def massi(high, low, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Mass Index (MASSI)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
high_low_range = non_zero_range(high, low)
fast = int(fast) if fast and fast > 0 else 9
slow = int(slow) if slow and slow > 0 else 25
if slow < fast:
fast, slow = slow, fast
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
# Calculate Result
hl_ema1 = ema(close=high_low_range, length=fast, **kwargs)
hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs)
hl_ratio = hl_ema1 / hl_ema2
massi = hl_ratio.rolling(slow, min_periods=slow).sum()
# Offset
if offset != 0:
massi = massi.shift(offset)
# Handle fills
if "fillna" in kwargs:
massi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
massi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
massi.name = f"MASSI_{fast}_{slow}"
massi.category = "volatility"
return massi
def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **kwargs):
"""Indicator: Keltner Channels (KC)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = int(length) if length and length > 0 else 20
scalar = float(scalar) if scalar and scalar > 0 else 2
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
use_tr = kwargs.pop("tr", True)
if use_tr:
range_ = true_range(high, low, close)
else:
range_ = high_low_range(high, low)
_mode = ""
if mamode == "sma":
basis = sma(close, length)
band = sma(range_, length=length)
_mode += "s"
elif mamode is None or mamode == "ema":
basis = ema(close, length=length)
band = ema(range_, length=length)
lower = basis - scalar * band
upper = basis + scalar * band
# Offset
if offset != 0:
lower = lower.shift(offset)
basis = basis.shift(offset)
upper = upper.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
basis.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
basis.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"{_mode if len(_mode) else ''}_{length}_{scalar}"
lower.name = f"KCL{_props}"
basis.name = f"KCB{_props}"
upper.name = f"KCU{_props}"
basis.category = upper.category = lower.category = "volatility"
# Prepare DataFrame to return
data = {lower.name: lower, basis.name: basis, upper.name: upper}
kcdf = DataFrame(data)
kcdf.name = f"KC{_props}"
kcdf.category = basis.category
return kcdf
def adosc(high, low, close, volume, open_=None, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Accumulation/Distribution Oscillator"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
volume = verify_series(volume)
fast = int(fast) if fast and fast > 0 else 3
slow = int(slow) if slow and slow > 0 else 10
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
# Calculate Result
ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
fast_ad = ema(close=ad_, length=fast, **kwargs)
slow_ad = ema(close=ad_, length=slow, **kwargs)
adosc = fast_ad - slow_ad
# Offset
if offset != 0:
adosc = adosc.shift(offset)
# Handle fills
if "fillna" in kwargs:
adosc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adosc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adosc.name = f"ADOSC_{fast}_{slow}"
adosc.category = "volume"
return adosc
def pvo(volume,
fast=None,
slow=None,
signal=None,
scalar=None,
offset=None,
**kwargs):
"""Indicator: Percentage Volume Oscillator (PVO)"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
scalar = float(scalar) if scalar else 100
if slow < fast:
fast, slow = slow, fast
volume = verify_series(volume, max(fast, slow, signal))
offset = get_offset(offset)
if volume is None: return
# Calculate Result
fastma = ema(volume, length=fast)
slowma = ema(volume, length=slow)
pvo = scalar * (fastma - slowma)
pvo /= slowma
signalma = ema(pvo, length=signal)
histogram = pvo - signalma
# Offset
if offset != 0:
pvo = pvo.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
pvo.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
pvo.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
pvo.name = f"PVO{_props}"
histogram.name = f"PVOh{_props}"
signalma.name = f"PVOs{_props}"
pvo.category = histogram.category = signalma.category = "momentum"
#
data = {pvo.name: pvo, histogram.name: histogram, signalma.name: signalma}
df = DataFrame(data)
df.name = pvo.name
df.category = pvo.category
return df
def amat(close=None, fast=None, slow=None, mamode=None, lookback=None, offset=None, **kwargs):
"""Indicator: Archer Moving Averages Trends (AMAT)"""
# Validate Arguments
close = verify_series(close)
fast = int(fast) if fast and fast > 0 else 8
slow = int(slow) if slow and slow > 0 else 21
lookback = int(lookback) if lookback and lookback > 0 else 2
mamode = mamode.lower() if mamode else "ema"
offset = get_offset(offset)
# Calculate Result
if mamode == "hma":
fast_ma = hma(close=close, length=fast, **kwargs)
slow_ma = hma(close=close, length=slow, **kwargs)
elif mamode == "linreg":
fast_ma = linreg(close=close, length=fast, **kwargs)
slow_ma = linreg(close=close, length=slow, **kwargs)
elif mamode == "rma":
fast_ma = rma(close=close, length=fast, **kwargs)
slow_ma = rma(close=close, length=slow, **kwargs)
elif mamode == "sma":
fast_ma = sma(close=close, length=fast, **kwargs)
slow_ma = sma(close=close, length=slow, **kwargs)
elif mamode == "wma":
fast_ma = wma(close=close, length=fast, **kwargs)
slow_ma = wma(close=close, length=slow, **kwargs)
else: # "ema"
fast_ma = ema(close=close, length=fast, **kwargs)
slow_ma = ema(close=close, length=slow, **kwargs)
mas_long = long_run(fast_ma, slow_ma, length=lookback)
mas_short = short_run(fast_ma, slow_ma, length=lookback)
# Offset
if offset != 0:
mas_long = mas_long.shift(offset)
mas_short = mas_short.shift(offset)
# # Handle fills
if "fillna" in kwargs:
mas_long.fillna(kwargs["fillna"], inplace=True)
mas_short.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
mas_long.fillna(method=kwargs["fill_method"], inplace=True)
mas_short.fillna(method=kwargs["fill_method"], inplace=True)
# Prepare DataFrame to return
amatdf = DataFrame({
f"AMAT_{mas_long.name}": mas_long,
f"AMAT_{mas_short.name}": mas_short
})
# Name and Categorize it
amatdf.name = f"AMAT_{mamode.upper()}_{fast}_{slow}_{lookback}"
amatdf.category = "trend"
return amatdf
def adosc(high,
low,
close,
volume,
open_=None,
fast=None,
slow=None,
talib=None,
offset=None,
**kwargs):
"""Indicator: Accumulation/Distribution Oscillator"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 3
slow = int(slow) if slow and slow > 0 else 10
_length = max(fast, slow)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
volume = verify_series(volume, _length)
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
mode_tal = bool(talib) if isinstance(talib, bool) else True
