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regressao-linear-v0.3-3-PT.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Felipe Soares
"""
import requests
import json
import random
import talib
import pandas as pd
import datetime as dt
from matplotlib import pyplot as plt
import urllib, os
from tqdm import tqdm
from time import sleep
import pyfiglet
ascii_banner = pyfiglet.figlet_format("DataCrypto Analytics")
print(ascii_banner)
#########################################################################################
#========= Esolhe a Criptomoedas e valores de negociação
########################################################################################
criptomoeda = input(' | Algoritmo: Regressão Linear '
'\n | Versão: v0.3-3-PT '
'\n\n | Twitter @DataCryptoML '
'\n | Github @datacrypto-analytics '
'\n | https://datacryptoanalytics.com/ '
'\n \nDigite o par de criptomoedas listada na Binance: ')
print('O par de criptomoeda informada foi: %s' %(criptomoeda))
#######################################################################################
#======== Cria URL da API Binance
#######################################################################################
root_url = 'https://api.binance.com/api/v1/klines'
symbol = criptomoeda
interval = input('Digite o Timeframe (Exemplo: 15m, 30m, 1h, 1d, 1M): ')
url = root_url + '?symbol=' + symbol + '&interval=' + interval
response = getattr(urllib, 'request', urllib).urlopen(url)
with tqdm.wrapattr(open(os.devnull, "wb"), "write",
miniters=1, desc="DataCrypto Analytics recuperando dados",
total=getattr(response, 'length', None)) as fout:
for chunk in response:
fout.write(chunk)
#print(url)
#######################################################################################
#======== Monta URL e o Gráfico e DataFrame
#######################################################################################
def get_bars(symbol, interval = interval):
url = root_url + '?symbol=' + symbol + '&interval=' + interval
data = json.loads(requests.get(url).text)
df = pd.DataFrame(data)
df.columns = ['open_time',
'o', 'h', 'l', 'c', 'v',
'close_time', 'qav', 'num_trades',
'taker_base_vol', 'taker_quote_vol', 'ignore']
df.index = [dt.datetime.fromtimestamp(x/1000.0) for x in df.close_time]
return df
#######################################################################################
#======== Organizando Variaveis
#######################################################################################
criptomoeda_df = get_bars(criptomoeda)
criptomoeda_fechamento = criptomoeda_df['c'].astype('float').values
criptomoeda_close = criptomoeda_df['c'].astype('float')
criptomoeda_abertura = criptomoeda_df['o'].astype('float').values
criptomoeda_num_trades = criptomoeda_df['num_trades'].astype('float')
criptomoeda_maxima = criptomoeda_df['h'].astype('float')
criptomoeda_minima = criptomoeda_df['l'].astype('float')
criptomoeda_volume = criptomoeda_df['v'].astype('float')
criptomoeda_datas_fechamento = criptomoeda_df['close_time'].astype('float')
criptomoeda_datas_abertura = criptomoeda_df['open_time'].astype('float')
taker_base_vol = criptomoeda_df['taker_base_vol'].astype('float')
taker_quote_vol = criptomoeda_df['taker_quote_vol'].astype('float')
closeprice = criptomoeda_close.iloc[-1]
cci = talib.CCI(criptomoeda_maxima, criptomoeda_minima, criptomoeda_close, timeperiod=14)
atr = talib.ATR(criptomoeda_maxima, criptomoeda_minima, criptomoeda_close, timeperiod=14)
midpoint = talib.MIDPOINT(criptomoeda_close, timeperiod=30)
sma6 = talib.SMA(criptomoeda_close, timeperiod=6)
sma9 = talib.SMA(criptomoeda_close, timeperiod=9)
real = talib.T3(criptomoeda_close, timeperiod=14)
print('________________________________________________________________________________')
print('\n Preço de Fechamento: $%.2f'%(closeprice))
"""
#print('\n \n DataCrypto Analytics buscando dados...'
