diff --git a/strategy_evaluation/StrategyLearner.py b/strategy_evaluation/StrategyLearner.py new file mode 100644 index 0000000..9248361 --- /dev/null +++ b/strategy_evaluation/StrategyLearner.py @@ -0,0 +1,89 @@ +""" +Template for implementing StrategyLearner (c) 2016 Tucker Balch + +Copyright 2018, Georgia Institute of Technology (Georgia Tech) +Atlanta, Georgia 30332 +All Rights Reserved + +Template code for CS 4646/7646 + +Georgia Tech asserts copyright ownership of this template and all derivative +works, including solutions to the projects assigned in this course. Students +and other users of this template code are advised not to share it with others +or to make it available on publicly viewable websites including repositories +such as github and gitlab. This copyright statement should not be removed +or edited. + +We do grant permission to share solutions privately with non-students such +as potential employers. However, sharing with other current or future +students of CS 7646 is prohibited and subject to being investigated as a +GT honor code violation. + +-----do not edit anything above this line--- + +Student Name: Tucker Balch (replace with your name) +GT User ID: tb34 (replace with your User ID) +GT ID: 900897987 (replace with your GT ID) +""" + +import datetime as dt +import pandas as pd +import util as ut +import random + +class StrategyLearner(object): + + # constructor + def __init__(self, verbose = False, impact=0.0, commission=0.0): + self.verbose = verbose + self.impact = impact + self.commission = commission + + # this method should create a QLearner, and train it for trading + def addEvidence(self, symbol = "IBM", \ + sd=dt.datetime(2008,1,1), \ + ed=dt.datetime(2009,1,1), \ + sv = 10000): + + # add your code to do learning here + + # example usage of the old backward compatible util function + syms=[symbol] + dates = pd.date_range(sd, ed) + prices_all = ut.get_data(syms, dates) # automatically adds SPY + prices = prices_all[syms] # only portfolio symbols + prices_SPY = prices_all['SPY'] # only SPY, for comparison later + if self.verbose: print(prices) + + # example use with new colname + volume_all = ut.get_data(syms, dates, colname = "Volume") # automatically adds SPY + volume = volume_all[syms] # only portfolio symbols + volume_SPY = volume_all['SPY'] # only SPY, for comparison later + if self.verbose: print(volume) + + # this method should use the existing policy and test it against new data + def testPolicy(self, symbol = "IBM", \ + sd=dt.datetime(2009,1,1), \ + ed=dt.datetime(2010,1,1), \ + sv = 10000): + + # here we build a fake set of trades + # your code should return the same sort of data + dates = pd.date_range(sd, ed) + prices_all = ut.get_data([symbol], dates) # automatically adds SPY + trades = prices_all[[symbol,]] # only portfolio symbols + trades_SPY = prices_all['SPY'] # only SPY, for comparison later + trades.values[:,:] = 0 # set them all to nothing + trades.values[0,:] = 1000 # add a BUY at the start + trades.values[40,:] = -1000 # add a SELL + trades.values[41,:] = 1000 # add a BUY + trades.values[60,:] = -2000 # go short from long + trades.values[61,:] = 2000 # go long from short + trades.values[-1,:] = -1000 #exit on the last day + if self.verbose: print(type(trades)) # it better be a DataFrame! + if self.verbose: print(trades) + if self.verbose: print(prices_all) + return trades + +if __name__=="__main__": + print("One does not simply think up a strategy") diff --git a/strategy_evaluation/grade_strategy_learner.py b/strategy_evaluation/grade_strategy_learner.py new file mode 100644 index 0000000..18c567d --- /dev/null +++ b/strategy_evaluation/grade_strategy_learner.py @@ -0,0 +1,339 @@ +"""MC3-P3: Strategy Learner - grading script. + +Usage: +- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd). +- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.: + PYTHONPATH=ml4t:MC1-P2/jdoe7 python ml4t/mc2_p1_grading/grade_marketsim.py + +Copyright 2017, Georgia Tech Research Corporation +Atlanta, Georgia 30332-0415 +All Rights Reserved + +Template code for CS 4646/7646 + +Georgia Tech asserts copyright ownership of this template and all derivative +works, including solutions to the projects assigned in this course. Students +and other users of this template code are advised not to share it with others +or to make it available on publicly viewable websites including repositories +such as github and gitlab. This copyright statement should not be removed +or edited. + +We do grant permission to share solutions privately with non-students such +as potential employers. However, sharing with other current or future +students of CS 7646 is prohibited and subject to being investigated as a +GT honor code violation. + +-----do not edit anything above this line--- + +Student Name: Tucker Balch (replace with your name) +GT User ID: tb34 (replace with your User ID) +GT ID: 900897987 (replace with your GT ID) +""" + +import pytest +from grading.grading import grader, GradeResult, run_with_timeout, IncorrectOutput + +import os +import sys +import traceback as tb + +import datetime as dt +import numpy as np +import pandas as pd +from collections import namedtuple + +import time +import util +import random + +# Test cases +StrategyTestCase = namedtuple('Strategy', ['description','insample_args','outsample_args','benchmark_type','benchmark','impact','train_time','test_time','max_time','seed']) +strategy_test_cases = [ + StrategyTestCase( + description="ML4T-220", + insample_args=dict(symbol="ML4T-220",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000), + outsample_args=dict(symbol="ML4T-220",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000), + benchmark_type='clean', + benchmark=1.0, #benchmark updated Apr 24 2017 + impact=0.0, + train_time=25, + test_time=5, + max_time=60, + seed=1481090000 + ), + StrategyTestCase( + description="AAPL", + insample_args=dict(symbol="AAPL",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000), + outsample_args=dict(symbol="AAPL",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000), + benchmark_type='stock', + benchmark=0.1581999999999999, #benchmark computed Nov 22 2017 + impact=0.0, + train_time=25, + test_time=5, + max_time=60, + seed=1481090000 + ), + StrategyTestCase( + description="SINE_FAST_NOISE", + insample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000), + outsample_args=dict(symbol="SINE_FAST_NOISE",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000), + benchmark_type='noisy', + benchmark=2.0, #benchmark updated Apr 24 2017 + impact=0.0, + train_time=25, + test_time=5, + max_time=60, + seed=1481090000 + ), + StrategyTestCase( + description="UNH - In sample", + insample_args=dict(symbol="UNH",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000), + outsample_args=dict(symbol="UNH",sd=dt.datetime(2010,1,1),ed=dt.datetime(2011,12,31),sv=100000), + benchmark_type='stock', + benchmark= -0.25239999999999996, #benchmark computed Nov 22 2017 + impact=0.0, + train_time=25, + test_time=5, + max_time=60, + seed=1481090000 + ), +] + +max_points = 60.0 +html_pre_block = True # surround comments with HTML 
