def macd(Neff_pos, Neff_neg, Nwindow):
"moving average convergence/divergence"
h_pos = ema(Neff_pos, Nwindow)
h_neg = ema(Neff_neg, Nwindow)
h = h_pos - h_neg
return h
def macd(Neff_pos, Neff_neg, Nwindow):
"moving average convergence/divergence"
h_pos = ema(Neff_pos, Nwindow)
h_neg = ema(Neff_neg, Nwindow)
h = h_pos - h_neg
return h
def test_ema(closes):
"""EMA test function."""
ema5 = ema(closes, 5)
ema10 = ema(closes, 10)
data = pd.concat([closes, ema5, ema10], axis=1)
# print(data)
data.plot(title="EMA Chart")
plt.show()
def dema(arg, window):
"""DEMA: Double Exponential Moving Average.
Params:
arg (Series): Time series data such as close prices.
window (int): Moving average window size.
Returns:
Series: Double exponential moving average of arg.
"""
ema1 = ema(arg, window)
ema2 = ema(ema1, window)
dema = ema1 * 2 - ema2
return Series(data = dema, name = "dema" + str(window))
def dema(arg, window):
"""DEMA: Double Exponential Moving Average.
Params:
arg (Series): Time series data such as close prices.
window (int): Moving average window size.
Returns:
Series: Double exponential moving average of arg.
"""
ema1 = ema(arg, window)
ema2 = ema(ema1, window)
dema = ema1 * 2 - ema2
return Series(data = dema, name = "dema" + str(window))
def tema(arg, window):
"""TEMA: Triple Exponential Moving Average.
Params:
arg (Series): Time series data such as close prices.
window (int): Moving average window size.
Returns:
Series: Triple exponential moving average of arg.
"""
ema1 = ema(arg, window)
ema2 = ema(ema1, window)
ema3 = ema(ema2, window)
tema = ema1 * 3 - ema2 * 3 + ema3
return Series(data = tema, name = "tema" + str(window))
def macd_poly(Neff, Nwindow):
"Macd-Poly differencer"
if Neff > 0:
h_pos = ema(Neff/3.0, Nwindow)
h_neg = ema_poly1(Neff, Nwindow)
h = h_pos - h_neg
else:
h = np.zeros(Nwindow)
h[0]=1
h[1]=-1
return h
def animate(i):
try:
ax1.clear()
em = ema.ema(h.bidHistory['price'])
ax1.plot(h.bidHistory['time'],
h.bidHistory['price'],
'-o',
label='second')
ax1.plot(h.bidHistory['time'], em, '-o', label='third')
ax1.xaxis.set_major_formatter(majorFormatter)
ax1.autoscale_view()
except:
pass
def pac(result, longueur):
#pacC
result = ema.ema(result, longueur, "close")
#pacL
result = ema.ema(result, longueur, "low")
#pacU
result = ema.ema(result, longueur, "high")
for i in range(0, len(result[0])):
try:
if (result[0][i]["close"] >=
result[0][i]["EMA" + str(longueur) + "high"]):
result[0][i]["pac"] = 1
elif (result[0][i]["close"] <=
result[0][i]["EMA" + str(longueur) + "low"]):
result[0][i]["pac"] = -1
else:
result[0][i]["pac"] = 0
except KeyError:
pass
return result
def macd_poly(Neff, Nwindow):
"Macd-Poly differencer"
if Neff > 0:
h_pos = ema(Neff / 3.0, Nwindow)
h_neg = ema_poly1(Neff, Nwindow)
h = h_pos - h_neg
else:
h = np.zeros(Nwindow)
h[0] = 1
h[1] = -1
return h
def calc_ema_imacd_spectra_mse(Neff_imacd, Neff_ema, Nwindow):
"Mean-Squared Error\
1) generates h_ema and h_imacd given the input parameters;\
2) takes the amplitude of the FFT for each response\
3) computes the cumulative sum of each gain spectrum\
4) computes and retuns the MSE of the two cumulative gain spectra"
h_ema = ema(Neff_ema, Nwindow)
h_imacd = int_macd_poly(Neff_imacd, Nwindow)
h_ema_fft = np.fft.fft(h_ema)
h_imacd_fft = np.fft.fft(h_imacd)
