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import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout
from tensorflow.keras.optimizers import Adam
def carregar_e_preparar_dados(caminho_csv):
df = pd.read_csv(caminho_csv, parse_dates=["Date"], index_col="Date")
df = df[["Open", "High", "Low", "Close", "Volume"]]
scaler = MinMaxScaler()
dados_escalados = scaler.fit_transform(df)
X, y = [], []
for i in range(len(dados_escalados) - 60):
X.append(dados_escalados[i:i+60])
y.append(dados_escalados[i+60][3])
return np.array(X), np.array(y), scaler
def criar_modelo_cnn(input_shape):
model = Sequential([
Conv1D(64, 3, activation='relu', input_shape=input_shape),
MaxPooling1D(2),
Conv1D(128, 3, activation='relu'),
MaxPooling1D(2),
Flatten(),
Dense(64, activation='relu'),
Dropout(0.2),
Dense(1)
])
model.compile(optimizer=Adam(0.001), loss='mean_squared_error')
return model
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