if high is None or low is None or close is None or volume is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import ADOSC
adosc = ADOSC(high, low, close, volume, fast, slow)
else:
ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
fast_ad = ema(close=ad_, length=fast, **kwargs)
slow_ad = ema(close=ad_, length=slow, **kwargs)
adosc = fast_ad - slow_ad
# Offset
if offset != 0:
adosc = adosc.shift(offset)
# Handle fills
if "fillna" in kwargs:
adosc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adosc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adosc.name = f"ADOSC_{fast}_{slow}"
adosc.category = "volume"
return adosc
def ppo(close,
fast=None,
slow=None,
signal=None,
scalar=None,
offset=None,
**kwargs):
"""Indicator: Percentage Price Oscillator (PPO)"""
# Validate Arguments
close = verify_series(close)
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
scalar = float(scalar) if scalar else 100
if slow < fast:
fast, slow = slow, fast
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else fast
offset = get_offset(offset)
# Calculate Result
fastma = sma(close, length=fast)
slowma = sma(close, length=slow)
ppo = scalar * (fastma - slowma)
ppo /= slowma
signalma = ema(ppo, length=signal)
histogram = ppo - signalma
# Offset
if offset != 0:
ppo = ppo.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
ppo.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ppo.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
ppo.name = f"PPO{_props}"
histogram.name = f"PPOh{_props}"
signalma.name = f"PPOs{_props}"
ppo.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {ppo.name: ppo, histogram.name: histogram, signalma.name: signalma}
df = DataFrame(data)
df.name = f"PPO{_props}"
df.category = ppo.category
return df
def pgo(high, low, close, length=None, offset=None, **kwargs):
"""Indicator: Pretty Good Oscillator (PGO)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = int(length) if length and length > 0 else 14
offset = get_offset(offset)
# Calculate Result
pgo = close - sma(close, length)
pgo /= ema(atr(high, low, close, length), length)
# Offset
if offset != 0:
pgo = pgo.shift(offset)
# Handle fills
if "fillna" in kwargs:
pgo.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
pgo.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
pgo.name = f"PGO_{length}"
pgo.category = "momentum"
return pgo
def tsi(close,
fast=None,
slow=None,
scalar=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: True Strength Index (TSI)"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 13
slow = int(slow) if slow and slow > 0 else 25
# if slow < fast:
# fast, slow = slow, fast
scalar = float(scalar) if scalar else 100
close = verify_series(close, max(fast, slow))
drift = get_drift(drift)
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
if close is None: return
# Calculate Result
diff = close.diff(drift)
slow_ema = ema(close=diff, length=slow, **kwargs)
fast_slow_ema = ema(close=slow_ema, length=fast, **kwargs)
abs_diff = diff.abs()
abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs)
abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs)
tsi = scalar * fast_slow_ema / abs_fast_slow_ema
# Offset
if offset != 0:
tsi = tsi.shift(offset)
# Handle fills
if "fillna" in kwargs:
tsi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
tsi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
tsi.name = f"TSI_{fast}_{slow}"
tsi.category = "momentum"
return tsi
def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
"""Indicator: Fisher Transform (FISHT)"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 5
offset = get_offset(offset)
# Calculate Result
m = high.size
hl2_ = hl2(high, low)
max_high = hl2_.rolling(length).max()
min_low = hl2_.rolling(length).min()
hl2_range = max_high - min_low
hl2_range[hl2_range < 1e-5] = 0.001
position = (hl2_ - min_low) / hl2_range
v = 0
fish = 0
result = [npNaN for _ in range(0, length - 1)]
for i in range(length - 1, m):
v = 0.66 * (position[i] - 0.5) + 0.67 * v
if v > 0.99: v = 0.999
if v < -0.99: v = -0.999
fish = 0.5 * (fish + nplog((1 + v) / (1 - v)))
result.append(fish)
fisher = Series(result, index=high.index)
signalma = ema(fisher, length=signal)
# Offset
if offset != 0:
fisher = fisher.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
fisher.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
fisher.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}_{signal}"
fisher.name = f"FISHERT{_props}"
signalma.name = f"FISHERTs{_props}"
fisher.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {fisher.name: fisher, signalma.name: signalma}
df = DataFrame(data)
df.name = f"FISHERT{_props}"
df.category = fisher.category
return df
def rvi_(source, length, scalar, mamode, drift):
"""RVI"""
std = stdev(source, length)
pos, neg = unsigned_differences(source, amount=drift)
pos_std = pos * std
neg_std = neg * std
if mamode == 'sma':
pos_avg = sma(pos_std, length)
neg_avg = sma(neg_std, length)
else: # 'ema'
pos_avg = ema(pos_std, length)
neg_avg = ema(neg_std, length)
result = scalar * pos_avg
result /= pos_avg + neg_avg
return result
def smi(close,
fast=None,
slow=None,
signal=None,
scalar=None,
offset=None,
**kwargs):
"""Indicator: SMI Ergodic Indicator (SMIIO)"""
# Validate arguments
close = verify_series(close)
fast = int(fast) if fast and fast > 0 else 5
slow = int(slow) if slow and slow > 0 else 20
signal = int(signal) if signal and signal > 0 else 5
if slow < fast:
fast, slow = slow, fast
scalar = float(scalar) if scalar else 1
offset = get_offset(offset)
# Calculate Result
smi = tsi(close, fast=fast, slow=slow, scalar=scalar)
signalma = ema(smi, signal)
osc = smi - signalma
# Offset
if offset != 0:
smi = smi.shift(offset)
signalma = signalma.shift(offset)
osc = osc.shift(offset)
# Handle fills
if "fillna" in kwargs:
smi.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
osc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
smi.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
osc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_scalar = f"_{scalar}" if scalar != 1 else ""
_props = f"_{fast}_{slow}_{signal}{_scalar}"
smi.name = f"SMI{_props}"
signalma.name = f"SMIs{_props}"
osc.name = f"SMIo{_props}"
smi.category = signalma.category = osc.category = "momentum"
# Prepare DataFrame to return
data = {smi.name: smi, signalma.name: signalma, osc.name: osc}
df = DataFrame(data)
df.name = f"SMI{_props}"
df.category = smi.category
return df
def bbands(close, length=None, std=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bollinger Bands (BBANDS)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 5
min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
std = float(std) if std and std > 0 else 2.
