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import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional
from keras.optimizers import SGD
import math
from sklearn.metrics import mean_squared_error

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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

from subprocess import check_output
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.cross_validation import  train_test_split
import time #helper libraries
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from numpy import newaxis

# importing libraries required for our model
from keras.models import Sequential
from keras.layers import LSTM,Dense,Dropout

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import datetime
import math, time
import itertools
from sklearn import preprocessing
import datetime
from operator import itemgetter
from sklearn.metrics import mean_squared_error
from math import sqrt
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM
from keras.models import load_model
import keras
import h5py
import requests
import os

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import os, warnings, random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import tensorflow as tf
import tensorflow.keras.layers as L
from tensorflow.keras import optimizers, Sequential, Model

13

import pandas as pd
import numpy as np
np.random.seed(10)

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

from keras.models import Sequential
from keras.layers import Dense, LSTM

2

import pandas as pd
import pyarrow.parquet as pq # Used to read the data
import os 
import numpy as np
from keras.layers import * # Keras is the most friendly Neural Network library, this Kernel use a lot of layers classes
from keras.models import Model
from tqdm import tqdm # Processing time measurement
from sklearn.model_selection import train_test_split 
from keras import backend as K # The backend give us access to tensorflow operations and allow us to create the Attention class
from keras import optimizers # Allow us to access the Adam class to modify some parameters
from sklearn.model_selection import GridSearchCV, StratifiedKFold # Used to use Kfold to train our model
from keras.callbacks import * # This object helps the model to train in a smarter way, avoiding overfitting

3

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
%matplotlib inline

# For reading stock data from yahoo
from pandas_datareader.data import DataReader

# For time stamps
from datetime import datetime

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from keras.models import Sequential
from keras.layers import Dense, LSTM

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import numpy as np
import pandas as pd
import math
import sklearn
import sklearn.preprocessing
import datetime
import os
import matplotlib.pyplot as plt
import tensorflow as tf 

6

##########################Load Libraries  ####################################
import pandas as pd
import numpy as np
import dask.dataframe as dd
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import preprocessing, metrics
from ipywidgets import widgets, interactive
import gc
import joblib
import warnings
warnings.filterwarnings('ignore')
from datetime import datetime, timedelta 
from typing import Union
from tqdm.notebook import tqdm_notebook as tqdm
from itertools import cycle
import datetime as dt
from torch.autograd import Variable
import random 
import os
from matplotlib.pyplot import figure
from fastprogress import master_bar, progress_bar
import torch
import torch.nn as nn
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import time 
from torch.utils.data import Dataset
from sklearn.metrics import mean_squared_error
import torch 

%matplotlib inline

#from gensim.models import Word2Vec
#import gensim.downloader as api

pd.set_option('max_columns', 50)
plt.style.use('bmh')
color_pal = plt.rcParams['axes.prop_cycle'].by_key()['color']
color_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])

 

 

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# Let`s import all packages that we may need:

import sys 
import numpy as np # linear algebra
from scipy.stats import randint
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL
import matplotlib.pyplot as plt # this is used for the plot the graph 
import seaborn as sns # used for plot interactive graph. 
from sklearn.model_selection import train_test_split # to split the data into two parts
from sklearn.cross_validation import KFold # use for cross validation
from sklearn.preprocessing import StandardScaler # for normalization
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline # pipeline making
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import SelectFromModel
from sklearn import metrics # for the check the error and accuracy of the model
from sklearn.metrics import mean_squared_error,r2_score

## for Deep-learing:
import keras
from keras.layers import Dense
from keras.models import Sequential
from keras.utils import to_categorical
from keras.optimizers import SGD 
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
import itertools
from keras.layers import LSTM
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers import Dropout

8

import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras import optimizers
from keras.utils import plot_model
from keras.models import Sequential, Model
from keras.layers.convolutional import Conv1D, MaxPooling1D
from keras.layers import Dense, LSTM, RepeatVector, TimeDistributed, Flatten
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot

%matplotlib inline
warnings.filterwarnings("ignore")
init_notebook_mode(connected=True)

# Set seeds to make the experiment more reproducible.
from tensorflow import set_random_seed
from numpy.random import seed
set_random_seed(1)
seed(1)

9

import matplotlib.pyplot as plt
import statsmodels.tsa.seasonal as smt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import datetime as dt
from sklearn import linear_model 
from sklearn.metrics import mean_absolute_error
import plotly

# import the relevant Keras modules
from keras.models import Sequential
from keras.layers import Activation, Dense
from keras.layers import LSTM
from keras.layers import Dropout

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
import os
os.chdir('../input/Data/Stocks/')