h_ema_fft_abs = np.absolute(h_ema_fft)
h_imacd_fft_abs = np.absolute(h_imacd_fft)
h_ema_fft_abs_sum = np.cumsum(h_ema_fft_abs)
h_imacd_fft_abs_sum = np.cumsum(h_imacd_fft_abs)
mse = np.sum(np.square(h_ema_fft_abs_sum - h_imacd_fft_abs_sum)) / Nwindow
return mse
def calc_box_ema_spectra_mse(Neff, Nbox, Nwindow):
"Mean-Squared Error\
1) generates h_ema and h_box given the input parameters;\
2) takes the amplitude of the FFT for each response\
3) computes the cumulative sum of each gain spectrum\
4) computes and retuns the MSE of the two cumulative gain spectra"
h_box = box(Nbox, Nwindow)
h_ema = ema(Neff, Nwindow)
h_box_fft = np.fft.fft(h_box)
h_ema_fft = np.fft.fft(h_ema)
h_box_fft_abs = np.absolute(h_box_fft)
h_ema_fft_abs = np.absolute(h_ema_fft)
h_box_fft_abs_sum = np.cumsum(h_box_fft_abs)
h_ema_fft_abs_sum = np.cumsum(h_ema_fft_abs)
mse = np.sum(np.square(h_box_fft_abs_sum - h_ema_fft_abs_sum)) / Nwindow
return mse
axarr[0, 1].set_title("Box")
axarr[0, 1].plot(impulse_response_fde)
axarr[0, 1].plot(impulse_response_direct, 'o', markerfacecolor='none')
# c) Ema
Neff = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
impulse_response_direct = ema(Neff, Nwindow)
axarr[1, 0].set_title("Ema")
axarr[1, 0].plot(impulse_response_fde)
axarr[1, 0].plot(impulse_response_direct, 'o', markerfacecolor='none')
# d) Ema poly1
Neff = 32
lag = 2
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_poly1_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
indicator = bbands.bbands(closes, 20, 2.0)
print("Calculate BOLL: \t %gs" % ((time.time() - t1) / 1.0))
t1 = time.time()
for i in range(10):
indicator = dchannel.dchannel(ohlc, 10)
print("Calculate DCHANNEL: \t %gs" % ((time.time() - t1) / 10.0))
t1 = time.time()
for i in range(10):
indicator = sma.sma(closes, 10)
print("Calculate SMA: \t\t %gs" % ((time.time() - t1) / 10.0))
t1 = time.time()
for i in range(10):
indicator = ema.ema(closes, 10)
print("Calculate EMA: \t\t %gs" % ((time.time() - t1) / 10.0))
t1 = time.time()
for i in range(10):
indicator = dema.dema(closes, 10)
print("Calculate DEMA: \t %gs" % ((time.time() - t1) / 10.0))
t1 = time.time()
for i in range(10):
indicator = tema.tema(closes, 10)
print("Calculate TEMA: \t %gs" % ((time.time() - t1) / 10.0))
t1 = time.time()
for i in range(1):
indicator = wma.wma(closes, 10)
from box import box
from delta import delta
from ema import ema
from macd import macd
from diff import diff
Nwindow = 400
Neff_pos = 20
Neff_neg = 40
Neff = 20
Nbox = 20
h_step = step(Nwindow)
h_box = box(Nbox, Nwindow)
h_delta = delta(Neff_pos, Nwindow)
h_ema = ema(Neff_pos, Nwindow)
h_macd = macd(Neff_pos, Neff_neg, Nwindow)
h_diff = diff(Neff_pos, Neff_neg, Nwindow)
f, axarr = plt.subplots(3, 2)
axarr[0, 0].plot(h_step)
axarr[0, 0].set_title("step")
axarr[0, 1].plot(h_box)
axarr[0, 1].set_title("box")
axarr[1, 0].plot(h_delta)
axarr[1, 0].set_title("delta")
axarr[1, 1].plot(h_diff)
# ГЛАВНАЯ ПРОГРАММА НАЧИНАЕТСЯ ЗДЕСЬ
stopFlag = False # если этот флаг=Истина, ордера не выставляем
print("BTC доступно: ", "%.8f" % wallet.checkAmountOnWallet())
print("CREX доступно: ", "%.8f" % wallet.checkAmountOnWalletCREX())
# startWallet=wallet.checkAmountOnWallet()
print(
"Запускаю основной цикл. Обновление раз в 30 секунд. Торгуем на 3 минутах."
