diff --git a/strategy_evaluation/StrategyLearner.py b/strategy_evaluation/StrategyLearner.py index d0708be..0d4d3ec 100644 --- a/strategy_evaluation/StrategyLearner.py +++ b/strategy_evaluation/StrategyLearner.py @@ -1,88 +1,94 @@ -""" -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 util +import indicators +from BagLearner import BagLearner +from RTLearner import RTLearner + class StrategyLearner(object): - # constructor - def __init__(self, verbose = False, impact=0.0, commission=0.0): + def __init__(self, verbose=False, impact=0.0, commission=0.0, testing=False): self.verbose = verbose self.impact = impact self.commission = commission + self.testing = testing - # 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 + def _get_volume(self): + """For reference.""" + volume_all = ut.get_data(syms, dates, colname="Volume") volume = volume_all[syms] # only portfolio symbols # volume_SPY = volume_all['SPY'] # only SPY, for comparison later - if self.verbose: print(volume) + 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): + def _add_indicators(self, df, symbol): + """Add indicators for learning to DataFrame.""" + df.drop(columns=["SPY"], inplace=True) + indicators.macd(df, symbol) + indicators.rsi(df, symbol) + indicators.price_sma(df, symbol, [8]) + indicators.price_delta(df, symbol, 3) + df.dropna(inplace=True) - # 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 + def addEvidence(self, symbol="IBM", + sd=dt.datetime(2008, 1, 1), + ed=dt.datetime(2009, 1, 1), + sv=10000): + + self.indicators = ['macd_diff', 'rsi', 'price_sma_8'] + df = util.get_data([symbol], pd.date_range(sd, ed)) + self._add_indicators(df, symbol) + + def classify_y(row): + if row > 0.1: + return 1 + elif row < -0.1: + return -1 + return 0 + + self.learner = RTLearner(leaf_size = 7) + # self.learner = BagLearner(RTLearner, 5, {'leaf_size': 5}) + data_x = df[self.indicators].to_numpy() + y = df['pct_3'].apply(classify_y) + self.learner.addEvidence(data_x, y.to_numpy()) + return y + + def strat(self, data_y, orders): + self.holding = 0 + + def strat(row): + y = int(data_y.loc[row.name][0]) + shares = 0 + if self.holding == 0 and y == 1: + shares = 1000 + elif self.holding == -1000 and y == 1: + shares = 2000 + elif self.holding == 0 and y == -1: + shares = -1000 + elif self.holding == 1000 and y == -1: + shares = -2000 + self.holding += shares + return shares + + orders["Shares"] = orders.apply(strat, axis=1) + + def testPolicy(self, symbol="IBM", + sd=dt.datetime(2009, 1, 1), + ed=dt.datetime(2010, 1, 1), + sv=10000): + df = util.get_data([symbol], pd.date_range(sd, ed)) + self._add_indicators(df, symbol) + data_x = df[self.indicators].to_numpy() + data_y = pd.DataFrame(index=df.index, data=self.learner.query(data_x)) + + orders = pd.DataFrame(index=df.index) + orders["Symbol"] = symbol + orders["Order"] = "" + orders["Shares"] = 0 + self.strat(data_y, orders) + if self.testing: + return orders + else: + return orders[["Shares"]] -if __name__=="__main__": - print("One does not simply think up a strategy") diff --git a/strategy_evaluation/experiment1.py b/strategy_evaluation/experiment1.py index 8e09aae..65aa2b8 100644 --- a/strategy_evaluation/experiment1.py +++ b/strategy_evaluation/experiment1.py @@ -93,29 +93,29 @@ def compare_manual_strategies(symbol, sv, sd, ed): def experiment1(): symbol = "JPM" - start_value = 10000 + sv = 10000 sd = dt.datetime(2008, 1, 1) # in-sample ed = dt.datetime(2009, 12, 31) # in-sample - # sd = dt.datetime(2010, 1, 1) # out-sample - # ed = dt.datetime(2011, 12, 31) # out-sample + sd_out = dt.datetime(2010, 1, 1) # out-sample + ed_out = dt.datetime(2011, 12, 31) # out-sample + + df = util.get_data([symbol], pd.date_range(sd, ed_out)) + df.drop(columns=["SPY"], inplace=True) # visualize_correlations(symbol, df) # plot_indicators(symbol, df) - # compare_manual_strategies(symbol, start_value, sd, ed) - - df = util.get_data([symbol], pd.date_range(sd, ed)) - df.drop(columns=["SPY"], inplace=True) + # compare_manual_strategies(symbol, sv, sd, ed) bs = BenchmarkStrategy() - orders = bs.testPolicy(symbol, sd, ed, start_value) - df["Benchmark"] = marketsim.compute_portvals(orders, start_value) + orders = bs.testPolicy(symbol, sd_out, ed_out, sv) + df["Benchmark"] = marketsim.compute_portvals(orders, sv) df["Orders Benchmark"] = orders["Shares"] - sl = StrategyLearner() - orders = ms.testPolicy(symbol, sd, ed, start_value) - df["SL"] = marketsim.compute_portvals(orders, start_value) + sl = StrategyLearner(testing=True) + sl.addEvidence(symbol, sd, ed, sv) + orders = sl.testPolicy(symbol, sd_out, ed_out, sv) + df["SL"] = marketsim.compute_portvals(orders, sv) df["Orders SL"] = orders["Shares"] - # df["Holding Manual"] = orders["Shares"].cumsum() fig, ax = plt.subplots(3, sharex=True) df[[symbol]].plot(ax=ax[0]) @@ -127,6 +127,12 @@ def experiment1(): MultiCursor(fig.canvas, ax, color='r', lw=0.5) plt.show() + # For debugging the classification learner: + # df["y_train"] = sl.addEvidence(symbol, sd, ed, sv) + # df["y_query"] = sl.testPolicy(symbol, sd, ed, sv) + # df[["y_train", "y_query"]].plot(ax=ax[1]) + + if __name__ == "__main__": experiment1() diff --git a/strategy_evaluation/grade_strategy_learner.py b/strategy_evaluation/grade_strategy_learner.py index 18c567d..0dc0e20 100644 --- a/strategy_evaluation/grade_strategy_learner.py +++ b/strategy_evaluation/grade_strategy_learner.py @@ -1,242 +1,242 @@ -"""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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+"""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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                   (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:  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+            #     ((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" \
@@ -244,96 +244,96 @@ def test_strategy(description, insample_args, outsample_args, benchmark_type, be
                          "    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__])  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+                         "    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__])