 tag (for T-Square comments field)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+MAX_HOLDINGS = 1000  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+# Test functon(s)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+@pytest.mark.parametrize("description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed", strategy_test_cases)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def test_strategy(description, insample_args, outsample_args, benchmark_type, benchmark, impact, train_time, test_time, max_time, seed, grader):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    """Test StrategyLearner.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    Requires test description, insample args (dict), outsample args (dict), benchmark_type (str), benchmark (float)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    max time (seconds), points for this test case (int), random seed (long), and a grader fixture.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    """  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    points_earned = 0.0  # initialize points for this test case  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    try:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if not 'StrategyLearner' in globals():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            import importlib  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            m = importlib.import_module('StrategyLearner')  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            globals()['StrategyLearner'] = m  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        outsample_cr_to_beat = None  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if benchmark_type == 'clean':  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            outsample_cr_to_beat = benchmark  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        def timeoutwrapper_strategylearner():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #Set fixed seed for repetability  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            np.random.seed(seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            random.seed(seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            learner = StrategyLearner.StrategyLearner(verbose=False,impact=impact)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp = time.time()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            learner.addEvidence(**insample_args)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            train_t = time.time()-tmp  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp = time.time()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            insample_trades_1 = learner.testPolicy(**insample_args)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            test_t = time.time()-tmp  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            insample_trades_2 = learner.testPolicy(**insample_args)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp = time.time()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            outsample_trades = learner.testPolicy(**outsample_args)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            out_test_t = time.time()-tmp  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            return insample_trades_1, insample_trades_2, outsample_trades, train_t, test_t, out_test_t  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msgs = []  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        in_trades_1, in_trades_2, out_trades, train_t, test_t, out_test_t = run_with_timeout(timeoutwrapper_strategylearner,max_time,(),{})  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        incorrect = False  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if len(in_trades_1.shape)!=2 or in_trades_1.shape[1]!=1:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msgs.append("  First insample trades DF has invalid shape: {}".format(in_trades_1.shape))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif len(in_trades_2.shape)!=2 or in_trades_2.shape[1]!=1:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msgs.append("  Second insample trades DF has invalid shape: {}".format(in_trades_2.shape))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif len(out_trades.shape)!=2 or out_trades.shape[1]!=1:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msgs.append("  Out-of-sample trades DF has invalid shape: {}".format(out_trades.shape))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp_csum=0.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            for date,trade in in_trades_1.iterrows():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                tmp_csum+= trade.iloc[0]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   msgs.append("  illegal trade in first insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                elif abs(tmp_csum)>MAX_HOLDINGS:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    msgs.append("  holdings more than {} long or short in first insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp_csum=0.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            for date,trade in in_trades_2.iterrows():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                tmp_csum+= trade.iloc[0]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   msgs.append("  illegal trade in second insample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                elif abs(tmp_csum)>MAX_HOLDINGS:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    msgs.append("  holdings more than {} long or short in second insample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tmp_csum=0.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            for date,trade in out_trades.iterrows():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                tmp_csum+= trade.iloc[0]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                if (trade.iloc[0]!=0) and\
+                   (trade.abs().iloc[0]!=MAX_HOLDINGS) and\
