def test_get_data_from_file(self):
self.assertIsInstance(
aux.get_data_from_file("BTC-SRN", interval='10m'),
pandas.core.frame.DataFrame)
self.assertIsInstance(
aux.get_data_from_file("BTC-DCT", interval='10s', filetype='hdf'),
pandas.core.frame.DataFrame)
def test_time_to_index(self):
self.assertEqual(aux.time_to_index(aux.get_data_from_file("BTC-SRN",interval='10m'),
['01-03-2018','04-03-2018']),
(33568, 33998))
self.assertEqual(aux.time_to_index(aux.get_data_from_file("BTC-SRN",interval='10m'),
['01-03-2018 00:00','04-03-2018']),
(33568, 33998))
def test_plot_data(self):
self.assertEqual(
aux.plot_data(aux.get_data_from_file("BTC-SRN", interval='10m'),
to_file=True), True)
self.assertEqual(
aux.plot_data(
aux.get_data_from_file("BTC-SRN", interval='10m'),
entry_points=[10, 30, 50],
exit_points=[20, 40, 60],
to_file=True,
show_smas=True,
show_emas=True,
show_bbands=True,
), True)
def test_cross_smas(self):
q = get_data_from_file("BTC-XRP", interval='10m')
self.assertEqual(cross_smas(q[4416:4416 + 50], [4, 8, 12], [4, 8, 12]),
False)
self.assertEqual(cross_smas(q[4417:4417 + 50], [4, 8, 12], [4, 8, 12]),
True)
self.assertEqual(cross_smas(q[4418:4418 + 50], [4, 8, 12], [4, 8, 12]),
False)
def test_cross_smas(self):
q = get_data_from_file("BTC-XRP", interval='10m')
self.assertEqual(cross_smas(q[4778:4778 + 50], [4, 8, 12], [4, 8, 12]),
False)
self.assertEqual(cross_smas(q[4779:4779 + 50], [4, 8, 12], [4, 8, 12]),
True)
self.assertEqual(cross_smas(q[4780:4780 + 50], [4, 8, 12], [4, 8, 12]),
False)
def test_is_time_to_exit(self):
q = get_data_from_file("BTC-XRP", interval='10m')
self.assertEqual(
is_time_to_exit(q[4778:4778 + 50], [exit_.cross_smas], [4, 8, 12],
[4, 8, 12]), False)
self.assertEqual(
is_time_to_exit(q[4779:4779 + 50], [exit_.cross_smas], [4, 8, 12],
[4, 8, 12]), True)
self.assertEqual(
is_time_to_exit(q[4780:4780 + 50], [exit_.cross_smas], [4, 8, 12],
[4, 8, 12]), False)
def test_is_time_to_buy(self):
q = get_data_from_file("BTC-XRP", interval='10m')
self.assertEqual(
is_time_to_buy(q[4416:4416 + 50], [entry.cross_smas], [4, 8, 12],
[4, 8, 12]), False)
self.assertEqual(
is_time_to_buy(q[4417:4417 + 50], [entry.cross_smas], [4, 8, 12],
[4, 8, 12]), True)
self.assertEqual(
is_time_to_buy(q[4418:4418 + 50], [entry.cross_smas], [4, 8, 12],
[4, 8, 12]), False)
def test_detect_init(self):
self.assertIsInstance(
aux.detect_init(aux.get_data_from_file("BTC-SRN", interval='10m')),
pandas.core.frame.DataFrame)
def backtest_market(entry_funcs, exit_funcs, interval, _date, smas, emas,
from_file, to_file, plot, exchange, db_client, log_level,
market):
'''
Backtests strategies for a specific market.
Args:
entry_funcs(list): list of entry functions to test.
exit_funcs(list): list of entry functions to test.
interval(string): time between measures.
_date(list): init and end point to backtest.
smas(list): list of SMA values to use.
emas(list): list of EMA values to use.
to_file(bool): plot to file.
from_file(bool): get data from file.
plot(bool): plot data.
markets(string): list with markets to backtest or empty to test all available markets.
