Update val_sim.py

val_sim.py

@@ -22,14 +22,12 @@ dates = []
 
 for i in range(minhistory, len(close)):
 	if t == "buy":
-		if buy[i] == 1: # if we’re interested in Buy signals
+		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))]
-			mn = mean(pct_change)
-			std = stdev(pct_change)
-			# generate much larger random number series with same broad characteristics 
-			simulated = [random.gauss(mn,std) for x in range(shots)]
-			# sort and pick 95% and 99%  - not distinguishing long/short risks here
+			mean_value = mean(pct_change)
+			std_value = stdev(pct_change)
+			simulated = [random.gauss(mean_value,std_value) for x in range(shots)]
 			simulated.sort(reverse=True)
 			var95 = simulated[int(len(simulated)*0.95)]
 			var99 = simulated[int(len(simulated)*0.99)]
@@ -37,14 +35,12 @@ for i in range(minhistory, len(close)):
 			var99_list.append(var99)
 			dates.append(str(dt[i]))
 	elif t == "sell":
-		if sell[i] == 1: # if we’re interested in Sell signals
+		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))]
-			mn = mean(pct_change)
-			std = stdev(pct_change)
-			# generate much larger random number series with same broad characteristics 
-			simulated = [random.gauss(mn,std) for x in range(shots)]
-			# sort and pick 95% and 99%  - not distinguishing long/short risks here
+			mean_value = mean(pct_change)
+			std_value = stdev(pct_change)
+			simulated = [random.gauss(mean_value,std_value) for x in range(shots)]
 			simulated.sort(reverse=True)
 			var95 = simulated[int(len(simulated)*0.95)]
 			var99 = simulated[int(len(simulated)*0.99)]