+                   (trade.abs().iloc[0]!=2*MAX_HOLDINGS):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   msgs.append("  illegal trade in out-of-sample DF. abs(trade) not one of ({},{},{}).\n  Date {}, Trade {}".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                elif abs(tmp_csum)>MAX_HOLDINGS:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    msgs.append("  holdings more than {} long or short in out-of-sample DF. Date {}, Trade {}".format(MAX_HOLDINGS,date,trade))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    break  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            # if (((in_trades_1.abs()!=0) & (in_trades_1.abs()!=MAX_HOLDINGS) & (in_trades_1.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((in_trades_2.abs()!=0) & (in_trades_2.abs()!=MAX_HOLDINGS) & (in_trades_2.abs()!=2*MAX_HOLDINGS)).any().any() or\
+            #     ((out_trades.abs()!=0)  & (out_trades.abs()!=MAX_HOLDINGS)  & (out_trades.abs()!=2*MAX_HOLDINGS)).any().any()):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     msgs.append("  illegal trade. abs(trades) not one of ({},{},{})".format(0,MAX_HOLDINGS,2*MAX_HOLDINGS))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            # if ((in_trades_1.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((in_trades_2.cumsum().abs()>MAX_HOLDINGS).any()[0]) or ((out_trades.cumsum().abs()>MAX_HOLDINGS).any()[0]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     msgs.append("  holdings more than {} long or short".format(MAX_HOLDINGS))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if not(incorrect):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if train_t>train_time:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect=True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("  addEvidence() took {} seconds, max allowed {}".format(train_t,train_time))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                points_earned += 1.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if test_t > test_time:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("  testPolicy() took {} seconds, max allowed {}".format(test_t,test_time))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                points_earned += 2.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if not((in_trades_1 == in_trades_2).all()[0]):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                mismatches = in_trades_1.join(in_trades_2,how='outer',lsuffix='1',rsuffix='2')  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                mismatches = mismatches[mismatches.iloc[:,0]!=mismatches.iloc[:,1]]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("  consecutive calls to testPolicy() with same input did not produce same output:")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("  Mismatched trades:\n {}".format(mismatches))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                points_earned += 2.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_insample_cr = evalPolicy2(insample_args['symbol'],in_trades_1,insample_args['sv'],insample_args['sd'],insample_args['ed'],market_impact=impact,commission_cost=0.0)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            student_outsample_cr = evalPolicy2(outsample_args['symbol'],out_trades, outsample_args['sv'],outsample_args['sd'],outsample_args['ed'],market_impact=impact,commission_cost=0.0)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if student_insample_cr <= benchmark:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                msgs.append("  in-sample return ({}) did not beat benchmark ({})".format(student_insample_cr,benchmark))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                points_earned += 5.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            if outsample_cr_to_beat is None:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                if out_test_t > test_time:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    msgs.append("  out-sample took {} seconds, max of {}".format(out_test_t,test_time))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    points_earned += 5.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                if student_outsample_cr < outsample_cr_to_beat:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    incorrect = True  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    msgs.append("  out-sample return ({}) did not beat benchmark ({})".format(student_outsample_cr,outsample_cr_to_beat))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                    points_earned += 5.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if incorrect:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            inputs_str = "    insample_args: {}\n" \
+                         "    outsample_args: {}\n" \
+                         "    benchmark_type: {}\n" \
+                         "    benchmark: {}\n" \
+                         "    train_time: {}\n" \
+                         "    test_time: {}\n" \
+                         "    max_time: {}\n" \
+                         "    seed: {}\n".format(insample_args, outsample_args, benchmark_type, benchmark, train_time, test_time, max_time,seed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            raise IncorrectOutput("Test failed on one or more output criteria.\n  Inputs:\n{}\n  Failures:\n{}".format(inputs_str, "\n".join(msgs)))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    except Exception as e:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Test result: failed  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msg = "Test case description: {}\n".format(description)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Generate a filtered stacktrace, only showing erroneous lines in student file(s)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        tb_list = tb.extract_tb(sys.exc_info()[2])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        for i in range(len(tb_list)):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            row = tb_list[i]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3])  # show only filename instead of long absolute path  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # tb_list = [row for row in tb_list if row[0] in ['QLearner.py','StrategyLearner.py']]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if tb_list:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += "Traceback:\n"  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += ''.join(tb.format_list(tb_list))  # contains newlines  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        elif 'grading_traceback' in dir(e):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += "Traceback:\n"  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            msg += ''.join(tb.format_list(e.grading_traceback))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        msg += "{}: {}".format(e.