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 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
