def kvo(candles: np.ndarray,
short_period=2,
long_period=5,
sequential=False) -> Union[float, np.ndarray]:
"""
KVO - Klinger Volume Oscillator
:param candles: np.ndarray
:param short_period: int - default: 2
:param long_period: int - default: 5
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
res = ti.kvo(np.ascontiguousarray(candles[:, 3]),
np.ascontiguousarray(candles[:, 4]),
np.ascontiguousarray(candles[:, 2]),
np.ascontiguousarray(candles[:, 5]),
short_period=short_period,
long_period=long_period)
return np.concatenate(
(np.full((candles.shape[0] - res.shape[0]), np.nan),
res), axis=0) if sequential else res[-1]
def get_indicators(self):
short_window = int(self.strategy_value['short_window'])
long_window = int(self.strategy_value['long_window'])
signal_window = int(self.strategy_value['signal_window'])
kvo = ti.kvo(self.high, self.low, self.close, self.volume,
short_window, long_window)
return kvo, ti.ema(kvo, signal_window)
async def evaluate(self, cryptocurrency, symbol, time_frame, high_candles, low_candles,
close_candles, volume_candles, candle):
if len(high_candles) >= self.short_period:
kvo = tulipy.kvo(high_candles,
low_candles,
close_candles,
volume_candles,
self.short_period,
self.long_period)
kvo = data_util.drop_nan(kvo)
if len(kvo) >= self.ema_signal_period:
kvo_ema = tulipy.ema(kvo, self.ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = EvaluatorUtil.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 = EvaluatorUtil.TrendAnalysis.min_has_just_been_reached(
ema_difference[-to_consider_kvo:],
acceptance_window=0.9, delay=1)
await self.evaluation_completed(cryptocurrency, symbol, time_frame,
eval_time=evaluators_util.get_eval_time(full_candle=candle,
time_frame=time_frame))
def klinger_kama(h, l, c, v): cslow = 55 cfast = 34 kvo = ti.kvo(np.array(h), np.array(l), np.array(c), np.array(v), cfast, cslow) signal = kama(kvo) return (kvo, signal)
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 compute(self, data_dict):
high = data_dict.get('high')
low = data_dict.get('low')
close = data_dict.get('close')
volume = data_dict.get('volume')
return ti.kvo(high, low, close, volume, self.short_period,
self.long_period)
def klinger_kama(h, l, c, v):
cslow = 55
cfast = 34
h2 = [float(i) for i in h]
l2 = [float(i) for i in l]
c2 = [float(i) for i in c]
v2 = [float(i) for i in v]
kvo = ti.kvo(np.array(h2), np.array(l2), np.array(c2), np.array(v2), 34.0,
55.0)
signal = kama(kvo)
return kvo, signal
async def evaluate(self, cryptocurrency, symbol, time_frame, high_candles, low_candles,
close_candles, volume_candles, candle):
eval_proposition = commons_constants.START_PENDING_EVAL_NOTE
kvo = tulipy.kvo(high_candles,
low_candles,
close_candles,
volume_candles,
self.short_period,
self.long_period)
kvo = data_util.drop_nan(kvo)
if len(kvo) >= self.ema_signal_period:
kvo_ema = tulipy.ema(kvo, self.ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = EvaluatorUtil.TrendAnalysis.get_threshold_change_indexes(ema_difference, 0)
current_difference = ema_difference[-1]
significant_move_threshold = numpy.std(ema_difference)
factor = 0.2
if EvaluatorUtil.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
await self.evaluation_completed(cryptocurrency, symbol, time_frame,
eval_time=evaluators_util.get_eval_time(full_candle=candle,
time_frame=time_frame))
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
def kvo(candles: np.ndarray,
short_period: int = 2,
long_period: int = 5,
sequential: bool = False) -> Union[float, np.ndarray]:
"""
KVO - Klinger Volume Oscillator
:param candles: np.ndarray
:param short_period: int - default: 2
:param long_period: int - default: 5
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
res = ti.kvo(np.ascontiguousarray(candles[:, 3]),
np.ascontiguousarray(candles[:, 4]),
np.ascontiguousarray(candles[:, 2]),
np.ascontiguousarray(candles[:, 5]),
short_period=short_period,
long_period=long_period)
return same_length(candles, res) if sequential else res[-1]
def get_KVI(high, low, close, volume, slow_signal, fast_signal):
kvo = np.zeros(len(high))
kvo[1:] = ti.kvo(high, low, close,volume, slow_signal, fast_signal)
kvo_signal = pd.Series(kvo).rolling(9).mean().values
return [kvo, kvo_signal]