Source code for pandas_ta.momentum.squeeze

# -*- coding: utf-8 -*-
from numpy import nan as npNaN
from pandas import DataFrame
from pandas_ta.momentum import mom
from pandas_ta.overlap import ema, linreg, sma
from pandas_ta.trend import decreasing, increasing
from pandas_ta.volatility import bbands, kc
from pandas_ta.utils import get_offset
from pandas_ta.utils import unsigned_differences, verify_series

[docs]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, mamode=None, offset=None, **kwargs): """Indicator: Squeeze Momentum (SQZ)""" # 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 = 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 _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) 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) lazybear = kwargs.pop("lazybear", 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 = 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.lower() == "ema": squeeze = ema(momo, length=mom_smooth) else: # "sma" 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 "" = f"SQZ{_props}" data = { 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.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
squeeze.__doc__ = \ """Squeeze (SQZ) The default is based on John Carter's "TTM Squeeze" indicator, as discussed in his book "Mastering the Trade" (chapter 11). The Squeeze indicator attempts to capture the relationship between two studies: Bollinger Bands® and Keltner's Channels. When the volatility increases, so does the distance between the bands, conversely, when the volatility declines, the distance also decreases. It finds sections of the Bollinger Bands® study which fall inside the Keltner's Channels. Sources: Calculation: Default Inputs: bb_length=20, bb_std=2, kc_length=20, kc_scalar=1.5, mom_length=12, mom_smooth=12, tr=True, lazybear=False, BB = Bollinger Bands KC = Keltner Channels MOM = Momentum SMA = Simple Moving Average EMA = Exponential Moving Average TR = True Range RANGE = TR(high, low, close) if using_tr else high - low BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std) KC_LOW, KC_MID, KC_HIGH = KC(high, low, close, kc_length, kc_scalar, TR) if lazybear: HH = high.rolling(kc_length).max() LL = low.rolling(kc_length).min() AVG = 0.25 * (HH + LL) + 0.5 * KC_MID SQZ = linreg(close - AVG, kc_length) else: MOMO = MOM(close, mom_length) if mamode == "ema": SQZ = EMA(MOMO, mom_smooth) else: SQZ = EMA(momo, mom_smooth) SQZ_ON = (BB_LOW > KC_LOW) and (BB_HIGH < KC_HIGH) SQZ_OFF = (BB_LOW < KC_LOW) and (BB_HIGH > KC_HIGH) NO_SQZ = !SQZ_ON and !SQZ_OFF Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's bb_length (int): Bollinger Bands period. Default: 20 bb_std (float): Bollinger Bands Std. Dev. Default: 2 kc_length (int): Keltner Channel period. Default: 20 kc_scalar (float): Keltner Channel scalar. Default: 1.5 mom_length (int): Momentum Period. Default: 12 mom_smooth (int): Smoothing Period of Momentum. Default: 6 mamode (str): Only "ema" or "sma". Default: "sma" offset (int): How many periods to offset the result. Default: 0 Kwargs: tr (value, optional): Use True Range for Keltner Channels. Default: True asint (value, optional): Use integers instead of bool. Default: True mamode (value, optional): Which MA to use. Default: "sma" lazybear (value, optional): Use LazyBear's TradingView implementation. Default: False detailed (value, optional): Return additional variations of SQZ for visualization. Default: False fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.DataFrame: SQZ, SQZ_ON, SQZ_OFF, NO_SQZ columns by default. More detailed columns if 'detailed' kwarg is True. """