Returns:
float: returns backtests profit & loss value for applied strategies.
'''
date = [0, 0]
total = 0
#market = check_market_name(market)
#global cached
is_cached = False
entry_points_x = []
entry_points_y = []
exit_points_x = []
exit_points_y = []
full_log = '[Market analysis]: ' + market + '\n'
if from_file:
try:
data = get_data_from_file(market, interval=interval)
except Exception as e:
log(str(e), 0, log_level)
log('[ERROR] Can\'t find ' + market + ' in files.', 0, log_level)
return 0
data_init = data
if isinstance(_date[0], str):
date[0], date[1] = time_to_index(data, _date)
else:
date = _date
if date[1] == 0:
data = data[date[0]:]
else:
data = data[date[0]:date[1]]
else:
if market in cached and \
cached[market]['interval'] == interval and \
cached[market]['init_date'] == _date[0] and \
cached[market]['end_date'] == _date[1]:
# Check if cached data is the same as you want.
data = cached[market]['data']
cached[market]['last'] = 2
is_cached = True
else:
try:
data = get_historical_data(market,
interval=interval,
init_date=_date[0],
end_date=_date[1],
exchange=exchange)
date[0], date[1] = 0, len(data)
except Exception as e:
log(str(e), 0, log_level)
log('[ERROR] Can\'t find ' + market + ' in BD.', 0, log_level)
return 0
#continue
data_init = data
aux_buy = False
buy_price = 0
high_price = 0
# # Test for volume.
# if data.BaseVolume.mean() < 20:
# log(full_log, 1, log_level)
# del data
# del data_init
# return 0
#Tests several functions.
for i in range(len(data) - 110):
if not aux_buy:
if is_time_to_buy(data[i:i + 110], entry_funcs, smas, emas):
buy_price = data_init.Ask.iloc[i + 109 + date[0]]
high_price = buy_price
entry_points_x.append(i + 109)
entry_points_y.append(data_init.Ask.iloc[i + 109 + date[0]])
if exit_funcs:
aux_buy = True
full_log += str(data_init.time.iloc[i + 109 + date[0]]) + \
' [BUY] @ ' + str(data_init.Ask.iloc[i + 109 + date[0]]) + '\n'
else:
# Used for trailing stop loss.
if data_init.Last.iloc[i + 109 + date[0]] > high_price:
high_price = data_init.Last.iloc[i + 109 + date[0]]
if is_time_to_exit(data[i:i + 110],
exit_funcs,
smas,
emas,
stop=0,
bought_at=buy_price,
max_price=high_price):
exit_points_x.append(i + 109)
exit_points_y.append(data_init.Bid.iloc[i + 109 + date[0]])
aux_buy = False
total += round(
((data_init.Bid.iloc[i + 109 + date[0]] - buy_price) /
buy_price) * 100, 2)
full_log += str(data_init.time.iloc[i + 109 + date[0]]) + \
' [SELL]@ ' + str(data_init.Bid.iloc[i + 109 + date[0]]) + '\n'
full_log += '[P&L] > ' + str(total) + '%.' + '\n'
