import pandas as pd
import numpy as np
import MAIN.Basics as basics
import MAIN.Reinforcement as RL
import tensorflow as tf
import seaborn as sns
import matplotlib.pyplot as plt
from UTIL import FileIO
from STRATEGY.Cointegration import EGCointegration
# Read config
config_path = 'CONFIG\config_train.yml'
config_train = FileIO.read_yaml(config_path)
# Read prices
x = pd.read_csv('STATICS\PRICE\JNJ.csv')
y = pd.read_csv('STATICS\PRICE\PG.csv')
x, y = EGCointegration.clean_data(x, y, 'date', 'close')
# Separate training and testing sets
train_pct = 0.7
train_len = round(len(x) * 0.7)
idx_train = list(range(0, train_len))
idx_test = list(range(train_len, len(x)))
EG_Train = EGCointegration(x.iloc[idx_train, :], y.iloc[idx_train, :], 'date', 'close')
EG_Test = EGCointegration(x.iloc[idx_test, :], y.iloc[idx_test, :], 'date', 'close')
# Create action space
n_hist = list(np.arange(60, 601, 60))
n_forward = list(np.arange(120, 1201, 120))
from oandapyV20.exceptions import V20Error
import oandapyV20.endpoints.accounts as accounts
import oandapyV20.endpoints.instruments as instruments
import oandapyV20.endpoints.orders as orders
import oandapyV20.endpoints.positions as positions
import oandapyV20.endpoints.trades as trades
# import telebot
# Bot Parameters
spread_limit = 3.5
Multi_Threading = True
Tgr_Verbose = True
# Load Settings
config_path = 'CONFIG\config_trading.yml'
config_trading = FileIO.read_yaml(config_path)
instrument_1 = config_trading['Instrument_A']
instrument_2 = config_trading['Instrument_B']
# OANDA Config
accountID = config_trading['DataOandaAccount']
access_token = config_trading['DataOandaToken']
# Telegram Config
TOKEN = config_trading['DataTgrChatID']
chatid = config_trading['DataTgrToken']
# tb = telebot.TeleBot(TOKEN)
# Do Not Touch
pairs_traded = '%s,%s' % (str(instrument_1), str(instrument_2))
pairs_traded_dict = {instrument_1, instrument_2}
import time
from UTIL import FileIO
from DATA import API
config_path = 'CONFIG\config_data.yml'
config = FileIO.read_yaml(config_path)
attr = ['date', 'close']
ticker_list = ['EURUSD', 'GBPUSD']
api = API.Tiingo(config)
start = time.time()
price_1 = api.fetch(ticker_list, attr)
end = time.time()
print('Normal processing time: {time}s.'.format(time=end - start))
config['DataAPIFetchMethod'] = 'async'
start = time.time()
price_2 = api.fetch(ticker_list, attr)
end = time.time()
print('Asynchronous processing time: {time}s.'.format(time=end - start))
FileIO.save_csv(price_1['EURUSD'], 'EURUSD', 'STATICS/FX')
FileIO.save_csv(price_2['GBPUSD'], 'GBPUSD', 'STATICS/FX')
import time
import sys
sys.path.insert(0, r'C:\DATA\Projects\PortFolioProjects\ThirdProject\Code')
from UTIL import FileIO
from DATA import API
config_path = 'CONFIG\config_data.yml'
config = FileIO.read_yaml(config_path)
attr = ['date', 'close']
# https://dailypik.com/top-50-companies-sp-500/
#ticker_list = ['MSFT', 'AAPL', 'AMZN', 'GOOGL', 'FB', 'GOOG']
#ticker_list = ['NVDA', 'CRM', 'PYPL', 'NFLX', 'ABT', 'TMO', 'ABBV', 'COST']
# ticker_list = ['QCOM', 'NKE', 'MCD', 'ACN', 'MDT', 'AVGO', 'NEE', 'DHR', 'BMY', 'TXN', 'HON', 'AMGN', 'LIN']
#ticker_list = ['BRK-B', 'V', 'JPM', 'JNJ', 'WMT']
#ticker_list = ['MA', 'PG', 'BAC', 'INTC', 'T']
#ticker_list = ['UNH', 'XOM', 'HD', 'DIS','KO']
#ticker_list = ['VZ', 'MRK', 'PFE', 'CVX', 'CSCO']
#ticker_list = ['CMCSA', 'PEP', 'WFC', 'BA', 'ADBE']
import pickle
def load_dic(name):
with open(r'DATA/' + name + '.pkl', 'rb') as f:
return pickle.load(f)
sec_sym_dic = load_dic('sec_sym_dic')
#curr_sector = 'Information Technology'
import time
from UTIL import FileIO
from DATA import API
config_path = 'CONFIG\config_data.yml'
config = FileIO.read_yaml(config_path)
attr = ['date', 'close']
ticker_list = ['GOOG', 'GB']
api = API.Tiingo(config)
start = time.time()
price_1 = api.fetch(ticker_list, attr)
end = time.time()
print('Normal processing time: {time}s.'.format(time=end - start))
config['DataAPIFetchMethod'] = 'async'
start = time.time()
price_2 = api.fetch(ticker_list, attr)
end = time.time()
print('Asynchronous processing time: {time}s.'.format(time=end - start))
FileIO.save_csv(price_1['GOOG'], 'GOOG', 'STATICS/PRICE')
FileIO.save_csv(price_2['FB'], 'FB', 'STATICS/PRICE')
