def calculate_signal(self, candle, compare_candle):
'''
Creates SignalSimulation object for candle provided.
'''
# default values for price difference
p_default = {1: 80, 3: 0.01}
if self.simulation_id in [1, 3]:
calc_signal = self.calculate_signal_v1(
candle, compare_candle, p_default[self.simulation_id])
elif self.simulation_id == 2:
calc_signal = self.calculate_signal_hindsight(
candle, compare_candle.period_close)
elif self.simulation_id == 4:
calc_signal = self.calculate_signal_prophet(compare_candle)
elif self.simulation_id == 5:
calc_signal = self.calculate_signal_prophet(compare_candle)
# Signal #5 is almost equivalent to #4 but takes into account Google Trend ratio
if candle.search_trend:
if candle.search_trend.trend_ratio and candle.search_trend.trend_ratio > 0.35:
calc_signal = "BUY"
else:
calc_signal = ""
my_sim = SignalSimulation(crypto_candle=candle,
simulation=self.simulation_obj,
candle_compare=compare_candle,
signal=calc_signal)
my_sim.save()
return my_sim
def update_signal(self):
'''
Loops through candle subset to update signal and bank estimates. One-time update each time the data sources are updated.
TODO (reminder): allow optional start/end date parameters
'''
# initiate variables to be used in loop
prior_candle = None
x = 0
conditional_message = ''
# Next, delete the existing simulation
SignalSimulation.objects.filter(
simulation=self.simulation_obj,
crypto_candle__crypto_traded=self.currency).delete()
# The only time we want to see the future first is when we are determining hindsight simulations.
if self.simulation_id in [2, 4, 5]:
loop_candles = self.candle_subset.order_by(
'-period_start_timestamp')
if self.simulation_id in [4, 5]:
conditional_message += ' ' + self.predict_price()
else:
loop_candles = self.candle_subset.filter(
search_trend__isnull=False).order_by('period_start_timestamp')
for candle in loop_candles:
# For initial candle, we will initialize the simulation and bank and do nothing else.
if prior_candle is None:
my_sim = SignalSimulation(crypto_candle=candle,
simulation=self.simulation_obj)
my_sim.save()
else:
days_diff = 0
if candle.search_trend and prior_candle.search_trend:
candle_delta = candle.search_trend.date - prior_candle.search_trend.date
days_diff = candle_delta.days
# below i want to make sure my delta is working along with my period interval. This will need to be updated once we introduce hourly intervals.
if ((self.period_interval == "1d" and days_diff == 1)
or self.simulation_id in [2, 4, 5]):
sim_result = self.calculate_signal(candle, prior_candle)
# counting our success instances
if sim_result:
x += 1
prior_candle = candle
# Once we have updated all signals, we can model transaction history.
transaction_sim = BankTransaction(self.candle_subset,
self.simulation_obj, self.currency)
transaction_sim.transaction_history()
return f"Inserted {x} signal records on {timezone.now()}.{conditional_message}"
def update_signal(self):
'''
Loops through candle subset to update signal and bank estimates. One-time update each time the data sources are updated.
TODO (reminder): allow optional start/end date parameters
'''
# initiate variables to be used in loop
prior_candle = ""
x = 0
# below i want to make sure my delta is working along with my period interval. This will need to be updated once we introduce hourly intervals.
if self.period_interval == "1d":
delta_requirement = 1
# The only time we want to see the future first is when we are determining hindsight simulations.
if self.simulation_id == 2:
loop_candles = self.candle_subset.order_by(
'-period_start_timestamp')
else:
loop_candles = self.candle_subset.order_by(
'period_start_timestamp')
for candle in loop_candles:
# First, delete the existing simulation
SignalSimulation.objects.filter(
crypto_candle=candle, simulation=self.simulation_obj).delete()
# For initial candle, we will initialize the simulation and bank and do nothing else.
if prior_candle == "":
my_sim = SignalSimulation(crypto_candle=candle,
simulation=self.simulation_obj)
my_sim.save()
# Make sure we have required values to calculate signal: (1) a candle instance to compare against, (2) trend ratio to compare
elif candle.search_trend.trend_ratio:
candle_delta = candle.search_trend.date - prior_candle.search_trend.date
# Make sure the two objects meet delta requirements
if abs(candle_delta.days) == delta_requirement:
sim_result = self.calculate_signal(candle, prior_candle)
# counting our success instances
if sim_result:
x += 1
prior_candle = candle
# Once we have updated all signals, we can model transaction history.
self.transaction_history()
return f"Inserted {x} records on {timezone.now()}."
def calculate_signal(self, candle, compare_candle):
'''
Creates SignalSimulation object for candle provided.
'''
# default values
if self.simulation_id == 1:
calc_signal = self.calculate_signal_v1(candle, compare_candle)
elif self.simulation_id == 2:
calc_signal = self.calculate_signal_actual(candle, compare_candle)
else:
calc_signal = ""
my_sim = SignalSimulation(crypto_candle=candle,
simulation=self.simulation_obj,
candle_compare=compare_candle,
signal=calc_signal)
my_sim.save()
return my_sim