def check_max_reward(self, max_reward: float) -> ModelStep:
"""
Returns whether the profit upper bound is acceptably high.
:param max_reward: highest profit to sell for: res_line - sup_line at 0.5t
"""
return ModelStep(passed=max_reward / self.period_low > 0.15 / 100,
value=f'{100 * max_reward / self.period_low:.2f}%',
step_id=Breakout1ModelSteps.MAX_REWARD)
def check_range_change_2(self, range_change_2: float) -> ModelStep:
"""
Returns whether the range decreased from [0.31t, 0.65t] to [0.66t, 0.85t].
:param range_change_2: change in price range between [0.66, 0.85t] and [0.66t, 0.85t]
"""
return ModelStep(
passed=range_change_2 / self.period_low < 0.1,
value=f'{100 * range_change_2 / self.period_low:0.2f}%',
step_id=Breakout1ModelSteps.RANGE_CHANGE_2)
def check_dist_from_res(self, dist_from_res: float) -> ModelStep:
"""
Returns whether the price exceeded the resistance line by at least 10% of the period's range.
:param dist_from_res: difference between the resistance line and the symbol's recent high
"""
return ModelStep(
passed=dist_from_res >= 0.1 * self.period_range,
value=f'{100 * dist_from_res / self.period_range:.2f}%',
step_id=Breakout1ModelSteps.BREAKS_RESISTANCE)
def check_dist_to_sup(self, dist_to_sup: float) -> ModelStep:
"""
Returns whether the price dipped to slightly above or just barely below the support.
:param dist_to_sup: difference between the symbol's recent low and the support line
"""
return ModelStep(passed=self.period_range * -15 / 100 <= dist_to_sup <=
self.period_range * 20 / 100,
value=f'{100 * dist_to_sup / self.period_range:.2f}%',
step_id=Breakout1ModelSteps.DIPS_TO_SUPPORT)
def check_high_volume_drop_ratio(
self, high_volume_drop_ratio: float) -> ModelStep:
"""
Returns whether the price drops too often when volume increases.
:param high_volume_drop_ratio: when a minute's volume is higher than usual, the pct of occurrences
that see price drop
"""
self.set_val('high_volume_drop_ratio', high_volume_drop_ratio)
return ModelStep(passed=high_volume_drop_ratio > 1.5,
value=f'{high_volume_drop_ratio}:1',
step_id=Breakout1ModelSteps.HIGH_VOL_DROP_RATIO)
def check_strongest_high_volume_dip(
self, strongest_high_volume_dip: float) -> ModelStep:
"""
Returns whether any single minute with atypically high volume drops in price too steeply.
:param strongest_high_volume_dip: strongest minute-resolution price drop that is accompanied by
higher-than-usual volume
"""
return ModelStep(
passed=strongest_high_volume_dip < self.avg_minute_range +
(1.7 * self.stdev_minute_range),
value=
f'{(strongest_high_volume_dip - self.avg_minute_range) / self.stdev_minute_range:.0f} '
f'stdevs',
step_id=Breakout1ModelSteps.STRONGEST_HIGH_VOL_DIP)
def check_ema_minute_volume(
self, ema_minute_volume: float,
med_ema_volume_prev_7_periods: float) -> ModelStep:
"""
Returns whether this period's moving-average volume is higher than the median of the moving-average volume
during the same period on the previous 7 days.
:param ema_minute_volume: average minute's volume exponentially weighted by time
:param med_ema_volume_prev_7_periods: median of: moving-average volume during the same period on each of
the last 7 days
"""
return ModelStep(
passed=ema_minute_volume > 1.2 * med_ema_volume_prev_7_periods,
value=
f'{100 * ema_minute_volume / med_ema_volume_prev_7_periods:.1f}%',
step_id=Breakout1ModelSteps.EMA_MINUTE_VOLUME)
def add_step(self,
step: ModelStep = None,
passed: bool = None,
value: str = None,
step_id: ModelSteps = None) -> None:
"""
Adds a step in the calculation of model output.
Parameters: either a single ModelStep, or:
passed: bool,
value: str,
step_id: ModelSteps
"""
if step is not None:
self.steps.append(step)
elif passed is not None and value is not None and step_id is not None:
self.steps.append(
ModelStep(passed=passed, value=value, step_id=step_id))
else:
raise ValueError(
'Invalid arguments passed to model\'s output.add_step()')
def _check_ema_volume_excitement(self,
output: Breakout1ModelOutput) -> bool:
ema_volumes = [ema(output.minute_volumes)]
prev_date = self.time().now().date()
for i in range(7):
# Load the previous day's data
prev_date = self.time().get_prev_mkt_day(prev_date)
day_data = self.mongo().load_symbol_day(output.symbol, prev_date)
if not SymbolDay.validate_candles(day_data.candles):
output.steps.append(
ModelStep(
passed=False,
value=
f'missing needed data on {prev_date.strftime(DATE_FORMAT)}',
step_id=Breakout1ModelSteps.EMA_MINUTE_VOLUME))
return False
# Aggregate the day's second-resolution candles into minute resolution
prev_period_start = datetime.combine(prev_date,
output.period.start_time)
prev_period_end = datetime.combine(prev_date,
output.period.end_time)
prev_second_candles = [
candle for candle in day_data.candles
if prev_period_start <= candle.moment <= prev_period_end
]
prev_minute_candles = aggregate_minute_candles(prev_second_candles)
# Calculate the previous day's moving-average volume
ema_volumes.append(
ema([candle.volume for candle in prev_minute_candles]))
# Calculate the median ema volume of the same period on each of the past 7 days
med_ema_volume_prev_7_periods = median(ema_volumes)
# Calculate the ema volume of this period today
ema_minute_volume = ema(output.minute_volumes)
# Perform next step: ema minute volume check
output.steps.append(
output.check_ema_minute_volume(ema_minute_volume,
med_ema_volume_prev_7_periods))
return output.steps[-1].passed