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server.py
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server.py
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from __future__ import print_function
from flask import Flask, render_template,request
import sys
from os import environ
import json
import numpy as np
import os
import pandas as pd
import urllib.request as urllib2
import socket
from datetime import datetime
import calendar
from keras import applications
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dropout, Flatten, Dense, Activation, Reshape, LeakyReLU
from keras.callbacks import CSVLogger
import tensorflow as tf
import random
from keras.layers import LSTM , GRU
from keras.layers import Conv1D, MaxPooling1D
from keras import backend as K
import keras
from keras.models import load_model
from keras.callbacks import CSVLogger, ModelCheckpoint
from keras.backend.tensorflow_backend import set_session
from keras import optimizers
import h5py
from sklearn.preprocessing import MinMaxScaler
from flask import jsonify
from json import encoder
import datetime
import plotly.offline as py
import plotly.graph_objs as go
import plotly.figure_factory as ff
import plotly
import gc
#app = Flask(__name__)
app = Flask(__name__, static_url_path='/static')
app.config['SECRET_KEY'] = 'I am a cryptocurrency predictor!'
model = load_model('models/LSTM_model.h5')
modelgru = load_model('models/GRU_model.h5')
pred_df = pd.DataFrame()
pred_df_past = pd.DataFrame()
pred_df_pastgru = pd.DataFrame()
buy_df_past = pd.DataFrame()
sell_df_past = pd.DataFrame()
fig = 0
actual = pd.DataFrame()
graphJSON = 0
counter = 0
check_empty_buy = 0
check_empty_sell = 0
# if (len(sys.argv)!= 2):
# sys.exit("Please provide an amount of dollars to start!")
# money = int(sys.argv[1])
money = float(input("Please provide an amount of dollars to start:\t"))
start_money = money
coins = 0
transactions = 0
def get_data():
d = datetime.datetime.utcnow()
unixtime = calendar.timegm(d.utctimetuple())
unixtime = unixtime /100 *100
past_unixtime = unixtime- 300*30
url = 'https://poloniex.com/public?command=returnChartData¤cyPair=USDT_ETH&start='+str(past_unixtime)+'&end=9999999999&period=300'
openUrl = urllib2.urlopen(url)
r = openUrl.read()
openUrl.close()
d = json.loads(r.decode())
df = pd.DataFrame(d)
original_columns=[u'close', u'date', u'high', u'low', u'open',u'volume',u'weightedAverage']
new_columns = ['Close','Timestamp','High','Low','Open','Volume','Weighted_Average']
df = df.loc[:,original_columns]
df.columns = new_columns
df = df.iloc[-6:,:]
datatimes = df.Timestamp
wa = df.Weighted_Average
datas1 = np.array(df.Close)
datas2 = np.array(df.Weighted_Average)
datas = np.column_stack((datas1,datas2))
datas.shape
return datas ,datatimes ,wa
def save_results_to_csv(actual, output, outputgru, buy_df, sell_df):
os.makedirs("data", exist_ok=True) #create folder data
actual_plotdata = pd.DataFrame(columns = ["Time", "Price"])
actual_plotdata.Time = actual['times']
#actual_plotdata.Timestamp = datetime.strptime(actual['times'], '%m/%d/%Y %H:%M:%S %p')
#actual_plotdata.Timestamp = int(datetime.datetime.timestamp(actual_plotdata.Time.values.astype('datetime64',copy=False)))
actual_plotdata.Price = actual['price']
actual_plotdata.to_csv("data/actual-plotdata.csv", index = False)
lstm_prediction_plotdata = pd.DataFrame(columns = ["Time", "Price"])
lstm_prediction_plotdata.Time = output['times']
#lstm_prediction_plotdata.Timestamp = datetime.strptime(output['times'], '%m/%d/%Y %H:%M:%S %p')
