def get_moving_average_analysis(data, current_moving_average, time_period):
time_period_unit_moving_average = tulipy.sma(data, time_period)
# equalize array size
min_len_arrays = min(len(time_period_unit_moving_average), len(current_moving_average))
# compute difference between 1 unit values and others ( >0 means currently up the other one)
values_difference = (current_moving_average[-min_len_arrays:] - time_period_unit_moving_average[-min_len_arrays:])
values_difference = DataUtil.drop_nan(values_difference)
if len(values_difference):
# indexes where current_unit_moving_average crosses time_period_unit_moving_average
crossing_indexes = TrendAnalysis.get_threshold_change_indexes(values_difference, 0)
multiplier = 1 if values_difference[-1] > 0 else -1
# check at least some data crossed 0
if crossing_indexes:
normalized_data = DataUtil.normalize_data(values_difference)
current_value = min(abs(normalized_data[-1])*2, 1)
if math.isnan(current_value):
return 0
# check <= values_difference.count()-1if current value is max/min
if current_value == 0 or current_value == 1:
chances_to_be_max = TrendAnalysis.get_estimation_of_move_state_relatively_to_previous_moves_length(
crossing_indexes,
values_difference)
return multiplier*current_value*chances_to_be_max
# other case: maxima already reached => return distance to max
else:
return multiplier*current_value
# just crossed the average => neutral
return 0
def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
period_length = 14
if len(self.data[
PriceIndexes.IND_PRICE_HIGH.value]) > period_length + 10:
min_adx = 7.5
max_adx = 45
neutral_adx = 25
adx = tulipy.adx(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
period_length)
instant_ema = tulipy.ema(
self.data[PriceIndexes.IND_PRICE_CLOSE.value], 2)
slow_ema = tulipy.ema(
self.data[PriceIndexes.IND_PRICE_CLOSE.value], 20)
adx = DataUtil.drop_nan(adx)
if len(adx):
current_adx = adx[-1]
current_slows_ema = slow_ema[-1]
current_instant_ema = instant_ema[-1]
multiplier = -1 if current_instant_ema < current_slows_ema else 1
# strong adx => strong trend
if current_adx > neutral_adx:
# if max adx already reached => when ADX forms a top and begins to turn down, you should look for a
# retracement that causes the price to move toward its 20-day exponential moving average (EMA).
adx_last_values = adx[-15:]
adx_last_value = adx_last_values[-1]
local_max_adx = adx_last_values.max()
# max already reached => trend will slow down
if adx_last_value < local_max_adx:
self.eval_note = multiplier * (
current_adx - neutral_adx) / (local_max_adx -
neutral_adx)
# max not reached => trend will continue, return chances to be max now
else:
crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
adx, neutral_adx)
chances_to_be_max = \
TrendAnalysis.get_estimation_of_move_state_relatively_to_previous_moves_length(
crossing_indexes, adx) if len(crossing_indexes) > 2 \
else 0.75
proximity_to_max = min(1, current_adx / max_adx)
self.eval_note = multiplier * proximity_to_max * chances_to_be_max
# weak adx => change to come
else:
self.eval_note = multiplier * min(1, (
(neutral_adx - current_adx) / (neutral_adx - min_adx)))