diff --git a/val_sim.py b/val_sim.py
index 0fedc9f29945edf2bf58a194ceb160a210c0299a..9425187269678361d96f4178292aa2d8e1a198f6 100644
--- a/val_sim.py
+++ b/val_sim.py
@@ -1,60 +1,59 @@
+#The below code was referenced and modified as per the requirement from the code provided on course work desciption document.
+#The handling of the input from the  GAE(codes from line 9 to 18) are adopted from Python3 documentation https://docs.python.org/3/library/
 #!/usr/bin/python3
 
 import math, random, sys, json
 from statistics import mean, stdev
-import logging
-
-logging.basicConfig(filename='/var/log/apache2/val_sim.log', level=logging.ERROR)
-
-try:
-    event = json.loads(sys.stdin.read())
-
-    dt = eval(event['key1'])
-    close = eval(event['key2'])
-    buy = eval(event['key3'])
-    sell = eval(event['key4'])
-    h = int(event['key5'])
-    d = int(event['key6'])
-    t = event['key7']
-    
-    minhistory = h
-    shots = d
-    var95_list = []
-    var99_list = []
-    dates = []
-
-    for i in range(minhistory, len(close)):
-        if t == "buy" and buy[i] == 1:
-            close_data = close[i-minhistory:i]
-            pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1, len(close_data))]
-            mean_val = mean(pct_change)
-            std = stdev(pct_change)
-            simulated = [random.gauss(mean_val, std) for x in range(shots)]
-            simulated.sort(reverse=True)
-            var95 = simulated[int(len(simulated) * 0.95)]
-            var99 = simulated[int(len(simulated) * 0.99)]
-            var95_list.append(var95)
-            var99_list.append(var99)
-            dates.append(str(dt[i]))
-
-        elif t == "sell" and sell[i] == 1:
-            close_data = close[i-minhistory:i]
-            pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1, len(close_data))]
-            mean_val = mean(pct_change)
-            std = stdev(pct_change)
-            simulated = [random.gauss(mean_val, std) for x in range(shots)]
-            simulated.sort(reverse=True)
-            var95 = simulated[int(len(simulated) * 0.95)]
-            var99 = simulated[int(len(simulated) * 0.99)]
-            var95_list.append(var95)
-            var99_list.append(var99)
-            dates.append(str(dt[i]))
-
-    output = {"dates": dates, "var95": var95_list, "var99": var99_list}
-    print("Content-Type: application/json")
-    print()
-    print(json.dumps(output))
-
-except Exception as e:
-    logging.error(f"Error: {e}")
-    sys.exit(1)
+
+
+event = json.loads(sys.stdin.read())
+
+
+dt = eval(event['key1'])
+close = eval(event['key2'])
+buy = eval(event['key3'])
+sell = eval(event['key4'])
+h = int(event['key5'])
+d = int(event['key6'])
+t = event['key7']
+minhistory = h
+shots = d
+var95_list = []
+var99_list = []
+dates = []
+
+for i in range(minhistory, len(close)):
+	if t == "buy":
+		if buy[i] == 1: 
+			close_data = close[i-minhistory:i]
+			pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]
+			mean = mean(pct_change)
+			std = stdev(pct_change)
+			simulated = [random.gauss(mean,std) for x in range(shots)]
+			simulated.sort(reverse=True)
+			var95 = simulated[int(len(simulated)*0.95)]
+			var99 = simulated[int(len(simulated)*0.99)]
+			var95_list.append(var95)
+			var99_list.append(var99)
+			dates.append(str(dt[i]))
+	elif t == "sell":
+		if sell[i] == 1:
+			close_data = close[i-minhistory:i]
+			pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))]
+			mean = mean(pct_change)
+			std = stdev(pct_change)
+			simulated = [random.gauss(mean,std) for x in range(shots)]
+			simulated.sort(reverse=True)
+			var95 = simulated[int(len(simulated)*0.95)]
+			var99 = simulated[int(len(simulated)*0.99)]
+			var95_list.append(var95)
+			var99_list.append(var99)
+			dates.append(str(dt[i]))
+
+output = {"dates" : dates,
+		"var95" : var95_list,
+		"var99" : var99_list
+		}
+
+output = json.dumps(output)
+