#lstm_prediction_plotdata.Timestamp = int(datetime.datetime.timestamp(lstm_prediction_plotdata.Time.values.astype('datetime64',copy=False)))
lstm_prediction_plotdata.Price = output['prediction']
lstm_prediction_plotdata.to_csv("data/lstm-prediction-plotdata.csv", index = False)
gru_prediction_plotdata = pd.DataFrame(columns = ["Time", "Price"])
gru_prediction_plotdata.Time = outputgru['times']
#gru_prediction_plotdata.Timestamp = datetime.strptime(outputgru['times'], '%m/%d/%Y %H:%M:%S %p')
#gru_prediction_plotdata.Timestamp = int(datetime.datetime.timestamp(gru_prediction_plotdata.Time.values.astype('datetime64',copy=False)))
gru_prediction_plotdata.Price = outputgru['prediction']
gru_prediction_plotdata.to_csv("data/gru-prediction-plotdata.csv", index = False)
buy_signal_plotdata = pd.DataFrame(columns = ["Time", "Price"])
buy_signal_plotdata.Time = buy_df['times']
#buy_signal_plotdata.Timestamp = datetime.strptime(buy_df['times'], '%m/%d/%Y %H:%M:%S %p')
#buy_signal_plotdata.Timestamp = int(datetime.datetime.timestamp(buy_signal_plotdata.Time.values.astype('datetime64',copy=False)))
buy_signal_plotdata.Price = buy_df['price']
buy_signal_plotdata.to_csv("data/buy-signals-plotdata.csv", index = False)
sell_signal_plotdata = pd.DataFrame(columns = ["Time", "Price"])
sell_signal_plotdata.Time = sell_df['times']
#sell_signal_plotdata.Timestamp = datetime.strptime(sell_df['times'], '%m/%d/%Y %H:%M:%S %p')
#sell_signal_plotdata.Timestamp = int(datetime.datetime.timestamp(sell_signal_plotdata.Time.values.astype('datetime64',copy=False)))
sell_signal_plotdata.Price = sell_df['price']
sell_signal_plotdata.to_csv("data/sell-signals-plotdata.csv", index = False)
@app.route('/plot')
def plot():
datas ,datatimes, wa = get_data()
global pred_df_past
global pred_df_pastgru
global buy_df_past, sell_df_past
global fig
global actual
global graphJSON
global check_empty_buy, check_empty_sell
global money, coins, transactions, start_money
temp_actual = pd.DataFrame()
temp_actual['price'] = datas[:,0] #keep the close actual price
temp_actual['times'] = pd.to_datetime(datatimes.values,unit='s') #keep the timestamps
temp_actual['WA'] = wa.values #keep the weighted average
actual = actual.append(temp_actual,ignore_index=True)
actual = actual.drop_duplicates(subset='times',keep='last')
with h5py.File(''.join(['ethereum_data.h5']), 'r') as hf:
original_datas = hf['original_datas'].value
scaler = MinMaxScaler()
scaler.fit(original_datas[:,0].reshape(-1,1))
datas = scaler.transform(datas)
datas = datas[None,:,:]
step_size = datas.shape[1]
batch_size= 843
nb_features = datas.shape[2]
epochs = 1
output_size=1
units= 50
second_units=30
predicted = model.predict(datas)
predictedgru = modelgru.predict(datas)
gc.collect()
####LSTM Prediction####
predicted_inverted = scaler.inverse_transform(predicted)
output={}
output['prediction'] = list(predicted_inverted.reshape(-1))
datatimes=np.array(datatimes)
####GRU Prediction####
predicted_invertedgru = scaler.inverse_transform(predictedgru)
outputgru={}
outputgru['prediction'] = list(predicted_invertedgru.reshape(-1))
outputtimes = []
times = datatimes[-1]
for i in range(output_size) :
if (i == 0):
outputtimes.append(times + 300)
else:
temp = outputtimes[i-1] + 300
outputtimes.append(temp)
output = pd.DataFrame(output)
output['times'] = list(outputtimes)
output.times = pd.to_datetime(output.times,unit='s')
####GRU### output times
outputgru = pd.DataFrame(outputgru)
outputgru['times'] = output.times
actual.times = pd.to_datetime(actual.times,unit='s')
print ('done', file = sys.stderr)
