def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
long_period_length = 26
if len(self.data[PriceIndexes.IND_PRICE_CLOSE.value]) >= long_period_length:
macd, macd_signal, macd_hist = tulipy.macd(self.data[PriceIndexes.IND_PRICE_CLOSE.value], 12,
long_period_length, 9)
# on macd hist => M pattern: bearish movement, W pattern: bullish movement
# max on hist: optimal sell or buy
macd_hist = DataUtil.drop_nan(macd_hist)
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(macd_hist, 0)
last_index = len(macd_hist) - 1
pattern, start_index, end_index = PatternAnalyser.find_pattern(macd_hist, zero_crossing_indexes,
last_index)
if pattern != PatternAnalyser.UNKNOWN_PATTERN:
# set sign (-1 buy or 1 sell)
sign_multiplier = -1 if pattern == "W" or pattern == "V" else 1
# set pattern time frame => W and M are on 2 time frames, others 1
pattern_move_time = 2 if (pattern == "W" or pattern == "M") and end_index == last_index else 1
# set weight according to the max value of the pattern and the current value
current_pattern_start = start_index
price_weight = macd_hist[-1] / macd_hist[current_pattern_start:].max() if sign_multiplier == 1 \
else macd_hist[-1] / macd_hist[current_pattern_start:].min()
if not math.isnan(price_weight):
self._analyse_pattern(pattern, macd_hist, zero_crossing_indexes, price_weight,
pattern_move_time, sign_multiplier)
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
async def eval_impl(self):
self.eval_note = False
short_period = 35 # standard with klinger
long_period = 55 # standard with klinger
ema_signal_period = 13 # standard ema signal for klinger
kvo = tulipy.kvo(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.data[PriceIndexes.IND_PRICE_VOL.value],
short_period, long_period)
kvo = DataUtil.drop_nan(kvo)
if len(kvo) >= ema_signal_period:
kvo_ema = tulipy.ema(kvo, ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
ema_difference, 0)
max_elements = 7
to_consider_kvo = min(
max_elements,
len(ema_difference) - zero_crossing_indexes[-1])
self.eval_note = TrendAnalysis.min_has_just_been_reached(
ema_difference[-to_consider_kvo:],
acceptance_window=0.9,
delay=1)
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)))
def _get_rsi_averages(self):
# compute the slow and fast RSI average
if len(self.data[
PriceIndexes.IND_PRICE_CLOSE.value]) > self.period_length:
rsi_v = tulipy.rsi(self.data[PriceIndexes.IND_PRICE_CLOSE.value],
period=self.period_length)
rsi_v = DataUtil.drop_nan(rsi_v)
if len(rsi_v):
slow_average = numpy.mean(rsi_v[-self.slow_eval_count:])
fast_average = numpy.mean(rsi_v[-self.fast_eval_count:])
return slow_average, fast_average, rsi_v
return None, None, None
def eval_impl(self):
eval_proposition = START_PENDING_EVAL_NOTE
short_period = 35 # standard with klinger
long_period = 55 # standard with klinger
ema_signal_period = 13 # standard ema signal for klinger
kvo = tulipy.kvo(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.data[PriceIndexes.IND_PRICE_VOL.value],
short_period, long_period)
kvo = DataUtil.drop_nan(kvo)
if len(kvo) >= ema_signal_period:
kvo_ema = tulipy.ema(kvo, ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
ema_difference, 0)
current_difference = ema_difference[-1]
significant_move_threshold = numpy.std(ema_difference)
factor = 0.2
if TrendAnalysis.peak_has_been_reached_already(
ema_difference[zero_crossing_indexes[-1]:]):
if abs(current_difference) > significant_move_threshold:
factor = 1
else:
factor = 0.5
eval_proposition = current_difference * factor / significant_move_threshold
if abs(eval_proposition) > 1:
eval_proposition = 1 if eval_proposition > 0 else -1
self.eval_note = eval_proposition