mamode = mamode.lower() if mamode else "sma"
offset = get_offset(offset)
# Calculate Result
standard_deviation = stdev(close=close, length=length)
deviations = std * standard_deviation
if mamode is None or mamode == "sma":
mid = sma(close=close, length=length)
elif mamode == "ema":
mid = ema(close=close, length=length, **kwargs)
lower = mid - deviations
upper = mid + deviations
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
mid.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
mid.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
lower.name = f"BBL_{length}_{std}"
mid.name = f"BBM_{length}_{std}"
upper.name = f"BBU_{length}_{std}"
mid.category = upper.category = lower.category = "volatility"
# Prepare DataFrame to return
data = {lower.name: lower, mid.name: mid, upper.name: upper}
bbandsdf = DataFrame(data)
bbandsdf.name = f"BBANDS_{length}_{std}"
bbandsdf.category = "volatility"
return bbandsdf
def atr(high,
low,
close,
length=None,
mamode=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: Average True Range (ATR)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = int(length) if length and length > 0 else 14
mamode = mamode = mamode.lower() if mamode else "rma"
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
_mode = ""
tr = true_range(high=high, low=low, close=close, drift=drift)
if mamode == "ema":
atr, _mode = ema(tr, length=length), "ema"
elif mamode == "sma":
atr, _mode = sma(tr, length=length), "sma"
elif mamode == "wma":
atr, _mode = wma(tr, length=length), "wma"
else: # "rma"
atr = rma(tr, length=length)
percentage = kwargs.pop("percent", False)
if percentage:
atr *= 100 / close
# Offset
if offset != 0:
atr = atr.shift(offset)
# Handle fills
if "fillna" in kwargs:
atr.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
atr.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
atr.name = f"ATR{_mode}_{length}{'p' if percentage else ''}"
atr.category = "volatility"
return atr
def eri(high, low, close, length=None, offset=None, **kwargs):
"""Indicator: Elder Ray Index (ERI)"""
# Validate arguments
length = int(length) if length and length > 0 else 13
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
if high is None or low is None or close is None: return
# Calculate Result
ema_ = ema(close, length)
bull = high - ema_
bear = low - ema_
# Offset
if offset != 0:
bull = bull.shift(offset)
bear = bear.shift(offset)
# Handle fills
if "fillna" in kwargs:
bull.fillna(kwargs["fillna"], inplace=True)
bear.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
bull.fillna(method=kwargs["fill_method"], inplace=True)
bear.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
bull.name = f"BULLP_{length}"
bear.name = f"BEARP_{length}"
bull.category = bear.category = "momentum"
# Prepare DataFrame to return
data = {bull.name: bull, bear.name: bear}
df = DataFrame(data)
df.name = f"ERI_{length}"
df.category = bull.category
return df
def qstick(open_, close, length=None, offset=None, **kwargs):
"""Indicator: Q Stick"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
ma = kwargs.pop("ma", "sma")
open_ = verify_series(open_, length)
close = verify_series(close, length)
offset = get_offset(offset)
if open_ is None or close is None: return
# Calculate Result
diff = non_zero_range(close, open_)
if ma == "dema":
qstick = dema(diff, length=length, **kwargs)
elif ma == "ema":
qstick = ema(diff, length=length, **kwargs)
elif ma == "hma":
qstick = hma(diff, length=length)
elif ma == "rma":
qstick = rma(diff, length=length)
else: # "sma"
qstick = sma(diff, length=length)
# Offset
if offset != 0:
qstick = qstick.shift(offset)
# Handle fills
if "fillna" in kwargs:
qstick.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
qstick.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
qstick.name = f"QS_{length}"
qstick.category = "trend"
return qstick
def efi(close,
volume,
length=None,
drift=None,
mamode=None,
offset=None,
**kwargs):
"""Indicator: Elder's Force Index (EFI)"""
# Validate arguments
close = verify_series(close)
volume = verify_series(volume)
length = int(length) if length and length > 0 else 13
drift = get_drift(drift)
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
pv_diff = close.diff(drift) * volume
if mamode == "sma":
efi = sma(pv_diff, length)
else:
efi = ema(pv_diff, length)
# Offset
if offset != 0:
efi = efi.shift(offset)
# Handle fills
if "fillna" in kwargs:
efi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
efi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
efi.name = f"EFI_{length}"
efi.category = "volume"
return efi
def bias(close, length=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bias (BIAS)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 26
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
if mamode == "ema":
ma = ema(close, length=length, **kwargs)
elif mamode == "hma":
ma = hma(close, length=length, **kwargs)
elif mamode == "rma":
ma = rma(close, length=length, **kwargs)
elif mamode == "wma":
ma = wma(close, length=length, **kwargs)
else: # "sma"
ma = sma(close, length=length, **kwargs)
bias = (close / ma) - 1
# Offset
if offset != 0:
bias = bias.shift(offset)
# Handle fills
if "fillna" in kwargs:
bias.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
bias.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
bias.name = f"BIAS_{ma.name}"
bias.category = "momentum"
return bias
def stc(close,
tclength=None,
fast=None,
slow=None,
factor=None,
offset=None,
**kwargs):
"""Indicator: Schaff Trend Cycle (STC)"""
# Validate arguments
tclength = int(tclength) if tclength and tclength > 0 else 10
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
factor = float(factor) if factor and factor > 0 else 0.5
if slow < fast: # mandatory condition, but might be confusing
fast, slow = slow, fast
_length = max(tclength, fast, slow)
close = verify_series(close, _length)
offset = get_offset(offset)