####LSTM past prediction to append####
pred_df_past = pred_df_past.append(output)
pred_df_past = pred_df_past.drop_duplicates(subset='times',keep='last')
output = pred_df_past
####GRU past prediction to append####
pred_df_pastgru = pred_df_pastgru.append(outputgru)
pred_df_pastgru = pred_df_pastgru.drop_duplicates(subset='times',keep='last')
outputgru = pred_df_pastgru
actualarr = np.array(actual['price'])
buy_df = pd.DataFrame(columns = ['times', 'price'])
sell_df = pd.DataFrame(columns = ['times', 'price'])
if ((actualarr[-1] <= predicted_inverted) & (money > 0) ):
buy_price = actualarr[-1].reshape(-1)
buy_time = actual.times.iloc[-1]
coins = coins + money/buy_price
money = 0 #spend all your money
transactions = transactions + 1
print ("BUY:\t",buy_price, buy_time)
buy_df={}
buy_df['price'] = list(buy_price)
buy_df = pd.DataFrame(buy_df)
buy_df['times'] = buy_time
if ((actualarr[-1] > predicted_inverted) & (coins > 0 )):
sell_price = actualarr[-1].reshape(-1)
sell_time = actual.times.iloc[-1]
money = money + coins*sell_price
coins = 0 #sell all your coins
transactions = transactions + 1
print("SELL:\t",sell_price, sell_time)
sell_df={}
sell_df['price'] = list(sell_price)
sell_df = pd.DataFrame(sell_df)
sell_df['times'] = sell_time
end_money = money + coins*(actualarr[-1].reshape(-1))
profit = end_money - start_money
print("START MONEY:\t", start_money,"\n")
print("END MONEY:\t", end_money,"\n")
print("COINS:\t", coins,"\n")
print("PROFIT:\t", profit,"\n")
print("TOTAL TRANSACTIONS:\t", transactions,"\n")
buy_df_past = buy_df_past.append(buy_df)
buy_df_past = buy_df_past.drop_duplicates(subset='times',keep='last')
buy_df = buy_df_past
sell_df_past = sell_df_past.append(sell_df)
sell_df_past = sell_df_past.drop_duplicates(subset='times',keep='last')
sell_df = sell_df_past
actual_chart = go.Scatter(x = actual['times'], y = actual['price'], name= 'Actual Price')
lstm_predict_chart = go.Scatter(x = output['times'], y = output['prediction'], name= 'LSTM Prediction Price',mode='lines+markers')
gru_predict_chart = go.Scatter(x = outputgru['times'], y = outputgru['prediction'], name= 'GRU Prediction Price',mode='lines+markers')
buy_chart = go.Scatter(x=buy_df['times'], y = buy_df['price'], name= 'BUY SIGNAL', mode = 'markers')
sell_chart = go.Scatter(x=sell_df['times'], y = sell_df['price'], name= 'SELL SIGNAL', mode = 'markers')
layout = go.Layout(
title='Ethereum Real time Prediction',
xaxis=dict(
title='Time(UTC)',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='ETH(USD)',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
data = [actual_chart,lstm_predict_chart,gru_predict_chart, buy_chart, sell_chart]
save_results_to_csv(actual, output, outputgru, buy_df, sell_df)
fig = go.Figure(data=data, layout=layout)
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
#@app.route('/predictor')
@app.route('/')
def api_predict():
print ('start', file = sys.stderr)
global pred_df
global pred_df_past
global pred_df_pastgru
global buy_df_past, sell_df_past
global fig
global actual
global graphJSON
global counter
global check_empty_buy, check_empty_sell
if (counter == 0):
graphJSON = plot() #call the plot function once
counter = 1
return render_template('index.html', graphJSON=graphJSON)
#@app.route('/')
def index():
website = api_predict()
return website
# app.run(port="8080")
if __name__ == '__main__':
# HOST = environ.get('HOST', 'localhost')
HOST=socket.gethostname()
try:
PORT = int(environ.get('PORT','5555'))
except ValueError:
PORT = 5555
app.run('0.0.0.0', PORT, threaded=False)