Source code for pandas_ta.momentum.squeeze_pro

# -*- 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, 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_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}" = f"SQZPRO{_props}" data = { 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.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
squeeze_pro.__doc__ = \ """Squeeze PRO(SQZPRO) This indicator is an extended version of "TTM Squeeze" from John Carter. 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_wide=2, kc_scalar_normal=1.5, kc_scalar_narrow=1, mom_length=12, mom_smooth=6, tr=True, 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_WIDE, KC_MID_WIDE, KC_HIGH_WIDE = KC(high, low, close, kc_length, kc_scalar_wide, TR) KC_LOW_NORMAL, KC_MID_NORMAL, KC_HIGH_NORMAL = KC(high, low, close, kc_length, kc_scalar_normal, TR) KC_LOW_NARROW, KC_MID_NARROW, KC_HIGH_NARROW = KC(high, low, close, kc_length, kc_scalar_narrow, TR) MOMO = MOM(close, mom_length) if mamode == "ema": SQZPRO = EMA(MOMO, mom_smooth) else: SQZPRO = EMA(momo, mom_smooth) SQZPRO_ON_WIDE = (BB_LOW > KC_LOW_WIDE) and (BB_HIGH < KC_HIGH_WIDE) SQZPRO_ON_NORMAL = (BB_LOW > KC_LOW_NORMAL) and (BB_HIGH < KC_HIGH_NORMAL) SQZPRO_ON_NARROW = (BB_LOW > KC_LOW_NARROW) and (BB_HIGH < KC_HIGH_NARROW) SQZPRO_OFF_WIDE = (BB_LOW < KC_LOW_WIDE) and (BB_HIGH > KC_HIGH_WIDE) SQZPRO_NO = !SQZ_ON_WIDE and !SQZ_OFF_WIDE 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_wide (float): Keltner Channel scalar for wider channel. Default: 2 kc_scalar_normal (float): Keltner Channel scalar for normal channel. Default: 1.5 kc_scalar_narrow (float): Keltner Channel scalar for narrow channel. Default: 1 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" 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: SQZPRO, SQZPRO_ON_WIDE, SQZPRO_ON_NORMAL, SQZPRO_ON_NARROW, SQZPRO_OFF_WIDE, SQZPRO_NO columns by default. More detailed columns if 'detailed' kwarg is True. """