__class__.__name__, str(e))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Report failure result to grader, with stacktrace  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        raise  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    else:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # Test result: passed (no exceptions)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def compute_benchmark(sd,ed,sv,symbol,market_impact,commission_cost,max_holdings):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    date_idx = util.get_data([symbol,],pd.date_range(sd,ed)).index  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    orders = pd.DataFrame(index=date_idx)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    orders['orders'] = 0; orders['orders'][0] = max_holdings; orders['orders'][-1] = -max_holdings  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return evalPolicy2(symbol,orders,sv,sd,ed,market_impact,commission_cost)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def evalPolicy(student_trades,sym_prices,startval):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ending_cash = startval - student_trades.mul(sym_prices,axis=0).sum()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    ending_stocks = student_trades.sum()*sym_prices.iloc[-1]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return float((ending_cash+ending_stocks)/startval)-1.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def evalPolicy2(symbol, student_trades, startval, sd, ed, market_impact,commission_cost):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    orders_df = pd.DataFrame(columns=['Shares','Order','Symbol'])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for row_idx in student_trades.index:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        nshares = student_trades.loc[row_idx][0]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if nshares == 0:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            continue  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        order = 'sell' if nshares < 0 else 'buy'  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        new_row = pd.DataFrame([[abs(nshares),order,symbol],],columns=['Shares','Order','Symbol'],index=[row_idx,])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        orders_df = orders_df.append(new_row)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    portvals = compute_portvals(orders_df, sd, ed, startval,market_impact,commission_cost)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return float(portvals[-1]/portvals[0])-1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+def compute_portvals(orders_df, start_date, end_date, startval, market_impact=0.0, commission_cost=0.0):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    """Simulate the market for the given date range and orders file."""  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    symbols = []  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    orders = []  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    orders_df = orders_df.sort_index()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for date, order in orders_df.iterrows():  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        shares = order['Shares']  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        action = order['Order']  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        symbol = order['Symbol']  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if action.lower() == 'sell':  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            shares *= -1  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        order = (date, symbol, shares)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        orders.append(order)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        symbols.append(symbol)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    symbols = list(set(symbols))  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    dates = pd.date_range(start_date, end_date)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    prices_all = util.get_data(symbols, dates)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    prices = prices_all[symbols]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    prices = prices.fillna(method='ffill').fillna(method='bfill')  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    prices['_CASH'] = 1.0  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    trades = pd.DataFrame(index=prices.index, columns=symbols)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    trades = trades.fillna(0)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    cash = pd.Series(index=prices.index)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    cash = cash.fillna(0)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    cash.iloc[0] = startval  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    for date, symbol, shares in orders:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        price = prices[symbol][date]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        val = shares * price  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # transaction cost model  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        val += commission_cost + (pd.np.abs(shares)*price*market_impact)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        positions = prices.loc[date] * trades.sum()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        totalcash = cash.sum()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if (date < prices.index.min()) or (date > prices.index.max()):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            continue  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades[symbol][date] += shares  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        cash[date] -= val  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    trades['_CASH'] = cash  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    holdings = trades.cumsum()  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    df_portvals = (prices * holdings).sum(axis=1)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    return df_portvals  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+if __name__ == "__main__":  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    pytest.main(["-s", __file__])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
diff --git a/zips/20Spring_strategy_evaluation.zip b/zips/20Spring_strategy_evaluation.zip
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