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ctbot_neural.py
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ctbot_neural.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import time
import sys
import argparse
import datetime
import numpy as np
import plotly as py
import plotly.graph_objs as go
# Local files
# poloniex API and API keys for account
# helper_functions random functions moved for readability
# math all the math functions
from poloniex import poloniex
from ctbot_helpers import init_variables, send_email
from ctbot_math import *
from ctbot_models import *
from keys import adrlar_key, adrlar_secret
from sklearn.preprocessing import MinMaxScaler
def nonlin(x,deriv=False):
if(deriv==True):
return x*(1-x)
return 1/(1+np.exp(-x))
polo = poloniex(adrlar_key, adrlar_secret)
tmp = polo.api_query("returnChartData",{"currencyPair":"USDT_BTC","start":1500542000,"end":1506812400,"period":14400})["candleStick"]
X_ini = []
y_ini = []
ini_i = 0
X_maxes = {}
for i in xrange(len(tmp)-1):
if tmp[i]['close'] < tmp[i+1]['close']:
y_ini.append([1])
else:
y_ini.append([0])
X_ini.append([])
for key in sorted(tmp[i].keys()):
if key not in ['date']:
X_ini[i].append(tmp[i][key])
X = np.array(X_ini)
y = np.array(y_ini)
np.random.seed(12)
# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((len(X_ini[0]),len(X_ini))) - 1
syn1 = 2*np.random.random((len(y_ini),len(y_ini[0]))) - 1
for j in xrange(6):
# Feed forward through layers 0, 1, and 2
l0 = X
l1 = nonlin(np.dot(l0,syn0))
l2 = nonlin(np.dot(l1,syn1))
# how much did we miss the target value?
l2_error = y - l2
if (j% 1) == 0:
print l1
print "Error:" + str(np.mean(np.abs(l2_error)))
# in what direction is the target value?
# were we really sure? if so, don't change too much.
l2_delta = l2_error*nonlin(l2,deriv=True)
# how much did each l1 value contribute to the l2 error (according to the weights)?
l1_error = l2_delta.dot(syn1.T)
# in what direction is the target l1?
# were we really sure? if so, don't change too much.
l1_delta = l1_error * nonlin(l1,deriv=True)
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)