)
while (True):
data = getdata.getData()
sred = ema.ema(data[-20:-1:], 0.5) #alpha была 0.5
bol = (4 * std(data[-20:-1:]) / sred[-1]) / 2
interval = abs(sred[-1] - sred[-1] * (1 + bol))
# покупаем-продаем (восходящий тренд)
if (getdata.currentPriceBid() < sred[-1] - interval * 0.7) and (stopFlag
== False):
buy.placeOrder(float(getdata.currentPriceBid()))
stopFlag = True
print("КУПИЛИ по цене ", getdata.currentPriceBid())
sell.placeOrder(sred[-1] + interval)
print("ОРДЕР НА ПРОДАЖУ по цене ", float(sred[-1] * (1 + bol)))
# продаем-покупаем (нисходящий тренд)
if (getdata.currentPriceBid() > sred[-1] + interval * 0.7) and (stopFlag
== False):
import csv
import numpy as np
import matplotlib.pyplot as plt
from ema import ema
f, axarr = plt.subplots(4, 2)
print("Problem 6: Ema on price series")
print("(a) Ema impulse response.")
Nwindow = 500
h_ema_1 = ema(2, Nwindow)
h_ema_2 = ema(5, Nwindow)
h_ema_3 = ema(10, Nwindow)
h_ema_4 = ema(20, Nwindow)
h_ema_5 = ema(50, Nwindow)
h_ema_6 = ema(100, Nwindow)
axarr[0, 0].plot(h_ema_1)
axarr[0, 0].plot(h_ema_2)
axarr[0, 0].plot(h_ema_3)
axarr[0, 0].plot(h_ema_4)
axarr[0, 0].plot(h_ema_5)
axarr[0, 0].plot(h_ema_6)
axarr[0, 0].set_title('Ema impulse responses with Neff = 2,5,10,20,50,100')
with open('jpm_trades.csv', 'r') as trades_csv:
from scipy import optimize
from ema import ema
from int_macd_poly import int_macd_poly
from calc_ema_imacd_spectra_mse import calc_ema_imacd_spectra_mse
Nwindow = 1024
Neff_ema = 16
Neff_imacd = 16
f, axarr = plt.subplots(2, 2)
# a) Indicative responses
h_ema = ema(Neff_ema, Nwindow)
h_imacd = int_macd_poly(Neff_imacd, Nwindow)
axarr[0, 0].plot(h_ema[:150])
axarr[0, 0].plot(h_imacd[:150])
axarr[0, 0].set_title('Ema and integrated macd poly.')
