#!/usr/bin/env python
# coding: utf-8

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# install python-poloniex
get_ipython().system('pip install https://github.com/s4w3d0ff/python-poloniex/archive/v0.4.6.zip')


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import poloniex


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import time

# prep poloniex API
polo = poloniex.Poloniex()

# 5min(300 seconds)100days
chart_data = polo.returnChartData('BTC_ETH', period=300, start=time.time()-polo.DAY*100, end=time.time())
#poloniex source code which explain how interval data we can get
#https://github.com/s4w3d0ff/python-poloniex


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# import pandas
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

price_data = pd.DataFrame([float(i.get('open')) for i in chart_data])
mss = MinMaxScaler()
input_dataframe = pd.DataFrame(mss.fit_transform(price_data))


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#devide into training data and validation data

import numpy as np

def _load_data(data, n_prev=50):
    docX, docY = [], []
    for i in range(len(data)-n_prev):
        docX.append(data.iloc[i:i+n_prev].as_matrix())
        docY.append(data.iloc[i+n_prev].as_matrix())
    alsX = np.array(docX)
    alsY = np.array(docY)
    return alsX, alsY

#trainning data is 70%. Validationdata is 30%
def train_test_split(df, test_size=0.3, n_prev=50):
    ntrn = round(len(df) * (1 - test_size))
    ntrn = int(ntrn)
    X_train, y_train = _load_data(df.iloc[0:ntrn], n_prev)
    X_test, y_test = _load_data(df.iloc[ntrn:], n_prev)
    return (X_train, y_train), (X_test, y_test)

(X_train, y_train), (X_test, y_test) = train_test_split(input_dataframe)


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get_ipython().system('pip install keras')


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from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM

in_out_neurons = 1
hidden_neurons = 300
length_of_sequences = 50

model = Sequential()
model.add(LSTM(hidden_neurons, batch_input_shape=(None, length_of_sequences, in_out_neurons), return_sequences=False))
model.add(Dense(in_out_neurons))
model.add(Activation("linear"))
model.compile(loss="mean_squared_error", optimizer="adam",)


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from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM

in_out_neurons = 1
hidden_neurons = 300
length_of_sequences = 50

model = Sequential()
model.add(LSTM(hidden_neurons, batch_input_shape=(None, length_of_sequences, in_out_neurons), return_sequences=False))
model.add(Dense(in_out_neurons))
model.add(Activation("linear"))
model.compile(loss="mean_squared_error", optimizer="adam",)


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from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', mode='auto', patience=0)
history = model.fit(X_train, y_train, batch_size=600, epochs=10, validation_split=0.1, callbacks=[early_stopping])


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get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib import pyplot as plt

pred_data = model.predict(X_train)
plt.plot(y_train, label='train')
plt.plot(pred_data, label='pred')
plt.legend(loc='upper left')
plt.show()


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pred_data = model.predict(X_test)
plt.plot(y_test, label='test')
plt.plot(pred_data, label='pred')
plt.legend(loc='upper left')
plt.show()


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pred_data = model.predict(X_test)
plt.plot(y_test, label='test')
plt.plot(pred_data, label='pred')
plt.legend(loc='upper left')
plt.xlim(7800, 8100)
plt.show()


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