# '\n Aguarde alguns segundos, algoritmo treinando e fazendo previsões!')
for i in tqdm(range(0, 100), desc =" Algoritmo treinando com dados recuperados"):
sleep(.2)
for i in tqdm(range(0, 100), desc =" Aguarde mais alguns segundos. Algoritmo fazendo previsões"):
sleep(.5)
"""
#######################################################################################
# Média movel de 14 dias do Fechamento
#######################################################################################
criptomoeda_fechamento_mediamovel = criptomoeda_df['c'].rolling(30).mean()
#######################################################################################
# Média movel de 30 dias do Fechamento
#######################################################################################
criptomoeda_fechamento_mediamovel100 = criptomoeda_df['c'].rolling(100).mean()
#######################################################################################
#======== Importar biblioteca SKLEARN
#######################################################################################
import numpy as np
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
from matplotlib import rcParams
#=========
criptomoeda_regressao = criptomoeda_abertura
#========= Treinar X
criptomoeda_X_train = criptomoeda_abertura
criptomoeda_X_test = criptomoeda_abertura
X_train = np.reshape(criptomoeda_X_train, (-1,1))
X_test = np.reshape(criptomoeda_X_test, (-1,1))
#========== Treinar Y
criptomoeda_y_train = criptomoeda_fechamento
criptomoeda_y_test = criptomoeda_fechamento
y_train = np.reshape(criptomoeda_y_train, (-1,1))
y_test = np.reshape(criptomoeda_y_test, (-1,1))
#######################################################################################
#========= Criar modelo de regressão linear
#######################################################################################
regr = linear_model.LinearRegression(fit_intercept=True, normalize=True, copy_X=True, n_jobs=15, positive=False)
#######################################################################################
#========= Treine o modelo usando os conjuntos de treinamento
#######################################################################################
regr.fit(X_train, y_train)
#######################################################################################
#======== Faça previsões usando o conjunto de teste
#######################################################################################
criptomoeda_y_pred = regr.predict(X_test)
for i in tqdm((criptomoeda_y_pred), desc =" DataCrypto Analytics criando previsões"):
sleep(.02)
#######################################################################################
#======== The coefficients
#######################################################################################
print('________________________________________________________________________________')
print('________________________________Resultados______________________________________')
print('\n Coefficients: ', regr.coef_)
print('________________________________________________________________________________')
#######################################################################################
#======== The mean squared error
#######################################################################################
print("\n Mean squared error (MSE): %.2f"
% mean_squared_error(y_test, criptomoeda_y_pred))
print('________________________________________________________________________________')
#######################################################################################
# ======== Explained variance score: 1 is perfect prediction
#######################################################################################
print('\n Score de variância (próximo de 1.0 bom > ruim): %.2f'
% r2_score(y_test, criptomoeda_y_pred))
print('________________________________________________________________________________')
print('\n Média previsões:', criptomoeda_y_pred.mean())
print('________________________________________________________________________________')
media = criptomoeda_y_pred.mean()
#######################################################################################
#============ Criar Gráfico
#######################################################################################
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = (10,6)
plt.rcParams['font.family'] = 'serif'
plt.subplot(2, 1, 1)
plt.plot(criptomoeda_close, '-', color="black", linewidth=1)
plt.plot(criptomoeda_fechamento_mediamovel, '-', color="red", linewidth=1)
plt.plot(criptomoeda_fechamento_mediamovel100, '-', color="black", linewidth=1)
plt.plot(midpoint, '--', color="red", linewidth=1, alpha=0.4)
#plt.text(10000, 3000, "@DataCryptoML", family="serif")
plt.legend(['Close $%.2f'%(closeprice), 'MA30', 'MA100', 'Midpoint'], loc=0)
plt.title('DataCrypto Analytics (@DataCryptoML) - %s' %(criptomoeda))
#plt.xlabel('DataCrypto Analytics (@DataCryptoML)', labelpad=1)
plt.ylabel('Price')
plt.subplot(2, 1, 2)
plt.scatter(X_test, y_test, color="black", linewidth=2, alpha=0.5, s=.8)
plt.scatter(media, media,color="red", linewidth=3, alpha=0.6)
plt.plot(X_test, criptomoeda_y_pred, color='blue', linewidth=1)
plt.legend(['Linear Regression', 'Close', 'Prediction Price $%.2f'%(media)], loc=0)
plt.xlabel('GitHub: @datacrypto-analytics', fontsize=9)
plt.ylabel('Linear Regression')
plt.show()
#######################################################################################
#------ Salvando o modelo -----------------
#######################################################################################
# importa o pickle
import pickle
# Define o nome do arquivo em disco que irá
# guardar o nosso modelo
filename='regressor_model.sav'
# salva o modelo no disco
pickle.dump(regr, open(filename, 'wb'))
# Carregando o modelo do disco
loaded_model = pickle.load(open(filename, 'rb'))
# Atribui a variável result o score do modelo
result = loaded_model.score(X_test, y_test)
#Imprime o resultado
print('\n Score do modelo salvo:', result)
print('________________________________________________________________________________')