# b) Spectra
h_ema_fft = np.fft.fft(h_ema)
h_imacd_fft = np.fft.fft(h_imacd)
h_ema_fft_abs = np.absolute(h_ema_fft)
h_imacd_fft_abs = np.absolute(h_imacd_fft)
axarr[0, 1].plot(h_ema_fft_abs)
axarr[0, 1].set_title("Box")
axarr[0, 1].plot(impulse_response_fde)
axarr[0, 1].plot(impulse_response_direct, 'o',markerfacecolor='none')
# c) Ema
Neff = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
impulse_response_direct = ema(Neff, Nwindow)
axarr[1, 0].set_title("Ema")
axarr[1, 0].plot(impulse_response_fde)
axarr[1, 0].plot(impulse_response_direct, 'o', markerfacecolor='none')
# d) Ema poly1
Neff = 32
lag = 2
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_poly1_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
def test_ema_50(self):
ema.ema(config.index, 50, refresh=config.refresh)
def loadCfg(self,
pid : int,
botId : str,
binance_apikey : str,
binance_sekkey : str,
work_path : str,
pid_file_path : str,
cmd_pipe_file_path : str,
binance_pair : str,
fast_ema : int,
fast_ema_offset : int,
slow_ema : int,
slow_ema_offset : int,
time_sample : int,
notification : str,
max_timeout_to_exit : int,
retry_timeout : int ) -> int:
# global auxPid_file_path
# global auxCmd_pipe_file_path
# auxPid_file_path = pid_file_path
# auxCmd_pipe_file_path = cmd_pipe_file_path
setPidFileAndPipeFile(pid_file_path, cmd_pipe_file_path)
self.cfg.set('binance_apikey' , binance_apikey)
self.cfg.set('binance_sekkey' , binance_sekkey)
self.cfg.set('work_path' , work_path)
self.cfg.set('pid' , pid)
self.cfg.set('pid_file_path' , pid_file_path)
self.cfg.set('bot_id' , botId)
self.cfg.set('cmd_pipe_file_path' , cmd_pipe_file_path)
self.cfg.set('binance_pair' , binance_pair)
self.cfg.set('fast_ema' , fast_ema)
self.cfg.set('fast_ema_offset' , fast_ema_offset)
self.cfg.set('slow_ema' , slow_ema)
self.cfg.set('slow_ema_offset' , slow_ema_offset)
self.cfg.set('max_timeout_to_exit', max_timeout_to_exit)
self.cfg.set('retry_timeout' , retry_timeout)
self.emaSlow = ema(self.cfg.get('slow_ema'), self.cfg.get('slow_ema_offset'))
self.emaFast = ema(self.cfg.get('fast_ema'), self.cfg.get('fast_ema_offset'))
if notification.lower() == 'twitter':
self.twtt.accessData(self.cfg.get('bot_id'))
try:
self.cfg.set('time_sample', klineAPIIntervals[time_sample])
except KeyError as e:
logging.info(f'Error: time sample {argvInit[6]} not defined. Use one of: ')
logging.info(klineAPIIntervals.keys())
raise
try:
mkfifo(cmd_pipe_file_path)
except (OSError, FileExistsError) as e:
logging.info(f"Erro creating cmd pipe file: {e.errno} - {e.strerror}")
raise
logging.info(f"\n================================================================\nBOT {self.cfg.get('bot_id')} Configuration:")
logging.info(f"\tPID = [{self.cfg.get('pid')}]")
logging.info(f"\tPID file = [{self.cfg.get('pid_file_path')}]")
logging.info(f"\tCMD pipe = [{self.cfg.get('cmd_pipe_file_path')}]")
logging.info(f"\tWorking path = [{self.cfg.get('work_path')}]")
logging.info(f"\tBinance pair = [{self.cfg.get('binance_pair')}]")
logging.info(f"\tEMA Slow/Fast = [{self.cfg.get('slow_ema')} / {self.cfg.get('fast_ema')}]")
logging.info(f"\tEMA Slow/Fast Offset = [{self.cfg.get('slow_ema_offset')} / {self.cfg.get('fast_ema_offset')}]")
logging.info(f"\tTime sample = [{self.cfg.get('time_sample')[0]}]")
logging.info(f"\tBinance API key = [{self.cfg.get('binance_apikey')}]")
logging.info(f"\tMaximum timeout attempts before exit = [{self.cfg.get('max_timeout_to_exit')}]")
logging.info(f"\tSeconds between timeouts = [{self.cfg.get('retry_timeout')}]\n")
return 0
from scipy import optimize
from ema import ema
from int_macd_poly import int_macd_poly
from calc_ema_imacd_spectra_mse import calc_ema_imacd_spectra_mse
Nwindow = 1024
Neff_ema = 16
Neff_imacd = 16
f, axarr = plt.subplots(2, 2)
# a) Indicative responses
h_ema = ema(Neff_ema, Nwindow)
h_imacd = int_macd_poly(Neff_imacd, Nwindow)
axarr[0, 0].plot(h_ema[:150])
axarr[0, 0].plot(h_imacd[:150])
axarr[0, 0].set_title('Ema and integrated macd poly.')
