def test_log(self):
self.assertEqual(aux.log("Running unit tests..."), 0)
def backtest(markets,
entry_funcs,
exit_funcs=[],
_date=[0, 0],
smas=var.default_smas,
emas=var.default_emas,
interval=var.default_interval,
plot=False,
to_file=True,
from_file=False,
exchange="bittrex",
base_market='BTC',
log_level=1,
mp_level="medium"):
'''
Backtests strategies.
Args:
markets(list): list with markets to backtest or empty to test all available markets.
entry_funcs(list): list of entry functions to test.
exit_funcs(list): list of entry functions to test.
_date(list): init and end point to backtest.
Ex: '1-1-2017 11:10'
smas(list): list of SMA values to use.
emas(list): list of EMA values to use.
interval(string): time between measures.
plot(bool): plot data.
to_file(bool): plot to file.
from_file(bool): get data from file.
base_market(string): base market to use.
log_level(int): log level - 0-2.
mp_level(string): multiprocessing level - [low, medium, high].
Returns:
bool: returns True in case of success.
log_level:
0 - Only presents total.
1 - Writes logs to file.
2 - Writes logs to file and prints on screen.
Default is 2.
'''
signal.signal(signal.SIGINT, signal_handler)
#global cached
if not from_file:
# Connects to DB.
try:
db_client = connect_db()
except Exception as e:
log(str(e), 0, log_level)
sys.exit(1)
else:
db_client = 0
# For all markets.
if not len(markets):
y = 'y' #raw_input("Want to run all markets? ")
if y == 'y':
if from_file:
markets = get_markets_on_files(interval, base=base_market)
else:
markets = get_markets_list(base_market, exchange)
else:
log("Without files to analyse.", 0, log_level)
# Prevents errors from markets and funcs as str.
if not isinstance(markets, list): markets = [markets]
if not isinstance(entry_funcs, list): entry_funcs = [entry_funcs]
if not isinstance(exit_funcs, list): exit_funcs = [exit_funcs]
# For selected markets.
if from_file:
markets = manage_files(markets, interval=interval)
log(str(len(markets)) + " files/chunks to analyse...", 1, log_level)
# Create a multiprocessing Pool
pool = Pool(num_processors(mp_level))
# Display information about pool.
total = pool.map(
partial(backtest_market, entry_funcs, exit_funcs, interval, _date,
smas, emas, from_file, to_file, plot, exchange, db_client,
log_level), markets)
pool.close()
pool.join()
log(" Total > " + str(sum(total)), 1, log_level)
for k in cached.keys():
if cached[k]['last'] < 1:
del cached[k]
else:
cached[k]['last'] = cached[k]['last'] - 1
return sum(total)
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