del data_init
# Use plot_data for just a few markets. If you try to run plot_data for several markets,
# computer can start run really slow.
try:
if plot:
plot_data(data,
name=market,
date=[0, 0],
smas=smas,
emas=None,
entry_points=(entry_points_x, entry_points_y),
exit_points=(exit_points_x, exit_points_y),
show_smas=True,
show_emas=True,
show_bbands=False,
to_file=to_file)
except Exception as e:
log("[ERROR] Ploting data: " + str(e), 0, log_level)
if not is_cached:
cached[market] = {
'interval': interval,
'init_date': _date[0],
'end_date': _date[1],
'data': data,
'last': 2
}
#if len(exit_points_x):
# log(market + ' > ' + str(total), log_level)
log('[' + market + '][TOTAL]> ' + str(total) + '%.', 0, log_level)
log(full_log, 1, log_level)
if isnan(total):
log("[ERROR] Total is isnan", 0, log_level)
return 0
return total
def tick_by_tick(market,
entry_funcs,
exit_funcs,
interval=var.default_interval,
smas=var.default_smas,
emas=var.default_emas,
refresh_interval=1,
from_file=True,
plot=False,
log_level=2):
'''
Simulates a working bot, in realtime or in faster speed,
using pre own data from DB or file,
to test an autonomous bot on a specific market.
Args:
markets(string): list with markets to backtest or
empty to run all available markets.
entry_funcs(list): list of entry functions to test.
exit_funcs(list): list of entry functions to test.
interval(string): time between measures.
smas(list): list of SMA values to use.
emas(list): list of EMA values to use.
refresh_interval(int): Refresh rate.
main_coins(list):
'''
#log_level:
# 0 - Only presents total.
# 1 - Writes logs to file.
# 2 - Writes logs to file and prints on screen.
# Default is 2.
#plt.ion()
var.global_log_level = log_level
signal.signal(signal.SIGINT, signal_handler)
date = [0, 0]
total = 0
#market = check_market_name(market)
entry_points_x = []
entry_points_y = []
exit_points_x = []
exit_points_y = []
if not isinstance(entry_funcs, list): entry_funcs = [entry_funcs]
if not isinstance(exit_funcs, list): exit_funcs = [exit_funcs]
print(f'[Market analysis]: {market}')
if from_file:
try:
data = get_data_from_file(market, interval=interval)
except Exception as e:
log(str(e), 1)
log('[ERROR] Can\'t find ' + market + ' in files.', 1)
return 0
data_init = data
#if type(_date[0]) is str:
# date[0], date[1] = time_to_index(data, _date)
#if date[1] == 0:
# data = data[date[0]:]
#else:
# data = data[date[0]:date[1]]
else:
try:
data = get_historical_data(market,
interval=interval,
init_date=_date[0],
end_date=_date[1])
date[0], date[1] = 0, len(data)
data_init = data
except Exception as e:
log(str(e), 1)
log('[ERROR] Can\'t find ' + market + ' in BD.', 1)
return 0
#continue
aux_buy = False
buy_price = 0
high_price = 0
#plt.show()
#Tests several functions.
for i in range(len(data) - 110):
start_time = time()
#print(data_init.Last.iloc[i])
if not aux_buy:
if is_time_to_buy(data[i:i + 110], entry_funcs, smas, emas):
buy_price = data_init.Ask.iloc[i + 109 + date[0]]
high_price = buy_price
entry_points_x.append(i + 109)
entry_points_y.append(data_init.Ask.iloc[i + 109 + date[0]])
if exit_funcs:
aux_buy = True
print(f'''{data_init.time.iloc[i + 109 + date[0]]} \
[BUY] @ {data_init.Ask.iloc[i + 109 + date[0]]}''')
else:
# Used for trailing stop loss.
if data_init.Last.iloc[i + 109 + date[0]] > high_price:
high_price = data_init.Last.iloc[i + 109 + date[0]]
if is_time_to_exit(data[i:i + 110],
exit_funcs,
smas,
emas,
stop=0,
bought_at=buy_price,
max_price=high_price):
exit_points_x.append(i + 109)
exit_points_y.append(data_init.Bid.iloc[i + 109 + date[0]])
aux_buy = False
total += round(
((data_init.Bid.iloc[i + 109 + date[0]] - buy_price) /
buy_price) * 100, 2)
print(f'''{data_init.time.iloc[i + 109 + date[0]]} \
[SELL]@ {data_init.Bid.iloc[i + 109 + date[0]]}''')
print(f'[P&L] > {total}%.')
