diff --git a/assignment8.py b/assignment8.py
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+++ b/assignment8.py
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+'''
+Created on Nov 24, 2016
+@author: danielamaranto
+This program accepts a list of integers from a user and assumes the user enters the list with surrounding brackets,
+as indicated in the instructions.  For any set of integers, it will create an array of individual trials of 
+daily return on investment for each position entered by the user.  It will generate 1 text file of the mean and
+standard deviation of each position, and it will generate a separate histogram for each position.
+
+I am the sole author of this program, but I have included my references where applicable.
+'''
+
+#Import functions and a class from separate modules
+import outputs
+from investment import investment_position
+
+def main():
+    complete = False
+    while complete is False:
+        # Get input from user: 1 list of positions and the number of trials
+        position_input = input('Enter a list of investments:').replace(' ','')[1:-1]  #Surrounding brackets deleted
+        trials_input = input('Enter a number of trials:').replace(' ','')
+        
+        try:
+            investments = [int(i) for i in position_input.split(sep=',')]
+            num_trials = int(trials_input)
+            files = 1
+            with open('results.txt', 'w') as file:    #If user is re-running, this will wipe the 
+                file.write('')                                     #text file clean
+            print('Generating output...')
+            for i in investments:
+                files = files + 1                                  #A histogram is generated for each position and a text
+                position = investment_position(i,num_trials)       #file is generated for all positions collectively.
+                outputs.generate_statistics(position)
+                outputs.generate_histogram(position)
+            print(str(files) + ' files generated.')
+            complete = True
+        
+        # All invalid entries are handled here, including floats, alphabetical entries, etc.
+        except ValueError:
+            print('Invalid input\n')
+        except IndexError:
+            print('Invalid input\n')
+        except UnboundLocalError:
+            print('Invalid input\n')
+        except TypeError:
+            print('Invalid input\n')
+        except KeyboardInterrupt:
+            print('Invalid input\n')
+
+
+if __name__ == '__main__':
+    main() 
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diff --git a/investment.py b/investment.py
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+++ b/investment.py
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+'''
+Created on Nov 24, 2016
+I created a class that would perform all of the required calculation for each
+position determined by the user.  Functions referenced by the class's attributes
+are located in the position_outcomes module.
+@author: danielamaranto
+'''
+
+import numpy as np
+from position_outcomes import cumu_ret
+
+class investment_position:
+    def __init__(self, shares, trials):
+        self.position = shares
+        self.position_value = 1000/shares
+        self.num_trials = trials
+    
+    def all_trials_result(self):
+        trial_array = np.array([cumu_ret(self.position) for i in range(self.num_trials)])
+        return trial_array
+    
+    def daily_ret(self):
+        normalized_result = np.array([i/1000-1 for i in self.all_trials_result()])
+        return normalized_result
+    
+    def mean(self):
+        return self.daily_ret().mean()
+    
+    def stdev(self):
+        return self.daily_ret().std()
+    
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diff --git a/outputs.py b/outputs.py
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+++ b/outputs.py
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+'''
+Created on Nov 24, 2016
+
+@author: danielamaranto
+'''
+from investment import investment_position
+import matplotlib.pyplot as plt
+
+def generate_statistics(investment):
+    '''I referred to python documentation to write to the text file.
+    https://docs.python.org/3/tutorial/inputoutput.html''' 
+    with open('results.txt', 'a') as file:
+        data =  'Number of shares: ' + str(investment.position) + \
+                '\nValue of each share: $' + str(investment.position_value) + \
+                '\nMean: ' + str(investment.mean()) + \
+                '\nStandard Deviation: ' + str(investment.stdev()) + '\n\n'
+        file.write(data)
+        
+def generate_histogram(investment):
+    '''I referred to python documentation to properly format the histogram filenames (zfill)
+    and to generate individual plots (savefig). 
+    https://docs.python.org/2/library/string.html
+    http://matplotlib.org/api/pyplot_api.html'''
+    plt.hist(investment.daily_ret(),100, range=[-1,1])
+    plt.xlabel('Daily Return')
+    plt.ylabel('Number of Trials')
+    plt.title('Histogram of investment trials')
+    plt.grid(True)
+    name = str(investment.position).zfill(4)
+    plt.savefig('histogram_'+ name +'_pos.pdf', format='pdf')
+    plt.close('all')
diff --git a/position_outcomes.py b/position_outcomes.py
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+++ b/position_outcomes.py
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+'''
+Created on Nov 27, 2016
+These functions assist the investment_portfolio class with generation of the daily investment outcome.
+@author: danielamaranto
+'''
+import numpy as np
+import random
+
+#Determines the outcome of a single share
+def share_result():
+    result = random.uniform(0,1)
+    if result <= 0.49:
+        win = False
+    else:
+        win = True
+    return win
+
+#Determines the outcome of one day of trading
+def cumu_ret(shares):
+    wins = np.array([share_result() for i in range(shares)])
+    return 2 * (1000/shares) * np.sum(wins)
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diff --git a/tests.py b/tests.py
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+++ b/tests.py
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+'''
+Created on Nov 27, 2016
+
+@author: danielamaranto
+'''
+import unittest
+import numpy as np
+from position_outcomes import *
+
+'''Given that all my functions rely on randomly generated data, my tests either
+confirm whether or not the functions work based on probabilities with large numbers
+of trials or they confirm that functions output the correct format of data.
+I found the assertAlmostEqual method from the python documentation website.
+https://docs.python.org/2/library/unittest.html
+'''
+
+class Test(unittest.TestCase):
+    
+    def testShareResult(self):
+        result = [share_result() for i in list(range(10000))]
+        proportion_true = sum(result)/10000
+        self.assertAlmostEqual(proportion_true, 0.51, 2)
+
+    def testCumulative(self):
+        result = np.array([cumu_ret(1) for i in list(range(100000))])
+        outcome = result.mean()
+        self.assertAlmostEqual(outcome, 1020, -2)      #Expected value of 1 share is .51 * 2000, or 1020
+        
+
+if __name__ == "__main__":
+    unittest.main()
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