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 cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
"""Indicator: Chande Forcast Oscillator (CFO)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 9
scalar = float(scalar) if scalar else 100
drift = get_drift(drift)
offset = get_offset(offset)
# Finding linear regression of Series
cfo = scalar * (close - linreg(close, length=length, tsf=True))
cfo /= close
# Offset
if offset != 0:
cfo = cfo.shift(offset)
# Handle fills
if "fillna" in kwargs:
cfo.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
cfo.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
cfo.name = f"CFO_{length}"
cfo.category = "momentum"
return cfo
def cti(close, length=None, offset=None, **kwargs) -> Series:
"""Indicator: Correlation Trend Indicator"""
length = int(length) if length and length > 0 else 12
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
cti = linreg(close, length=length, r=True)
# Offset
if offset != 0:
cti = cti.shift(offset)
# Handle fills
if "fillna" in kwargs:
cti.fillna(method=kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
cti.fillna(method=kwargs["fill_method"], inplace=True)
cti.name = f"CTI_{length}"
cti.category = "momentum"
return cti
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 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 inertia(close=None,
high=None,
low=None,
length=None,
rvi_length=None,
scalar=None,
refined=None,
thirds=None,
mamode=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: Inertia (INERTIA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 20
rvi_length = int(rvi_length) if rvi_length and rvi_length > 0 else 14
scalar = float(scalar) if scalar and scalar > 0 else 100
refined = False if refined is None else True
thirds = False if thirds is None else True
mamode = mamode if isinstance(mamode, str) else "ema"
_length = max(length, rvi_length)
close = verify_series(close, _length)
drift = get_drift(drift)
offset = get_offset(offset)
if close is None: return
if refined or thirds:
high = verify_series(high, _length)
low = verify_series(low, _length)
if high is None or low is None: return
# Calculate Result
if refined:
_mode = "r"
rvi_ = rvi(close,
high=high,
low=low,
length=rvi_length,
scalar=scalar,
refined=refined,
mamode=mamode)
elif thirds:
_mode = "t"
rvi_ = rvi(close,
high=high,
low=low,
length=rvi_length,
scalar=scalar,
thirds=thirds,
mamode=mamode)
else:
_mode = ""
rvi_ = rvi(close, length=rvi_length, scalar=scalar, mamode=mamode)
inertia = linreg(rvi_, length=length)
# Offset
if offset != 0:
inertia = inertia.shift(offset)
# Handle fills
if "fillna" in kwargs:
inertia.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
inertia.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
_props = f"_{length}_{rvi_length}"
inertia.name = f"INERTIA{_mode}{_props}"
inertia.category = "momentum"
return inertia
