diff --git a/problem1/problem1_template.py b/problem1/problem1_template.py
index aa03626..2bebb0a 100644
--- a/problem1/problem1_template.py
+++ b/problem1/problem1_template.py
@@ -10,12 +10,18 @@
 from backtester.constants import *
 from backtester.features.feature import Feature
 from backtester.logger import *
+from backtester.versions import versions
 import pandas as pd
 import numpy as np
 import sys
 from sklearn import linear_model
 from sklearn import metrics as sm
 from problem1_trading_params import MyTradingParams
+try:
+    from urllib2 import urlopen
+except ImportError:
+    from urllib.request import urlopen
+import collections
 
 ## Make your changes to the functions below.
 ## SPECIFY the symbols you are modeling for in getSymbolsToTrade() below
@@ -25,16 +31,13 @@
 ## Don't change any other function
 ## The toolbox does the rest for you, from downloading and loading data to running backtest
 
-
 class MyTradingFunctions():
 
+
     def __init__(self):  #Put any global variables here
         self.lookback = 1200  ## max number of historical datapoints you want at any given time
         self.targetVariable = 'Y'
-        if datetime.today() < datetime(2018, 7, 3):
-            self.dataSetId = 'QQ3DataSample'
-        else:
-            self.dataSetId = 'QQ3DataDownSampled'
+        self.dataSetId = ''
         self.params = {}
 
         # for example you can import and store an ML model from scikit learn in this dict
@@ -74,7 +77,6 @@ def getSymbolsToTrade(self):
     '''
 
     def getInstrumentFeatureConfigDicts(self):
-
     ##############################################################################
     ### TODO 2a: FILL THIS FUNCTION TO CREATE DESIRED FEATURES for each symbol. ###
     ### USE TEMPLATE BELOW AS EXAMPLE                                          ###
@@ -104,7 +106,6 @@ def getMarketFeatureConfigDicts(self):
     ### TODO 2b: FILL THIS FUNCTION TO CREATE features that use multiple symbols ###
     ### USE TEMPLATE BELOW AS EXAMPLE                                           ###
     ###############################################################################
-
         # customFeatureDict = {'featureKey': 'custom_mrkt_feature',
         #                      'featureId': 'my_custom_mrkt_feature',
         #                      'params': {'param1': 'value1'}}
@@ -155,7 +156,6 @@ def getPrediction(self, time, updateNum, instrumentManager,predictions):
         ma1 = lookbackInstrumentFeatures.getFeatureDf('ma_5')       #DF with rows=timestamp and columns=stockIDS
         ma2 = lookbackInstrumentFeatures.getFeatureDf('ma_10')      #DF with rows=timestamp and columns=stockIDS
         factor2Values = (ma1/ma2)                                   #DF with rows=timestamp and columns=stockIDS
-
         # Now looping over all stocks:
         for s in self.getSymbolsToTrade():
             #Creating a dataframe to hold features for this stock
@@ -194,7 +194,6 @@ def getPrediction(self, time, updateNum, instrumentManager,predictions):
             threshold = 0.8
             predictions[s] = 1 if y_predict>threshold else 0.5
             predictions[s] = 0 if y_predict<(1-threshold) else 0.5
-
         return predictions
 
     ###########################################
@@ -243,7 +242,6 @@ def getCustomFeatures(self):
     def computeForInstrument(cls, updateNum, time, featureParams, featureKey, instrumentManager):
         # Custom parameter which can be used as input to computation of this feature
         param1Value = featureParams['param1']
-
         # A holder for the all the instrument features
         lookbackInstrumentFeatures = instrumentManager.getLookbackInstrumentFeatures()
 
@@ -254,14 +252,13 @@ def computeForInstrument(cls, updateNum, time, featureParams, featureKey, instru
         # The last row of the previous dataframe gives the last calculated value for that feature (basis in this case)
         # This returns a series with symbols/instrumentIds as the index.
         currentValue = lookbackInstrumentValue.iloc[-1]
-
         if param1Value == 'value1':
             return currentValue * 0.1
         else:
             return currentValue * 0.5
 
-
 if __name__ == "__main__":
+    versions()
     if updateCheck():
         print('Your version of the auquan toolbox package is old. Please update by running the following command:')
         print('pip install -U auquan_toolbox')
diff --git a/problem1/problem1_trading_params.py b/problem1/problem1_trading_params.py
index 00377f7..e379dbf 100644
--- a/problem1/problem1_trading_params.py
+++ b/problem1/problem1_trading_params.py
@@ -15,6 +15,10 @@
 import sys
 from sklearn import linear_model
 from sklearn import metrics as sm
+try:
+    from urllib2 import urlopen
+except ImportError:
+    from urllib.request import urlopen
 
 ## Make your changes to the functions below.
 ## SPECIFY the symbols you are modeling for in getSymbolsToTrade() below
@@ -32,7 +36,11 @@ class MyTradingParams(TradingSystemParameters):
     '''
     def __init__(self, tradingFunctions):
         self.__tradingFunctions = tradingFunctions
-        self.__dataSetId = self.__tradingFunctions.getDataSetId()
+
+        url = "https://raw.githubusercontent.com/Auquan/data_set_id/master/DataSetId.txt"
+        response = urlopen(url)
+        self.__dataSetId = response.read().decode('utf8').rstrip() + self.__tradingFunctions.getDataSetId()
+
         self.__instrumentIds = self.__tradingFunctions.getSymbolsToTrade()
         self.__priceKey = 'F5'
         self.__additionalInstrumentFeatureConfigDicts = []