plt.use("TkAgg")
from matplotlib import pyplot as plt
import json
iter = 1
file_location = 'data/1.csv'
startDate = {"year": 2013, "week": 1}
instrument = 'EURUSD'
data = ld.load(ld.Interval.HOURE, instrument, startDate, 54*4)
candles = candle.Candles(data)
candles.calc_gradients([2,3,4,5,6,7,8,9,10,20,40,80,100])
candles.calc_sma_seq([2,3,4,5,6,7,8,9,10])
candles.norm_by_column_sma()
candles.norm_by_column_grad()
candles.setGradToSimulation()
env = FOREX(candles)
dense_lstm_net = [
dict(type='dense', size=32),
dict(type='internal_lstm', size=10)
]
dense_net = [
dict(type='dense', size=32),
dict(type='dense', size=64),
from tensorforce.execution import Runner
from forex import FOREX
import candle
import numpy as np
import FXCMDataLoader as ld
import matplotlib as plt
plt.use("TkAgg")
from matplotlib import pyplot as plt
import json
startDate = {"year": 2018, "week": 1}
instrument = 'EURUSD'
traindata = ld.load(ld.Interval.HOURE, instrument, startDate, 50)
candles = candle.Candles(traindata)
candles.calc_sma([2, 3, 4, 5, 6, 7, 8, 9, 10])
candles.setSMAToSimulation()
train_env = FOREX(candles)
mid = candles.closeMid
print("asdasdasd")
print(mid[-1] / mid[0])
dense_lstm_net = [
dict(type='dense', size=32),
dict(type='internal_lstm', size=64)
]
dense_net = [
EU_USD.rename(index=str,
columns={
"BidOpen": "Open",
"BidHigh": "High",
"BidLow": "Low",
"BidClose": "Close"
},
inplace=True)
EU_USD.drop('AskOpen', axis=1, inplace=True)
EU_USD.drop('AskClose', axis=1, inplace=True)
EU_USD.drop('AskHigh', axis=1, inplace=True)
EU_USD.drop('AskLow', axis=1, inplace=True)
EU_USD.index = pd.to_datetime(EU_USD.index)
candles = cn.Candles(raw_data)
candles.calc_sma_seq([
5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220,
240, 280, 300
])
candles.norm_by_column_sma()
candles.setSMAToSimulation()
env = FOREX(candles)
dense_lstm_net = [
dict(type='dense', size=32),
dict(type='internal_lstm', size=10)
]
dense_net = [
dict(type='dense', size=32),
#data['HLDiff'].plot()
file_location = 'data/1.csv'
http_location = 'https://candledata.fxcorporate.com/D1/EURUSD/2017.csv.gz'
#AUDCAD, AUDCHF, AUDJPY, AUDNZD, CADCHF, EURAUD,
#EURCHF, EURGBP, EURJPY, EURUSD, GBPCHF, GBPJPY,
#GBPNZD, GBPUSD, NZDCAD, NZDCHF, NZDJPY, NZDUSD,
#USDCAD, USDCHF, USDJPY, AUDUSD, CADJPY, GBPCAD,
#USDTRY, EURNZD
startDate = {"year": 2012, "week": 1}
instrument = 'EURUSD'
data = ld.load(ld.Interval.HOURE, instrument, startDate, 54 * 7)
candles = candle.Candles(data)
data_brit = ld.load(ld.Interval.HOURE, 'EURGBP', startDate, 54 * 7)
cand_brit = candle.Candles(data_brit)
#candles.calc_gradients([50,100,150,200])
#candles.calc_sma_seq([10,30,50,70,90,110,130,150,170,190,210])
#candles.calc_sma([3,4,5,6])
#for i in list(range(2,14)):
# result = gradient_linreg_slidewindow(closeAsk_y, window_size)
# plt.plot(result['gradiens'])
# window_size = window_size + 1
#plt.plot(closeAsk_y)