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import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split

# Set the seed for reproducibility
np.random.seed(0)

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from IPython.core.debugger import Pdb; Pdb().set_trace()

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# https://pytorch.org/docs/stable/generated/torch.from_numpy.html
import torch
import numpy as np

x = np.array([1,2,3])

print(x)

x = torch.from_numpy(x)

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import numpy as np
import pandas as pd
from oandapyV20 import API
import oandapyV20.endpoints.instruments as instruments
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error