if close is None: return
# kwargs allows for three more series (ma1, ma2 and osc) which can be passed
# here ma1 and ma2 input negate internal ema calculations, osc substitutes
# both ma's.
ma1 = kwargs.pop("ma1", False)
ma2 = kwargs.pop("ma2", False)
osc = kwargs.pop("osc", False)
# 3 different modes of calculation..
if isinstance(ma1, Series) and isinstance(ma2, Series) and not osc:
ma1 = verify_series(ma1, _length)
ma2 = verify_series(ma2, _length)
if ma1 is None or ma2 is None: return
# Calculate Result based on external feeded series
xmacd = ma1 - ma2
# invoke shared calculation
pff, pf = schaff_tc(close, xmacd, tclength, factor)
elif isinstance(osc, Series):
osc = verify_series(osc, _length)
if osc is None: return
# Calculate Result based on feeded oscillator
# (should be ranging around 0 x-axis)
xmacd = osc
# invoke shared calculation
pff, pf = schaff_tc(close, xmacd, tclength, factor)
else:
# Calculate Result .. (traditionel/full)
# MACD line
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
xmacd = fastma - slowma
# invoke shared calculation
pff, pf = schaff_tc(close, xmacd, tclength, factor)
# Resulting Series
stc = Series(pff, index=close.index)
macd = Series(xmacd, index=close.index)
stoch = Series(pf, index=close.index)
# Offset
if offset != 0:
stc = stc.shift(offset)
macd = macd.shift(offset)
stoch = stoch.shift(offset)
# Handle fills
if "fillna" in kwargs:
stc.fillna(kwargs["fillna"], inplace=True)
macd.fillna(kwargs["fillna"], inplace=True)
stoch.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
stc.fillna(method=kwargs["fill_method"], inplace=True)
macd.fillna(method=kwargs["fill_method"], inplace=True)
stoch.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{tclength}_{fast}_{slow}_{factor}"
stc.name = f"STC{_props}"
macd.name = f"STCmacd{_props}"
stoch.name = f"STCstoch{_props}"
stc.category = macd.category = stoch.category = "momentum"
# Prepare DataFrame to return
data = {stc.name: stc, macd.name: macd, stoch.name: stoch}
df = DataFrame(data)
df.name = f"STC{_props}"
df.category = stc.category
return df
def accbands(high,
low,
close,
length=None,
c=None,
drift=None,
mamode=None,
offset=None,
**kwargs):
"""Indicator: Acceleration Bands (ACCBANDS)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
high_low_range = non_zero_range(high, low)
length = int(length) if length and length > 0 else 20
c = float(c) if c and c > 0 else 4
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else length
mamode = mamode.lower() if mamode else "sma"
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
hl_ratio = high_low_range / (high + low)
hl_ratio *= c
_lower = low * (1 - hl_ratio)
_upper = high * (1 + hl_ratio)
if mamode == "ema":
lower = ema(_lower, length=length)
mid = ema(close, length=length)
upper = ema(_upper, length=length)
else: # "sma"
lower = sma(_lower, length=length)
mid = sma(close, length=length)
upper = sma(_upper, length=length)
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
mid.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
mid.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
lower.name = f"ACCBL_{length}"
mid.name = f"ACCBM_{length}"
upper.name = f"ACCBU_{length}"
mid.category = upper.category = lower.category = "volatility"
# Prepare DataFrame to return
data = {lower.name: lower, mid.name: mid, upper.name: upper}
accbandsdf = DataFrame(data)
accbandsdf.name = f"ACCBANDS_{length}"
accbandsdf.category = mid.category
return accbandsdf
def aobv(close,
volume,
fast=None,
slow=None,
mamode=None,
max_lookback=None,
min_lookback=None,
offset=None,
**kwargs):
"""Indicator: Archer On Balance Volume (AOBV)"""
# Validate arguments
close = verify_series(close)
volume = verify_series(volume)
offset = get_offset(offset)
fast = int(fast) if fast and fast > 0 else 4
slow = int(slow) if slow and slow > 0 else 12
max_lookback = int(
max_lookback) if max_lookback and max_lookback > 0 else 2
min_lookback = int(
min_lookback) if min_lookback and min_lookback > 0 else 2
if slow < fast:
fast, slow = slow, fast
mamode = mamode.upper() if mamode else None
run_length = kwargs.pop("run_length", 2)
# Calculate Result
obv_ = obv(close=close, volume=volume, **kwargs)
if mamode is None or mamode == "EMA":
mamode = "EMA"
maf = ema(close=obv_, length=fast, **kwargs)
mas = ema(close=obv_, length=slow, **kwargs)
elif mamode == "HMA":
maf = hma(close=obv_, length=fast, **kwargs)
mas = hma(close=obv_, length=slow, **kwargs)
elif mamode == "LINREG":
maf = linreg(close=obv_, length=fast, **kwargs)
mas = linreg(close=obv_, length=slow, **kwargs)
elif mamode == "SMA":
maf = sma(close=obv_, length=fast, **kwargs)
mas = sma(close=obv_, length=slow, **kwargs)
elif mamode == "WMA":
maf = wma(close=obv_, length=fast, **kwargs)
mas = wma(close=obv_, length=slow, **kwargs)
# When MAs are long and short
obv_long = long_run(maf, mas, length=run_length)
obv_short = short_run(maf, mas, length=run_length)
# Offset
if offset != 0:
obv_ = obv_.shift(offset)
maf = maf.shift(offset)
mas = mas.shift(offset)
obv_long = obv_long.shift(offset)
obv_short = obv_short.shift(offset)
# # Handle fills
if "fillna" in kwargs:
obv_.fillna(kwargs["fillna"], inplace=True)
maf.fillna(kwargs["fillna"], inplace=True)
mas.fillna(kwargs["fillna"], inplace=True)
obv_long.fillna(kwargs["fillna"], inplace=True)