# b) Spectra
h_ema_fft = np.fft.fft(h_ema)
h_imacd_fft = np.fft.fft(h_imacd)
h_ema_fft_abs = np.absolute(h_ema_fft)
h_imacd_fft_abs = np.absolute(h_imacd_fft)
axarr[0, 1].plot(h_ema_fft_abs)
import csv
import numpy as np
import matplotlib.pyplot as plt
from ema import ema
f, axarr = plt.subplots(4, 2)
print("Problem 6: Ema on price series")
print("(a) Ema impulse response.")
Nwindow = 500
h_ema_1 = ema(2, Nwindow)
h_ema_2 = ema(5, Nwindow)
h_ema_3 = ema(10, Nwindow)
h_ema_4 = ema(20, Nwindow)
h_ema_5 = ema(50, Nwindow)
h_ema_6 = ema(100, Nwindow)
axarr[0, 0].plot(h_ema_1)
axarr[0, 0].plot(h_ema_2)
axarr[0, 0].plot(h_ema_3)
axarr[0, 0].plot(h_ema_4)
axarr[0, 0].plot(h_ema_5)
axarr[0, 0].plot(h_ema_6)
axarr[0, 0].set_title('Ema impulse responses with Neff = 2,5,10,20,50,100')
with open('jpm_trades.csv', 'r') as trades_csv:
from box import box
from ema import ema
from calc_box_ema_spectra_mse import calc_box_ema_spectra_mse
Nwindow = 1024
Nbox = 16
f, axarr = plt.subplots(2, 2)
# a) Indicative responses
Neff = Nbox / (1-np.exp(-1))
h_box = box(Nbox, Nwindow)
h_ema = ema(Neff, Nwindow)
axarr[0, 0].plot(h_box[:150])
axarr[0, 0].plot(h_ema[:150])
axarr[0, 0].set_title('Box and ema impulse response')
# b) Spectra
h_box_fft = np.fft.fft(h_box)
h_ema_fft = np.fft.fft(h_ema)
h_box_fft_abs = np.absolute(h_box_fft)
h_ema_fft_abs = np.absolute(h_ema_fft)
axarr[0, 1].plot(h_box_fft_abs)
axarr[0, 1].plot(h_ema_fft_abs, 'r--')
#!/usr/bin/anaconda3/bin/python
import csv
import numpy as np
import matplotlib.pyplot as plt
from ema import ema
from macd import macd
f, axarr = plt.subplots(2, 3)
print("Problem 5: Gain")
Neff = 20
Nwindow = 100
h_ema = ema(Neff, Nwindow)
Neff_pos = 20
Neff_neg = 40
h_macd = macd(Neff_pos, Neff_neg, Nwindow)
with open('jpm_trades.csv', 'r') as trades_csv:
trades = np.array(list(csv.reader(trades_csv))[1:]).astype('double')
prices = trades[:,1]
candidate = np.convolve(prices, h_ema)
truncated = candidate[:len(prices)]
print("(a) Ema gain")
# The gain = 1 is the correct choice.
# The factor g must be multiplied with the offset prices[0]
# because
from box import box
from ema import ema
from calc_box_ema_spectra_mse import calc_box_ema_spectra_mse
Nwindow = 1024
Nbox = 16
f, axarr = plt.subplots(2, 2)
# a) Indicative responses
Neff = Nbox / (1 - np.exp(-1))
h_box = box(Nbox, Nwindow)
h_ema = ema(Neff, Nwindow)
axarr[0, 0].plot(h_box[:150])
axarr[0, 0].plot(h_ema[:150])
axarr[0, 0].set_title('Box and ema impulse response')
# b) Spectra
h_box_fft = np.fft.fft(h_box)
h_ema_fft = np.fft.fft(h_ema)
h_box_fft_abs = np.absolute(h_box_fft)
h_ema_fft_abs = np.absolute(h_ema_fft)
axarr[0, 1].plot(h_box_fft_abs)
axarr[0, 1].plot(h_ema_fft_abs, 'r--')
