def handle(self, *args, **options):
from history.poloniex import poloniex
from history.models import Price
import time
poo = poloniex(settings.API_KEY,settings.API_SECRET)
now = get_utc_unixtime()
r = poo.returnDepositHistory(0,now)
deposits = r['deposits'] + r['withdrawals']
for d in deposits:
print(d)
currency = d['currency']
amount = float(d['amount']) * ( -1 if 'withdrawalNumber' in d.keys() else 1 )
timestamp = d['timestamp']
txid = d['withdrawalNumber'] if 'withdrawalNumber' in d.keys() else d['txid']
status = d['status']
created_on = datetime.datetime.fromtimestamp(timestamp)
try:
d = Deposit.objects.get(txid=txid)
except:
d = Deposit()
d.symbol=currency
d.amount = amount
d.txid = txid
d.type = 'deposit' if amount > 0 else 'withdrawal'
d.status = status
d.created_on = created_on
d.modified_on = created_on
d.created_on_str = datetime.datetime.strftime(created_on - datetime.timedelta(hours=int(7)),'%Y-%m-%d %H:%M')
d.save()
def handle(self, *args, **options):
# setup
self.poo = poloniex(settings.API_KEY, settings.API_SECRET)
self.setup()
print_and_log("(t){} ---- ****** STARTING TRAINERS ******* ".format(str(datetime.datetime.now())))
self.get_traders()
print_and_log("(t){} ---- ****** DONE TRAINING ALL TRAINERS ******* ".format(str(datetime.datetime.now())))
while True:
# TLDR -- which NNs should run at this granularity?
should_run = []
recommendations = dict.fromkeys(range(0, len(self.predictors)))
for i in range(0, len(self.predictor_configs)):
config = self.predictor_configs[i]
if (int(get_utc_unixtime() / 60) % config['granularity'] == 0 and datetime.datetime.now().second < 1):
should_run.append(i)
# TLDR -- update open orders bfore placing new ones
if len(should_run) > 0:
self.handle_open_orders()
# TLDR -- run the NNs specified at this granularity
for i in should_run:
config = self.predictor_configs[i]
recommend = self.run_predictor(i)
recommendations[i] = recommend
time.sleep(1)
# TLDR - act upon recommendations
for i in range(0, len(recommendations)):
recommendation = recommendations[i]
config = self.predictor_configs[i]
if recommendation is not None:
print_and_log("(t)recommendation {} - {} : {}".format(i, str(config['name']), recommendation))
self.act_upon_recommendation(i, recommendation)
# TLDR - cleanup and stats
if len(should_run) > 0:
pct_buy = round(100.0 * sum(recommendations[i] == 'BUY' for
i in recommendations) / len(recommendations))
pct_sell = round(100.0 * sum(recommendations[i] == 'SELL' for
i in recommendations) / len(recommendations))
print_and_log("(t)TLDR - {}% buy & {}% sell: {}".format(pct_buy, pct_sell, recommendations))
print_and_log("(t) ******************************************************************************* ")
print_and_log("(t) portfolio is {}".format(self.get_portfolio_breakdown_pct()))
print_and_log("(t) ******************************************************************************* ")
print_and_log("(t) {} ..... waiting again ..... ".format(str(datetime.datetime.now())))
print_and_log("(t) ******************************************************************************* ")
time.sleep(1)
def handle(self, *args, **options):
# setup
self.poo = poloniex(settings.API_KEY, settings.API_SECRET)
self.setup()
print_and_log("(t){} ---- ****** STARTING TRAINERS ******* ".format(
str(datetime.datetime.now())))
self.get_traders()
print_and_log(
"(t){} ---- ****** DONE TRAINING ALL TRAINERS ******* ".format(
str(datetime.datetime.now())))
while True:
# TLDR -- which NNs should run at this granularity?
should_run = []
recommendations = dict.fromkeys(range(0, len(self.predictors)))
for i in range(0, len(self.predictor_configs)):
config = self.predictor_configs[i]
if (int(get_utc_unixtime() / 60) % config['granularity'] == 0
and datetime.datetime.now().second < 1):
should_run.append(i)
# TLDR -- update open orders bfore placing new ones
if len(should_run) > 0:
self.handle_open_orders()
# TLDR -- run the NNs specified at this granularity
for i in should_run:
config = self.predictor_configs[i]
recommend = self.run_predictor(i)
recommendations[i] = recommend
time.sleep(1)
# TLDR - act upon recommendations
for i in range(0, len(recommendations)):
recommendation = recommendations[i]
config = self.predictor_configs[i]
if recommendation is not None:
print_and_log("(t)recommendation {} - {} : {}".format(
i, str(config['name']), recommendation))
self.act_upon_recommendation(i, recommendation)
# TLDR - cleanup and stats
if len(should_run) > 0:
pct_buy = round(100.0 * sum(recommendations[i] == 'BUY'
for i in recommendations) /
len(recommendations))
pct_sell = round(100.0 * sum(recommendations[i] == 'SELL'
for i in recommendations) /
len(recommendations))
print_and_log("(t)TLDR - {}% buy & {}% sell: {}".format(
pct_buy, pct_sell, recommendations))
print_and_log(
"(t) ******************************************************************************* "
)
print_and_log("(t) portfolio is {}".format(
self.get_portfolio_breakdown_pct()))
print_and_log(
"(t) ******************************************************************************* "
)
print_and_log("(t) {} ..... waiting again ..... ".format(
str(datetime.datetime.now())))
print_and_log(
"(t) ******************************************************************************* "
)
time.sleep(1)