obv_short.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
obv_.fillna(method=kwargs["fill_method"], inplace=True)
maf.fillna(method=kwargs["fill_method"], inplace=True)
mas.fillna(method=kwargs["fill_method"], inplace=True)
obv_long.fillna(method=kwargs["fill_method"], inplace=True)
obv_short.fillna(method=kwargs["fill_method"], inplace=True)
# Prepare DataFrame to return
data = {
obv_.name: obv_,
f"OBV_min_{min_lookback}": obv_.rolling(min_lookback).min(),
f"OBV_max_{max_lookback}": obv_.rolling(max_lookback).max(),
f"OBV_{maf.name}": maf,
f"OBV_{mas.name}": mas,
f"AOBV_LR_{run_length}": obv_long,
f"AOBV_SR_{run_length}": obv_short,
}
aobvdf = DataFrame(data)
# Name and Categorize it
aobvdf.name = (
f"AOBV_{mamode}_{fast}_{slow}_{min_lookback}_{max_lookback}_{run_length}"
)
aobvdf.category = "volume"
return aobvdf
def macd(close,
fast=None,
slow=None,
signal=None,
talib=None,
offset=None,
**kwargs):
"""Indicator: Moving Average, Convergence/Divergence (MACD)"""
# Validate arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow, signal))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
as_mode = kwargs.setdefault("asmode", False)
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import MACD
macd, signalma, histogram = MACD(close, fast, slow, signal)
else:
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
macd = fastma - slowma
signalma = ema(close=macd.loc[macd.first_valid_index():, ],
length=signal)
histogram = macd - signalma
if as_mode:
macd = macd - signalma
signalma = ema(close=macd.loc[macd.first_valid_index():, ],
length=signal)
histogram = macd - signalma
# Offset
if offset != 0:
macd = macd.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
macd.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
macd.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_asmode = "AS" if as_mode else ""
_props = f"_{fast}_{slow}_{signal}"
macd.name = f"MACD{_asmode}{_props}"
histogram.name = f"MACD{_asmode}h{_props}"
signalma.name = f"MACD{_asmode}s{_props}"
macd.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {
macd.name: macd,
histogram.name: histogram,
signalma.name: signalma
}
df = DataFrame(data)
df.name = f"MACD{_asmode}{_props}"
df.category = macd.category
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
df,
signals(
indicator=histogram,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", True),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
signals(
indicator=macd,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return df
def thermo(high,
low,
length=None,
long=None,
short=None,
mamode=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: Elders Thermometer (THERMO)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
drift = get_drift(drift)
offset = get_offset(offset)
length = int(length) if length and length > 0 else 20
long = float(long) if long and long > 0 else 2
short = float(short) if short and short > 0 else 0.5
mamode = mamode.lower() if mamode else "ema"
asint = kwargs.pop("asint", True)
# Calculate Result
thermoL = (low.shift(drift) - low).abs()
thermoH = (high - high.shift(drift)).abs()
thermo = thermoL
thermo = thermo.where(thermoH < thermoL, thermoH)
thermo.index = high.index
if mamode == "sma":
thermo_ma = sma(thermo, length)
if mamode == "hma":
thermo_ma = hma(thermo, length)
else: # "ema"
thermo_ma = ema(thermo, length)
# Create signals
thermo_long = thermo < (thermo_ma * long)
thermo_short = thermo > (thermo_ma * short)
# Binary output, useful for signals
if asint:
thermo_long = thermo_long.astype(int)
thermo_short = thermo_short.astype(int)
# Offset
if offset != 0:
thermo = thermo.shift(offset)
thermo_ma = thermo_ma.shift(offset)
therthermo_longmo_ma = thermo_ma.shift(offset)
thermo_short = thermo_ma.shift(offset)
# Handle fills
if "fillna" in kwargs:
thermo.fillna(kwargs["fillna"], inplace=True)
thermo_ma.fillna(kwargs["fillna"], inplace=True)
thermo_long.fillna(kwargs["fillna"], inplace=True)
thermo_short.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
thermo.fillna(method=kwargs["fill_method"], inplace=True)
thermo_ma.fillna(method=kwargs["fill_method"], inplace=True)
thermo_long.fillna(method=kwargs["fill_method"], inplace=True)
thermo_short.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}_{long}_{short}"
thermo.name = f"THERMO{_props}"
thermo_ma.name = f"THERMOma{_props}"
thermo_long.name = f"THERMOl{_props}"
thermo_short.name = f"THERMOs{_props}"
thermo.category = thermo_ma.category = thermo_long.category = thermo_short.category = "volatility"
# Prepare Dataframe to return
data = {
thermo.name: thermo,
thermo_ma.name: thermo_ma,
thermo_long.name: thermo_long,
thermo_short.name: thermo_short
}
df = DataFrame(data)
df.name = f"THERMO{_props}"
df.category = thermo.category
return df
def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
"""Indicator: Moving Average, Convergence/Divergence (MACD)"""
# Validate arguments
close = verify_series(close)
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
if slow < fast:
fast, slow = slow, fast
offset = get_offset(offset)
# Calculate Result
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
macd = fastma - slowma
signalma = ema(close=macd, length=signal)
histogram = macd - signalma
# Offset
if offset != 0:
macd = macd.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
macd.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
