diff --git a/__pycache__/functions.cpython-310.pyc b/__pycache__/functions.cpython-310.pyc
new file mode 100644
index 0000000..196fa8e
Binary files /dev/null and b/__pycache__/functions.cpython-310.pyc differ
diff --git a/agent/__pycache__/__init__.cpython-310.pyc b/agent/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000..62b50ce
Binary files /dev/null and b/agent/__pycache__/__init__.cpython-310.pyc differ
diff --git a/agent/__pycache__/agent.cpython-310.pyc b/agent/__pycache__/agent.cpython-310.pyc
new file mode 100644
index 0000000..28bcca5
Binary files /dev/null and b/agent/__pycache__/agent.cpython-310.pyc differ
diff --git a/agent/agent.py b/agent/agent.py
index 07e163b..b8c6364 100644
--- a/agent/agent.py
+++ b/agent/agent.py
@@ -44,7 +44,7 @@ def act(self, state):
 	def expReplay(self, batch_size):
 		mini_batch = []
 		l = len(self.memory)
-		for i in xrange(l - batch_size + 1, l):
+		for i in range(l - batch_size + 1, l):
 			mini_batch.append(self.memory[i])
 
 		for state, action, reward, next_state, done in mini_batch:
diff --git a/evaluate.py b/evaluate.py
index 0055e9d..dc980ae 100644
--- a/evaluate.py
+++ b/evaluate.py
@@ -6,7 +6,7 @@
 import sys
 
 if len(sys.argv) != 3:
-	print "Usage: python evaluate.py [stock] [model]"
+	print ("Usage: python evaluate.py [stock] [model]")
 	exit()
 
 stock_name, model_name = sys.argv[1], sys.argv[2]
@@ -22,7 +22,7 @@
 total_profit = 0
 agent.inventory = []
 
-for t in xrange(l):
+for t in range(l):
 	action = agent.act(state)
 
 	# sit
@@ -31,19 +31,19 @@
 
 	if action == 1: # buy
 		agent.inventory.append(data[t])
-		print "Buy: " + formatPrice(data[t])
+		print ("Buy: " + formatPrice(data[t]))
 
 	elif action == 2 and len(agent.inventory) > 0: # sell
 		bought_price = agent.inventory.pop(0)
 		reward = max(data[t] - bought_price, 0)
 		total_profit += data[t] - bought_price
-		print "Sell: " + formatPrice(data[t]) + " | Profit: " + formatPrice(data[t] - bought_price)
+		print ("Sell: " + formatPrice(data[t]) + " | Profit: " + formatPrice(data[t] - bought_price))
 
 	done = True if t == l - 1 else False
 	agent.memory.append((state, action, reward, next_state, done))
 	state = next_state
 
 	if done:
-		print "--------------------------------"
-		print stock_name + " Total Profit: " + formatPrice(total_profit)
-		print "--------------------------------"
+		print ("--------------------------------")
+		print (stock_name + " Total Profit: " + formatPrice(total_profit))
+		print ("--------------------------------")
diff --git a/functions.py b/functions.py
index 5129e0c..f8d216e 100644
--- a/functions.py
+++ b/functions.py
@@ -24,7 +24,7 @@ def getState(data, t, n):
 	d = t - n + 1
 	block = data[d:t + 1] if d >= 0 else -d * [data[0]] + data[0:t + 1] # pad with t0
 	res = []
-	for i in xrange(n - 1):
+	for i in range(n - 1):
 		res.append(sigmoid(block[i + 1] - block[i]))
 
 	return np.array([res])
diff --git a/train.py b/train.py
index c4ba8ed..bd5775f 100644
--- a/train.py
+++ b/train.py
@@ -3,24 +3,24 @@
 import sys
 
 if len(sys.argv) != 4:
-	print "Usage: python train.py [stock] [window] [episodes]"
+	print ("Usage: python train.py [stock] [window] [episodes]")
 	exit()
 
 stock_name, window_size, episode_count = sys.argv[1], int(sys.argv[2]), int(sys.argv[3])
 
 agent = Agent(window_size)
-data = getStockDataVec(stock_name)
+data = cha(stock_name)
 l = len(data) - 1
 batch_size = 32
 
-for e in xrange(episode_count + 1):
-	print "Episode " + str(e) + "/" + str(episode_count)
+for e in range(episode_count + 1):
+	print ("Episode " + str(e) + "/" + str(episode_count))
 	state = getState(data, 0, window_size + 1)
 
 	total_profit = 0
 	agent.inventory = []
 
-	for t in xrange(l):
+	for t in range(l):
 		action = agent.act(state)
 
 		# sit
@@ -29,22 +29,22 @@
 
 		if action == 1: # buy
 			agent.inventory.append(data[t])
-			print "Buy: " + formatPrice(data[t])
+			print ("Buy: " + formatPrice(data[t]))
 
 		elif action == 2 and len(agent.inventory) > 0: # sell
 			bought_price = agent.inventory.pop(0)
 			reward = max(data[t] - bought_price, 0)
 			total_profit += data[t] - bought_price
-			print "Sell: " + formatPrice(data[t]) + " | Profit: " + formatPrice(data[t] - bought_price)
+			print ("Sell: " + formatPrice(data[t]) + " | Profit: " + formatPrice(data[t] - bought_price))
 
 		done = True if t == l - 1 else False
 		agent.memory.append((state, action, reward, next_state, done))
 		state = next_state
 
 		if done:
-			print "--------------------------------"
-			print "Total Profit: " + formatPrice(total_profit)
-			print "--------------------------------"
+			print ("--------------------------------")
+			print ("Total Profit: " + formatPrice(total_profit))
+			print ("--------------------------------")
 
 		if len(agent.memory) > batch_size:
 			agent.expReplay(batch_size)