()
featureGen = FeatureGenerator()
modelAlgo = AlgoUtility()
sysUtil = TradingSystemUtility()

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]
    ext_input_dict = sys.argv[2]
from Code.lib.candle_indicators import CandleIndicators
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.utilities.stat_tests import stationarity_tests
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__':
    from Code.lib.plot_utils import PlotUtility
    plotIt = PlotUtility()

    # set to existing system name OR set to blank if creating new
    if len(sys.argv) < 2:
"""
Created on Fri Mar  8 17:04:07 2019

@author: kruegkj

Re-use existing input_dicts to generate new results
"""

import os
import glob

from Code.lib.retrieve_data import DataRetrieve

current_directory = os.getcwd()

dSet = DataRetrieve()

# create
list_of_input_dicts = []

num_dicts_to_make = 40
dict_ctr = 0

dicts_to_use = glob.glob("input_dict*")

program_name = "IS_Day_by_day_TLT_v1.py"

for dict_name in dicts_to_use:
    print(dict_name)
    current_directory = os.getcwd()
    # add try/catch block later
from Code.lib.retrieve_system_info import TradingSystemUtility
from Code.lib.candle_indicators import CandleIndicators
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.utilities.stat_tests import stationarity_tests
from Code.lib.config import current_feature, feature_dict
from Code.models import models_utils
from Code.lib.model_algos import AlgoUtility

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()

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]