macd.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
macd.name = f"MACD{_props}"
histogram.name = f"MACDh{_props}"
signalma.name = f"MACDs{_props}"
macd.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {macd.name: macd, histogram.name: histogram, signalma.name: signalma}
df = DataFrame(data)
df.name = f"MACD{_props}"
df.category = macd.category
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
df,
signals(
indicator=histogram,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", True),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
signals(
indicator=macd,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return df
def tsi(close,
fast=None,
slow=None,
signal=None,
scalar=None,
mamode=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: True Strength Index (TSI)"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 13
slow = int(slow) if slow and slow > 0 else 25
signal = int(signal) if signal and signal > 0 else 13
# if slow < fast:
# fast, slow = slow, fast
scalar = float(scalar) if scalar else 100
close = verify_series(close, max(fast, slow))
drift = get_drift(drift)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "ema"
if "length" in kwargs: kwargs.pop("length")
if close is None: return
# Calculate Result
diff = close.diff(drift)
slow_ema = ema(close=diff, length=slow, **kwargs)
fast_slow_ema = ema(close=slow_ema, length=fast, **kwargs)
abs_diff = diff.abs()
abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs)
abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs)
tsi = scalar * fast_slow_ema / abs_fast_slow_ema
tsi_signal = ma(mamode, tsi, length=signal)
# Offset
if offset != 0:
tsi = tsi.shift(offset)
tsi_signal = tsi_signal.shift(offset)
# Handle fills
if "fillna" in kwargs:
tsi.fillna(kwargs["fillna"], inplace=True)
tsi_signal.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
tsi.fillna(method=kwargs["fill_method"], inplace=True)
tsi_signal.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
tsi.name = f"TSI_{fast}_{slow}_{signal}"
tsi_signal.name = f"TSIs_{fast}_{slow}_{signal}"
tsi.category = tsi_signal.category = "momentum"
# Prepare DataFrame to return
df = DataFrame({tsi.name: tsi, tsi_signal.name: tsi_signal})
df.name = f"TSI_{fast}_{slow}_{signal}"
df.category = "momentum"
return df
def squeeze_pro(high,
low,
close,
bb_length=None,
bb_std=None,
kc_length=None,
kc_scalar_wide=None,
kc_scalar_normal=None,
kc_scalar_narrow=None,
mom_length=None,
mom_smooth=None,
use_tr=None,
mamode=None,
offset=None,
**kwargs):
"""Indicator: Squeeze Momentum (SQZ) PRO"""
# Validate arguments
bb_length = int(bb_length) if bb_length and bb_length > 0 else 20
bb_std = float(bb_std) if bb_std and bb_std > 0 else 2.0
kc_length = int(kc_length) if kc_length and kc_length > 0 else 20
kc_scalar_wide = float(
kc_scalar_wide) if kc_scalar_wide and kc_scalar_wide > 0 else 2
kc_scalar_normal = float(
kc_scalar_normal) if kc_scalar_normal and kc_scalar_normal > 0 else 1.5
kc_scalar_narrow = float(
kc_scalar_narrow) if kc_scalar_narrow and kc_scalar_narrow > 0 else 1
mom_length = int(mom_length) if mom_length and mom_length > 0 else 12
mom_smooth = int(mom_smooth) if mom_smooth and mom_smooth > 0 else 6
_length = max(bb_length, kc_length, mom_length, mom_smooth)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
valid_kc_scaler = kc_scalar_wide > kc_scalar_normal and kc_scalar_normal > kc_scalar_narrow
if not valid_kc_scaler: return
if high is None or low is None or close is None: return
use_tr = kwargs.setdefault("tr", True)
asint = kwargs.pop("asint", True)
detailed = kwargs.pop("detailed", False)
mamode = mamode if isinstance(mamode, str) else "sma"
def simplify_columns(df, n=3):
df.columns = df.columns.str.lower()
return [c.split("_")[0][n - 1:n] for c in df.columns]
# Calculate Result
bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode)
kch_wide = kc(high,
low,
close,
length=kc_length,
scalar=kc_scalar_wide,
mamode=mamode,
tr=use_tr)
kch_normal = kc(high,
low,
close,
length=kc_length,
scalar=kc_scalar_normal,
mamode=mamode,
tr=use_tr)
kch_narrow = kc(high,
low,
close,
length=kc_length,
scalar=kc_scalar_narrow,
mamode=mamode,
tr=use_tr)
# Simplify KC and BBAND column names for dynamic access
bbd.columns = simplify_columns(bbd)
kch_wide.columns = simplify_columns(kch_wide)
kch_normal.columns = simplify_columns(kch_normal)
kch_narrow.columns = simplify_columns(kch_narrow)
momo = mom(close, length=mom_length)
if mamode.lower() == "ema":
squeeze = ema(momo, length=mom_smooth)
else: # "sma"
squeeze = sma(momo, length=mom_smooth)
# Classify Squeezes
squeeze_on_wide = (bbd.l > kch_wide.l) & (bbd.u < kch_wide.u)
squeeze_on_normal = (bbd.l > kch_normal.l) & (bbd.u < kch_normal.u)
squeeze_on_narrow = (bbd.l > kch_narrow.l) & (bbd.u < kch_narrow.u)
squeeze_off_wide = (bbd.l < kch_wide.l) & (bbd.u > kch_wide.u)
no_squeeze = ~squeeze_on_wide & ~squeeze_off_wide
# Offset
if offset != 0:
squeeze = squeeze.shift(offset)
squeeze_on_wide = squeeze_on_wide.shift(offset)
squeeze_on_normal = squeeze_on_normal.shift(offset)
squeeze_on_narrow = squeeze_on_narrow.shift(offset)
squeeze_off_wide = squeeze_off_wide.shift(offset)
no_squeeze = no_squeeze.shift(offset)
# Handle fills
if "fillna" in kwargs:
squeeze.fillna(kwargs["fillna"], inplace=True)
squeeze_on_wide.fillna(kwargs["fillna"], inplace=True)
