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Restore compatibility with modern Python, pandas, and TensorFlow #43

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Restore compatibility with modern Python, pandas, and TensorFlow #43

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74c63ec
fix: restore compatibility with modern Python/pandas/TensorFlow
victalejo May 20, 2026
8f41d64
fix: use .keras checkpoint extension for Keras 3 compatibility
victalejo May 20, 2026
d587065
Merge pull request #1 from victalejo/fix/modernize-deps
victalejo May 20, 2026
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4 changes: 2 additions & 2 deletions iq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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'})
Expand All @@ -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):
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11 changes: 5 additions & 6 deletions requirements.txt
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Original file line number Diff line number Diff line change
@@ -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
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
4 changes: 2 additions & 2 deletions testing.py
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Expand Up @@ -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()
Expand All @@ -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,
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6 changes: 3 additions & 3 deletions training.py
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Expand Up @@ -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()
Expand Down Expand Up @@ -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(
Expand All @@ -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(
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