#plt.plot(data.Last.iloc[i:i+50])
#plt.draw()
#plt.clf()
# In case of processing time is bigger than *refresh_interval* doesn't sleep.
if refresh_interval - (time() - start_time) >= 0:
sleep(refresh_interval - (time() - start_time))
return total
def tick_by_tick(
market,
entry_funcs,
exit_funcs,
interval=var.default_interval,
smas=var.default_smas,
emas=var.default_emas,
refresh_interval=1,
from_file=True,
# plot=False
):
"""
Simulates a working bot, in realtime or in faster speed,
using pre own data from DB or file,
to test an autonomous bot on a specific market.
Args:
market(string): list with markets to backtest or
empty to run all available markets.
entry_funcs(list): list of entry functions to test.
exit_funcs(list): list of entry functions to test.
interval(string): time between measures.
smas(list): list of SMA values to use.
emas(list): list of EMA values to use.
refresh_interval(int): Refresh rate.
from_file(bool): Select the origin of data.
# plot(bool: plots data.
"""
# plt.ion()
signal.signal(signal.SIGINT, signal_handler)
date = [0, 0]
total = 0
# market = check_market_name(market)
entry_points_x = []
entry_points_y = []
exit_points_x = []
exit_points_y = []
if not isinstance(entry_funcs, list):
entry_funcs = [entry_funcs]
if not isinstance(exit_funcs, list):
exit_funcs = [exit_funcs]
log.info(f"[Market analysis]: {market}")
if from_file:
try:
data = get_data_from_file(market, interval=interval)
except Exception as e:
log.error(f"Unable to get data from file: {e}")
log.error(f"Unable to find {market} in files.")
return 0
data_init = data
# if type(_date[0]) is str:
# date[0], date[1] = time_to_index(data, _date)
# if date[1] == 0:
# data = data[date[0]:]
# else:
# data = data[date[0]:date[1]]
else:
try:
data = get_historical_data(market, interval=interval)
date[0], date[1] = 0, len(data)
data_init = data
except Exception as e:
log.error(f"Unable to get data from file: {e}")
log.error(f"Unable to find {market} in DB.")
return 0
aux_buy = False
buy_price = 0
high_price = 0
# plt.show()
# Tests several functions.
for i in range(len(data) - 110):
start_time = time()
# print(data_init.Last.iloc[i])
if not aux_buy:
if is_time_to_buy(data[i:i + 110], entry_funcs, smas, emas):
buy_price = data_init.Ask.iloc[i + 109 + date[0]]
high_price = buy_price
entry_points_x.append(i + 109)
entry_points_y.append(data_init.Ask.iloc[i + 109 + date[0]])
if exit_funcs:
aux_buy = True
log.info(f'''{data_init.time.iloc[i + 109 + date[0]]} \
[BUY] @ {data_init.Ask.iloc[i + 109 + date[0]]}''')
else:
# Used for trailing stop loss.
if data_init.Last.iloc[i + 109 + date[0]] > high_price:
high_price = data_init.Last.iloc[i + 109 + date[0]]
if is_time_to_exit(data[i:i + 110],
exit_funcs,
smas,
emas,
stop=var.stop_type,
bought_at=buy_price,
max_price=high_price):
exit_points_x.append(i + 109)
exit_points_y.append(data_init.Bid.iloc[i + 109 + date[0]])
aux_buy = False
total += round(
((data_init.Bid.iloc[i + 109 + date[0]] - buy_price) /
buy_price) * 100, 2)
log.info(f'''{data_init.time.iloc[i + 109 + date[0]]} \
[SELL]@ {data_init.Bid.iloc[i + 109 + date[0]]}''')
log.info(f'[P&L] > {total}%.')
# plt.plot(data.Last.iloc[i:i+50])
# plt.draw()
# plt.clf()
# In case of processing time is bigger than *refresh_interval* doesn't sleep.
if refresh_interval - (time() - start_time) >= 0:
sleep(refresh_interval - (time() - start_time))
return total