squeeze_on_normal.fillna(kwargs["fillna"], inplace=True)
squeeze_on_narrow.fillna(kwargs["fillna"], inplace=True)
squeeze_off_wide.fillna(kwargs["fillna"], inplace=True)
no_squeeze.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
squeeze.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_on_wide.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_on_normal.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_on_narrow.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_off_wide.fillna(method=kwargs["fill_method"], inplace=True)
no_squeeze.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = "" if use_tr else "hlr"
_props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar_wide}_{kc_scalar_normal}_{kc_scalar_narrow}"
squeeze.name = f"SQZPRO{_props}"
data = {
squeeze.name: squeeze,
f"SQZPRO_ON_WIDE":
squeeze_on_wide.astype(int) if asint else squeeze_on_wide,
f"SQZPRO_ON_NORMAL":
squeeze_on_normal.astype(int) if asint else squeeze_on_normal,
f"SQZPRO_ON_NARROW":
squeeze_on_narrow.astype(int) if asint else squeeze_on_narrow,
f"SQZPRO_OFF":
squeeze_off_wide.astype(int) if asint else squeeze_off_wide,
f"SQZPRO_NO": no_squeeze.astype(int) if asint else no_squeeze,
}
df = DataFrame(data)
df.name = squeeze.name
df.category = squeeze.category = "momentum"
# Detailed Squeeze Series
if detailed:
pos_squeeze = squeeze[squeeze >= 0]
neg_squeeze = squeeze[squeeze < 0]
pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True)
neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True)
pos_inc *= squeeze
pos_dec *= squeeze
neg_dec *= squeeze
neg_inc *= squeeze
pos_inc.replace(0, npNaN, inplace=True)
pos_dec.replace(0, npNaN, inplace=True)
neg_dec.replace(0, npNaN, inplace=True)
neg_inc.replace(0, npNaN, inplace=True)
sqz_inc = squeeze * increasing(squeeze)
sqz_dec = squeeze * decreasing(squeeze)
sqz_inc.replace(0, npNaN, inplace=True)
sqz_dec.replace(0, npNaN, inplace=True)
# Handle fills
if "fillna" in kwargs:
sqz_inc.fillna(kwargs["fillna"], inplace=True)
sqz_dec.fillna(kwargs["fillna"], inplace=True)
pos_inc.fillna(kwargs["fillna"], inplace=True)
pos_dec.fillna(kwargs["fillna"], inplace=True)
neg_dec.fillna(kwargs["fillna"], inplace=True)
neg_inc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
sqz_inc.fillna(method=kwargs["fill_method"], inplace=True)
sqz_dec.fillna(method=kwargs["fill_method"], inplace=True)
pos_inc.fillna(method=kwargs["fill_method"], inplace=True)
pos_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_inc.fillna(method=kwargs["fill_method"], inplace=True)
df[f"SQZPRO_INC"] = sqz_inc
df[f"SQZPRO_DEC"] = sqz_dec
df[f"SQZPRO_PINC"] = pos_inc
df[f"SQZPRO_PDEC"] = pos_dec
df[f"SQZPRO_NDEC"] = neg_dec
df[f"SQZPRO_NINC"] = neg_inc
return df
def squeeze(high,
low,
close,
bb_length=None,
bb_std=None,
kc_length=None,
kc_scalar=None,
mom_length=None,
mom_smooth=None,
use_tr=None,
offset=None,
**kwargs):
"""Indicator: Squeeze Momentum (SQZ)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
bb_length = int(bb_length) if bb_length and bb_length > 0 else 20
bb_std = float(bb_std) if bb_std and bb_std > 0 else 2.
kc_length = int(kc_length) if kc_length and kc_length > 0 else 20
kc_scalar = float(kc_scalar) if kc_scalar and kc_scalar > 0 else 1.5
mom_length = int(mom_length) if mom_length and mom_length > 0 else 12
mom_smooth = int(mom_smooth) if mom_smooth and mom_smooth > 0 else 6
use_tr = kwargs.setdefault("tr", True)
asint = kwargs.pop("asint", True)
mamode = kwargs.pop("mamode", "sma").lower()
lazybear = kwargs.pop("lazybear", False)
detailed = kwargs.pop("detailed", False)
def simplify_columns(df, n=3):
df.columns = df.columns.str.lower()
return [c.split('_')[0][n - 1:n] for c in df.columns]
# Calculate Result
bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode)
kch = kc(high,
low,
close,
length=kc_length,
scalar=kc_scalar,
mamode=mamode,
tr=use_tr)
# Simplify KC and BBAND column names for dynamic access
bbd.columns = simplify_columns(bbd)
kch.columns = simplify_columns(kch)
if lazybear:
highest_high = high.rolling(kc_length).max()
lowest_low = low.rolling(kc_length).min()
avg_ = 0.25 * (highest_high + lowest_low) + 0.5 * kch.b
squeeze = linreg(close - avg_, length=kc_length)
else:
momo = mom(close, length=mom_length)
if mamode == "ema":
squeeze = ema(momo, length=mom_smooth)
else:
squeeze = sma(momo, length=mom_smooth)
# Classify Squeezes
squeeze_on = (bbd.l > kch.l) & (bbd.u < kch.u)
squeeze_off = (bbd.l < kch.l) & (bbd.u > kch.u)
no_squeeze = ~squeeze_on & ~squeeze_off
# Offset
if offset != 0:
squeeze = squeeze.shift(offset)
squeeze_on = squeeze_on.shift(offset)
squeeze_off = squeeze_off.shift(offset)
no_squeeze = no_squeeze.shift(offset)
# Handle fills
if "fillna" in kwargs:
squeeze.fillna(kwargs["fillna"], inplace=True)
squeeze_on.fillna(kwargs["fillna"], inplace=True)
squeeze_off.fillna(kwargs["fillna"], inplace=True)
no_squeeze.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
squeeze.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_on.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_off.fillna(method=kwargs["fill_method"], inplace=True)
no_squeeze.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = "" if use_tr else "hlr"
_props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar}"
_props += "_LB" if lazybear else ""
squeeze.name = f"SQZ{_props}"
