import pandas as pd
import time
import seaborn as sns
import matplotlib.pyplot as plt
import datetime
import numpy as np

# get market info for bitcoin from the start of 2016 to the current day
bitcoin_market_info = pd.read_html("https://coinmarketcap.com/currencies/bitcoin/historical-data/?start=20130428&end="+time.strftime("%Y%m%d"))[0]
# convert the date string to the correct date format
bitcoin_market_info = bitcoin_market_info.assign(Date=pd.to_datetime(bitcoin_market_info['Date']))
# when Volume is equal to '-' convert it to 0
bitcoin_market_info.loc[bitcoin_market_info['Volume']=="-",'Volume']=0
# convert to int
bitcoin_market_info['Volume'] = bitcoin_market_info['Volume'].astype('int64')
# look at the first few rows
bitcoin_market_info.head()
from keras.models import Sequential
from keras.layers import Activation, Dense
from keras.layers import LSTM
from keras.layers import Dropout

def build_model(inputs, output_size, neurons, activ_func = "linear",
                dropout =0.25, loss="mae", optimizer="adam"):
    model = Sequential()

    model.add(LSTM(neurons, input_shape=(inputs.shape[1], inputs.shape[2])))
    model.add(Dropout(dropout))

    model.add(Dense(units=output_size))
    model.add(Activation(activ_func))

    model.compile(loss=loss, optimizer=optimizer)
    return model
# random seed for reproducibility
np.random.seed(202)
# initialise model architecture
eth_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)
# model output is next price normalised to 10th previous closing price
LSTM_training_outputs = (training_set['eth_Close'][window_len:].values/training_set['eth_Close'][:-window_len].values)-1
# train model on data
# note: eth_history contains information on the training error per epoch
eth_history = eth_model.fit(LSTM_training_inputs, LSTM_training_outputs, 
                            epochs=50, batch_size=1, verbose=2, shuffle=True)
#eth_preds = np.loadtxt('eth_preds.txt')