diff --git a/iq.py b/iq.py
index 02337e0..26f3ae7 100644
--- a/iq.py
+++ b/iq.py
@@ -89,7 +89,7 @@ def get_data_needed(iq): #function to gather all the data
         for candle in current:
             useful_frame = pd.DataFrame(list(candle.values()),index = list(candle.keys())).T.drop(columns = ['at'])
             useful_frame = useful_frame.set_index(useful_frame['id']).drop(columns = ['id'])
-            main = main.append(useful_frame)
+            main = pd.concat([main, useful_frame])
             main.drop_duplicates()
         if active == 'EURUSD':
             final_data = main.drop(columns = {'from','to'})
@@ -108,7 +108,7 @@ def fast_data(iq,ratio): #function to gather reduced data for the testing
     for candle in candles:
         useful_frame = pd.DataFrame(list(candle.values()),index = list(candle.keys())).T.drop(columns = ['at'])
         useful_frame = useful_frame.set_index(useful_frame['id']).drop(columns = ['id'])
-        main = main.append(useful_frame)
+        main = pd.concat([main, useful_frame])
     return main
     
 def get_balance(iq):
diff --git a/requirements.txt b/requirements.txt
index eecf703..2a996ee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
-iqoptionapi @ git+git://github.com/Lu-Yi-Hsun/iqoptionapi.git@e96ba2c5b905a139a4765167b08c5df48cf57773
-numpy==1.18.0
-pandas==1.1.1
-tensorflow==2.3.1
-scikit-learn==0.23.2
-grpcio==1.24.3
\ No newline at end of file
+iqoptionapi @ git+https://github.com/Lu-Yi-Hsun/iqoptionapi.git@e96ba2c5b905a139a4765167b08c5df48cf57773
+numpy>=1.23,<2.0
+pandas>=2.0,<3.0
+tensorflow>=2.13,<3.0
+scikit-learn>=1.3,<2.0
diff --git a/testing.py b/testing.py
index 7717240..d91848f 100644
--- a/testing.py
+++ b/testing.py
@@ -122,7 +122,7 @@ def preprocess_prediciton(iq):
 
 
 
-NAME = train_data() + '.model'
+NAME = train_data() + '.keras'
 model = tf.keras.models.load_model(f'models/{NAME}')
 
 iq = login()
@@ -136,7 +136,7 @@ def preprocess_prediciton(iq):
 
 while(1):
     if i >= 10 and i % 2 == 0:
-        NAME = train_data() + '.model'
+        NAME = train_data() + '.keras'
         model = tf.keras.models.load_model(f'models/{NAME}')
         i = 0
     if datetime.datetime.now().second < 30 and i % 2 == 0: #GARANTE QUE ELE VAI APOSTAR NA SEGUNDA, POIS AQUI ELE JÁ PEGA OS DADOS DE UMA NA FRENTE,
diff --git a/training.py b/training.py
index 1a1479a..78219f7 100644
--- a/training.py
+++ b/training.py
@@ -32,7 +32,7 @@ def classify(current,future):
         return 0
 
 def preprocess_df(df):
-    df = df.drop("future", 1) 
+    df = df.drop("future", axis=1)
     
     from sklearn.preprocessing import MinMaxScaler
     scaler = MinMaxScaler()
@@ -204,7 +204,7 @@ def train_data():
     model.add(Dense(2, activation='softmax'))
     
     
-    opt = tf.keras.optimizers.Adam(lr=LEARNING_RATE, decay=5e-5)
+    opt = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
     
     # Compile model
     model.compile(
@@ -217,7 +217,7 @@ def train_data():
     
     
     filepath = "LSTM-best"  # unique file name that will include the epoch and the validation acc for that epoch
-    checkpoint = ModelCheckpoint("models/{}.model".format(filepath), monitor='val_acc', verbose=1, save_best_only=True, mode='max') # saves only the best ones
+    checkpoint = ModelCheckpoint("models/{}.keras".format(filepath), monitor='val_accuracy', verbose=1, save_best_only=True, mode='max') # saves only the best ones
     
     # Train model
     history = model.fit(