diff --git a/Histogram_1000_pos.pdf b/Histogram_1000_pos.pdf
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diff --git a/Histogram_100_pos.pdf b/Histogram_100_pos.pdf
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diff --git a/Histogram_10_pos.pdf b/Histogram_10_pos.pdf
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diff --git a/Histogram_1_pos.pdf b/Histogram_1_pos.pdf
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diff --git a/assignment8.py b/assignment8.py
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+++ b/assignment8.py
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+# Author: Yang Sun
+import sys
+from simulation import *
+
+'''Users should input a list of numbers of shares to buy, and a number of times to repeat to do the
+simulation of the investment. The program will yield mean and standard deviation of each position,
+and histogram of daily return.'''
+
+while True:
+    try:
+        pos_num = raw_input('A list of number of shares to buy in parallel? '
+                            'Valid numbers to input are 1,10,100 and 1000\n')
+        positions = pos_num.split()
+        for i, position in enumerate(positions):
+            positions[i] = int(position)
+        break
+    except EOFError:
+        sys.exit(1)
+    except KeyboardInterrupt:
+        sys.exit(2)
+
+while True:
+    try:
+        num_trials = int(raw_input('How many times to randomly repeat the test?\n'))
+        break
+    except ValueError:
+        print('Invalid number!')
+    except EOFError:
+        sys.exit(3)
+    except KeyboardInterrupt:
+        sys.exit(4)
+
+f = open ('results.txt','w')
+for i in positions:
+    s = simulation(i, num_trials)
+    f.write('Position: ' + str(i) + ', Mean: ' + str(s.mean)+ ', Std:  ' + str(s.std) + "\r\n")
+    s.histogram('Histogram_' + str(i) + '_pos.pdf')
+
+f.close()
diff --git a/results.txt b/results.txt
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+++ b/results.txt
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+Position: 1, Mean: 0.0182, Std:  0.999834366283
+Position: 10, Mean: 0.01614, Std:  0.314145667486
+Position: 100, Mean: 0.020338, Std:  0.0997569333731
+Position: 1000, Mean: 0.0195904, Std:  0.0314628197694
diff --git a/simulation.py b/simulation.py
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+++ b/simulation.py
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+import numpy as np
+import matplotlib.pyplot as plt
+
+'''This module "simulation" have two functions. The first function takes in two parameters, number of
+positions bought and number of times to simulate. Daily return, the mean and standard deviation of daily
+return are all calculated. The second function takes in one parameter and draws a histogram for each
+ daily return'''
+
+class simulation:
+    def __init__(self, position, num_trials):
+        position_value = 1000 / position
+        self.cumu_ret = []
+        ret_list = []
+        for i in range(num_trials):
+            cumu_ret = 0
+            for j in range(position):
+                chance = np.random.uniform(0, 1)
+                if chance <= 0.51:
+                    cumu_ret += position_value * 2
+                else:
+                    cumu_ret += 0
+            ret_list.append(cumu_ret/1000.0 - 1)
+        self.daily_ret = np.asarray(ret_list)
+        self.mean = self.daily_ret.mean()
+        self.std = self.daily_ret.std()
+
+    def histogram(self,f_name):
+        plt.hist(self.daily_ret, 100, range = [-1,1])
+        plt.xlabel('Daily_ret')
+        plt.ylabel('Number of trials')
+        plt.savefig(f_name)
+        plt.close()
diff --git a/simulation.pyc b/simulation.pyc
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diff --git a/test.py b/test.py
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+++ b/test.py
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+import unittest
+from simulation import *
+
+'''This class contains two functions. First one tests the mean generated by the module "simulation", and
+the second one tests the standard deviation of the previous result. Both pass successfully'''
+
+class tests(unittest.TestCase):
+    def test_mean(self):
+        s1 = simulation(10, 10000)
+        s2 = simulation(100, 10000)
+        self.assertTrue(abs(s1.mean - 0.01588) < 0.01)
+        self.assertTrue(abs(s2.mean - 0.021166) < 0.01)
+
+    def test_std(self):
+        s3 = simulation(100, 10000)
+        s4 = simulation(1000, 10000)
+        self.assertTrue(abs(s3.std - 0.099479648391) < 0.01)
+        self.assertTrue(abs(s4.std - 0.03175307467) < 0.01)
+
+
+if __name__ == "__main__":
+    unittest.main()
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