data = {
squeeze.name: squeeze,
f"SQZ_ON": squeeze_on.astype(int) if asint else squeeze_on,
f"SQZ_OFF": squeeze_off.astype(int) if asint else squeeze_off,
f"SQZ_NO": no_squeeze.astype(int) if asint else no_squeeze
}
df = DataFrame(data)
df.name = squeeze.name
df.category = squeeze.category = "momentum"
# Detailed Squeeze Series
if detailed:
pos_squeeze = squeeze[squeeze >= 0]
neg_squeeze = squeeze[squeeze < 0]
pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True)
neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True)
pos_inc *= squeeze
pos_dec *= squeeze
neg_dec *= squeeze
neg_inc *= squeeze
pos_inc.replace(0, npNaN, inplace=True)
pos_dec.replace(0, npNaN, inplace=True)
neg_dec.replace(0, npNaN, inplace=True)
neg_inc.replace(0, npNaN, inplace=True)
sqz_inc = squeeze * increasing(squeeze)
sqz_dec = squeeze * decreasing(squeeze)
sqz_inc.replace(0, npNaN, inplace=True)
sqz_dec.replace(0, npNaN, inplace=True)
# Handle fills
if "fillna" in kwargs:
sqz_inc.fillna(kwargs["fillna"], inplace=True)
sqz_dec.fillna(kwargs["fillna"], inplace=True)
pos_inc.fillna(kwargs["fillna"], inplace=True)
pos_dec.fillna(kwargs["fillna"], inplace=True)
neg_dec.fillna(kwargs["fillna"], inplace=True)
neg_inc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
sqz_inc.fillna(method=kwargs["fill_method"], inplace=True)
sqz_dec.fillna(method=kwargs["fill_method"], inplace=True)
pos_inc.fillna(method=kwargs["fill_method"], inplace=True)
pos_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_inc.fillna(method=kwargs["fill_method"], inplace=True)
df[f"SQZ_INC"] = sqz_inc
df[f"SQZ_DEC"] = sqz_dec
df[f"SQZ_PINC"] = pos_inc
df[f"SQZ_PDEC"] = pos_dec
df[f"SQZ_NDEC"] = neg_dec
df[f"SQZ_NINC"] = neg_inc
return df
def qqe(close, length=None, smooth=None, factor=None, mamode=None, drift=None, offset=None, **kwargs):
"""Indicator: Quantitative Qualitative Estimation (QQE)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
smooth = int(smooth) if smooth and smooth > 0 else 5
factor = float(factor) if factor else 4.236
mamode = mamode.lower() if mamode else "ema"
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
rsi_ = rsi(close, length)
if mamode == "hma":
rsi_ma, _mode = hma(rsi_, length=smooth), "h"
elif mamode == "rma":
rsi_ma, _mode = rma(rsi_, length=smooth), "r"
elif mamode == "sma":
rsi_ma, _mode = sma(rsi_, length=smooth), "s"
elif mamode == "wma":
rsi_ma, _mode = wma(rsi_, length=smooth), "w"
else: # "ema"
rsi_ma, _mode = ema(rsi_, length=smooth), ""
# RSI MA True Range
rsi_ma_tr = rsi_ma.diff(drift).abs()
# Double Smooth the RSI MA True Range using Wilder's Length with a default
# width of 4.236.
wilders_length = 2 * length - 1
smoothed_rsi_tr_ma = ema(rsi_ma_tr, length=wilders_length)
dar = factor * ema(smoothed_rsi_tr_ma, length=wilders_length)
# Create the Upper and Lower Bands around RSI MA.
upperband = rsi_ma + dar
lowerband = rsi_ma - dar
m = close.size
long = Series(0, index=close.index)
short = Series(0, index=close.index)
trend = Series(1, index=close.index)
qqe = Series(rsi_ma.iloc[0], index=close.index)
qqe_long = Series(npNaN, index=close.index)
qqe_short = Series(npNaN, index=close.index)
for i in range(1, m):
c_rsi, p_rsi = rsi_ma.iloc[i], rsi_ma.iloc[i - 1]
c_long, p_long = long.iloc[i - 1], long.iloc[i - 2]
c_short, p_short = short.iloc[i - 1], short.iloc[i - 2]
# Long Line
if p_rsi > c_long and c_rsi > c_long:
long.iloc[i] = npMaximum(c_long, lowerband.iloc[i])
else:
long.iloc[i] = lowerband.iloc[i]
# Short Line
if p_rsi < c_short and c_rsi < c_short:
short.iloc[i] = npMinimum(c_short, upperband.iloc[i])
else:
short.iloc[i] = upperband.iloc[i]
# Trend & QQE Calculation
# Long: Current RSI_MA value Crosses the Prior Short Line Value
# Short: Current RSI_MA Crosses the Prior Long Line Value
if (c_rsi > c_short and p_rsi < p_short) or (c_rsi <= c_short and p_rsi >= p_short):
trend.iloc[i] = 1
qqe.iloc[i] = qqe_long.iloc[i] = long.iloc[i]
elif (c_rsi > c_long and p_rsi < p_long) or (c_rsi <= c_long and p_rsi >= p_long):
trend.iloc[i] = -1
qqe.iloc[i] = qqe_short.iloc[i] = short.iloc[i]
else:
trend.iloc[i] = trend.iloc[i - 1]
if trend.iloc[i] == 1:
qqe.iloc[i] = qqe_long.iloc[i] = long.iloc[i]
else:
qqe.iloc[i] = qqe_short.iloc[i] = short.iloc[i]
# Offset
if offset != 0:
rsi_ma = rsi_ma.shift(offset)
qqe = qqe.shift(offset)
long = long.shift(offset)
short = short.shift(offset)
# Handle fills
if "fillna" in kwargs:
rsi_ma.fillna(kwargs["fillna"], inplace=True)
qqe.fillna(kwargs["fillna"], inplace=True)
long.fillna(kwargs["fillna"], inplace=True)
short.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
rsi_ma.fillna(method=kwargs["fill_method"], inplace=True)
qqe.fillna(method=kwargs["fill_method"], inplace=True)
long.fillna(method=kwargs["fill_method"], inplace=True)
short.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"{_mode}_{length}_{smooth}_{factor}"
qqe.name = f"QQE{_props}"
rsi_ma.name = f"QQE{_props}_RSI{_mode.upper()}MA"
long.name = f"QQEl{_props}"
short.name = f"QQEs{_props}"
qqe.category = rsi_ma.category = "momentum"
long.category = short.category = qqe.category
# Prepare DataFrame to return
data = {
qqe.name: qqe, rsi_ma.name: rsi_ma,
# long.name: long, short.name: short
long.name: qqe_long, short.name: qqe_short
}
df = DataFrame(data)
df.name = f"QQE{_props}"
df.category = qqe.category
return df